Merge branch 'master' into mdrxy/post-patch-fixes

.github/CODEOWNERS

@@ -1,3 +1,3 @@
-/.github/   @ccurme @eyurtsev @mdrxy
+/.github/   @baskaryan @ccurme @eyurtsev
 /libs/core/ @eyurtsev
 /libs/partners/ @ccurme @mdrxy

.github/ISSUE_TEMPLATE/bug-report.yml

@@ -53,6 +53,7 @@ body:
         - label: langchain-anthropic
         - label: langchain-classic
         - label: langchain-core
+        - label: langchain-cli
         - label: langchain-model-profiles
         - label: langchain-tests
         - label: langchain-text-splitters
@@ -70,16 +71,6 @@ body:
         - label: langchain-qdrant
         - label: langchain-xai
         - label: Other / not sure / general
-  - type: textarea
-    id: related
-    validations:
-      required: false
-    attributes:
-      label: Related Issues / PRs
-      description: |
-        If this bug is related to any existing issues or pull requests, please link them here.
-      placeholder: |
-        * e.g. #123, #456
   - type: textarea
     id: reproduction
     validations:

.github/ISSUE_TEMPLATE/feature-request.yml

@@ -50,6 +50,7 @@ body:
         - label: langchain-anthropic
         - label: langchain-classic
         - label: langchain-core
+        - label: langchain-cli
         - label: langchain-model-profiles
         - label: langchain-tests
         - label: langchain-text-splitters

.github/ISSUE_TEMPLATE/privileged.yml

@@ -30,6 +30,7 @@ body:
         - label: langchain-anthropic
         - label: langchain-classic
         - label: langchain-core
+        - label: langchain-cli
         - label: langchain-model-profiles
         - label: langchain-tests
         - label: langchain-text-splitters

.github/ISSUE_TEMPLATE/task.yml

@@ -101,6 +101,7 @@ body:
         - label: langchain-anthropic
         - label: langchain-classic
         - label: langchain-core
+        - label: langchain-cli
         - label: langchain-model-profiles
         - label: langchain-tests
         - label: langchain-text-splitters

.github/PULL_REQUEST_TEMPLATE.md

@@ -1,7 +1,6 @@
 (Replace this entire block of text)
 
 Read the full contributing guidelines: https://docs.langchain.com/oss/python/contributing/overview
-If you paste a large clearly AI generated description here your PR may be IGNORED or CLOSED!
 
 Thank you for contributing to LangChain! Follow these steps to have your pull request considered as ready for review.
 
@@ -24,15 +23,8 @@ Thank you for contributing to LangChain! Follow these steps to have your pull re
 
   - We will not consider a PR unless these three are passing in CI.
 
-4. How did you verify your code works?
-
 Additional guidelines:
 
   - We ask that if you use generative AI for your contribution, you include a disclaimer.
   - PRs should not touch more than one package unless absolutely necessary.
   - Do not update the `uv.lock` files or add dependencies to `pyproject.toml` files (even optional ones) unless you have explicit permission to do so by a maintainer.
-
-## Social handles (optional)
-<!-- If you'd like a shoutout on release, add your socials below -->
-Twitter: @
-LinkedIn: https://linkedin.com/in/

.github/pr-file-labeler.yml

@@ -17,6 +17,11 @@ langchain:
       - any-glob-to-any-file:
           - "libs/langchain_v1/**/*"
 
+cli:
+  - changed-files:
+      - any-glob-to-any-file:
+          - "libs/cli/**/*"
+
 standard-tests:
   - changed-files:
       - any-glob-to-any-file:

.github/scripts/check_diff.py

@@ -56,7 +56,7 @@ def all_package_dirs() -> Set[str]:
     return {
         "/".join(path.split("/")[:-1]).lstrip("./")
         for path in glob.glob("./libs/**/pyproject.toml", recursive=True)
-        if "libs/standard-tests" not in path
+        if "libs/cli" not in path and "libs/standard-tests" not in path
     }
 
 
@@ -286,6 +286,10 @@ if __name__ == "__main__":
             dirs_to_run["test"].add("libs/partners/fireworks")
             dirs_to_run["test"].add("libs/partners/groq")
 
+        elif file.startswith("libs/cli"):
+            dirs_to_run["lint"].add("libs/cli")
+            dirs_to_run["test"].add("libs/cli")
+
         elif file.startswith("libs/partners"):
             partner_dir = file.split("/")[2]
             if os.path.isdir(f"libs/partners/{partner_dir}") and [

.github/workflows/_release.yml

@@ -394,9 +394,10 @@ jobs:
     runs-on: ubuntu-latest
     permissions:
       contents: read
+    if: false # temporarily skip
     strategy:
       matrix:
-        partner: [anthropic]
+        partner: [openai, anthropic]
       fail-fast: false # Continue testing other partners if one fails
     env:
       ANTHROPIC_API_KEY: ${{ secrets.ANTHROPIC_API_KEY }}
@@ -539,7 +540,7 @@ jobs:
       - test-pypi-publish
       - pre-release-checks
       - test-dependents
-      - test-prior-published-packages-against-new-core
+      # - test-prior-published-packages-against-new-core
     # Run if all needed jobs succeeded or were skipped (test-dependents only runs for core/langchain_v1)
     if: ${{ !cancelled() && !failure() }}
     runs-on: ubuntu-latest

.github/workflows/auto-label-by-package.yml

@@ -30,6 +30,7 @@ jobs:
               "langchain-anthropic": "anthropic",
               "langchain-classic": "langchain-classic",
               "langchain-core": "core",
+              "langchain-cli": "cli",
               "langchain-model-profiles": "model-profiles",
               "langchain-tests": "standard-tests",
               "langchain-text-splitters": "text-splitters",

.github/workflows/check_agents_sync.yml

@@ -1,42 +0,0 @@
-# Ensures CLAUDE.md and AGENTS.md stay synchronized.
-#
-# These files contain the same development guidelines but are named differently
-# for compatibility with different AI coding assistants (Claude Code uses CLAUDE.md,
-# other tools may use AGENTS.md).
-
-name: "🔄 Check CLAUDE.md / AGENTS.md Sync"
-
-on:
-  push:
-    branches: [master]
-    paths:
-      - "CLAUDE.md"
-      - "AGENTS.md"
-  pull_request:
-    paths:
-      - "CLAUDE.md"
-      - "AGENTS.md"
-
-permissions:
-  contents: read
-
-jobs:
-  check-sync:
-    name: "verify files are identical"
-    runs-on: ubuntu-latest
-    steps:
-      - name: "📋 Checkout Code"
-        uses: actions/checkout@v6
-
-      - name: "🔍 Check CLAUDE.md and AGENTS.md are in sync"
-        run: |
-          if ! diff -q CLAUDE.md AGENTS.md > /dev/null 2>&1; then
-            echo "❌ CLAUDE.md and AGENTS.md are out of sync!"
-            echo ""
-            echo "These files must contain identical content."
-            echo "Differences:"
-            echo ""
-            diff --color=always CLAUDE.md AGENTS.md || true
-            exit 1
-          fi
-          echo "✅ CLAUDE.md and AGENTS.md are in sync"

.github/workflows/pr_lint.yml

@@ -8,7 +8,7 @@
 #
 # Examples:
 #     feat(core): add multi‐tenant support
-#     fix(langchain): resolve error
+#     fix(cli): resolve flag parsing error
 #     docs: update API usage examples
 #     docs(openai): update API usage examples
 #
@@ -27,18 +27,12 @@
 #   * release    — prepare a new release
 #
 # Allowed Scope(s) (optional):
-#   core, langchain, langchain-classic, model-profiles,
+#   core, cli, langchain, langchain-classic, model-profiles,
 #   standard-tests, text-splitters, docs, anthropic, chroma, deepseek, exa,
 #   fireworks, groq, huggingface, mistralai, nomic, ollama, openai,
 #   perplexity, prompty, qdrant, xai, infra, deps
 #
-# Multiple scopes can be used by separating them with a comma. For example:
-#
-#   feat(core,langchain): add multi‐tenant support to core and langchain
-#
-# Note: PRs touching the langchain package should use the 'langchain' scope. It is not
-#   acceptable to omit the scope for changes to the langchain package, despite it being
-#   the main package & name of the repo.
+# Multiple scopes can be used by separating them with a comma.
 #
 # Rules:
 #   1. The 'Type' must start with a lowercase letter.
@@ -85,6 +79,7 @@ jobs:
             release
           scopes: |
             core
+            cli
             langchain
             langchain-classic
             model-profiles

.github/workflows/tag-external-contributions.yml

@@ -32,14 +32,14 @@ jobs:
     steps:
       - name: Generate GitHub App token
         id: app-token
-        uses: actions/create-github-app-token@v2
+        uses: actions/create-github-app-token@v1
         with:
           app-id: ${{ secrets.ORG_MEMBERSHIP_APP_ID }}
           private-key: ${{ secrets.ORG_MEMBERSHIP_APP_PRIVATE_KEY }}
 
       - name: Check if contributor is external
         id: check-membership
-        uses: actions/github-script@v8
+        uses: actions/github-script@v7
         with:
           github-token: ${{ steps.app-token.outputs.token }}
           script: |
@@ -77,7 +77,7 @@ jobs:
 
       - name: Add external label to issue
         if: steps.check-membership.outputs.is-external == 'true' && github.event_name == 'issues'
-        uses: actions/github-script@v8
+        uses: actions/github-script@v7
         with:
           github-token: ${{ secrets.GITHUB_TOKEN }}
           script: |
@@ -95,7 +95,7 @@ jobs:
 
       - name: Add external label to pull request
         if: steps.check-membership.outputs.is-external == 'true' && github.event_name == 'pull_request_target'
-        uses: actions/github-script@v8
+        uses: actions/github-script@v7
         with:
           github-token: ${{ secrets.GITHUB_TOKEN }}
           script: |
@@ -113,7 +113,7 @@ jobs:
 
       - name: Add internal label to issue
         if: steps.check-membership.outputs.is-external == 'false' && github.event_name == 'issues'
-        uses: actions/github-script@v8
+        uses: actions/github-script@v7
         with:
           github-token: ${{ secrets.GITHUB_TOKEN }}
           script: |
@@ -131,7 +131,7 @@ jobs:
 
       - name: Add internal label to pull request
         if: steps.check-membership.outputs.is-external == 'false' && github.event_name == 'pull_request_target'
-        uses: actions/github-script@v8
+        uses: actions/github-script@v7
         with:
           github-token: ${{ secrets.GITHUB_TOKEN }}
           script: |

AGENTS.md

@@ -22,6 +22,7 @@ langchain/
 │   ├── text-splitters/   # Document chunking utilities
 │   ├── standard-tests/   # Shared test suite for integrations
 │   ├── model-profiles/   # Model configuration profiles
+│   └── cli/              # Command-line interface tools
 ├── .github/              # CI/CD workflows and templates
 ├── .vscode/              # VSCode IDE standard settings and recommended extensions
 └── README.md             # Information about LangChain
@@ -32,7 +33,7 @@ langchain/
 - **Integration layer** (`partners/`): Third-party service integrations. Note that this monorepo is not exhaustive of all LangChain integrations; some are maintained in separate repos, such as `langchain-ai/langchain-google` and `langchain-ai/langchain-aws`. Usually these repos are cloned at the same level as this monorepo, so if needed, you can refer to their code directly by navigating to `../langchain-google/` from this monorepo.
 - **Testing layer** (`standard-tests/`): Standardized integration tests for partner integrations
 
-### Development tools & commands
+### Development tools & commands**
 
 - `uv` – Fast Python package installer and resolver (replaces pip/poetry)
 - `make` – Task runner for common development commands. Feel free to look at the `Makefile` for available commands and usage patterns.
@@ -44,16 +45,6 @@ This monorepo uses `uv` for dependency management. Local development uses editab
 
 Each package in `libs/` has its own `pyproject.toml` and `uv.lock`.
 
-Before running your tests, setup all packages by running:
-
-```bash
-# For all groups
-uv sync --all-groups
-
-# or, to install a specific group only:
-uv sync --group test
-```
-
 ```bash
 # Run unit tests (no network)
 make test
@@ -81,15 +72,7 @@ uv run --group lint mypy .
 
 #### Commit standards
 
-Suggest PR titles that follow Conventional Commits format. Refer to .github/workflows/pr_lint for allowed types and scopes. Note that all commit/PR titles should be in lowercase with the exception of proper nouns/named entities. All PR titles should include a scope with no exceptions. For example:
-
-```txt
-feat(langchain): add new chat completion feature
-fix(core): resolve type hinting issue in vector store
-chore(anthropic): update infrastructure dependencies
-```
-
-Note how `feat(langchain)` includes a scope even though it is the main package and name of the repo.
+Suggest PR titles that follow Conventional Commits format. Refer to .github/workflows/pr_lint for allowed types and scopes.
 
 #### Pull request guidelines
 
@@ -128,7 +111,7 @@ def filter_unknown_users(users: list[str], known_users: set[str]) -> list[str]:
         known_users: Set of known/valid user identifiers.
 
     Returns:
-        List of users that are not in the `known_users` set.
+        List of users that are not in the known_users set.
     """
 ```
 
@@ -196,4 +179,4 @@ def send_email(to: str, msg: str, *, priority: str = "normal") -> bool:
 ## Additional resources
 
 - **Documentation:** https://docs.langchain.com/oss/python/langchain/overview and source at https://github.com/langchain-ai/docs or `../docs/`. Prefer the local install and use file search tools for best results. If needed, use the docs MCP server as defined in `.mcp.json` for programmatic access.
-- **Contributing Guide:** [Contributing Guide](https://docs.langchain.com/oss/python/contributing/overview)
+- **Contributing Guide:** [`.github/CONTRIBUTING.md`](https://docs.langchain.com/oss/python/contributing/overview)

CLAUDE.md

@@ -22,6 +22,7 @@ langchain/
 │   ├── text-splitters/   # Document chunking utilities
 │   ├── standard-tests/   # Shared test suite for integrations
 │   ├── model-profiles/   # Model configuration profiles
+│   └── cli/              # Command-line interface tools
 ├── .github/              # CI/CD workflows and templates
 ├── .vscode/              # VSCode IDE standard settings and recommended extensions
 └── README.md             # Information about LangChain
@@ -32,7 +33,7 @@ langchain/
 - **Integration layer** (`partners/`): Third-party service integrations. Note that this monorepo is not exhaustive of all LangChain integrations; some are maintained in separate repos, such as `langchain-ai/langchain-google` and `langchain-ai/langchain-aws`. Usually these repos are cloned at the same level as this monorepo, so if needed, you can refer to their code directly by navigating to `../langchain-google/` from this monorepo.
 - **Testing layer** (`standard-tests/`): Standardized integration tests for partner integrations
 
-### Development tools & commands
+### Development tools & commands**
 
 - `uv` – Fast Python package installer and resolver (replaces pip/poetry)
 - `make` – Task runner for common development commands. Feel free to look at the `Makefile` for available commands and usage patterns.
@@ -44,16 +45,6 @@ This monorepo uses `uv` for dependency management. Local development uses editab
 
 Each package in `libs/` has its own `pyproject.toml` and `uv.lock`.
 
-Before running your tests, setup all packages by running:
-
-```bash
-# For all groups
-uv sync --all-groups
-
-# or, to install a specific group only:
-uv sync --group test
-```
-
 ```bash
 # Run unit tests (no network)
 make test
@@ -81,15 +72,7 @@ uv run --group lint mypy .
 
 #### Commit standards
 
-Suggest PR titles that follow Conventional Commits format. Refer to .github/workflows/pr_lint for allowed types and scopes. Note that all commit/PR titles should be in lowercase with the exception of proper nouns/named entities. All PR titles should include a scope with no exceptions. For example:
-
-```txt
-feat(langchain): add new chat completion feature
-fix(core): resolve type hinting issue in vector store
-chore(anthropic): update infrastructure dependencies
-```
-
-Note how `feat(langchain)` includes a scope even though it is the main package and name of the repo.
+Suggest PR titles that follow Conventional Commits format. Refer to .github/workflows/pr_lint for allowed types and scopes.
 
 #### Pull request guidelines
 
@@ -128,7 +111,7 @@ def filter_unknown_users(users: list[str], known_users: set[str]) -> list[str]:
         known_users: Set of known/valid user identifiers.
 
     Returns:
-        List of users that are not in the `known_users` set.
+        List of users that are not in the known_users set.
     """
 ```
 
@@ -196,4 +179,4 @@ def send_email(to: str, msg: str, *, priority: str = "normal") -> bool:
 ## Additional resources
 
 - **Documentation:** https://docs.langchain.com/oss/python/langchain/overview and source at https://github.com/langchain-ai/docs or `../docs/`. Prefer the local install and use file search tools for best results. If needed, use the docs MCP server as defined in `.mcp.json` for programmatic access.
-- **Contributing Guide:** [Contributing Guide](https://docs.langchain.com/oss/python/contributing/overview)
+- **Contributing Guide:** [`.github/CONTRIBUTING.md`](https://docs.langchain.com/oss/python/contributing/overview)

CONTRIBUTING.md

@@ -1,15 +0,0 @@
-# Contributing to LangChain
-
-Thanks for your interest in contributing to LangChain!
-
-We have moved our contributing guidelines to our documentation site to keep them up-to-date and easy to access.
-
-👉 **[Read the Contributing Guide](https://docs.langchain.com/oss/python/contributing/overview)**
-
-This guide includes instructions on:
-- How to set up your development environment
-- How to run tests and linting
-- How to submit a Pull Request
-- Coding standards and best practices
-
-We look forward to your contributions!

README.md

@@ -36,7 +36,6 @@ If you're looking for more advanced customization or agent orchestration, check
 
 - [docs.langchain.com](https://docs.langchain.com/oss/python/langchain/overview) – Comprehensive documentation, including conceptual overviews and guides
 - [reference.langchain.com/python](https://reference.langchain.com/python) – API reference docs for LangChain packages
-- [Chat LangChain](https://chat.langchain.com/) – Chat with the LangChain documentation and get answers to your questions
 
 **Discussions**: Visit the [LangChain Forum](https://forum.langchain.com) to connect with the community and share all of your technical questions, ideas, and feedback.
 
@@ -62,11 +61,11 @@ While the LangChain framework can be used standalone, it also integrates seamles
 
 To improve your LLM application development, pair LangChain with:
 
-- [Deep Agents](https://github.com/langchain-ai/deepagents) *(new!)* – Build agents that can plan, use subagents, and leverage file systems for complex tasks
 - [LangGraph](https://docs.langchain.com/oss/python/langgraph/overview) – Build agents that can reliably handle complex tasks with LangGraph, our low-level agent orchestration framework. LangGraph offers customizable architecture, long-term memory, and human-in-the-loop workflows – and is trusted in production by companies like LinkedIn, Uber, Klarna, and GitLab.
 - [Integrations](https://docs.langchain.com/oss/python/integrations/providers/overview) – List of LangChain integrations, including chat & embedding models, tools & toolkits, and more
 - [LangSmith](https://www.langchain.com/langsmith) – Helpful for agent evals and observability. Debug poor-performing LLM app runs, evaluate agent trajectories, gain visibility in production, and improve performance over time.
 - [LangSmith Deployment](https://docs.langchain.com/langsmith/deployments) – Deploy and scale agents effortlessly with a purpose-built deployment platform for long-running, stateful workflows. Discover, reuse, configure, and share agents across teams – and iterate quickly with visual prototyping in [LangSmith Studio](https://docs.langchain.com/langsmith/studio).
+- [Deep Agents](https://github.com/langchain-ai/deepagents) *(new!)* – Build agents that can plan, use subagents, and leverage file systems for complex tasks
 
 ## Additional resources
 

libs/cli/.gitignore

@@ -0,0 +1,159 @@
+# Byte-compiled / optimized / DLL files
+__pycache__/
+*.py[cod]
+*$py.class
+
+# C extensions
+*.so
+
+# Distribution / packaging
+.Python
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+wheels/
+share/python-wheels/
+*.egg-info/
+.installed.cfg
+*.egg
+MANIFEST
+
+# PyInstaller
+#  Usually these files are written by a python script from a template
+#  before PyInstaller builds the exe, so as to inject date/other infos into it.
+*.manifest
+*.spec
+
+# Installer logs
+pip-log.txt
+pip-delete-this-directory.txt
+
+# Unit test / coverage reports
+htmlcov/
+.tox/
+.nox/
+.coverage
+.coverage.*
+.cache
+nosetests.xml
+coverage.xml
+*.cover
+*.py,cover
+.hypothesis/
+.pytest_cache/
+cover/
+
+# Translations
+*.mo
+*.pot
+
+# Django stuff:
+*.log
+local_settings.py
+db.sqlite3
+db.sqlite3-journal
+
+# Flask stuff:
+instance/
+.webassets-cache
+
+# Scrapy stuff:
+.scrapy
+
+# PyBuilder
+.pybuilder/
+target/
+
+# Jupyter Notebook
+.ipynb_checkpoints
+
+# IPython
+profile_default/
+ipython_config.py
+
+# pyenv
+#   For a library or package, you might want to ignore these files since the code is
+#   intended to run in multiple environments; otherwise, check them in:
+# .python-version
+
+# pipenv
+#   According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
+#   However, in case of collaboration, if having platform-specific dependencies or dependencies
+#   having no cross-platform support, pipenv may install dependencies that don't work, or not
+#   install all needed dependencies.
+#Pipfile.lock
+
+# poetry
+#   Similar to Pipfile.lock, it is generally recommended to include poetry.lock in version control.
+#   This is especially recommended for binary packages to ensure reproducibility, and is more
+#   commonly ignored for libraries.
+#   https://python-poetry.org/docs/basic-usage/#commit-your-poetrylock-file-to-version-control
+#poetry.lock
+
+# pdm
+#   Similar to Pipfile.lock, it is generally recommended to include pdm.lock in version control.
+#pdm.lock
+#   pdm stores project-wide configurations in .pdm.toml, but it is recommended to not include it
+#   in version control.
+#   https://pdm.fming.dev/#use-with-ide
+.pdm.toml
+
+# PEP 582; used by e.g. github.com/David-OConnor/pyflow and github.com/pdm-project/pdm
+__pypackages__/
+
+# Celery stuff
+celerybeat-schedule
+celerybeat.pid
+
+# SageMath parsed files
+*.sage.py
+
+# Environments
+.env
+.venv
+env/
+venv/
+ENV/
+env.bak/
+venv.bak/
+
+# Spyder project settings
+.spyderproject
+.spyproject
+
+# Rope project settings
+.ropeproject
+
+# mkdocs documentation
+/site
+
+# mypy
+.mypy_cache/
+.dmypy.json
+dmypy.json
+
+# Pyre type checker
+.pyre/
+
+# pytype static type analyzer
+.pytype/
+
+# Cython debug symbols
+cython_debug/
+
+# PyCharm
+#  JetBrains specific template is maintained in a separate JetBrains.gitignore that can
+#  be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore
+#  and can be added to the global gitignore or merged into this file.  For a more nuclear
+#  option (not recommended) you can uncomment the following to ignore the entire idea folder.
+#.idea/
+
+.integration_test

libs/cli/DOCS.md

@@ -0,0 +1,189 @@
+# `langchain`
+
+**Usage**:
+
+```console
+$ langchain [OPTIONS] COMMAND [ARGS]...
+```
+
+**Options**:
+
+* `--help`: Show this message and exit.
+* `-v, --version`: Print current CLI version.
+
+**Commands**:
+
+* `app`: Manage LangChain apps
+* `serve`: Start the LangServe app, whether it's a...
+* `template`: Develop installable templates.
+
+## `langchain app`
+
+Manage LangChain apps
+
+**Usage**:
+
+```console
+$ langchain app [OPTIONS] COMMAND [ARGS]...
+```
+
+**Options**:
+
+* `--help`: Show this message and exit.
+
+**Commands**:
+
+* `add`: Adds the specified template to the current...
+* `new`: Create a new LangServe application.
+* `remove`: Removes the specified package from the...
+* `serve`: Starts the LangServe app.
+
+### `langchain app add`
+
+Adds the specified template to the current LangServe app.
+
+e.g.:
+langchain app add extraction-openai-functions
+langchain app add git+ssh://git@github.com/efriis/simple-pirate.git
+
+**Usage**:
+
+```console
+$ langchain app add [OPTIONS] [DEPENDENCIES]...
+```
+
+**Arguments**:
+
+* `[DEPENDENCIES]...`: The dependency to add
+
+**Options**:
+
+* `--api-path TEXT`: API paths to add
+* `--project-dir PATH`: The project directory
+* `--repo TEXT`: Install templates from a specific github repo instead
+* `--branch TEXT`: Install templates from a specific branch
+* `--help`: Show this message and exit.
+
+### `langchain app new`
+
+Create a new LangServe application.
+
+**Usage**:
+
+```console
+$ langchain app new [OPTIONS] NAME
+```
+
+**Arguments**:
+
+* `NAME`: The name of the folder to create  [required]
+
+**Options**:
+
+* `--package TEXT`: Packages to seed the project with
+* `--help`: Show this message and exit.
+
+### `langchain app remove`
+
+Removes the specified package from the current LangServe app.
+
+**Usage**:
+
+```console
+$ langchain app remove [OPTIONS] API_PATHS...
+```
+
+**Arguments**:
+
+* `API_PATHS...`: The API paths to remove  [required]
+
+**Options**:
+
+* `--help`: Show this message and exit.
+
+### `langchain app serve`
+
+Starts the LangServe app.
+
+**Usage**:
+
+```console
+$ langchain app serve [OPTIONS]
+```
+
+**Options**:
+
+* `--port INTEGER`: The port to run the server on
+* `--host TEXT`: The host to run the server on
+* `--app TEXT`: The app to run, e.g. `app.server:app`
+* `--help`: Show this message and exit.
+
+## `langchain serve`
+
+Start the LangServe app, whether it's a template or an app.
+
+**Usage**:
+
+```console
+$ langchain serve [OPTIONS]
+```
+
+**Options**:
+
+* `--port INTEGER`: The port to run the server on
+* `--host TEXT`: The host to run the server on
+* `--help`: Show this message and exit.
+
+## `langchain template`
+
+Develop installable templates.
+
+**Usage**:
+
+```console
+$ langchain template [OPTIONS] COMMAND [ARGS]...
+```
+
+**Options**:
+
+* `--help`: Show this message and exit.
+
+**Commands**:
+
+* `new`: Creates a new template package.
+* `serve`: Starts a demo app for this template.
+
+### `langchain template new`
+
+Creates a new template package.
+
+**Usage**:
+
+```console
+$ langchain template new [OPTIONS] NAME
+```
+
+**Arguments**:
+
+* `NAME`: The name of the folder to create  [required]
+
+**Options**:
+
+* `--with-poetry / --no-poetry`: Don't run poetry install  [default: no-poetry]
+* `--help`: Show this message and exit.
+
+### `langchain template serve`
+
+Starts a demo app for this template.
+
+**Usage**:
+
+```console
+$ langchain template serve [OPTIONS]
+```
+
+**Options**:
+
+* `--port INTEGER`: The port to run the server on
+* `--host TEXT`: The host to run the server on
+* `--help`: Show this message and exit.

libs/cli/LICENSE

@@ -0,0 +1,21 @@
+MIT License
+
+Copyright (c) LangChain, Inc.
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.

libs/cli/Makefile

@@ -0,0 +1,53 @@
+
+######################
+# LINTING AND FORMATTING
+######################
+
+.EXPORT_ALL_VARIABLES:
+UV_FROZEN = true
+
+# Define a variable for Python and notebook files.
+PYTHON_FILES=.
+MYPY_CACHE=.mypy_cache
+lint format: PYTHON_FILES=.
+lint_diff format_diff: PYTHON_FILES=$(shell git diff --relative=libs/cli --name-only --diff-filter=d master | grep -E '\.py$$|\.ipynb$$')
+lint_package: PYTHON_FILES=langchain_cli
+lint_tests: PYTHON_FILES=tests
+lint_tests: MYPY_CACHE=.mypy_cache_test
+
+lint lint_diff lint_package lint_tests:
+	[ "$(PYTHON_FILES)" = "" ] || uv run --group typing --group lint ruff check $(PYTHON_FILES)
+	[ "$(PYTHON_FILES)" = "" ] || uv run --group typing --group lint ruff format $(PYTHON_FILES) --diff
+	[ "$(PYTHON_FILES)" = "" ] || mkdir -p $(MYPY_CACHE) && uv run --group typing --group lint mypy $(PYTHON_FILES) --cache-dir $(MYPY_CACHE)
+
+format format_diff:
+	[ "$(PYTHON_FILES)" = "" ] || uv run --group typing --group lint ruff format $(PYTHON_FILES)
+	[ "$(PYTHON_FILES)" = "" ] || uv run --group typing --group lint ruff check --fix $(PYTHON_FILES)
+
+test tests: _test _e2e_test
+
+PYTHON = .venv/bin/python
+
+_test:
+	uv run --group test pytest tests
+
+# custom integration testing for cli integration flow
+# currently ignores vectorstores test because lacks implementation
+_e2e_test:
+	rm -rf .integration_test
+	mkdir .integration_test
+	cd .integration_test && \
+		python3 -m venv .venv && \
+		$(PYTHON) -m pip install --upgrade uv && \
+		$(PYTHON) -m pip install -e .. && \
+		$(PYTHON) -m langchain_cli.cli integration new --name parrot-link --name-class ParrotLink && \
+		$(PYTHON) -m langchain_cli.cli integration new --name parrot-link --name-class ParrotLinkB --src=integration_template/chat_models.py --dst=langchain-parrot-link/langchain_parrot_link/chat_models_b.py && \
+		$(PYTHON) -m langchain_cli.cli integration create-doc --name parrot-link --name-class ParrotLinkB --component-type ChatModel --destination-dir langchain-parrot-link/docs && \
+		cd langchain-parrot-link && \
+			unset UV_FROZEN && \
+			unset VIRTUAL_ENV && \
+			uv sync && \
+			uv add --editable ../../../standard-tests && \
+			make format lint tests && \
+			uv add --editable ../../../core && \
+			make integration_test 

libs/cli/README.md

@@ -0,0 +1,30 @@
+# langchain-cli
+
+[![PyPI - Version](https://img.shields.io/pypi/v/langchain-cli?label=%20)](https://pypi.org/project/langchain-cli/#history)
+[![PyPI - License](https://img.shields.io/pypi/l/langchain-cli)](https://opensource.org/licenses/MIT)
+[![PyPI - Downloads](https://img.shields.io/pepy/dt/langchain-cli)](https://pypistats.org/packages/langchain-cli)
+[![Twitter](https://img.shields.io/twitter/url/https/twitter.com/langchain.svg?style=social&label=Follow%20%40LangChain)](https://x.com/langchain)
+
+## Quick Install
+
+```bash
+pip install langchain-cli
+```
+
+## 🤔 What is this?
+
+This package implements the official CLI for LangChain. Right now, it is most useful for getting started with LangChain Templates!
+
+## 📖 Documentation
+
+[CLI Docs](https://github.com/langchain-ai/langchain/blob/master/libs/cli/DOCS.md)
+
+## 📕 Releases & Versioning
+
+See our [Releases](https://docs.langchain.com/oss/python/release-policy) and [Versioning](https://docs.langchain.com/oss/python/versioning) policies.
+
+## 💁 Contributing
+
+As an open-source project in a rapidly developing field, we are extremely open to contributions, whether it be in the form of a new feature, improved infrastructure, or better documentation.
+
+For detailed information on how to contribute, see the [Contributing Guide](https://docs.langchain.com/oss/python/contributing/overview).

libs/cli/langchain_cli/__init__.py

@@ -0,0 +1,7 @@
+"""LangChain CLI."""
+
+from langchain_cli._version import __version__
+
+__all__ = [
+    "__version__",
+]

libs/cli/langchain_cli/_version.py

@@ -0,0 +1,10 @@
+from importlib import metadata
+
+try:
+    __version__ = metadata.version(__package__)
+except metadata.PackageNotFoundError:
+    # Case where package metadata is not available.
+    __version__ = ""
+del metadata  # optional, avoids polluting the results of dir(__package__)
+
+__all__ = ["__version__"]

libs/cli/langchain_cli/cli.py

@@ -0,0 +1,88 @@
+"""LangChain CLI."""
+
+from __future__ import annotations
+
+from typing import Annotated
+
+import typer
+
+from langchain_cli._version import __version__
+from langchain_cli.namespaces import app as app_namespace
+from langchain_cli.namespaces import integration as integration_namespace
+from langchain_cli.namespaces import template as template_namespace
+from langchain_cli.namespaces.migrate import main as migrate_namespace
+from langchain_cli.utils.packages import get_langserve_export, get_package_root
+
+app = typer.Typer(no_args_is_help=True, add_completion=False)
+app.add_typer(
+    template_namespace.package_cli,
+    name="template",
+    help=template_namespace.__doc__,
+)
+app.add_typer(app_namespace.app_cli, name="app", help=app_namespace.__doc__)
+app.add_typer(
+    integration_namespace.integration_cli,
+    name="integration",
+    help=integration_namespace.__doc__,
+)
+
+app.command(
+    name="migrate",
+    context_settings={
+        # Let Grit handle the arguments
+        "allow_extra_args": True,
+        "ignore_unknown_options": True,
+    },
+)(
+    migrate_namespace.migrate,
+)
+
+
+def _version_callback(*, show_version: bool) -> None:
+    if show_version:
+        typer.echo(f"langchain-cli {__version__}")
+        raise typer.Exit
+
+
+@app.callback()
+def _main(
+    *,
+    version: bool = typer.Option(
+        False,  # noqa: FBT003
+        "--version",
+        "-v",
+        help="Print the current CLI version.",
+        callback=_version_callback,
+        is_eager=True,
+    ),
+) -> None:
+    pass
+
+
+@app.command()
+def serve(
+    *,
+    port: Annotated[
+        int | None,
+        typer.Option(help="The port to run the server on"),
+    ] = None,
+    host: Annotated[
+        str | None,
+        typer.Option(help="The host to run the server on"),
+    ] = None,
+) -> None:
+    """Start the LangServe app, whether it's a template or an app."""
+    try:
+        project_dir = get_package_root()
+        pyproject = project_dir / "pyproject.toml"
+        get_langserve_export(pyproject)
+    except (KeyError, FileNotFoundError):
+        # not a template
+        app_namespace.serve(port=port, host=host)
+    else:
+        # is a template
+        template_namespace.serve(port=port, host=host)
+
+
+if __name__ == "__main__":
+    app()

libs/cli/langchain_cli/constants.py

@@ -0,0 +1,5 @@
+"""LangChain CLI constants."""
+
+DEFAULT_GIT_REPO = "https://github.com/langchain-ai/langchain.git"
+DEFAULT_GIT_SUBDIRECTORY = "templates"
+DEFAULT_GIT_REF = "master"

libs/cli/langchain_cli/dev_scripts.py

@@ -0,0 +1,70 @@
+"""Development Scripts for template packages."""
+
+from collections.abc import Sequence
+from typing import Literal
+
+from fastapi import FastAPI
+from langserve import add_routes
+
+from langchain_cli.utils.packages import get_langserve_export, get_package_root
+
+
+def create_demo_server(
+    *,
+    config_keys: Sequence[str] = (),
+    playground_type: Literal["default", "chat"] = "default",
+) -> FastAPI:
+    """Create a demo server for the current template.
+
+    Args:
+        config_keys: Optional sequence of config keys to expose in the playground.
+        playground_type: The type of playground to use.
+
+    Returns:
+        The demo server.
+
+    Raises:
+        KeyError: If the `pyproject.toml` file is missing required fields.
+        ImportError: If the module defined in `pyproject.toml` cannot be imported.
+    """
+    app = FastAPI()
+    package_root = get_package_root()
+    pyproject = package_root / "pyproject.toml"
+    try:
+        package = get_langserve_export(pyproject)
+
+        mod = __import__(package["module"], fromlist=[package["attr"]])
+
+        chain = getattr(mod, package["attr"])
+        add_routes(
+            app,
+            chain,
+            config_keys=config_keys,
+            playground_type=playground_type,
+        )
+    except KeyError as e:
+        msg = "Missing fields from pyproject.toml"
+        raise KeyError(msg) from e
+    except ImportError as e:
+        msg = "Could not import module defined in pyproject.toml"
+        raise ImportError(msg) from e
+
+    return app
+
+
+def create_demo_server_configurable() -> FastAPI:
+    """Create a configurable demo server.
+
+    Returns:
+        The configurable demo server.
+    """
+    return create_demo_server(config_keys=["configurable"])
+
+
+def create_demo_server_chat() -> FastAPI:
+    """Create a chat demo server.
+
+    Returns:
+        The chat demo server.
+    """
+    return create_demo_server(playground_type="chat")

libs/cli/langchain_cli/integration_template/.gitignore

@@ -0,0 +1 @@
+__pycache__

libs/cli/langchain_cli/integration_template/LICENSE

@@ -0,0 +1,21 @@
+MIT License
+
+Copyright (c) 2024 LangChain, Inc.
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.

libs/cli/langchain_cli/integration_template/Makefile

@@ -0,0 +1,58 @@
+.PHONY: all format lint test tests integration_tests help extended_tests
+
+# Default target executed when no arguments are given to make.
+all: help
+
+# Define a variable for the test file path.
+TEST_FILE ?= tests/unit_tests/
+integration_test integration_tests: TEST_FILE = tests/integration_tests/
+
+
+# unit tests are run with the --disable-socket flag to prevent network calls
+test tests:
+	uv run pytest --disable-socket --allow-unix-socket $(TEST_FILE)
+
+test_watch:
+	uv run ptw --snapshot-update --now . -- -vv $(TEST_FILE)
+
+# integration tests are run without the --disable-socket flag to allow network calls
+integration_test integration_tests:
+	uv run pytest $(TEST_FILE)
+
+######################
+# LINTING AND FORMATTING
+######################
+
+# Define a variable for Python and notebook files.
+PYTHON_FILES=.
+MYPY_CACHE=.mypy_cache
+lint format: PYTHON_FILES=.
+lint_diff format_diff: PYTHON_FILES=$(shell git diff --relative=libs/partners/__package_name_short__ --name-only --diff-filter=d master | grep -E '\.py$$|\.ipynb$$')
+lint_package: PYTHON_FILES=__module_name__
+lint_tests: PYTHON_FILES=tests
+lint_tests: MYPY_CACHE=.mypy_cache_test
+
+lint lint_diff lint_package lint_tests:
+	[ "$(PYTHON_FILES)" = "" ] || uv run ruff check $(PYTHON_FILES)
+	[ "$(PYTHON_FILES)" = "" ] || uv run ruff format $(PYTHON_FILES) --diff
+	[ "$(PYTHON_FILES)" = "" ] || mkdir -p $(MYPY_CACHE) && uv run mypy $(PYTHON_FILES) --cache-dir $(MYPY_CACHE)
+
+format format_diff:
+	[ "$(PYTHON_FILES)" = "" ] || uv run ruff format $(PYTHON_FILES)
+	[ "$(PYTHON_FILES)" = "" ] || uv run ruff check --fix $(PYTHON_FILES)
+
+check_imports: $(shell find __module_name__ -name '*.py')
+	uv run python ./scripts/check_imports.py $^
+
+######################
+# HELP
+######################
+
+help:
+	@echo '----'
+	@echo 'check_imports				- check imports'
+	@echo 'format                       - run code formatters'
+	@echo 'lint                         - run linters'
+	@echo 'test                         - run unit tests'
+	@echo 'tests                        - run unit tests'
+	@echo 'test TEST_FILE=<test_file>   - run all tests in file'

libs/cli/langchain_cli/integration_template/README.md

@@ -0,0 +1,46 @@
+# __package_name__
+
+This package contains the LangChain integration with __ModuleName__
+
+## Installation
+
+```bash
+pip install -U __package_name__
+```
+
+And you should configure credentials by setting the following environment variables:
+
+* TODO: fill this out
+
+## Chat Models
+
+`Chat__ModuleName__` class exposes chat models from __ModuleName__.
+
+```python
+from __module_name__ import Chat__ModuleName__
+
+model = Chat__ModuleName__()
+model.invoke("Sing a ballad of LangChain.")
+```
+
+## Embeddings
+
+`__ModuleName__Embeddings` class exposes embeddings from __ModuleName__.
+
+```python
+from __module_name__ import __ModuleName__Embeddings
+
+embeddings = __ModuleName__Embeddings()
+embeddings.embed_query("What is the meaning of life?")
+```
+
+## LLMs
+
+`__ModuleName__LLM` class exposes LLMs from __ModuleName__.
+
+```python
+from __module_name__ import __ModuleName__LLM
+
+model = __ModuleName__LLM()
+model.invoke("The meaning of life is")
+```

libs/cli/langchain_cli/integration_template/docs/chat.ipynb

@@ -0,0 +1,264 @@
+{
+  "cells": [
+    {
+      "cell_type": "raw",
+      "id": "afaf8039",
+      "metadata": {},
+      "source": [
+        "---\n",
+        "sidebar_label: __ModuleName__\n",
+        "---"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "e49f1e0d",
+      "metadata": {},
+      "source": [
+        "# Chat__ModuleName__\n",
+        "\n",
+        "- TODO: Make sure API reference link is correct.\n",
+        "\n",
+        "This will help you get started with __ModuleName__ [chat models](/docs/concepts/chat_models). For detailed documentation of all Chat__ModuleName__ features and configurations head to the [API reference](https://python.langchain.com/api_reference/__package_name_short_snake__/chat_models/__module_name__.chat_models.Chat__ModuleName__.html).\n",
+        "\n",
+        "- TODO: Add any other relevant links, like information about models, prices, context windows, etc. See https://python.langchain.com/docs/integrations/chat/openai/ for an example.\n",
+        "\n",
+        "## Overview\n",
+        "### Integration details\n",
+        "\n",
+        "- TODO: Fill in table features.\n",
+        "- TODO: Remove JS support link if not relevant, otherwise ensure link is correct.\n",
+        "- TODO: Make sure API reference links are correct.\n",
+        "\n",
+        "| Class | Package | Local | Serializable | [JS support](https://js.langchain.com/docs/integrations/chat/__package_name_short_snake__) | Package downloads | Package latest |\n",
+        "| :--- | :--- | :---: | :---: |  :---: | :---: | :---: |\n",
+        "| [Chat__ModuleName__](https://python.langchain.com/api_reference/__package_name_short_snake__/chat_models/__module_name__.chat_models.Chat__ModuleName__.html) | [__package_name__](https://python.langchain.com/api_reference/__package_name_short_snake__/) | ✅/❌ | beta/❌ | ✅/❌ | ![PyPI - Downloads](https://img.shields.io/pypi/dm/__package_name__&label=%20) | ![PyPI - Version](https://img.shields.io/pypi/v/__package_name__&label=%20) |\n",
+        "\n",
+        "### Model features\n",
+        "| [Tool calling](/docs/how_to/tool_calling) | [Structured output](/docs/how_to/structured_output/) | JSON mode | [Image input](/docs/how_to/multimodal_inputs/) | Audio input | Video input | [Token-level streaming](/docs/how_to/chat_streaming/) | Native async | [Token usage](/docs/how_to/chat_token_usage_tracking/) | [Logprobs](/docs/how_to/logprobs/) |\n",
+        "| :---: | :---: | :---: | :---: |  :---: | :---: | :---: | :---: | :---: | :---: |\n",
+        "| ✅/❌ | ✅/❌ | ✅/❌ | ✅/❌ | ✅/❌ | ✅/❌ | ✅/❌ | ✅/❌ | ✅/❌ | ✅/❌ |\n",
+        "\n",
+        "## Setup\n",
+        "\n",
+        "- TODO: Update with relevant info.\n",
+        "\n",
+        "To access __ModuleName__ models you'll need to create a/an __ModuleName__ account, get an API key, and install the `__package_name__` integration package.\n",
+        "\n",
+        "### Credentials\n",
+        "\n",
+        "- TODO: Update with relevant info.\n",
+        "\n",
+        "Head to (TODO: link) to sign up to __ModuleName__ and generate an API key. Once you've done this set the __MODULE_NAME___API_KEY environment variable:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "433e8d2b-9519-4b49-b2c4-7ab65b046c94",
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "import getpass\n",
+        "import os\n",
+        "\n",
+        "if not os.getenv(\"__MODULE_NAME___API_KEY\"):\n",
+        "    os.environ[\"__MODULE_NAME___API_KEY\"] = getpass.getpass(\n",
+        "        \"Enter your __ModuleName__ API key: \"\n",
+        "    )"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "72ee0c4b-9764-423a-9dbf-95129e185210",
+      "metadata": {},
+      "source": [
+        "To enable automated tracing of your model calls, set your [LangSmith](https://docs.smith.langchain.com/) API key:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "a15d341e-3e26-4ca3-830b-5aab30ed66de",
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# os.environ[\"LANGSMITH_TRACING\"] = \"true\"\n",
+        "# os.environ[\"LANGSMITH_API_KEY\"] = getpass.getpass(\"Enter your LangSmith API key: \")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "0730d6a1-c893-4840-9817-5e5251676d5d",
+      "metadata": {},
+      "source": [
+        "### Installation\n",
+        "\n",
+        "The LangChain __ModuleName__ integration lives in the `__package_name__` package:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "652d6238-1f87-422a-b135-f5abbb8652fc",
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "%pip install -qU __package_name__"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "a38cde65-254d-4219-a441-068766c0d4b5",
+      "metadata": {},
+      "source": [
+        "## Instantiation\n",
+        "\n",
+        "Now we can instantiate our model object and generate chat completions:\n",
+        "\n",
+        "- TODO: Update model instantiation with relevant params."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "cb09c344-1836-4e0c-acf8-11d13ac1dbae",
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from __module_name__ import Chat__ModuleName__\n",
+        "\n",
+        "model = Chat__ModuleName__(\n",
+        "    model=\"model-name\",\n",
+        "    temperature=0,\n",
+        "    max_tokens=None,\n",
+        "    timeout=None,\n",
+        "    max_retries=2,\n",
+        "    # other params...\n",
+        ")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "2b4f3e15",
+      "metadata": {},
+      "source": [
+        "## Invocation\n",
+        "\n",
+        "- TODO: Run cells so output can be seen."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "62e0dbc3",
+      "metadata": {
+        "tags": []
+      },
+      "outputs": [],
+      "source": [
+        "messages = [\n",
+        "    (\n",
+        "        \"system\",\n",
+        "        \"You are a helpful assistant that translates English to French. Translate the user sentence.\",\n",
+        "    ),\n",
+        "    (\"human\", \"I love programming.\"),\n",
+        "]\n",
+        "ai_msg = model.invoke(messages)\n",
+        "ai_msg"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "d86145b3-bfef-46e8-b227-4dda5c9c2705",
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "print(ai_msg.content)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "18e2bfc0-7e78-4528-a73f-499ac150dca8",
+      "metadata": {},
+      "source": [
+        "## Chaining\n",
+        "\n",
+        "We can [chain](/docs/how_to/sequence/) our model with a prompt template like so:\n",
+        "\n",
+        "- TODO: Run cells so output can be seen."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "e197d1d7-a070-4c96-9f8a-a0e86d046e0b",
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from langchain_core.prompts import ChatPromptTemplate\n",
+        "\n",
+        "prompt = ChatPromptTemplate(\n",
+        "    [\n",
+        "        (\n",
+        "            \"system\",\n",
+        "            \"You are a helpful assistant that translates {input_language} to {output_language}.\",\n",
+        "        ),\n",
+        "        (\"human\", \"{input}\"),\n",
+        "    ]\n",
+        ")\n",
+        "\n",
+        "chain = prompt | model\n",
+        "chain.invoke(\n",
+        "    {\n",
+        "        \"input_language\": \"English\",\n",
+        "        \"output_language\": \"German\",\n",
+        "        \"input\": \"I love programming.\",\n",
+        "    }\n",
+        ")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "d1ee55bc-ffc8-4cfa-801c-993953a08cfd",
+      "metadata": {},
+      "source": [
+        "## TODO: Any functionality specific to this model provider\n",
+        "\n",
+        "E.g. creating/using finetuned models via this provider. Delete if not relevant."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "3a5bb5ca-c3ae-4a58-be67-2cd18574b9a3",
+      "metadata": {},
+      "source": [
+        "## API reference\n",
+        "\n",
+        "For detailed documentation of all Chat__ModuleName__ features and configurations head to the [API reference](https://python.langchain.com/api_reference/__package_name_short_snake__/chat_models/__module_name__.chat_models.Chat__ModuleName__.html)"
+      ]
+    }
+  ],
+  "metadata": {
+    "kernelspec": {
+      "display_name": "Python 3 (ipykernel)",
+      "language": "python",
+      "name": "python3"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.11.9"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 5
+}

libs/cli/langchain_cli/integration_template/docs/document_loaders.ipynb

@@ -0,0 +1,219 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "---\n",
+    "sidebar_label: __ModuleName__\n",
+    "---"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# __ModuleName__Loader\n",
+    "\n",
+    "- TODO: Make sure API reference link is correct.\n",
+    "\n",
+    "This notebook provides a quick overview for getting started with __ModuleName__ [document loader](https://python.langchain.com/docs/concepts/document_loaders). For detailed documentation of all __ModuleName__Loader features and configurations head to the [API reference](https://python.langchain.com/v0.2/api_reference/community/document_loaders/langchain_community.document_loaders.__module_name___loader.__ModuleName__Loader.html).\n",
+    "\n",
+    "- TODO: Add any other relevant links, like information about underlying API, etc.\n",
+    "\n",
+    "## Overview\n",
+    "### Integration details\n",
+    "\n",
+    "- TODO: Fill in table features.\n",
+    "- TODO: Remove JS support link if not relevant, otherwise ensure link is correct.\n",
+    "- TODO: Make sure API reference links are correct.\n",
+    "\n",
+    "| Class | Package | Local | Serializable | [JS support](https://js.langchain.com/docs/integrations/document_loaders/web_loaders/__module_name___loader)|\n",
+    "| :--- | :--- | :---: | :---: |  :---: |\n",
+    "| [__ModuleName__Loader](https://python.langchain.com/v0.2/api_reference/community/document_loaders/langchain_community.document_loaders.__module_name__loader.__ModuleName__Loader.html) | [langchain_community](https://api.python.langchain.com/en/latest/community_api_reference.html) | ✅/❌ | beta/❌ | ✅/❌ | \n",
+    "### Loader features\n",
+    "| Source | Document Lazy Loading | Native Async Support\n",
+    "| :---: | :---: | :---: | \n",
+    "| __ModuleName__Loader | ✅/❌ | ✅/❌ | \n",
+    "\n",
+    "## Setup\n",
+    "\n",
+    "- TODO: Update with relevant info.\n",
+    "\n",
+    "To access __ModuleName__ document loader you'll need to install the `__package_name__` integration package, and create a **ModuleName** account and get an API key.\n",
+    "\n",
+    "### Credentials\n",
+    "\n",
+    "- TODO: Update with relevant info.\n",
+    "\n",
+    "Head to (TODO: link) to sign up to __ModuleName__ and generate an API key. Once you've done this set the __MODULE_NAME___API_KEY environment variable:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import getpass\n",
+    "import os\n",
+    "\n",
+    "os.environ[\"__MODULE_NAME___API_KEY\"] = getpass.getpass(\n",
+    "    \"Enter your __ModuleName__ API key: \"\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": "To enable automated tracing of your model calls, set your [LangSmith](https://docs.smith.langchain.com/) API key:"
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# os.environ[\"LANGSMITH_API_KEY\"] = getpass.getpass(\"Enter your LangSmith API key: \")\n",
+    "# os.environ[\"LANGSMITH_TRACING\"] = \"true\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Installation\n",
+    "\n",
+    "Install **langchain_community**.\n",
+    "\n",
+    "- TODO: Add any other required packages"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%pip install -qU langchain_community"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Initialization\n",
+    "\n",
+    "Now we can instantiate our model object and load documents:\n",
+    "\n",
+    "- TODO: Update model instantiation with relevant params."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain_community.document_loaders import __ModuleName__Loader\n",
+    "\n",
+    "loader = __ModuleName__Loader(\n",
+    "    # required params = ...\n",
+    "    # optional params = ...\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Load\n",
+    "\n",
+    "- TODO: Run cells to show loading capabilities"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "docs = loader.load()\n",
+    "docs[0]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "print(docs[0].metadata)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Lazy Load\n",
+    "\n",
+    "- TODO: Run cells to show lazy loading capabilities. Delete if lazy loading is not implemented."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "page = []\n",
+    "for doc in loader.lazy_load():\n",
+    "    page.append(doc)\n",
+    "    if len(page) >= 10:\n",
+    "        # do some paged operation, e.g.\n",
+    "        # index.upsert(page)\n",
+    "\n",
+    "        page = []"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## TODO: Any functionality specific to this document loader\n",
+    "\n",
+    "E.g. using specific configs for different loading behavior. Delete if not relevant."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## API reference\n",
+    "\n",
+    "For detailed documentation of all __ModuleName__Loader features and configurations head to the API reference: https://python.langchain.com/v0.2/api_reference/community/document_loaders/langchain_community.document_loaders.__module_name___loader.__ModuleName__Loader.html"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}

libs/cli/langchain_cli/integration_template/docs/llms.ipynb

@@ -0,0 +1,238 @@
+{
+  "cells": [
+    {
+      "cell_type": "raw",
+      "id": "67db2992",
+      "metadata": {},
+      "source": [
+        "---\n",
+        "sidebar_label: __ModuleName__\n",
+        "---"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "9597802c",
+      "metadata": {},
+      "source": [
+        "# __ModuleName__LLM\n",
+        "\n",
+        "- [ ] TODO: Make sure API reference link is correct\n",
+        "\n",
+        "This will help you get started with __ModuleName__ completion models (LLMs) using LangChain. For detailed documentation on `__ModuleName__LLM` features and configuration options, please refer to the [API reference](https://api.python.langchain.com/en/latest/llms/__module_name__.llms.__ModuleName__LLM.html).\n",
+        "\n",
+        "## Overview\n",
+        "### Integration details\n",
+        "\n",
+        "- TODO: Fill in table features.\n",
+        "- TODO: Remove JS support link if not relevant, otherwise ensure link is correct.\n",
+        "- TODO: Make sure API reference links are correct.\n",
+        "\n",
+        "| Class | Package | Local | Serializable | [JS support](https://js.langchain.com/docs/integrations/llms/__package_name_short_snake__) | Package downloads | Package latest |\n",
+        "| :--- | :--- | :---: | :---: |  :---: | :---: | :---: |\n",
+        "| [__ModuleName__LLM](https://api.python.langchain.com/en/latest/llms/__module_name__.llms.__ModuleName__LLM.html) | [__package_name__](https://api.python.langchain.com/en/latest/__package_name_short_snake___api_reference.html) | ✅/❌ | beta/❌ | ✅/❌ | ![PyPI - Downloads](https://img.shields.io/pypi/dm/__package_name__&label=%20) | ![PyPI - Version](https://img.shields.io/pypi/v/__package_name__&label=%20) |\n",
+        "\n",
+        "## Setup\n",
+        "\n",
+        "- TODO: Update with relevant info.\n",
+        "\n",
+        "To access __ModuleName__ models you'll need to create a/an __ModuleName__ account, get an API key, and install the `__package_name__` integration package.\n",
+        "\n",
+        "### Credentials\n",
+        "\n",
+        "- TODO: Update with relevant info.\n",
+        "\n",
+        "Head to (TODO: link) to sign up to __ModuleName__ and generate an API key. Once you've done this set the __MODULE_NAME___API_KEY environment variable:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "bc51e756",
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "import getpass\n",
+        "import os\n",
+        "\n",
+        "if not os.getenv(\"__MODULE_NAME___API_KEY\"):\n",
+        "    os.environ[\"__MODULE_NAME___API_KEY\"] = getpass.getpass(\n",
+        "        \"Enter your __ModuleName__ API key: \"\n",
+        "    )"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "4b6e1ca6",
+      "metadata": {},
+      "source": [
+        "To enable automated tracing of your model calls, set your [LangSmith](https://docs.smith.langchain.com/) API key:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "196c2b41",
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# os.environ[\"LANGSMITH_TRACING\"] = \"true\"\n",
+        "# os.environ[\"LANGSMITH_API_KEY\"] = getpass.getpass(\"Enter your LangSmith API key: \")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "809c6577",
+      "metadata": {},
+      "source": [
+        "### Installation\n",
+        "\n",
+        "The LangChain __ModuleName__ integration lives in the `__package_name__` package:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "59c710c4",
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "%pip install -qU __package_name__"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "0a760037",
+      "metadata": {},
+      "source": [
+        "## Instantiation\n",
+        "\n",
+        "Now we can instantiate our model object and generate chat completions:\n",
+        "\n",
+        "- TODO: Update model instantiation with relevant params."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "a0562a13",
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from __module_name__ import __ModuleName__LLM\n",
+        "\n",
+        "model = __ModuleName__LLM(\n",
+        "    model=\"model-name\",\n",
+        "    temperature=0,\n",
+        "    max_tokens=None,\n",
+        "    timeout=None,\n",
+        "    max_retries=2,\n",
+        "    # other params...\n",
+        ")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "0ee90032",
+      "metadata": {},
+      "source": [
+        "## Invocation\n",
+        "\n",
+        "- [ ] TODO: Run cells so output can be seen."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "035dea0f",
+      "metadata": {
+        "tags": []
+      },
+      "outputs": [],
+      "source": [
+        "input_text = \"__ModuleName__ is an AI company that \"\n",
+        "\n",
+        "completion = model.invoke(input_text)\n",
+        "completion"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "add38532",
+      "metadata": {},
+      "source": [
+        "## Chaining\n",
+        "\n",
+        "We can [chain](/docs/how_to/sequence/) our completion model with a prompt template like so:\n",
+        "\n",
+        "- TODO: Run cells so output can be seen."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "078e9db2",
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from langchain_core.prompts import PromptTemplate\n",
+        "\n",
+        "prompt = PromptTemplate(\"How to say {input} in {output_language}:\\n\")\n",
+        "\n",
+        "chain = prompt | model\n",
+        "chain.invoke(\n",
+        "    {\n",
+        "        \"output_language\": \"German\",\n",
+        "        \"input\": \"I love programming.\",\n",
+        "    }\n",
+        ")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "e99eef30",
+      "metadata": {},
+      "source": [
+        "## TODO: Any functionality specific to this model provider\n",
+        "\n",
+        "E.g. creating/using finetuned models via this provider. Delete if not relevant"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "e9bdfcef",
+      "metadata": {},
+      "source": [
+        "## API reference\n",
+        "\n",
+        "For detailed documentation of all `__ModuleName__LLM` features and configurations head to the API reference: https://api.python.langchain.com/en/latest/llms/__module_name__.llms.__ModuleName__LLM.html"
+      ]
+    }
+  ],
+  "metadata": {
+    "kernelspec": {
+      "display_name": "Python 3.11.1 64-bit",
+      "language": "python",
+      "name": "python3"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.9.7"
+    },
+    "vscode": {
+      "interpreter": {
+        "hash": "e971737741ff4ec9aff7dc6155a1060a59a8a6d52c757dbbe66bf8ee389494b1"
+      }
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 5
+}

libs/cli/langchain_cli/integration_template/docs/provider.ipynb

@@ -0,0 +1,50 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# __ModuleName__\n",
+    "\n",
+    "__ModuleName__ is a platform that offers..."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "id": "y8ku6X96sebl"
+   },
+   "outputs": [],
+   "source": [
+    "from __module_name__ import Chat__ModuleName__\n",
+    "from __module_name__ import __ModuleName__LLM\n",
+    "from __module_name__ import __ModuleName__VectorStore"
+   ]
+  }
+ ],
+ "metadata": {
+  "colab": {
+   "provenance": []
+  },
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.11"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 1
+}

libs/cli/langchain_cli/integration_template/docs/retrievers.ipynb

@@ -0,0 +1,245 @@
+{
+ "cells": [
+  {
+   "cell_type": "raw",
+   "id": "afaf8039",
+   "metadata": {},
+   "source": [
+    "---\n",
+    "sidebar_label: __ModuleName__\n",
+    "---"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e49f1e0d",
+   "metadata": {},
+   "source": [
+    "# __ModuleName__Retriever\n",
+    "\n",
+    "- TODO: Make sure API reference link is correct.\n",
+    "\n",
+    "This will help you get started with the __ModuleName__ [retriever](/docs/concepts/retrievers). For detailed documentation of all __ModuleName__Retriever features and configurations head to the [API reference](https://api.python.langchain.com/en/latest/retrievers/__module_name__.retrievers.__ModuleName__.__ModuleName__Retriever.html).\n",
+    "\n",
+    "### Integration details\n",
+    "\n",
+    "TODO: Select one of the tables below, as appropriate.\n",
+    "\n",
+    "1: Bring-your-own data (i.e., index and search a custom corpus of documents):\n",
+    "\n",
+    "| Retriever | Self-host | Cloud offering | Package |\n",
+    "| :--- | :--- | :---: | :---: |\n",
+    "[__ModuleName__Retriever](https://api.python.langchain.com/en/latest/retrievers/__package_name__.retrievers.__module_name__.__ModuleName__Retriever.html) | ❌ | ❌ | __package_name__ |\n",
+    "\n",
+    "2: External index (e.g., constructed from Internet data or similar)):\n",
+    "\n",
+    "| Retriever | Source | Package |\n",
+    "| :--- | :--- | :---: |\n",
+    "[__ModuleName__Retriever](https://api.python.langchain.com/en/latest/retrievers/__package_name__.retrievers.__module_name__.__ModuleName__Retriever.html) | Source description | __package_name__ |\n",
+    "\n",
+    "## Setup\n",
+    "\n",
+    "- TODO: Update with relevant info."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "72ee0c4b-9764-423a-9dbf-95129e185210",
+   "metadata": {},
+   "source": [
+    "If you want to get automated tracing from individual queries, you can also set your [LangSmith](https://docs.smith.langchain.com/) API key by uncommenting below:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a15d341e-3e26-4ca3-830b-5aab30ed66de",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# os.environ[\"LANGSMITH_API_KEY\"] = getpass.getpass(\"Enter your LangSmith API key: \")\n",
+    "# os.environ[\"LANGSMITH_TRACING\"] = \"true\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0730d6a1-c893-4840-9817-5e5251676d5d",
+   "metadata": {},
+   "source": [
+    "### Installation\n",
+    "\n",
+    "This retriever lives in the `__package_name__` package:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "652d6238-1f87-422a-b135-f5abbb8652fc",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%pip install -qU __package_name__"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a38cde65-254d-4219-a441-068766c0d4b5",
+   "metadata": {},
+   "source": [
+    "## Instantiation\n",
+    "\n",
+    "Now we can instantiate our retriever:\n",
+    "\n",
+    "- TODO: Update model instantiation with relevant params."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "70cc8e65-2a02-408a-bbc6-8ef649057d82",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from __module_name__ import __ModuleName__Retriever\n",
+    "\n",
+    "retriever = __ModuleName__Retriever(\n",
+    "    # ...\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5c5f2839-4020-424e-9fc9-07777eede442",
+   "metadata": {},
+   "source": [
+    "## Usage"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "51a60dbe-9f2e-4e04-bb62-23968f17164a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "query = \"...\"\n",
+    "\n",
+    "retriever.invoke(query)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "dfe8aad4-8626-4330-98a9-7ea1ca5d2e0e",
+   "metadata": {},
+   "source": [
+    "## Use within a chain\n",
+    "\n",
+    "Like other retrievers, __ModuleName__Retriever can be incorporated into LLM applications via [chains](/docs/how_to/sequence/).\n",
+    "\n",
+    "We will need a LLM or chat model:\n",
+    "\n",
+    "import ChatModelTabs from \"@theme/ChatModelTabs\";\n",
+    "\n",
+    "<ChatModelTabs customVarName=\"llm\" />"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "25b647a3-f8f2-4541-a289-7a241e43f9df",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# | output: false\n",
+    "# | echo: false\n",
+    "\n",
+    "from langchain_openai import ChatOpenAI\n",
+    "\n",
+    "model = ChatOpenAI(model=\"gpt-3.5-turbo-0125\", temperature=0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "23e11cc9-abd6-4855-a7eb-799f45ca01ae",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain_core.output_parsers import StrOutputParser\n",
+    "from langchain_core.prompts import ChatPromptTemplate\n",
+    "from langchain_core.runnables import RunnablePassthrough\n",
+    "\n",
+    "prompt = ChatPromptTemplate.from_template(\n",
+    "    \"\"\"Answer the question based only on the context provided.\n",
+    "\n",
+    "Context: {context}\n",
+    "\n",
+    "Question: {question}\"\"\"\n",
+    ")\n",
+    "\n",
+    "\n",
+    "def format_docs(docs):\n",
+    "    return \"\\n\\n\".join(doc.page_content for doc in docs)\n",
+    "\n",
+    "\n",
+    "chain = (\n",
+    "    {\"context\": retriever | format_docs, \"question\": RunnablePassthrough()}\n",
+    "    | prompt\n",
+    "    | model\n",
+    "    | StrOutputParser()\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "d47c37dd-5c11-416c-a3b6-bec413cd70e8",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "chain.invoke(\"...\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d1ee55bc-ffc8-4cfa-801c-993953a08cfd",
+   "metadata": {},
+   "source": [
+    "## TODO: Any functionality or considerations specific to this retriever\n",
+    "\n",
+    "Fill in or delete if not relevant."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3a5bb5ca-c3ae-4a58-be67-2cd18574b9a3",
+   "metadata": {},
+   "source": [
+    "## API reference\n",
+    "\n",
+    "For detailed documentation of all __ModuleName__Retriever features and configurations head to the [API reference](https://api.python.langchain.com/en/latest/retrievers/__module_name__.retrievers.__ModuleName__.__ModuleName__Retriever.html)."
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

libs/cli/langchain_cli/integration_template/docs/stores.ipynb

@@ -0,0 +1,204 @@
+{
+  "cells": [
+    {
+      "cell_type": "raw",
+      "metadata": {
+        "vscode": {
+          "languageId": "raw"
+        }
+      },
+      "source": [
+        "---\n",
+        "sidebar_label: __ModuleName__ByteStore\n",
+        "---"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "# __ModuleName__ByteStore\n",
+        "\n",
+        "- TODO: Make sure API reference link is correct.\n",
+        "\n",
+        "This will help you get started with __ModuleName__ [key-value stores](/docs/concepts/#key-value-stores). For detailed documentation of all __ModuleName__ByteStore features and configurations head to the [API reference](https://python.langchain.com/v0.2/api_reference/core/stores/langchain_core.stores.__module_name__ByteStore.html).\n",
+        "\n",
+        "- TODO: Add any other relevant links, like information about models, prices, context windows, etc. See https://python.langchain.com/docs/integrations/stores/in_memory/ for an example.\n",
+        "\n",
+        "## Overview\n",
+        "\n",
+        "- TODO: (Optional) A short introduction to the underlying technology/API.\n",
+        "\n",
+        "### Integration details\n",
+        "\n",
+        "- TODO: Fill in table features.\n",
+        "- TODO: Remove JS support link if not relevant, otherwise ensure link is correct.\n",
+        "- TODO: Make sure API reference links are correct.\n",
+        "\n",
+        "| Class | Package | Local | [JS support](https://js.langchain.com/docs/integrations/stores/_package_name_) | Package downloads | Package latest |\n",
+        "| :--- | :--- | :---: | :---: |  :---: | :---: |\n",
+        "| [__ModuleName__ByteStore](https://api.python.langchain.com/en/latest/stores/__module_name__.stores.__ModuleName__ByteStore.html) | [__package_name__](https://api.python.langchain.com/en/latest/__package_name_short_snake___api_reference.html) | ✅/❌ | ✅/❌ | ![PyPI - Downloads](https://img.shields.io/pypi/dm/__package_name__&label=%20) | ![PyPI - Version](https://img.shields.io/pypi/v/__package_name__&label=%20) |\n",
+        "\n",
+        "## Setup\n",
+        "\n",
+        "- TODO: Update with relevant info.\n",
+        "\n",
+        "To create a __ModuleName__ byte store, you'll need to create a/an __ModuleName__ account, get an API key, and install the `__package_name__` integration package.\n",
+        "\n",
+        "### Credentials\n",
+        "\n",
+        "- TODO: Update with relevant info, or omit if the service does not require any credentials.\n",
+        "\n",
+        "Head to (TODO: link) to sign up to __ModuleName__ and generate an API key. Once you've done this set the __MODULE_NAME___API_KEY environment variable:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "import getpass\n",
+        "import os\n",
+        "\n",
+        "if not os.getenv(\"__MODULE_NAME___API_KEY\"):\n",
+        "    os.environ[\"__MODULE_NAME___API_KEY\"] = getpass.getpass(\n",
+        "        \"Enter your __ModuleName__ API key: \"\n",
+        "    )"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Installation\n",
+        "\n",
+        "The LangChain __ModuleName__ integration lives in the `__package_name__` package:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "%pip install -qU __package_name__"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Instantiation\n",
+        "\n",
+        "Now we can instantiate our byte store:\n",
+        "\n",
+        "- TODO: Update model instantiation with relevant params."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from __module_name__ import __ModuleName__ByteStore\n",
+        "\n",
+        "kv_store = __ModuleName__ByteStore(\n",
+        "    # params...\n",
+        ")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Usage\n",
+        "\n",
+        "- TODO: Run cells so output can be seen.\n",
+        "\n",
+        "You can set data under keys like this using the `mset` method:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "kv_store.mset(\n",
+        "    [\n",
+        "        [\"key1\", b\"value1\"],\n",
+        "        [\"key2\", b\"value2\"],\n",
+        "    ]\n",
+        ")\n",
+        "\n",
+        "kv_store.mget(\n",
+        "    [\n",
+        "        \"key1\",\n",
+        "        \"key2\",\n",
+        "    ]\n",
+        ")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "And you can delete data using the `mdelete` method:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "kv_store.mdelete(\n",
+        "    [\n",
+        "        \"key1\",\n",
+        "        \"key2\",\n",
+        "    ]\n",
+        ")\n",
+        "\n",
+        "kv_store.mget(\n",
+        "    [\n",
+        "        \"key1\",\n",
+        "        \"key2\",\n",
+        "    ]\n",
+        ")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## TODO: Any functionality specific to this key-value store provider\n",
+        "\n",
+        "E.g. extra initialization. Delete if not relevant."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## API reference\n",
+        "\n",
+        "For detailed documentation of all __ModuleName__ByteStore features and configurations, head to the API reference: https://api.python.langchain.com/en/latest/stores/__module_name__.stores.__ModuleName__ByteStore.html"
+      ]
+    }
+  ],
+  "metadata": {
+    "kernelspec": {
+      "display_name": "Python 3",
+      "language": "python",
+      "name": "python3"
+    },
+    "language_info": {
+      "name": "python",
+      "version": "3.10.5"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 2
+}

libs/cli/langchain_cli/integration_template/docs/text_embedding.ipynb

@@ -0,0 +1,246 @@
+{
+ "cells": [
+  {
+   "cell_type": "raw",
+   "id": "afaf8039",
+   "metadata": {},
+   "source": [
+    "---\n",
+    "sidebar_label: __ModuleName__\n",
+    "---"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9a3d6f34",
+   "metadata": {},
+   "source": [
+    "# __ModuleName__Embeddings\n",
+    "\n",
+    "- [ ] TODO: Make sure API reference link is correct\n",
+    "\n",
+    "This will help you get started with __ModuleName__ embedding models using LangChain. For detailed documentation on `__ModuleName__Embeddings` features and configuration options, please refer to the [API reference](https://python.langchain.com/v0.2/api_reference/__package_name_short__/embeddings/__module_name__.embeddings__ModuleName__Embeddings.html).\n",
+    "\n",
+    "## Overview\n",
+    "### Integration details\n",
+    "\n",
+    "| Provider | Package |\n",
+    "|:--------:|:-------:|\n",
+    "| [__ModuleName__](/docs/integrations/providers/__package_name_short__/) | [__package_name__](https://python.langchain.com/v0.2/api_reference/__module_name__/embeddings/__module_name__.embeddings__ModuleName__Embeddings.html) |\n",
+    "\n",
+    "## Setup\n",
+    "\n",
+    "- [ ] TODO: Update with relevant info.\n",
+    "\n",
+    "To access __ModuleName__ embedding models you'll need to create a/an __ModuleName__ account, get an API key, and install the `__package_name__` integration package.\n",
+    "\n",
+    "### Credentials\n",
+    "\n",
+    "- TODO: Update with relevant info.\n",
+    "\n",
+    "Head to (TODO: link) to sign up to __ModuleName__ and generate an API key. Once you've done this set the __MODULE_NAME___API_KEY environment variable:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "36521c2a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import getpass\n",
+    "import os\n",
+    "\n",
+    "if not os.getenv(\"__MODULE_NAME___API_KEY\"):\n",
+    "    os.environ[\"__MODULE_NAME___API_KEY\"] = getpass.getpass(\n",
+    "        \"Enter your __ModuleName__ API key: \"\n",
+    "    )"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c84fb993",
+   "metadata": {},
+   "source": "To enable automated tracing of your model calls, set your [LangSmith](https://docs.smith.langchain.com/) API key:"
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "39a4953b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# os.environ[\"LANGSMITH_TRACING\"] = \"true\"\n",
+    "# os.environ[\"LANGSMITH_API_KEY\"] = getpass.getpass(\"Enter your LangSmith API key: \")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d9664366",
+   "metadata": {},
+   "source": [
+    "### Installation\n",
+    "\n",
+    "The LangChain __ModuleName__ integration lives in the `__package_name__` package:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "64853226",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%pip install -qU __package_name__"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "45dd1724",
+   "metadata": {},
+   "source": [
+    "## Instantiation\n",
+    "\n",
+    "Now we can instantiate our model object and generate chat completions:\n",
+    "\n",
+    "- TODO: Update model instantiation with relevant params."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "9ea7a09b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from __module_name__ import __ModuleName__Embeddings\n",
+    "\n",
+    "embeddings = __ModuleName__Embeddings(\n",
+    "    model=\"model-name\",\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "77d271b6",
+   "metadata": {},
+   "source": [
+    "## Indexing and Retrieval\n",
+    "\n",
+    "Embedding models are often used in retrieval-augmented generation (RAG) flows, both as part of indexing data as well as later retrieving it. For more detailed instructions, please see our [RAG tutorials](/docs/tutorials/).\n",
+    "\n",
+    "Below, see how to index and retrieve data using the `embeddings` object we initialized above. In this example, we will index and retrieve a sample document in the `InMemoryVectorStore`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "d817716b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Create a vector store with a sample text\n",
+    "from langchain_core.vectorstores import InMemoryVectorStore\n",
+    "\n",
+    "text = \"LangChain is the framework for building context-aware reasoning applications\"\n",
+    "\n",
+    "vectorstore = InMemoryVectorStore.from_texts(\n",
+    "    [text],\n",
+    "    embedding=embeddings,\n",
+    ")\n",
+    "\n",
+    "# Use the vectorstore as a retriever\n",
+    "retriever = vectorstore.as_retriever()\n",
+    "\n",
+    "# Retrieve the most similar text\n",
+    "retrieved_documents = retriever.invoke(\"What is LangChain?\")\n",
+    "\n",
+    "# show the retrieved document's content\n",
+    "retrieved_documents[0].page_content"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e02b9855",
+   "metadata": {},
+   "source": [
+    "## Direct Usage\n",
+    "\n",
+    "Under the hood, the vectorstore and retriever implementations are calling `embeddings.embed_documents(...)` and `embeddings.embed_query(...)` to create embeddings for the text(s) used in `from_texts` and retrieval `invoke` operations, respectively.\n",
+    "\n",
+    "You can directly call these methods to get embeddings for your own use cases.\n",
+    "\n",
+    "### Embed single texts\n",
+    "\n",
+    "You can embed single texts or documents with `embed_query`:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "0d2befcd",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "single_vector = embeddings.embed_query(text)\n",
+    "print(str(single_vector)[:100])  # Show the first 100 characters of the vector"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1b5a7d03",
+   "metadata": {},
+   "source": [
+    "### Embed multiple texts\n",
+    "\n",
+    "You can embed multiple texts with `embed_documents`:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "2f4d6e97",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "text2 = (\n",
+    "    \"LangGraph is a library for building stateful, multi-actor applications with LLMs\"\n",
+    ")\n",
+    "two_vectors = embeddings.embed_documents([text, text2])\n",
+    "for vector in two_vectors:\n",
+    "    print(str(vector)[:100])  # Show the first 100 characters of the vector"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "98785c12",
+   "metadata": {},
+   "source": [
+    "## API Reference\n",
+    "\n",
+    "For detailed documentation on `__ModuleName__Embeddings` features and configuration options, please refer to the [API reference](https://api.python.langchain.com/en/latest/embeddings/__module_name__.embeddings.__ModuleName__Embeddings.html).\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

libs/cli/langchain_cli/integration_template/docs/toolkits.ipynb

@@ -0,0 +1,199 @@
+{
+  "cells": [
+    {
+      "cell_type": "raw",
+      "id": "afaf8039",
+      "metadata": {},
+      "source": [
+        "---\n",
+        "sidebar_label: __ModuleName__\n",
+        "---"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "e49f1e0d",
+      "metadata": {},
+      "source": [
+        "# __ModuleName__Toolkit\n",
+        "\n",
+        "- TODO: Make sure API reference link is correct.\n",
+        "\n",
+        "This will help you get started with the __ModuleName__ [toolkit](/docs/concepts/tools/#toolkits). For detailed documentation of all __ModuleName__Toolkit features and configurations head to the [API reference](https://api.python.langchain.com/en/latest/agent_toolkits/__module_name__.agent_toolkits.__ModuleName__.toolkit.__ModuleName__Toolkit.html).\n",
+        "\n",
+        "## Setup\n",
+        "\n",
+        "- TODO: Update with relevant info."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "72ee0c4b-9764-423a-9dbf-95129e185210",
+      "metadata": {},
+      "source": "To enable automated tracing of individual tools, set your [LangSmith](https://docs.smith.langchain.com/) API key:"
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "a15d341e-3e26-4ca3-830b-5aab30ed66de",
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# os.environ[\"LANGSMITH_API_KEY\"] = getpass.getpass(\"Enter your LangSmith API key: \")\n",
+        "# os.environ[\"LANGSMITH_TRACING\"] = \"true\""
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "0730d6a1-c893-4840-9817-5e5251676d5d",
+      "metadata": {},
+      "source": [
+        "### Installation\n",
+        "\n",
+        "This toolkit lives in the `__package_name__` package:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "652d6238-1f87-422a-b135-f5abbb8652fc",
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "%pip install -qU __package_name__"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "a38cde65-254d-4219-a441-068766c0d4b5",
+      "metadata": {},
+      "source": [
+        "## Instantiation\n",
+        "\n",
+        "Now we can instantiate our toolkit:\n",
+        "\n",
+        "- TODO: Update model instantiation with relevant params."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "cb09c344-1836-4e0c-acf8-11d13ac1dbae",
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from __module_name__ import __ModuleName__Toolkit\n",
+        "\n",
+        "toolkit = __ModuleName__Toolkit(\n",
+        "    # ...\n",
+        ")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "5c5f2839-4020-424e-9fc9-07777eede442",
+      "metadata": {},
+      "source": [
+        "## Tools\n",
+        "\n",
+        "View available tools:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "51a60dbe-9f2e-4e04-bb62-23968f17164a",
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "toolkit.get_tools()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "d11245ad-3661-4405-8558-1188896347ec",
+      "metadata": {},
+      "source": [
+        "TODO: list API reference pages for individual tools."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "dfe8aad4-8626-4330-98a9-7ea1ca5d2e0e",
+      "metadata": {},
+      "source": [
+        "## Use within an agent"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "310bf18e-6c9a-4072-b86e-47bc1fcca29d",
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from langgraph.prebuilt import create_react_agent\n",
+        "\n",
+        "agent_executor = create_react_agent(llm, tools)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "23e11cc9-abd6-4855-a7eb-799f45ca01ae",
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "example_query = \"...\"\n",
+        "\n",
+        "events = agent_executor.stream(\n",
+        "    {\"messages\": [(\"user\", example_query)]},\n",
+        "    stream_mode=\"values\",\n",
+        ")\n",
+        "for event in events:\n",
+        "    event[\"messages\"][-1].pretty_print()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "d1ee55bc-ffc8-4cfa-801c-993953a08cfd",
+      "metadata": {},
+      "source": [
+        "## TODO: Any functionality or considerations specific to this toolkit\n",
+        "\n",
+        "Fill in or delete if not relevant."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "3a5bb5ca-c3ae-4a58-be67-2cd18574b9a3",
+      "metadata": {},
+      "source": [
+        "## API reference\n",
+        "\n",
+        "For detailed documentation of all __ModuleName__Toolkit features and configurations head to the [API reference](https://api.python.langchain.com/en/latest/agent_toolkits/__module_name__.agent_toolkits.__ModuleName__.toolkit.__ModuleName__Toolkit.html)."
+      ]
+    }
+  ],
+  "metadata": {
+    "kernelspec": {
+      "display_name": "Python 3 (ipykernel)",
+      "language": "python",
+      "name": "python3"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.10.4"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 5
+}

libs/cli/langchain_cli/integration_template/docs/tools.ipynb

@@ -0,0 +1,271 @@
+{
+  "cells": [
+    {
+      "cell_type": "raw",
+      "id": "10238e62-3465-4973-9279-606cbb7ccf16",
+      "metadata": {},
+      "source": [
+        "---\n",
+        "sidebar_label: __ModuleName__\n",
+        "---"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "a6f91f20",
+      "metadata": {},
+      "source": [
+        "# __ModuleName__\n",
+        "\n",
+        "- TODO: Make sure API reference link is correct.\n",
+        "\n",
+        "This notebook provides a quick overview for getting started with __ModuleName__ [tool](/docs/integrations/tools/). For detailed documentation of all __ModuleName__ features and configurations head to the [API reference](https://python.langchain.com/v0.2/api_reference/community/tools/langchain_community.tools.__module_name__.tool.__ModuleName__.html).\n",
+        "\n",
+        "- TODO: Add any other relevant links, like information about underlying API, etc.\n",
+        "\n",
+        "## Overview\n",
+        "\n",
+        "### Integration details\n",
+        "\n",
+        "- TODO: Make sure links and features are correct\n",
+        "\n",
+        "| Class | Package | Serializable | [JS support](https://js.langchain.com/docs/integrations/tools/__module_name__) |  Package latest |\n",
+        "| :--- | :--- | :---: | :---: | :---: |\n",
+        "| [__ModuleName__](https://python.langchain.com/v0.2/api_reference/community/tools/langchain_community.tools.__module_name__.tool.__ModuleName__.html) | [langchain-community](https://api.python.langchain.com/en/latest/community_api_reference.html) | beta/❌ | ✅/❌ |  ![PyPI - Version](https://img.shields.io/pypi/v/langchain-community&label=%20) |\n",
+        "\n",
+        "### Tool features\n",
+        "\n",
+        "- TODO: Add feature table if it makes sense\n",
+        "\n",
+        "\n",
+        "## Setup\n",
+        "\n",
+        "- TODO: Add any additional deps\n",
+        "\n",
+        "The integration lives in the `langchain-community` package."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "f85b4089",
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "%pip install --quiet -U langchain-community"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "b15e9266",
+      "metadata": {},
+      "source": [
+        "### Credentials\n",
+        "\n",
+        "- TODO: Add any credentials that are needed"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 2,
+      "id": "e0b178a2-8816-40ca-b57c-ccdd86dde9c9",
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "import getpass\n",
+        "import os\n",
+        "\n",
+        "# if not os.environ.get(\"__MODULE_NAME___API_KEY\"):\n",
+        "#     os.environ[\"__MODULE_NAME___API_KEY\"] = getpass.getpass(\"__MODULE_NAME__ API key:\\n\")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "bc5ab717-fd27-4c59-b912-bdd099541478",
+      "metadata": {},
+      "source": [
+        "It's also helpful (but not needed) to set up [LangSmith](https://smith.langchain.com/) for best-in-class observability:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 3,
+      "id": "a6c2f136-6367-4f1f-825d-ae741e1bf281",
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# os.environ[\"LANGSMITH_TRACING\"] = \"true\"\n",
+        "# os.environ[\"LANGSMITH_API_KEY\"] = getpass.getpass()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "1c97218f-f366-479d-8bf7-fe9f2f6df73f",
+      "metadata": {},
+      "source": [
+        "## Instantiation\n",
+        "\n",
+        "- TODO: Fill in instantiation params\n",
+        "\n",
+        "Here we show how to instantiate an instance of the __ModuleName__ tool, with "
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 4,
+      "id": "8b3ddfe9-ca79-494c-a7ab-1f56d9407a64",
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from langchain_community.tools import __ModuleName__\n",
+        "\n",
+        "\n",
+        "tool = __ModuleName__(...)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "74147a1a",
+      "metadata": {},
+      "source": [
+        "## Invocation\n",
+        "\n",
+        "### [Invoke directly with args](/docs/concepts/tools/#use-the-tool-directly)\n",
+        "\n",
+        "- TODO: Describe what the tool args are, fill them in, run cell"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "65310a8b-eb0c-4d9e-a618-4f4abe2414fc",
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "tool.invoke({...})"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "d6e73897",
+      "metadata": {},
+      "source": [
+        "### [Invoke with ToolCall](/docs/concepts/tool_calling/#tool-execution)\n",
+        "\n",
+        "We can also invoke the tool with a model-generated ToolCall, in which case a ToolMessage will be returned:\n",
+        "\n",
+        "- TODO: Fill in tool args and run cell"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "f90e33a7",
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# This is usually generated by a model, but we'll create a tool call directly for demo purposes.\n",
+        "model_generated_tool_call = {\n",
+        "    \"args\": {...},  # TODO: FILL IN\n",
+        "    \"id\": \"1\",\n",
+        "    \"name\": tool.name,\n",
+        "    \"type\": \"tool_call\",\n",
+        "}\n",
+        "tool.invoke(model_generated_tool_call)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "659f9fbd-6fcf-445f-aa8c-72d8e60154bd",
+      "metadata": {},
+      "source": [
+        "## Use within an agent\n",
+        "\n",
+        "- TODO: Add user question and run cells\n",
+        "\n",
+        "We can use our tool in an [agent](/docs/concepts/agents/). For this we will need a LLM with [tool-calling](/docs/how_to/tool_calling/) capabilities:\n",
+        "\n",
+        "import ChatModelTabs from \"@theme/ChatModelTabs\";\n",
+        "\n",
+        "<ChatModelTabs customVarName=\"llm\" />\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "af3123ad-7a02-40e5-b58e-7d56e23e5830",
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# | output: false\n",
+        "# | echo: false\n",
+        "\n",
+        "# !pip install -qU langchain langchain-openai\n",
+        "from langchain.chat_models import init_chat_model\n",
+        "\n",
+        "model = init_chat_model(model=\"gpt-4o\", model_provider=\"openai\")"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "bea35fa1",
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from langgraph.prebuilt import create_react_agent\n",
+        "\n",
+        "tools = [tool]\n",
+        "agent = create_react_agent(model, tools)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "fdbf35b5-3aaf-4947-9ec6-48c21533fb95",
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "example_query = \"...\"\n",
+        "\n",
+        "events = agent.stream(\n",
+        "    {\"messages\": [(\"user\", example_query)]},\n",
+        "    stream_mode=\"values\",\n",
+        ")\n",
+        "for event in events:\n",
+        "    event[\"messages\"][-1].pretty_print()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "4ac8146c",
+      "metadata": {},
+      "source": [
+        "## API reference\n",
+        "\n",
+        "For detailed documentation of all __ModuleName__ features and configurations head to the API reference: https://python.langchain.com/v0.2/api_reference/community/tools/langchain_community.tools.__module_name__.tool.__ModuleName__.html"
+      ]
+    }
+  ],
+  "metadata": {
+    "kernelspec": {
+      "display_name": "poetry-venv-311",
+      "language": "python",
+      "name": "poetry-venv-311"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.11.9"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 5
+}

libs/cli/langchain_cli/integration_template/docs/vectorstores.ipynb

@@ -0,0 +1,333 @@
+{
+ "cells": [
+  {
+   "cell_type": "raw",
+   "id": "1957f5cb",
+   "metadata": {},
+   "source": [
+    "---\n",
+    "sidebar_label: __ModuleName__\n",
+    "---"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ef1f0986",
+   "metadata": {},
+   "source": [
+    "# __ModuleName__VectorStore\n",
+    "\n",
+    "This notebook covers how to get started with the __ModuleName__ vector store."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "36fdc060",
+   "metadata": {},
+   "source": [
+    "## Setup\n",
+    "\n",
+    "- TODO: Update with relevant info.\n",
+    "- TODO: Update minimum version to be correct.\n",
+    "\n",
+    "To access __ModuleName__ vector stores you'll need to create a/an __ModuleName__ account, get an API key, and install the `__package_name__` integration package."
+   ]
+  },
+  {
+   "cell_type": "raw",
+   "id": "64e28aa6",
+   "metadata": {
+    "vscode": {
+     "languageId": "raw"
+    }
+   },
+   "source": [
+    "%pip install -qU \"__package_name__>=MINIMUM_VERSION\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9695dee7",
+   "metadata": {},
+   "source": [
+    "### Credentials\n",
+    "\n",
+    "- TODO: Update with relevant info.\n",
+    "\n",
+    "Head to (TODO: link) to sign up to __ModuleName__ and generate an API key. Once you've done this set the __MODULE_NAME___API_KEY environment variable:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "894c30e4",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import getpass\n",
+    "import os\n",
+    "\n",
+    "if not os.getenv(\"__MODULE_NAME___API_KEY\"):\n",
+    "    os.environ[\"__MODULE_NAME___API_KEY\"] = getpass.getpass(\n",
+    "        \"Enter your __ModuleName__ API key: \"\n",
+    "    )"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7f98392b",
+   "metadata": {},
+   "source": "To enable automated tracing of your model calls, set your [LangSmith](https://docs.smith.langchain.com/) API key:"
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "e7b6a6e0",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# os.environ[\"LANGSMITH_API_KEY\"] = getpass.getpass(\"Enter your LangSmith API key: \")\n",
+    "# os.environ[\"LANGSMITH_TRACING\"] = \"true\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "93df377e",
+   "metadata": {},
+   "source": [
+    "## Initialization\n",
+    "\n",
+    "- TODO: Fill out with relevant init params\n",
+    "\n",
+    "\n",
+    "```{=mdx}\n",
+    "import EmbeddingTabs from \"@theme/EmbeddingTabs\";\n",
+    "\n",
+    "<EmbeddingTabs/>\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "dc37144c-208d-4ab3-9f3a-0407a69fe052",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "from __module_name__.vectorstores import __ModuleName__VectorStore\n",
+    "\n",
+    "vector_store = __ModuleName__VectorStore(embeddings=embeddings)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ac6071d4",
+   "metadata": {},
+   "source": [
+    "## Manage vector store\n",
+    "\n",
+    "### Add items to vector store\n",
+    "\n",
+    "- TODO: Edit and then run code cell to generate output"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "17f5efc0",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain_core.documents import Document\n",
+    "\n",
+    "document_1 = Document(page_content=\"foo\", metadata={\"source\": \"https://example.com\"})\n",
+    "\n",
+    "document_2 = Document(page_content=\"bar\", metadata={\"source\": \"https://example.com\"})\n",
+    "\n",
+    "document_3 = Document(page_content=\"baz\", metadata={\"source\": \"https://example.com\"})\n",
+    "\n",
+    "documents = [document_1, document_2, document_3]\n",
+    "\n",
+    "vector_store.add_documents(documents=documents, ids=[\"1\", \"2\", \"3\"])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c738c3e0",
+   "metadata": {},
+   "source": [
+    "### Update items in vector store\n",
+    "\n",
+    "- TODO: Edit and then run code cell to generate output"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "f0aa8b71",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "updated_document = Document(\n",
+    "    page_content=\"qux\", metadata={\"source\": \"https://another-example.com\"}\n",
+    ")\n",
+    "\n",
+    "vector_store.update_documents(document_id=\"1\", document=updated_document)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "dcf1b905",
+   "metadata": {},
+   "source": [
+    "### Delete items from vector store\n",
+    "\n",
+    "- TODO: Edit and then run code cell to generate output"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "ef61e188",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vector_store.delete(ids=[\"3\"])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c3620501",
+   "metadata": {},
+   "source": [
+    "## Query vector store\n",
+    "\n",
+    "Once your vector store has been created and the relevant documents have been added you will most likely wish to query it during the running of your chain or agent.\n",
+    "\n",
+    "### Query directly\n",
+    "\n",
+    "Performing a simple similarity search can be done as follows:\n",
+    "\n",
+    "- TODO: Edit and then run code cell to generate output"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "aa0a16fa",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "results = vector_store.similarity_search(\n",
+    "    query=\"thud\", k=1, filter={\"source\": \"https://another-example.com\"}\n",
+    ")\n",
+    "for doc in results:\n",
+    "    print(f\"* {doc.page_content} [{doc.metadata}]\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3ed9d733",
+   "metadata": {},
+   "source": [
+    "If you want to execute a similarity search and receive the corresponding scores you can run:\n",
+    "\n",
+    "- TODO: Edit and then run code cell to generate output"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "5efd2eaa",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "results = vector_store.similarity_search_with_score(\n",
+    "    query=\"thud\", k=1, filter={\"source\": \"https://example.com\"}\n",
+    ")\n",
+    "for doc, score in results:\n",
+    "    print(f\"* [SIM={score:3f}] {doc.page_content} [{doc.metadata}]\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0c235cdc",
+   "metadata": {},
+   "source": [
+    "### Query by turning into retriever\n",
+    "\n",
+    "You can also transform the vector store into a retriever for easier usage in your chains.\n",
+    "\n",
+    "- TODO: Edit and then run code cell to generate output"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "f3460093",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "retriever = vector_store.as_retriever(search_type=\"mmr\", search_kwargs={\"k\": 1})\n",
+    "retriever.invoke(\"thud\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "901c75dc",
+   "metadata": {},
+   "source": [
+    "## Usage for retrieval-augmented generation\n",
+    "\n",
+    "For guides on how to use this vector store for retrieval-augmented generation (RAG), see the following sections:\n",
+    "\n",
+    "- [Tutorials](/docs/tutorials/)\n",
+    "- [How-to: Question and answer with RAG](https://python.langchain.com/docs/how_to/#qa-with-rag)\n",
+    "- [Retrieval conceptual docs](https://python.langchain.com/docs/concepts/retrieval/)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "069f1b5f",
+   "metadata": {},
+   "source": [
+    "## TODO: Any functionality specific to this vector store\n",
+    "\n",
+    "E.g. creating a persistent database to save to your disk, etc."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8a27244f",
+   "metadata": {},
+   "source": [
+    "## API reference\n",
+    "\n",
+    "For detailed documentation of all __ModuleName__VectorStore features and configurations head to the API reference: https://api.python.langchain.com/en/latest/vectorstores/__module_name__.vectorstores.__ModuleName__VectorStore.html"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

libs/cli/langchain_cli/integration_template/integration_template/__init__.py

@@ -0,0 +1,27 @@
+from importlib import metadata
+
+from __module_name__.chat_models import Chat__ModuleName__
+from __module_name__.document_loaders import __ModuleName__Loader
+from __module_name__.embeddings import __ModuleName__Embeddings
+from __module_name__.retrievers import __ModuleName__Retriever
+from __module_name__.toolkits import __ModuleName__Toolkit
+from __module_name__.tools import __ModuleName__Tool
+from __module_name__.vectorstores import __ModuleName__VectorStore
+
+try:
+    __version__ = metadata.version(__package__)
+except metadata.PackageNotFoundError:
+    # Case where package metadata is not available.
+    __version__ = ""
+del metadata  # optional, avoids polluting the results of dir(__package__)
+
+__all__ = [
+    "Chat__ModuleName__",
+    "__ModuleName__VectorStore",
+    "__ModuleName__Embeddings",
+    "__ModuleName__Loader",
+    "__ModuleName__Retriever",
+    "__ModuleName__Toolkit",
+    "__ModuleName__Tool",
+    "__version__",
+]

libs/cli/langchain_cli/integration_template/integration_template/chat_models.py

@@ -0,0 +1,423 @@
+"""__ModuleName__ chat models."""
+
+from typing import Any, Dict, Iterator, List
+
+from langchain_core.callbacks import (
+    CallbackManagerForLLMRun,
+)
+from langchain_core.language_models import BaseChatModel
+from langchain_core.messages import (
+    AIMessage,
+    AIMessageChunk,
+    BaseMessage,
+)
+from langchain_core.messages.ai import UsageMetadata
+from langchain_core.outputs import ChatGeneration, ChatGenerationChunk, ChatResult
+from pydantic import Field
+
+
+class Chat__ModuleName__(BaseChatModel):
+    # TODO: Replace all TODOs in docstring. See example docstring:
+    # https://github.com/langchain-ai/langchain/blob/7ff05357bac6eaedf5058a2af88f23a1817d40fe/libs/partners/openai/langchain_openai/chat_models/base.py#L1120
+    """__ModuleName__ chat model integration.
+
+    The default implementation echoes the first `parrot_buffer_length` characters of
+    the input.
+
+    # TODO: Replace with relevant packages, env vars.
+    Setup:
+        Install `__package_name__` and set environment variable
+        `__MODULE_NAME___API_KEY`.
+
+        ```bash
+        pip install -U __package_name__
+        export __MODULE_NAME___API_KEY="your-api-key"
+        ```
+
+    # TODO: Populate with relevant params.
+    Key init args — completion params:
+        model:
+            Name of __ModuleName__ model to use.
+        temperature:
+            Sampling temperature.
+        max_tokens:
+            Max number of tokens to generate.
+
+    # TODO: Populate with relevant params.
+    Key init args — client params:
+        timeout:
+            Timeout for requests.
+        max_retries:
+            Max number of retries.
+        api_key:
+            __ModuleName__ API key. If not passed in will be read from env var
+            __MODULE_NAME___API_KEY.
+
+    See full list of supported init args and their descriptions in the params section.
+
+    # TODO: Replace with relevant init params.
+    Instantiate:
+        ```python
+        from __module_name__ import Chat__ModuleName__
+
+        model = Chat__ModuleName__(
+            model="...",
+            temperature=0,
+            max_tokens=None,
+            timeout=None,
+            max_retries=2,
+            # api_key="...",
+            # other params...
+        )
+        ```
+
+    Invoke:
+        ```python
+        messages = [
+            ("system", "You are a helpful translator. Translate the user sentence to French."),
+            ("human", "I love programming."),
+        ]
+        model.invoke(messages)
+        ```
+
+        ```python
+        # TODO: Example output.
+        ```
+
+    # TODO: Delete if token-level streaming isn't supported.
+    Stream:
+        ```python
+        for chunk in model.stream(messages):
+            print(chunk.text, end="")
+        ```
+
+        ```python
+        # TODO: Example output.
+        ```
+
+        ```python
+        stream = model.stream(messages)
+        full = next(stream)
+        for chunk in stream:
+            full += chunk
+        full
+        ```
+
+        ```python
+        # TODO: Example output.
+        ```
+
+    # TODO: Delete if native async isn't supported.
+    Async:
+        ```python
+        await model.ainvoke(messages)
+
+        # stream:
+        # async for chunk in (await model.astream(messages))
+
+        # batch:
+        # await model.abatch([messages])
+        ```
+
+        ```python
+        # TODO: Example output.
+        ```
+    # TODO: Delete if .bind_tools() isn't supported.
+    Tool calling:
+        ```python
+        from pydantic import BaseModel, Field
+
+        class GetWeather(BaseModel):
+            '''Get the current weather in a given location'''
+
+            location: str = Field(..., description="The city and state, e.g. San Francisco, CA")
+
+        class GetPopulation(BaseModel):
+            '''Get the current population in a given location'''
+
+            location: str = Field(..., description="The city and state, e.g. San Francisco, CA")
+
+        model_with_tools = model.bind_tools([GetWeather, GetPopulation])
+        ai_msg = model_with_tools.invoke("Which city is hotter today and which is bigger: LA or NY?")
+        ai_msg.tool_calls
+        ```
+
+        ```python
+        # TODO: Example output.
+        ```
+
+        See `Chat__ModuleName__.bind_tools()` method for more.
+
+    # TODO: Delete if .with_structured_output() isn't supported.
+    Structured output:
+        ```python
+        from typing import Optional
+
+        from pydantic import BaseModel, Field
+
+        class Joke(BaseModel):
+            '''Joke to tell user.'''
+
+            setup: str = Field(description="The setup of the joke")
+            punchline: str = Field(description="The punchline to the joke")
+            rating: int | None = Field(description="How funny the joke is, from 1 to 10")
+
+        structured_model = model.with_structured_output(Joke)
+        structured_model.invoke("Tell me a joke about cats")
+        ```
+
+        ```python
+        # TODO: Example output.
+        ```
+
+        See `Chat__ModuleName__.with_structured_output()` for more.
+
+    # TODO: Delete if JSON mode response format isn't supported.
+    JSON mode:
+        ```python
+        # TODO: Replace with appropriate bind arg.
+        json_model = model.bind(response_format={"type": "json_object"})
+        ai_msg = json_model.invoke("Return a JSON object with key 'random_ints' and a value of 10 random ints in [0-99]")
+        ai_msg.content
+        ```
+
+        ```python
+        # TODO: Example output.
+        ```
+
+    # TODO: Delete if image inputs aren't supported.
+    Image input:
+        ```python
+        import base64
+        import httpx
+        from langchain_core.messages import HumanMessage
+
+        image_url = "https://upload.wikimedia.org/wikipedia/commons/thumb/d/dd/Gfp-wisconsin-madison-the-nature-boardwalk.jpg/2560px-Gfp-wisconsin-madison-the-nature-boardwalk.jpg"
+        image_data = base64.b64encode(httpx.get(image_url).content).decode("utf-8")
+        # TODO: Replace with appropriate message content format.
+        message = HumanMessage(
+            content=[
+                {"type": "text", "text": "describe the weather in this image"},
+                {
+                    "type": "image_url",
+                    "image_url": {"url": f"data:image/jpeg;base64,{image_data}"},
+                },
+            ],
+        )
+        ai_msg = model.invoke([message])
+        ai_msg.content
+        ```
+
+        ```python
+        # TODO: Example output.
+        ```
+
+    # TODO: Delete if audio inputs aren't supported.
+    Audio input:
+        ```python
+        # TODO: Example input
+        ```
+
+        ```python
+        # TODO: Example output
+        ```
+
+    # TODO: Delete if video inputs aren't supported.
+    Video input:
+        ```python
+        # TODO: Example input
+        ```
+
+        ```python
+        # TODO: Example output
+        ```
+
+    # TODO: Delete if token usage metadata isn't supported.
+    Token usage:
+        ```python
+        ai_msg = model.invoke(messages)
+        ai_msg.usage_metadata
+        ```
+
+        ```python
+        {'input_tokens': 28, 'output_tokens': 5, 'total_tokens': 33}
+        ```
+
+    # TODO: Delete if logprobs aren't supported.
+    Logprobs:
+        ```python
+        # TODO: Replace with appropriate bind arg.
+        logprobs_model = model.bind(logprobs=True)
+        ai_msg = logprobs_model.invoke(messages)
+        ai_msg.response_metadata["logprobs"]
+        ```
+
+        ```python
+        # TODO: Example output.
+        ```
+    Response metadata
+        ```python
+        ai_msg = model.invoke(messages)
+        ai_msg.response_metadata
+        ```
+
+        ```python
+        # TODO: Example output.
+
+        ```
+    """  # noqa: E501
+
+    model_name: str = Field(alias="model")
+    """The name of the model"""
+    parrot_buffer_length: int
+    """The number of characters from the last message of the prompt to be echoed."""
+    temperature: float | None = None
+    max_tokens: int | None = None
+    timeout: int | None = None
+    stop: list[str] | None = None
+    max_retries: int = 2
+
+    @property
+    def _llm_type(self) -> str:
+        """Return type of chat model."""
+        return "chat-__package_name_short__"
+
+    @property
+    def _identifying_params(self) -> Dict[str, Any]:
+        """Return a dictionary of identifying parameters.
+
+        This information is used by the LangChain callback system, which
+        is used for tracing purposes make it possible to monitor LLMs.
+        """
+        return {
+            # The model name allows users to specify custom token counting
+            # rules in LLM monitoring applications (e.g., in LangSmith users
+            # can provide per token pricing for their model and monitor
+            # costs for the given LLM.)
+            "model_name": self.model_name,
+        }
+
+    def _generate(
+        self,
+        messages: List[BaseMessage],
+        stop: list[str] | None = None,
+        run_manager: CallbackManagerForLLMRun | None = None,
+        **kwargs: Any,
+    ) -> ChatResult:
+        """Override the _generate method to implement the chat model logic.
+
+        This can be a call to an API, a call to a local model, or any other
+        implementation that generates a response to the input prompt.
+
+        Args:
+            messages: the prompt composed of a list of messages.
+            stop: a list of strings on which the model should stop generating.
+                If generation stops due to a stop token, the stop token itself
+                SHOULD BE INCLUDED as part of the output. This is not enforced
+                across models right now, but it's a good practice to follow since
+                it makes it much easier to parse the output of the model
+                downstream and understand why generation stopped.
+            run_manager: A run manager with callbacks for the LLM.
+        """
+        # Replace this with actual logic to generate a response from a list
+        # of messages.
+        last_message = messages[-1]
+        tokens = last_message.content[: self.parrot_buffer_length]
+        ct_input_tokens = sum(len(message.content) for message in messages)
+        ct_output_tokens = len(tokens)
+        message = AIMessage(
+            content=tokens,
+            additional_kwargs={},  # Used to add additional payload to the message
+            response_metadata={  # Use for response metadata
+                "time_in_seconds": 3,
+                "model_name": self.model_name,
+            },
+            usage_metadata={
+                "input_tokens": ct_input_tokens,
+                "output_tokens": ct_output_tokens,
+                "total_tokens": ct_input_tokens + ct_output_tokens,
+            },
+        )
+        ##
+
+        generation = ChatGeneration(message=message)
+        return ChatResult(generations=[generation])
+
+    def _stream(
+        self,
+        messages: List[BaseMessage],
+        stop: list[str] | None = None,
+        run_manager: CallbackManagerForLLMRun | None = None,
+        **kwargs: Any,
+    ) -> Iterator[ChatGenerationChunk]:
+        """Stream the output of the model.
+
+        This method should be implemented if the model can generate output
+        in a streaming fashion. If the model does not support streaming,
+        do not implement it. In that case streaming requests will be automatically
+        handled by the _generate method.
+
+        Args:
+            messages: the prompt composed of a list of messages.
+            stop: a list of strings on which the model should stop generating.
+                If generation stops due to a stop token, the stop token itself
+                SHOULD BE INCLUDED as part of the output. This is not enforced
+                across models right now, but it's a good practice to follow since
+                it makes it much easier to parse the output of the model
+                downstream and understand why generation stopped.
+            run_manager: A run manager with callbacks for the LLM.
+        """
+        last_message = messages[-1]
+        tokens = str(last_message.content[: self.parrot_buffer_length])
+        ct_input_tokens = sum(len(message.content) for message in messages)
+
+        for token in tokens:
+            usage_metadata = UsageMetadata(
+                {
+                    "input_tokens": ct_input_tokens,
+                    "output_tokens": 1,
+                    "total_tokens": ct_input_tokens + 1,
+                }
+            )
+            ct_input_tokens = 0
+            chunk = ChatGenerationChunk(
+                message=AIMessageChunk(content=token, usage_metadata=usage_metadata)
+            )
+
+            if run_manager:
+                # This is optional in newer versions of LangChain
+                # The on_llm_new_token will be called automatically
+                run_manager.on_llm_new_token(token, chunk=chunk)
+
+            yield chunk
+
+        # Let's add some other information (e.g., response metadata)
+        chunk = ChatGenerationChunk(
+            message=AIMessageChunk(
+                content="",
+                response_metadata={"time_in_sec": 3, "model_name": self.model_name},
+            )
+        )
+        if run_manager:
+            # This is optional in newer versions of LangChain
+            # The on_llm_new_token will be called automatically
+            run_manager.on_llm_new_token(token, chunk=chunk)
+        yield chunk
+
+    # TODO: Implement if Chat__ModuleName__ supports async streaming. Otherwise delete.
+    # async def _astream(
+    #     self,
+    #     messages: List[BaseMessage],
+    #     stop: list[str] | None = None,
+    #     run_manager: AsyncCallbackManagerForLLMRun | None = None,
+    #     **kwargs: Any,
+    # ) -> AsyncIterator[ChatGenerationChunk]:
+
+    # TODO: Implement if Chat__ModuleName__ supports async generation. Otherwise delete.
+    # async def _agenerate(
+    #     self,
+    #     messages: List[BaseMessage],
+    #     stop: list[str] | None = None,
+    #     run_manager: AsyncCallbackManagerForLLMRun | None = None,
+    #     **kwargs: Any,
+    # ) -> ChatResult:

libs/cli/langchain_cli/integration_template/integration_template/document_loaders.py

@@ -0,0 +1,74 @@
+"""__ModuleName__ document loader."""
+
+from typing import Iterator
+
+from langchain_core.document_loaders.base import BaseLoader
+from langchain_core.documents import Document
+
+
+class __ModuleName__Loader(BaseLoader):
+    # TODO: Replace all TODOs in docstring. See example docstring:
+    # https://github.com/langchain-ai/langchain/blob/869523ad728e6b76d77f170cce13925b4ebc3c1e/libs/community/langchain_community/document_loaders/recursive_url_loader.py#L54
+    """
+    __ModuleName__ document loader integration
+
+    # TODO: Replace with relevant packages, env vars.
+    Setup:
+        Install `__package_name__` and set environment variable
+        `__MODULE_NAME___API_KEY`.
+
+        ```bash
+        pip install -U __package_name__
+        export __MODULE_NAME___API_KEY="your-api-key"
+        ```
+
+    # TODO: Replace with relevant init params.
+    Instantiate:
+        ```python
+        from langchain_community.document_loaders import __ModuleName__Loader
+
+        loader = __ModuleName__Loader(
+            # required params = ...
+            # other params = ...
+        )
+        ```
+
+    Lazy load:
+        ```python
+        docs = []
+        docs_lazy = loader.lazy_load()
+
+        # async variant:
+        # docs_lazy = await loader.alazy_load()
+
+        for doc in docs_lazy:
+            docs.append(doc)
+        print(docs[0].page_content[:100])
+        print(docs[0].metadata)
+        ```
+
+        ```python
+        TODO: Example output
+        ```
+
+    # TODO: Delete if async load is not implemented
+    Async load:
+        ```python
+        docs = await loader.aload()
+        print(docs[0].page_content[:100])
+        print(docs[0].metadata)
+        ```
+
+        ```python
+        TODO: Example output
+
+        ```
+    """
+
+    # TODO: This method must be implemented to load documents.
+    # Do not implement load(), a default implementation is already available.
+    def lazy_load(self) -> Iterator[Document]:
+        raise NotImplementedError()
+
+    # TODO: Implement if you would like to change default BaseLoader implementation
+    # async def alazy_load(self) -> AsyncIterator[Document]:

libs/cli/langchain_cli/integration_template/integration_template/embeddings.py

@@ -0,0 +1,96 @@
+from typing import List
+
+from langchain_core.embeddings import Embeddings
+
+
+class __ModuleName__Embeddings(Embeddings):
+    """__ModuleName__ embedding model integration.
+
+    # TODO: Replace with relevant packages, env vars.
+    Setup:
+        Install `__package_name__` and set environment variable
+        `__MODULE_NAME___API_KEY`.
+
+        ```bash
+        pip install -U __package_name__
+        export __MODULE_NAME___API_KEY="your-api-key"
+        ```
+
+    # TODO: Populate with relevant params.
+    Key init args — completion params:
+        model: str
+            Name of __ModuleName__ model to use.
+
+    See full list of supported init args and their descriptions in the params section.
+
+    # TODO: Replace with relevant init params.
+    Instantiate:
+        ```python
+        from __module_name__ import __ModuleName__Embeddings
+
+        embed = __ModuleName__Embeddings(
+            model="...",
+            # api_key="...",
+            # other params...
+        )
+        ```
+
+    Embed single text:
+        ```python
+        input_text = "The meaning of life is 42"
+        embed.embed_query(input_text)
+        ```
+
+        ```python
+        # TODO: Example output.
+        ```
+
+    # TODO: Delete if token-level streaming isn't supported.
+    Embed multiple text:
+        ```python
+        input_texts = ["Document 1...", "Document 2..."]
+        embed.embed_documents(input_texts)
+        ```
+
+        ```python
+        # TODO: Example output.
+        ```
+
+    # TODO: Delete if native async isn't supported.
+    Async:
+        ```python
+        await embed.aembed_query(input_text)
+
+        # multiple:
+        # await embed.aembed_documents(input_texts)
+        ```
+
+        ```python
+        # TODO: Example output.
+
+        ```
+    """
+
+    def __init__(self, model: str):
+        self.model = model
+
+    def embed_documents(self, texts: List[str]) -> List[List[float]]:
+        """Embed search docs."""
+        return [[0.5, 0.6, 0.7] for _ in texts]
+
+    def embed_query(self, text: str) -> List[float]:
+        """Embed query text."""
+        return self.embed_documents([text])[0]
+
+    # optional: add custom async implementations here
+    # you can also delete these, and the base class will
+    # use the default implementation, which calls the sync
+    # version in an async executor:
+
+    # async def aembed_documents(self, texts: List[str]) -> List[List[float]]:
+    #     """Asynchronous Embed search docs."""
+    #     ...
+
+    # async def aembed_query(self, text: str) -> List[float]:
+    #     """Asynchronous Embed query text."""
+    #     ...

libs/cli/langchain_cli/integration_template/integration_template/retrievers.py

@@ -0,0 +1,107 @@
+"""__ModuleName__ retrievers."""
+
+from typing import Any, List
+
+from langchain_core.callbacks import CallbackManagerForRetrieverRun
+from langchain_core.documents import Document
+from langchain_core.retrievers import BaseRetriever
+
+
+class __ModuleName__Retriever(BaseRetriever):
+    # TODO: Replace all TODOs in docstring. See example docstring:
+    # https://github.com/langchain-ai/langchain/blob/master/libs/community/langchain_community/retrievers/tavily_search_api.py#L17
+    """__ModuleName__ retriever.
+
+    # TODO: Replace with relevant packages, env vars, etc.
+    Setup:
+        Install `__package_name__` and set environment variable
+        `__MODULE_NAME___API_KEY`.
+
+        ```bash
+        pip install -U __package_name__
+        export __MODULE_NAME___API_KEY="your-api-key"
+        ```
+
+    # TODO: Populate with relevant params.
+    Key init args:
+        arg 1: type
+            description
+        arg 2: type
+            description
+
+    # TODO: Replace with relevant init params.
+    Instantiate:
+        ```python
+        from __package_name__ import __ModuleName__Retriever
+
+        retriever = __ModuleName__Retriever(
+            # ...
+        )
+        ```
+
+    Usage:
+        ```python
+        query = "..."
+
+        retriever.invoke(query)
+        ```
+
+        ```txt
+        # TODO: Example output.
+        ```
+
+    Use within a chain:
+        ```python
+        from langchain_core.output_parsers import StrOutputParser
+        from langchain_core.prompts import ChatPromptTemplate
+        from langchain_core.runnables import RunnablePassthrough
+        from langchain_openai import ChatOpenAI
+
+        prompt = ChatPromptTemplate.from_template(
+            \"\"\"Answer the question based only on the context provided.
+
+        Context: {context}
+
+        Question: {question}\"\"\"
+        )
+
+        model = ChatOpenAI(model="gpt-3.5-turbo-0125")
+
+        def format_docs(docs):
+            return "\\n\\n".join(doc.page_content for doc in docs)
+
+        chain = (
+            {"context": retriever | format_docs, "question": RunnablePassthrough()}
+            | prompt
+            | model
+            | StrOutputParser()
+        )
+
+        chain.invoke("...")
+        ```
+
+        ```
+        # TODO: Example output.
+        ```
+
+    """
+
+    k: int = 3
+
+    # TODO: This method must be implemented to retrieve documents.
+    def _get_relevant_documents(
+        self, query: str, *, run_manager: CallbackManagerForRetrieverRun, **kwargs: Any
+    ) -> List[Document]:
+        k = kwargs.get("k", self.k)
+        return [
+            Document(page_content=f"Result {i} for query: {query}") for i in range(k)
+        ]
+
+    # optional: add custom async implementations here
+    # async def _aget_relevant_documents(
+    #     self,
+    #     query: str,
+    #     *,
+    #     run_manager: AsyncCallbackManagerForRetrieverRun,
+    #     **kwargs: Any,
+    # ) -> List[Document]: ...

libs/cli/langchain_cli/integration_template/integration_template/toolkits.py

@@ -0,0 +1,73 @@
+"""__ModuleName__ toolkits."""
+
+from typing import List
+
+from langchain_core.tools import BaseTool, BaseToolkit
+
+
+class __ModuleName__Toolkit(BaseToolkit):
+    # TODO: Replace all TODOs in docstring. See example docstring:
+    # https://github.com/langchain-ai/langchain/blob/c123cb2b304f52ab65db4714eeec46af69a861ec/libs/community/langchain_community/agent_toolkits/sql/toolkit.py#L19
+    """__ModuleName__ toolkit.
+
+    # TODO: Replace with relevant packages, env vars, etc.
+    Setup:
+        Install `__package_name__` and set environment variable
+        `__MODULE_NAME___API_KEY`.
+
+        ```bash
+        pip install -U __package_name__
+        export __MODULE_NAME___API_KEY="your-api-key"
+        ```
+
+    # TODO: Populate with relevant params.
+    Key init args:
+        arg 1: type
+            description
+        arg 2: type
+            description
+
+    # TODO: Replace with relevant init params.
+    Instantiate:
+        ```python
+        from __package_name__ import __ModuleName__Toolkit
+
+        toolkit = __ModuleName__Toolkit(
+            # ...
+        )
+        ```
+
+    Tools:
+        ```python
+        toolkit.get_tools()
+        ```
+
+        ```txt
+        # TODO: Example output.
+        ```
+
+    Use within an agent:
+        ```python
+        from langgraph.prebuilt import create_react_agent
+
+        agent_executor = create_react_agent(llm, tools)
+
+        example_query = "..."
+
+        events = agent_executor.stream(
+            {"messages": [("user", example_query)]},
+            stream_mode="values",
+        )
+        for event in events:
+            event["messages"][-1].pretty_print()
+        ```
+
+        ```txt
+        # TODO: Example output.
+        ```
+
+    """
+
+    # TODO: This method must be implemented to list tools.
+    def get_tools(self) -> List[BaseTool]:
+        raise NotImplementedError()

libs/cli/langchain_cli/integration_template/integration_template/tools.py

@@ -0,0 +1,95 @@
+"""__ModuleName__ tools."""
+
+from typing import Type
+
+from langchain_core.callbacks import (
+    CallbackManagerForToolRun,
+)
+from langchain_core.tools import BaseTool
+from pydantic import BaseModel, Field
+
+
+class __ModuleName__ToolInput(BaseModel):
+    """Input schema for __ModuleName__ tool.
+
+    This docstring is **not** part of what is sent to the model when performing tool
+    calling. The Field default values and descriptions **are** part of what is sent to
+    the model when performing tool calling.
+    """
+
+    # TODO: Add input args and descriptions.
+    a: int = Field(..., description="first number to add")
+    b: int = Field(..., description="second number to add")
+
+
+class __ModuleName__Tool(BaseTool):  # type: ignore[override]
+    """__ModuleName__ tool.
+
+    Setup:
+        # TODO: Replace with relevant packages, env vars.
+        Install `__package_name__` and set environment variable
+        `__MODULE_NAME___API_KEY`.
+
+        ```bash
+        pip install -U __package_name__
+        export __MODULE_NAME___API_KEY="your-api-key"
+        ```
+
+    Instantiation:
+        ```python
+        tool = __ModuleName__Tool(
+            # TODO: init params
+        )
+        ```
+
+    Invocation with args:
+        ```python
+        # TODO: invoke args
+        tool.invoke({...})
+        ```
+
+        ```python
+        # TODO: output of invocation
+        ```
+
+    Invocation with ToolCall:
+
+        ```python
+        # TODO: invoke args
+        tool.invoke({"args": {...}, "id": "1", "name": tool.name, "type": "tool_call"})
+        ```
+
+        ```python
+        # TODO: output of invocation
+
+        ```
+    """  # noqa: E501
+
+    # TODO: Set tool name and description
+    name: str = "TODO: Tool name"
+    """The name that is passed to the model when performing tool calling."""
+    description: str = "TODO: Tool description."
+    """The description that is passed to the model when performing tool calling."""
+    args_schema: Type[BaseModel] = __ModuleName__ToolInput
+    """The schema that is passed to the model when performing tool calling."""
+
+    # TODO: Add any other init params for the tool.
+    # param1: str | None
+    # """param1 determines foobar"""
+
+    # TODO: Replaced (a, b) with real tool arguments.
+    def _run(
+        self, a: int, b: int, *, run_manager: CallbackManagerForToolRun | None = None
+    ) -> str:
+        return str(a + b + 80)
+
+    # TODO: Implement if tool has native async functionality, otherwise delete.
+
+    # async def _arun(
+    #     self,
+    #     a: int,
+    #     b: int,
+    #     *,
+    #     run_manager: AsyncCallbackManagerForToolRun | None = None,
+    # ) -> str:
+    #     ...

libs/cli/langchain_cli/integration_template/integration_template/vectorstores.py

@@ -0,0 +1,438 @@
+"""__ModuleName__ vector stores."""
+
+from __future__ import annotations
+
+import uuid
+from typing import (
+    Any,
+    Callable,
+    Iterator,
+    List,
+    Sequence,
+    Tuple,
+    Type,
+    TypeVar,
+)
+
+from langchain_core.documents import Document
+from langchain_core.embeddings import Embeddings
+from langchain_core.vectorstores import VectorStore
+from langchain_core.vectorstores.utils import _cosine_similarity as cosine_similarity
+
+VST = TypeVar("VST", bound=VectorStore)
+
+
+class __ModuleName__VectorStore(VectorStore):
+    # TODO: Replace all TODOs in docstring.
+    """__ModuleName__ vector store integration.
+
+    # TODO: Replace with relevant packages, env vars.
+    Setup:
+        Install `__package_name__` and set environment variable `__MODULE_NAME___API_KEY`.
+
+        ```bash
+        pip install -U __package_name__
+        export __MODULE_NAME___API_KEY="your-api-key"
+        ```
+
+    # TODO: Populate with relevant params.
+    Key init args — indexing params:
+        collection_name:
+            Name of the collection.
+        embedding_function:
+            Embedding function to use.
+
+    # TODO: Populate with relevant params.
+    Key init args — client params:
+        client:
+            Client to use.
+        connection_args:
+            Connection arguments.
+
+    # TODO: Replace with relevant init params.
+    Instantiate:
+        ```python
+        from __module_name__.vectorstores import __ModuleName__VectorStore
+        from langchain_openai import OpenAIEmbeddings
+
+        vector_store = __ModuleName__VectorStore(
+            collection_name="foo",
+            embedding_function=OpenAIEmbeddings(),
+            connection_args={"uri": "./foo.db"},
+            # other params...
+        )
+        ```
+
+    # TODO: Populate with relevant variables.
+    Add Documents:
+        ```python
+        from langchain_core.documents import Document
+
+        document_1 = Document(page_content="foo", metadata={"baz": "bar"})
+        document_2 = Document(page_content="thud", metadata={"bar": "baz"})
+        document_3 = Document(page_content="i will be deleted :(")
+
+        documents = [document_1, document_2, document_3]
+        ids = ["1", "2", "3"]
+        vector_store.add_documents(documents=documents, ids=ids)
+        ```
+
+    # TODO: Populate with relevant variables.
+    Delete Documents:
+        ```python
+        vector_store.delete(ids=["3"])
+        ```
+
+    # TODO: Fill out with relevant variables and example output.
+    Search:
+        ```python
+        results = vector_store.similarity_search(query="thud",k=1)
+        for doc in results:
+            print(f"* {doc.page_content} [{doc.metadata}]")
+        ```
+
+        ```python
+        # TODO: Example output
+        ```
+
+    # TODO: Fill out with relevant variables and example output.
+    Search with filter:
+        ```python
+        results = vector_store.similarity_search(query="thud",k=1,filter={"bar": "baz"})
+        for doc in results:
+            print(f"* {doc.page_content} [{doc.metadata}]")
+        ```
+
+        ```python
+        # TODO: Example output
+        ```
+
+    # TODO: Fill out with relevant variables and example output.
+    Search with score:
+        ```python
+        results = vector_store.similarity_search_with_score(query="qux",k=1)
+        for doc, score in results:
+            print(f"* [SIM={score:3f}] {doc.page_content} [{doc.metadata}]")
+        ```
+
+        ```python
+        # TODO: Example output
+        ```
+
+    # TODO: Fill out with relevant variables and example output.
+    Async:
+        ```python
+        # add documents
+        # await vector_store.aadd_documents(documents=documents, ids=ids)
+
+        # delete documents
+        # await vector_store.adelete(ids=["3"])
+
+        # search
+        # results = vector_store.asimilarity_search(query="thud",k=1)
+
+        # search with score
+        results = await vector_store.asimilarity_search_with_score(query="qux",k=1)
+        for doc,score in results:
+            print(f"* [SIM={score:3f}] {doc.page_content} [{doc.metadata}]")
+        ```
+
+        ```python
+        # TODO: Example output
+        ```
+
+    # TODO: Fill out with relevant variables and example output.
+    Use as Retriever:
+        ```python
+        retriever = vector_store.as_retriever(
+            search_type="mmr",
+            search_kwargs={"k": 1, "fetch_k": 2, "lambda_mult": 0.5},
+        )
+        retriever.invoke("thud")
+        ```
+
+        ```python
+        # TODO: Example output
+
+        ```
+    """  # noqa: E501
+
+    def __init__(self, embedding: Embeddings) -> None:
+        """Initialize with the given embedding function.
+
+        Args:
+            embedding: embedding function to use.
+        """
+        self._database: dict[str, dict[str, Any]] = {}
+        self.embedding = embedding
+
+    @classmethod
+    def from_texts(
+        cls: Type[__ModuleName__VectorStore],
+        texts: List[str],
+        embedding: Embeddings,
+        metadatas: list[dict] | None = None,
+        **kwargs: Any,
+    ) -> __ModuleName__VectorStore:
+        store = cls(
+            embedding=embedding,
+        )
+        store.add_texts(texts=texts, metadatas=metadatas, **kwargs)
+        return store
+
+    # optional: add custom async implementations
+    # @classmethod
+    # async def afrom_texts(
+    #     cls: Type[VST],
+    #     texts: List[str],
+    #     embedding: Embeddings,
+    #     metadatas: list[dict] | None = None,
+    #     **kwargs: Any,
+    # ) -> VST:
+    #     return await asyncio.get_running_loop().run_in_executor(
+    #         None, partial(cls.from_texts, **kwargs), texts, embedding, metadatas
+    #     )
+
+    @property
+    def embeddings(self) -> Embeddings:
+        return self.embedding
+
+    def add_documents(
+        self,
+        documents: List[Document],
+        ids: list[str] | None = None,
+        **kwargs: Any,
+    ) -> List[str]:
+        """Add documents to the store."""
+        texts = [doc.page_content for doc in documents]
+        vectors = self.embedding.embed_documents(texts)
+
+        if ids and len(ids) != len(texts):
+            msg = (
+                f"ids must be the same length as texts. "
+                f"Got {len(ids)} ids and {len(texts)} texts."
+            )
+            raise ValueError(msg)
+
+        id_iterator: Iterator[str | None] = (
+            iter(ids) if ids else iter(doc.id for doc in documents)
+        )
+
+        ids_ = []
+
+        for doc, vector in zip(documents, vectors):
+            doc_id = next(id_iterator)
+            doc_id_ = doc_id if doc_id else str(uuid.uuid4())
+            ids_.append(doc_id_)
+            self._database[doc_id_] = {
+                "id": doc_id_,
+                "vector": vector,
+                "text": doc.page_content,
+                "metadata": doc.metadata,
+            }
+
+        return ids_
+
+    # optional: add custom async implementations
+    # async def aadd_documents(
+    #     self,
+    #     documents: List[Document],
+    #     ids: list[str] | None = None,
+    #     **kwargs: Any,
+    # ) -> List[str]:
+    #     raise NotImplementedError
+
+    def delete(self, ids: list[str] | None = None, **kwargs: Any) -> None:
+        if ids:
+            for _id in ids:
+                self._database.pop(_id, None)
+
+    # optional: add custom async implementations
+    # async def adelete(
+    #     self, ids: list[str] | None = None, **kwargs: Any
+    # ) -> None:
+    #     raise NotImplementedError
+
+    def get_by_ids(self, ids: Sequence[str], /) -> list[Document]:
+        """Get documents by their ids.
+
+        Args:
+            ids: The ids of the documents to get.
+
+        Returns:
+            A list of Document objects.
+        """
+        documents = []
+
+        for doc_id in ids:
+            doc = self._database.get(doc_id)
+            if doc:
+                documents.append(
+                    Document(
+                        id=doc["id"],
+                        page_content=doc["text"],
+                        metadata=doc["metadata"],
+                    )
+                )
+        return documents
+
+    # optional: add custom async implementations
+    # async def aget_by_ids(self, ids: Sequence[str], /) -> list[Document]:
+    #     raise NotImplementedError
+
+    # NOTE: the below helper method implements similarity search for in-memory
+    # storage. It is optional and not a part of the vector store interface.
+    def _similarity_search_with_score_by_vector(
+        self,
+        embedding: List[float],
+        k: int = 4,
+        filter: Callable[[Document], bool] | None = None,
+        **kwargs: Any,
+    ) -> List[tuple[Document, float, List[float]]]:
+        # get all docs with fixed order in list
+        docs = list(self._database.values())
+
+        if filter is not None:
+            docs = [
+                doc
+                for doc in docs
+                if filter(Document(page_content=doc["text"], metadata=doc["metadata"]))
+            ]
+
+        if not docs:
+            return []
+
+        similarity = cosine_similarity([embedding], [doc["vector"] for doc in docs])[0]
+
+        # get the indices ordered by similarity score
+        top_k_idx = similarity.argsort()[::-1][:k]
+
+        return [
+            (
+                # Document
+                Document(
+                    id=doc_dict["id"],
+                    page_content=doc_dict["text"],
+                    metadata=doc_dict["metadata"],
+                ),
+                # Score
+                float(similarity[idx].item()),
+                # Embedding vector
+                doc_dict["vector"],
+            )
+            for idx in top_k_idx
+            # Assign using walrus operator to avoid multiple lookups
+            if (doc_dict := docs[idx])
+        ]
+
+    def similarity_search(
+        self, query: str, k: int = 4, **kwargs: Any
+    ) -> List[Document]:
+        embedding = self.embedding.embed_query(query)
+        return [
+            doc
+            for doc, _, _ in self._similarity_search_with_score_by_vector(
+                embedding=embedding, k=k, **kwargs
+            )
+        ]
+
+    # optional: add custom async implementations
+    # async def asimilarity_search(
+    #     self, query: str, k: int = 4, **kwargs: Any
+    # ) -> List[Document]:
+    #     # This is a temporary workaround to make the similarity search
+    #     # asynchronous. The proper solution is to make the similarity search
+    #     # asynchronous in the vector store implementations.
+    #     func = partial(self.similarity_search, query, k=k, **kwargs)
+    #     return await asyncio.get_event_loop().run_in_executor(None, func)
+
+    def similarity_search_with_score(
+        self, query: str, k: int = 4, **kwargs: Any
+    ) -> List[Tuple[Document, float]]:
+        embedding = self.embedding.embed_query(query)
+        return [
+            (doc, similarity)
+            for doc, similarity, _ in self._similarity_search_with_score_by_vector(
+                embedding=embedding, k=k, **kwargs
+            )
+        ]
+
+    # optional: add custom async implementations
+    # async def asimilarity_search_with_score(
+    #     self, *args: Any, **kwargs: Any
+    # ) -> List[Tuple[Document, float]]:
+    #     # This is a temporary workaround to make the similarity search
+    #     # asynchronous. The proper solution is to make the similarity search
+    #     # asynchronous in the vector store implementations.
+    #     func = partial(self.similarity_search_with_score, *args, **kwargs)
+    #     return await asyncio.get_event_loop().run_in_executor(None, func)
+
+    ### ADDITIONAL OPTIONAL SEARCH METHODS BELOW ###
+
+    # def similarity_search_by_vector(
+    #     self, embedding: List[float], k: int = 4, **kwargs: Any
+    # ) -> List[Document]:
+    #     raise NotImplementedError
+
+    # optional: add custom async implementations
+    # async def asimilarity_search_by_vector(
+    #     self, embedding: List[float], k: int = 4, **kwargs: Any
+    # ) -> List[Document]:
+    #     # This is a temporary workaround to make the similarity search
+    #     # asynchronous. The proper solution is to make the similarity search
+    #     # asynchronous in the vector store implementations.
+    #     func = partial(self.similarity_search_by_vector, embedding, k=k, **kwargs)
+    #     return await asyncio.get_event_loop().run_in_executor(None, func)
+
+    # def max_marginal_relevance_search(
+    #     self,
+    #     query: str,
+    #     k: int = 4,
+    #     fetch_k: int = 20,
+    #     lambda_mult: float = 0.5,
+    #     **kwargs: Any,
+    # ) -> List[Document]:
+    #     raise NotImplementedError
+
+    # optional: add custom async implementations
+    # async def amax_marginal_relevance_search(
+    #     self,
+    #     query: str,
+    #     k: int = 4,
+    #     fetch_k: int = 20,
+    #     lambda_mult: float = 0.5,
+    #     **kwargs: Any,
+    # ) -> List[Document]:
+    #     # This is a temporary workaround to make the similarity search
+    #     # asynchronous. The proper solution is to make the similarity search
+    #     # asynchronous in the vector store implementations.
+    #     func = partial(
+    #         self.max_marginal_relevance_search,
+    #         query,
+    #         k=k,
+    #         fetch_k=fetch_k,
+    #         lambda_mult=lambda_mult,
+    #         **kwargs,
+    #     )
+    #     return await asyncio.get_event_loop().run_in_executor(None, func)
+
+    # def max_marginal_relevance_search_by_vector(
+    #     self,
+    #     embedding: List[float],
+    #     k: int = 4,
+    #     fetch_k: int = 20,
+    #     lambda_mult: float = 0.5,
+    #     **kwargs: Any,
+    # ) -> List[Document]:
+    #     raise NotImplementedError
+
+    # optional: add custom async implementations
+    # async def amax_marginal_relevance_search_by_vector(
+    #     self,
+    #     embedding: List[float],
+    #     k: int = 4,
+    #     fetch_k: int = 20,
+    #     lambda_mult: float = 0.5,
+    #     **kwargs: Any,
+    # ) -> List[Document]:
+    #     raise NotImplementedError

libs/cli/langchain_cli/integration_template/pyproject.toml

@@ -0,0 +1,53 @@
+[build-system]
+requires = ["pdm-backend"]
+build-backend = "pdm.backend"
+
+[project]
+name = "__package_name__"
+version = "0.1.0"
+description = "An integration package connecting __ModuleName__ and LangChain"
+authors = []
+readme = "README.md"
+license = "MIT"
+requires-python = ">=3.10.0,<4.0.0"
+dependencies = [
+    "langchain-core>=0.3.15",
+]
+
+[project.urls]
+"Source Code" = "https://github.com/langchain-ai/langchain/tree/master/libs/partners/__package_name_short__"
+"Release Notes" = "https://github.com/langchain-ai/langchain/releases?q=tag%3A%22__package_name_short__%3D%3D0%22&expanded=true"
+"Repository" = "https://github.com/langchain-ai/langchain"
+
+[tool.mypy]
+disallow_untyped_defs = "True"
+
+[tool.uv]
+dev-dependencies = [
+    "pytest>=7.4.3",
+    "pytest-asyncio>=0.23.2",
+    "pytest-socket>=0.7.0",
+    "pytest-watcher>=0.3.4",
+    "langchain-tests>=0.3.5",
+    "ruff>=0.5",
+    "mypy>=1.10",
+]
+
+[tool.ruff.lint]
+select = ["E", "F", "I", "T201"]
+
+[tool.ruff.lint.flake8-tidy-imports]
+ban-relative-imports = "all"
+
+[tool.ruff.lint.per-file-ignores]
+"docs/**" = [ "ALL",]
+
+[tool.coverage.run]
+omit = ["tests/*"]
+
+[tool.pytest.ini_options]
+addopts = "--strict-markers --strict-config --durations=5"
+markers = [
+    "compile: mark placeholder test used to compile integration tests without running them",
+]
+asyncio_mode = "auto"

libs/cli/langchain_cli/integration_template/scripts/check_imports.py

@@ -0,0 +1,17 @@
+import sys
+import traceback
+from importlib.machinery import SourceFileLoader
+
+if __name__ == "__main__":
+    files = sys.argv[1:]
+    has_failure = False
+    for file in files:
+        try:
+            SourceFileLoader("x", file).load_module()
+        except Exception:
+            has_failure = True
+            print(file)  # noqa: T201
+            traceback.print_exc()
+            print()  # noqa: T201
+
+    sys.exit(1 if has_failure else 0)

libs/cli/langchain_cli/integration_template/scripts/lint_imports.sh

@@ -0,0 +1,18 @@
+#!/bin/bash
+
+set -eu
+
+# Initialize a variable to keep track of errors
+errors=0
+
+# make sure not importing from langchain, langchain_experimental, or langchain_community
+git --no-pager grep '^from langchain\.' . && errors=$((errors+1))
+git --no-pager grep '^from langchain_experimental\.' . && errors=$((errors+1))
+git --no-pager grep '^from langchain_community\.' . && errors=$((errors+1))
+
+# Decide on an exit status based on the errors
+if [ "$errors" -gt 0 ]; then
+    exit 1
+else
+    exit 0
+fi

libs/cli/langchain_cli/integration_template/tests/integration_tests/test_chat_models.py

@@ -0,0 +1,21 @@
+"""Test Chat__ModuleName__ chat model."""
+
+from typing import Type
+
+from __module_name__.chat_models import Chat__ModuleName__
+from langchain_tests.integration_tests import ChatModelIntegrationTests
+
+
+class TestChatParrotLinkIntegration(ChatModelIntegrationTests):
+    @property
+    def chat_model_class(self) -> Type[Chat__ModuleName__]:
+        return Chat__ModuleName__
+
+    @property
+    def chat_model_params(self) -> dict:
+        # These should be parameters used to initialize your integration for testing
+        return {
+            "model": "bird-brain-001",
+            "temperature": 0,
+            "parrot_buffer_length": 50,
+        }

libs/cli/langchain_cli/integration_template/tests/integration_tests/test_compile.py

@@ -0,0 +1,7 @@
+import pytest
+
+
+@pytest.mark.compile
+def test_placeholder() -> None:
+    """Used for compiling integration tests without running any real tests."""
+    pass

libs/cli/langchain_cli/integration_template/tests/integration_tests/test_embeddings.py

@@ -0,0 +1,16 @@
+"""Test __ModuleName__ embeddings."""
+
+from typing import Type
+
+from __module_name__.embeddings import __ModuleName__Embeddings
+from langchain_tests.integration_tests import EmbeddingsIntegrationTests
+
+
+class TestParrotLinkEmbeddingsIntegration(EmbeddingsIntegrationTests):
+    @property
+    def embeddings_class(self) -> Type[__ModuleName__Embeddings]:
+        return __ModuleName__Embeddings
+
+    @property
+    def embedding_model_params(self) -> dict:
+        return {"model": "nest-embed-001"}

libs/cli/langchain_cli/integration_template/tests/integration_tests/test_retrievers.py

@@ -0,0 +1,22 @@
+from typing import Type
+
+from __module_name__.retrievers import __ModuleName__Retriever
+from langchain_tests.integration_tests import (
+    RetrieversIntegrationTests,
+)
+
+
+class Test__ModuleName__Retriever(RetrieversIntegrationTests):
+    @property
+    def retriever_constructor(self) -> Type[__ModuleName__Retriever]:
+        """Get an empty vectorstore for unit tests."""
+        return __ModuleName__Retriever
+
+    @property
+    def retriever_constructor_params(self) -> dict:
+        return {"k": 2}
+
+    @property
+    def retriever_query_example(self) -> str:
+        """Returns a str representing the "query" of an example retriever call."""
+        return "example query"

libs/cli/langchain_cli/integration_template/tests/integration_tests/test_tools.py

@@ -0,0 +1,27 @@
+from typing import Type
+
+from __module_name__.tools import __ModuleName__Tool
+from langchain_tests.integration_tests import ToolsIntegrationTests
+
+
+class TestParrotMultiplyToolIntegration(ToolsIntegrationTests):
+    @property
+    def tool_constructor(self) -> Type[__ModuleName__Tool]:
+        return __ModuleName__Tool
+
+    @property
+    def tool_constructor_params(self) -> dict:
+        # if your tool constructor instead required initialization arguments like
+        # `def __init__(self, some_arg: int):`, you would return those here
+        # as a dictionary, e.g.: `return {'some_arg': 42}`
+        return {}
+
+    @property
+    def tool_invoke_params_example(self) -> dict:
+        """
+        Returns a dictionary representing the "args" of an example tool call.
+
+        This should NOT be a ToolCall dict - i.e. it should not have
+        `{"name", "id", "args"}` keys.
+        """
+        return {"a": 2, "b": 3}

libs/cli/langchain_cli/integration_template/tests/integration_tests/test_vectorstores.py

@@ -0,0 +1,20 @@
+from typing import Generator
+
+import pytest
+from __module_name__.vectorstores import __ModuleName__VectorStore
+from langchain_core.vectorstores import VectorStore
+from langchain_tests.integration_tests import VectorStoreIntegrationTests
+
+
+class Test__ModuleName__VectorStore(VectorStoreIntegrationTests):
+    @pytest.fixture()
+    def vectorstore(self) -> Generator[VectorStore, None, None]:  # type: ignore
+        """Get an empty vectorstore for unit tests."""
+        store = __ModuleName__VectorStore(self.get_embeddings())
+        # note: store should be EMPTY at this point
+        # if you need to delete data, you may do so here
+        try:
+            yield store
+        finally:
+            # cleanup operations, or deleting data
+            pass

libs/cli/langchain_cli/integration_template/tests/unit_tests/test_chat_models.py

@@ -0,0 +1,21 @@
+"""Test chat model integration."""
+
+from typing import Type
+
+from __module_name__.chat_models import Chat__ModuleName__
+from langchain_tests.unit_tests import ChatModelUnitTests
+
+
+class TestChat__ModuleName__Unit(ChatModelUnitTests):
+    @property
+    def chat_model_class(self) -> Type[Chat__ModuleName__]:
+        return Chat__ModuleName__
+
+    @property
+    def chat_model_params(self) -> dict:
+        # These should be parameters used to initialize your integration for testing
+        return {
+            "model": "bird-brain-001",
+            "temperature": 0,
+            "parrot_buffer_length": 50,
+        }

libs/cli/langchain_cli/integration_template/tests/unit_tests/test_embeddings.py

@@ -0,0 +1,16 @@
+"""Test embedding model integration."""
+
+from typing import Type
+
+from __module_name__.embeddings import __ModuleName__Embeddings
+from langchain_tests.unit_tests import EmbeddingsUnitTests
+
+
+class TestParrotLinkEmbeddingsUnit(EmbeddingsUnitTests):
+    @property
+    def embeddings_class(self) -> Type[__ModuleName__Embeddings]:
+        return __ModuleName__Embeddings
+
+    @property
+    def embedding_model_params(self) -> dict:
+        return {"model": "nest-embed-001"}

libs/cli/langchain_cli/integration_template/tests/unit_tests/test_tools.py

@@ -0,0 +1,27 @@
+from typing import Type
+
+from __module_name__.tools import __ModuleName__Tool
+from langchain_tests.unit_tests import ToolsUnitTests
+
+
+class TestParrotMultiplyToolUnit(ToolsUnitTests):
+    @property
+    def tool_constructor(self) -> Type[__ModuleName__Tool]:
+        return __ModuleName__Tool
+
+    @property
+    def tool_constructor_params(self) -> dict:
+        # If your tool constructor instead required initialization arguments like
+        # `def __init__(self, some_arg: int):`, you would return those here
+        # as a dictionary, e.g.: `return {'some_arg': 42}`
+        return {}
+
+    @property
+    def tool_invoke_params_example(self) -> dict:
+        """
+        Returns a dictionary representing the "args" of an example tool call.
+
+        This should NOT be a ToolCall dict - i.e. it should not have
+        `{"name", "id", "args"}` keys.
+        """
+        return {"a": 2, "b": 3}

libs/cli/langchain_cli/namespaces/__init__.py

@@ -0,0 +1 @@
+"""Namespaces."""

libs/cli/langchain_cli/namespaces/app.py

@@ -0,0 +1,371 @@
+"""Manage LangChain apps."""
+
+import shutil
+import subprocess
+import sys
+import warnings
+from pathlib import Path
+from typing import Annotated
+
+import typer
+import uvicorn
+
+from langchain_cli.utils.events import create_events
+from langchain_cli.utils.git import (
+    DependencySource,
+    copy_repo,
+    parse_dependencies,
+    update_repo,
+)
+from langchain_cli.utils.packages import (
+    LangServeExport,
+    get_langserve_export,
+    get_package_root,
+)
+from langchain_cli.utils.pyproject import (
+    add_dependencies_to_pyproject_toml,
+    remove_dependencies_from_pyproject_toml,
+)
+
+REPO_DIR = Path(typer.get_app_dir("langchain")) / "git_repos"
+
+app_cli = typer.Typer(no_args_is_help=True, add_completion=False)
+
+
+@app_cli.command()
+def new(
+    name: Annotated[
+        str | None,
+        typer.Argument(
+            help="The name of the folder to create",
+        ),
+    ] = None,
+    *,
+    package: Annotated[
+        list[str] | None,
+        typer.Option(help="Packages to seed the project with"),
+    ] = None,
+    pip: Annotated[
+        bool | None,
+        typer.Option(
+            "--pip/--no-pip",
+            help="Pip install the template(s) as editable dependencies",
+        ),
+    ] = None,
+    noninteractive: Annotated[
+        bool,
+        typer.Option(
+            "--non-interactive/--interactive",
+            help="Don't prompt for any input",
+        ),
+    ] = False,
+) -> None:
+    """Create a new LangServe application."""
+    has_packages = package is not None and len(package) > 0
+
+    if noninteractive:
+        if name is None:
+            msg = "name is required when --non-interactive is set"
+            raise typer.BadParameter(msg)
+        name_str = name
+        pip_bool = bool(pip)  # None should be false
+    else:
+        name_str = name or typer.prompt("What folder would you like to create?")
+        if not has_packages:
+            package = []
+            package_prompt = "What package would you like to add? (leave blank to skip)"
+            while True:
+                package_str = typer.prompt(
+                    package_prompt,
+                    default="",
+                    show_default=False,
+                )
+                if not package_str:
+                    break
+                package.append(package_str)
+                package_prompt = (
+                    f"{len(package)} added. Any more packages (leave blank to end)?"
+                )
+
+            has_packages = len(package) > 0
+
+        pip_bool = False
+        if pip is None and has_packages:
+            pip_bool = typer.confirm(
+                "Would you like to install these templates into your environment "
+                "with pip?",
+                default=False,
+            )
+    # copy over template from ../project_template
+    project_template_dir = Path(__file__).parents[1] / "project_template"
+    destination_dir = Path.cwd() / name_str if name_str != "." else Path.cwd()
+    app_name = name_str if name_str != "." else Path.cwd().name
+    shutil.copytree(project_template_dir, destination_dir, dirs_exist_ok=name == ".")
+
+    readme = destination_dir / "README.md"
+    readme_contents = readme.read_text()
+    readme.write_text(readme_contents.replace("__app_name__", app_name))
+
+    pyproject = destination_dir / "pyproject.toml"
+    pyproject_contents = pyproject.read_text()
+    pyproject.write_text(pyproject_contents.replace("__app_name__", app_name))
+
+    # add packages if specified
+    if has_packages:
+        add(package, project_dir=destination_dir, pip=pip_bool)
+
+    typer.echo(f'\n\nSuccess! Created a new LangChain app under "./{app_name}"!\n\n')
+    typer.echo("Next, enter your new app directory by running:\n")
+    typer.echo(f"    cd ./{app_name}\n")
+    typer.echo("Then add templates with commands like:\n")
+    typer.echo("    langchain app add extraction-openai-functions")
+    typer.echo(
+        "    langchain app add git+ssh://git@github.com/efriis/simple-pirate.git\n\n",
+    )
+
+
+@app_cli.command()
+def add(
+    dependencies: Annotated[
+        list[str] | None,
+        typer.Argument(help="The dependency to add"),
+    ] = None,
+    *,
+    api_path: Annotated[
+        list[str] | None,
+        typer.Option(help="API paths to add"),
+    ] = None,
+    project_dir: Annotated[
+        Path | None,
+        typer.Option(help="The project directory"),
+    ] = None,
+    repo: Annotated[
+        list[str] | None,
+        typer.Option(help="Install templates from a specific github repo instead"),
+    ] = None,
+    branch: Annotated[
+        list[str] | None,
+        typer.Option(help="Install templates from a specific branch"),
+    ] = None,
+    pip: Annotated[
+        bool,
+        typer.Option(
+            "--pip/--no-pip",
+            help="Pip install the template(s) as editable dependencies",
+            prompt="Would you like to `pip install -e` the template(s)?",
+        ),
+    ],
+) -> None:
+    """Add the specified template to the current LangServe app.
+
+    e.g.:
+    `langchain app add extraction-openai-functions`
+    `langchain app add git+ssh://git@github.com/efriis/simple-pirate.git`
+    """
+    if branch is None:
+        branch = []
+    if repo is None:
+        repo = []
+    if api_path is None:
+        api_path = []
+    if not branch and not repo:
+        warnings.warn(
+            "Adding templates from the default branch and repo is deprecated."
+            " At a minimum, you will have to add `--branch v0.2` for this to work",
+            stacklevel=2,
+        )
+
+    parsed_deps = parse_dependencies(dependencies, repo, branch, api_path)
+
+    project_root = get_package_root(project_dir)
+
+    package_dir = project_root / "packages"
+
+    create_events(
+        [{"event": "serve add", "properties": {"parsed_dep": d}} for d in parsed_deps],
+    )
+
+    # group by repo/ref
+    grouped: dict[tuple[str, str | None], list[DependencySource]] = {}
+    for dep in parsed_deps:
+        key_tup = (dep["git"], dep["ref"])
+        lst = grouped.get(key_tup, [])
+        lst.append(dep)
+        grouped[key_tup] = lst
+
+    installed_destination_paths: list[Path] = []
+    installed_destination_names: list[str] = []
+    installed_exports: list[LangServeExport] = []
+
+    for (git, ref), group_deps in grouped.items():
+        if len(group_deps) == 1:
+            typer.echo(f"Adding {git}@{ref}...")
+        else:
+            typer.echo(f"Adding {len(group_deps)} templates from {git}@{ref}")
+        source_repo_path = update_repo(git, ref, REPO_DIR)
+
+        for dep in group_deps:
+            source_path = (
+                source_repo_path / dep["subdirectory"]
+                if dep["subdirectory"]
+                else source_repo_path
+            )
+            pyproject_path = source_path / "pyproject.toml"
+            if not pyproject_path.exists():
+                typer.echo(f"Could not find {pyproject_path}")
+                continue
+            langserve_export = get_langserve_export(pyproject_path)
+
+            # default path to package_name
+            inner_api_path = dep["api_path"] or langserve_export["package_name"]
+
+            destination_path = package_dir / inner_api_path
+            if destination_path.exists():
+                typer.echo(
+                    f"Folder {inner_api_path} already exists. Skipping...",
+                )
+                continue
+            copy_repo(source_path, destination_path)
+            typer.echo(f" - Downloaded {dep['subdirectory']} to {inner_api_path}")
+            installed_destination_paths.append(destination_path)
+            installed_destination_names.append(inner_api_path)
+            installed_exports.append(langserve_export)
+
+    if len(installed_destination_paths) == 0:
+        typer.echo("No packages installed. Exiting.")
+        return
+
+    try:
+        add_dependencies_to_pyproject_toml(
+            project_root / "pyproject.toml",
+            zip(installed_destination_names, installed_destination_paths, strict=False),
+        )
+    except Exception:
+        # Can fail if user modified/removed pyproject.toml
+        typer.echo("Failed to add dependencies to pyproject.toml, continuing...")
+
+    try:
+        cwd = Path.cwd()
+        installed_destination_strs = [
+            str(p.relative_to(cwd)) for p in installed_destination_paths
+        ]
+    except ValueError:
+        # Can fail if the cwd is not a parent of the package
+        typer.echo("Failed to print install command, continuing...")
+    else:
+        if pip:
+            cmd = ["pip", "install", "-e", *installed_destination_strs]
+            cmd_str = " \\\n  ".join(installed_destination_strs)
+            typer.echo(f"Running: pip install -e \\\n  {cmd_str}")
+            subprocess.run(cmd, cwd=cwd, check=True)  # noqa: S603
+
+    chain_names = []
+    for e in installed_exports:
+        original_candidate = f"{e['package_name'].replace('-', '_')}_chain"
+        candidate = original_candidate
+        i = 2
+        while candidate in chain_names:
+            candidate = original_candidate + "_" + str(i)
+            i += 1
+        chain_names.append(candidate)
+
+    api_paths = [
+        str(Path("/") / path.relative_to(package_dir))
+        for path in installed_destination_paths
+    ]
+
+    imports = [
+        f"from {e['module']} import {e['attr']} as {name}"
+        for e, name in zip(installed_exports, chain_names, strict=False)
+    ]
+    routes = [
+        f'add_routes(app, {name}, path="{path}")'
+        for name, path in zip(chain_names, api_paths, strict=False)
+    ]
+
+    t = (
+        "this template"
+        if len(chain_names) == 1
+        else f"these {len(chain_names)} templates"
+    )
+    lines = [
+        "",
+        f"To use {t}, add the following to your app:\n\n```",
+        "",
+        *imports,
+        "",
+        *routes,
+        "```",
+    ]
+    typer.echo("\n".join(lines))
+
+
+@app_cli.command()
+def remove(
+    api_paths: Annotated[list[str], typer.Argument(help="The API paths to remove")],
+    *,
+    project_dir: Annotated[
+        Path | None,
+        typer.Option(help="The project directory"),
+    ] = None,
+) -> None:
+    """Remove the specified package from the current LangServe app."""
+    project_root = get_package_root(project_dir)
+
+    project_pyproject = project_root / "pyproject.toml"
+
+    package_root = project_root / "packages"
+
+    remove_deps: list[str] = []
+
+    for api_path in api_paths:
+        package_dir = package_root / api_path
+        if not package_dir.exists():
+            typer.echo(f"Package {api_path} does not exist. Skipping...")
+            continue
+        try:
+            pyproject = package_dir / "pyproject.toml"
+            langserve_export = get_langserve_export(pyproject)
+            typer.echo(f"Removing {langserve_export['package_name']}...")
+
+            shutil.rmtree(package_dir)
+            remove_deps.append(api_path)
+        except OSError as exc:
+            typer.echo(f"Failed to remove {api_path}: {exc}")
+
+    try:
+        remove_dependencies_from_pyproject_toml(project_pyproject, remove_deps)
+    except Exception:
+        # Can fail if user modified/removed pyproject.toml
+        typer.echo("Failed to remove dependencies from pyproject.toml.")
+
+
+@app_cli.command()
+def serve(
+    *,
+    port: Annotated[
+        int | None,
+        typer.Option(help="The port to run the server on"),
+    ] = None,
+    host: Annotated[
+        str | None,
+        typer.Option(help="The host to run the server on"),
+    ] = None,
+    app: Annotated[
+        str | None,
+        typer.Option(help="The app to run, e.g. `app.server:app`"),
+    ] = None,
+) -> None:
+    """Start the LangServe app."""
+    # add current dir as first entry of path
+    sys.path.append(str(Path.cwd()))
+
+    app_str = app if app is not None else "app.server:app"
+    host_str = host if host is not None else "127.0.0.1"
+
+    uvicorn.run(
+        app_str,
+        host=host_str,
+        port=port if port is not None else 8000,
+        reload=True,
+    )

libs/cli/langchain_cli/namespaces/integration.py

@@ -0,0 +1,260 @@
+"""Develop integration packages for LangChain."""
+
+import os
+import re
+import shutil
+import subprocess
+from pathlib import Path
+from typing import Annotated, cast
+
+import typer
+from typing_extensions import TypedDict
+
+from langchain_cli.utils.find_replace import replace_file, replace_glob
+
+integration_cli = typer.Typer(no_args_is_help=True, add_completion=False)
+
+
+class Replacements(TypedDict):
+    """Replacements."""
+
+    __package_name__: str
+    __module_name__: str
+    __ModuleName__: str
+    __MODULE_NAME__: str
+    __package_name_short__: str
+    __package_name_short_snake__: str
+
+
+def _process_name(name: str, *, community: bool = False) -> Replacements:
+    preprocessed = name.replace("_", "-").lower()
+
+    preprocessed = preprocessed.removeprefix("langchain-")
+
+    if not re.match(r"^[a-z][a-z0-9-]*$", preprocessed):
+        msg = (
+            "Name should only contain lowercase letters (a-z), numbers, and hyphens"
+            ", and start with a letter."
+        )
+        raise ValueError(msg)
+    if preprocessed.endswith("-"):
+        msg = "Name should not end with `-`."
+        raise ValueError(msg)
+    if preprocessed.find("--") != -1:
+        msg = "Name should not contain consecutive hyphens."
+        raise ValueError(msg)
+    replacements: Replacements = {
+        "__package_name__": f"langchain-{preprocessed}",
+        "__module_name__": "langchain_" + preprocessed.replace("-", "_"),
+        "__ModuleName__": preprocessed.title().replace("-", ""),
+        "__MODULE_NAME__": preprocessed.upper().replace("-", ""),
+        "__package_name_short__": preprocessed,
+        "__package_name_short_snake__": preprocessed.replace("-", "_"),
+    }
+    if community:
+        replacements["__module_name__"] = preprocessed.replace("-", "_")
+    return replacements
+
+
+@integration_cli.command()
+def new(
+    name: Annotated[
+        str,
+        typer.Option(
+            help="The name of the integration to create (e.g. `my-integration`)",
+            prompt="The name of the integration to create (e.g. `my-integration`)",
+        ),
+    ],
+    name_class: Annotated[
+        str | None,
+        typer.Option(
+            help="The name of the integration in PascalCase. e.g. `MyIntegration`."
+            " This is used to name classes like `MyIntegrationVectorStore`",
+        ),
+    ] = None,
+    src: Annotated[
+        list[str] | None,
+        typer.Option(
+            help="The name of the single template file to copy."
+            " e.g. `--src integration_template/chat_models.py "
+            "--dst my_integration/chat_models.py`. Can be used multiple times.",
+        ),
+    ] = None,
+    dst: Annotated[
+        list[str] | None,
+        typer.Option(
+            help="The relative path to the integration package to place the new file in"
+            ". e.g. `my-integration/my_integration.py`",
+        ),
+    ] = None,
+) -> None:
+    """Create a new integration package."""
+    try:
+        replacements = _process_name(name)
+    except ValueError as e:
+        typer.echo(e)
+        raise typer.Exit(code=1) from None
+
+    if name_class:
+        if not re.match(r"^[A-Z][a-zA-Z0-9]*$", name_class):
+            typer.echo(
+                "Name should only contain letters (a-z, A-Z), numbers, and underscores"
+                ", and start with a capital letter.",
+            )
+            raise typer.Exit(code=1)
+        replacements["__ModuleName__"] = name_class
+    else:
+        replacements["__ModuleName__"] = typer.prompt(
+            "Name of integration in PascalCase",
+            default=replacements["__ModuleName__"],
+        )
+
+    project_template_dir = Path(__file__).parents[1] / "integration_template"
+    destination_dir = Path.cwd() / replacements["__package_name__"]
+    if not src and not dst:
+        if destination_dir.exists():
+            typer.echo(f"Folder {destination_dir} exists.")
+            raise typer.Exit(code=1)
+
+        # Copy over template from ../integration_template
+        shutil.copytree(project_template_dir, destination_dir, dirs_exist_ok=False)
+
+        # Folder movement
+        package_dir = destination_dir / replacements["__module_name__"]
+        shutil.move(destination_dir / "integration_template", package_dir)
+
+        # Replacements in files
+        replace_glob(destination_dir, "**/*", cast("dict[str, str]", replacements))
+
+        # Dependency install
+        try:
+            # Use --no-progress to avoid tty issues in CI/test environments
+            env = os.environ.copy()
+            env.pop("UV_FROZEN", None)
+            env.pop("VIRTUAL_ENV", None)
+            subprocess.run(
+                ["uv", "sync", "--dev", "--no-progress"],  # noqa: S607
+                cwd=destination_dir,
+                check=True,
+                env=env,
+            )
+        except FileNotFoundError:
+            typer.echo(
+                "uv is not installed. Skipping dependency installation; run "
+                "`uv sync --dev` manually if needed.",
+            )
+        except subprocess.CalledProcessError:
+            typer.echo(
+                "Failed to install dependencies. You may need to run "
+                "`uv sync --dev` manually in the package directory.",
+            )
+    else:
+        # Confirm src and dst are the same length
+        if not src:
+            typer.echo("Cannot provide --dst without --src.")
+            raise typer.Exit(code=1)
+        src_paths = [project_template_dir / p for p in src]
+        if dst and len(src) != len(dst):
+            typer.echo("Number of --src and --dst arguments must match.")
+            raise typer.Exit(code=1)
+        if not dst:
+            # Assume we're in a package dir, copy to equivalent path
+            dst_paths = [destination_dir / p for p in src]
+        else:
+            dst_paths = [Path.cwd() / p for p in dst]
+            dst_paths = [
+                p / f"{replacements['__package_name_short_snake__']}.ipynb"
+                if not p.suffix
+                else p
+                for p in dst_paths
+            ]
+
+        # Confirm no duplicate dst_paths
+        if len(dst_paths) != len(set(dst_paths)):
+            typer.echo(
+                "Duplicate destination paths provided or computed - please "
+                "specify them explicitly with --dst.",
+            )
+            raise typer.Exit(code=1)
+
+        # Confirm no files exist at dst_paths
+        for dst_path in dst_paths:
+            if dst_path.exists():
+                typer.echo(f"File {dst_path} exists.")
+                raise typer.Exit(code=1)
+
+        for src_path, dst_path in zip(src_paths, dst_paths, strict=False):
+            shutil.copy(src_path, dst_path)
+            replace_file(dst_path, cast("dict[str, str]", replacements))
+
+
+TEMPLATE_MAP: dict[str, str] = {
+    "ChatModel": "chat.ipynb",
+    "DocumentLoader": "document_loaders.ipynb",
+    "Tool": "tools.ipynb",
+    "VectorStore": "vectorstores.ipynb",
+    "Embeddings": "text_embedding.ipynb",
+    "ByteStore": "kv_store.ipynb",
+    "LLM": "llms.ipynb",
+    "Provider": "provider.ipynb",
+    "Toolkit": "toolkits.ipynb",
+    "Retriever": "retrievers.ipynb",
+}
+
+_component_types_str = ", ".join(f"`{k}`" for k in TEMPLATE_MAP)
+
+
+@integration_cli.command()
+def create_doc(
+    name: Annotated[
+        str,
+        typer.Option(
+            help=(
+                "The kebab-case name of the integration (e.g. `openai`, "
+                "`google-vertexai`). Do not include a 'langchain-' prefix."
+            ),
+            prompt=(
+                "The kebab-case name of the integration (e.g. `openai`, "
+                "`google-vertexai`). Do not include a 'langchain-' prefix."
+            ),
+        ),
+    ],
+    name_class: Annotated[
+        str | None,
+        typer.Option(
+            help=(
+                "The PascalCase name of the integration (e.g. `OpenAI`, "
+                "`VertexAI`). Do not include a 'Chat', 'VectorStore', etc. "
+                "prefix/suffix."
+            ),
+        ),
+    ] = None,
+    component_type: Annotated[
+        str,
+        typer.Option(
+            help=(
+                f"The type of component. Currently supported: {_component_types_str}."
+            ),
+        ),
+    ] = "ChatModel",
+    destination_dir: Annotated[
+        str,
+        typer.Option(
+            help="The relative path to the docs directory to place the new file in.",
+            prompt="The relative path to the docs directory to place the new file in.",
+        ),
+    ] = "docs/docs/integrations/chat/",
+) -> None:
+    """Create a new integration doc."""
+    if component_type not in TEMPLATE_MAP:
+        typer.echo(
+            f"Unrecognized {component_type=}. Expected one of {_component_types_str}.",
+        )
+        raise typer.Exit(code=1)
+
+    new(
+        name=name,
+        name_class=name_class,
+        src=[f"docs/{TEMPLATE_MAP[component_type]}"],
+        dst=[destination_dir],
+    )

libs/cli/langchain_cli/namespaces/migrate/.grit/.gitignore

@@ -0,0 +1,2 @@
+.gritmodules*
+*.log

libs/cli/langchain_cli/namespaces/migrate/.grit/grit.yaml

@@ -0,0 +1,3 @@
+version: 0.0.1
+patterns:
+  - name: github.com/getgrit/stdlib#*

libs/cli/langchain_cli/namespaces/migrate/.grit/patterns/_test_replace_imports.md

@@ -0,0 +1,56 @@
+# Testing the replace_imports migration
+
+This runs the v0.2 migration with a desired set of rules.
+
+```grit
+language python
+
+langchain_all_migrations()
+```
+
+## Single import
+
+Before:
+
+```python
+from langchain.chat_models import ChatOpenAI
+```
+
+After:
+
+```python
+from langchain_community.chat_models import ChatOpenAI
+```
+
+## Community to partner
+
+```python
+from langchain_community.chat_models import ChatOpenAI
+```
+
+```python
+from langchain_openai import ChatOpenAI
+```
+
+## Noop
+
+This file should not match at all.
+
+```python
+from foo import ChatOpenAI
+```
+
+## Mixed imports
+
+```python
+from langchain_community.chat_models import ChatOpenAI, ChatAnthropic, foo
+```
+
+```python
+from langchain_community.chat_models import foo
+
+from langchain_openai import ChatOpenAI
+
+from langchain_anthropic import ChatAnthropic
+
+```

libs/cli/langchain_cli/namespaces/migrate/.grit/patterns/anthropic.grit

@@ -0,0 +1,15 @@
+
+language python
+
+// This migration is generated automatically - do not manually edit this file
+pattern langchain_migrate_anthropic() {
+  find_replace_imports(list=[
+    [`langchain_community.chat_models.anthropic`, `ChatAnthropic`, `langchain_anthropic`, `ChatAnthropic`],
+    [`langchain_community.llms.anthropic`, `Anthropic`, `langchain_anthropic`, `Anthropic`],
+    [`langchain_community.chat_models`, `ChatAnthropic`, `langchain_anthropic`, `ChatAnthropic`],
+    [`langchain_community.llms`, `Anthropic`, `langchain_anthropic`, `Anthropic`]
+  ])
+}
+
+// Add this for invoking directly
+langchain_migrate_anthropic()

libs/cli/langchain_cli/namespaces/migrate/.grit/patterns/astradb.grit

@@ -0,0 +1,67 @@
+
+language python
+
+// This migration is generated automatically - do not manually edit this file
+pattern langchain_migrate_astradb() {
+  find_replace_imports(list=[
+
+            [
+                `langchain_community.vectorstores.astradb`,
+                `AstraDB`,
+                `langchain_astradb`,
+                `AstraDBVectorStore`
+            ]
+            ,
+
+            [
+                `langchain_community.storage.astradb`,
+                `AstraDBByteStore`,
+                `langchain_astradb`,
+                `AstraDBByteStore`
+            ]
+            ,
+
+            [
+                `langchain_community.storage.astradb`,
+                `AstraDBStore`,
+                `langchain_astradb`,
+                `AstraDBStore`
+            ]
+            ,
+
+            [
+                `langchain_community.cache`,
+                `AstraDBCache`,
+                `langchain_astradb`,
+                `AstraDBCache`
+            ]
+            ,
+
+            [
+                `langchain_community.cache`,
+                `AstraDBSemanticCache`,
+                `langchain_astradb`,
+                `AstraDBSemanticCache`
+            ]
+            ,
+
+            [
+                `langchain_community.chat_message_histories.astradb`,
+                `AstraDBChatMessageHistory`,
+                `langchain_astradb`,
+                `AstraDBChatMessageHistory`
+            ]
+            ,
+
+            [
+                `langchain_community.document_loaders.astradb`,
+                `AstraDBLoader`,
+                `langchain_astradb`,
+                `AstraDBLoader`
+            ]
+            
+  ])
+}
+
+// Add this for invoking directly
+langchain_migrate_astradb()

libs/cli/langchain_cli/namespaces/migrate/.grit/patterns/community_to_core.grit

@@ -0,0 +1,38 @@
+
+language python
+
+// This migration is generated automatically - do not manually edit this file
+pattern langchain_migrate_community_to_core() {
+  find_replace_imports(list=[
+    [`langchain_community.callbacks.tracers`, `ConsoleCallbackHandler`, `langchain_core.tracers`, `ConsoleCallbackHandler`],
+    [`langchain_community.callbacks.tracers`, `FunctionCallbackHandler`, `langchain_core.tracers.stdout`, `FunctionCallbackHandler`],
+    [`langchain_community.callbacks.tracers`, `LangChainTracer`, `langchain_core.tracers`, `LangChainTracer`],
+    [`langchain_community.callbacks.tracers`, `LangChainTracerV1`, `langchain_core.tracers.langchain_v1`, `LangChainTracerV1`],
+    [`langchain_community.docstore.document`, `Document`, `langchain_core.documents`, `Document`],
+    [`langchain_community.document_loaders`, `Blob`, `langchain_core.document_loaders`, `Blob`],
+    [`langchain_community.document_loaders`, `BlobLoader`, `langchain_core.document_loaders`, `BlobLoader`],
+    [`langchain_community.document_loaders.base`, `BaseBlobParser`, `langchain_core.document_loaders`, `BaseBlobParser`],
+    [`langchain_community.document_loaders.base`, `BaseLoader`, `langchain_core.document_loaders`, `BaseLoader`],
+    [`langchain_community.document_loaders.blob_loaders`, `Blob`, `langchain_core.document_loaders`, `Blob`],
+    [`langchain_community.document_loaders.blob_loaders`, `BlobLoader`, `langchain_core.document_loaders`, `BlobLoader`],
+    [`langchain_community.document_loaders.blob_loaders.schema`, `Blob`, `langchain_core.document_loaders`, `Blob`],
+    [`langchain_community.document_loaders.blob_loaders.schema`, `BlobLoader`, `langchain_core.document_loaders`, `BlobLoader`],
+    [`langchain_community.tools`, `BaseTool`, `langchain_core.tools`, `BaseTool`],
+    [`langchain_community.tools`, `StructuredTool`, `langchain_core.tools`, `StructuredTool`],
+    [`langchain_community.tools`, `Tool`, `langchain_core.tools`, `Tool`],
+    [`langchain_community.tools`, `format_tool_to_openai_function`, `langchain_core.utils.function_calling`, `format_tool_to_openai_function`],
+    [`langchain_community.tools`, `tool`, `langchain_core.tools`, `tool`],
+    [`langchain_community.tools.convert_to_openai`, `format_tool_to_openai_function`, `langchain_core.utils.function_calling`, `format_tool_to_openai_function`],
+    [`langchain_community.tools.convert_to_openai`, `format_tool_to_openai_tool`, `langchain_core.utils.function_calling`, `format_tool_to_openai_tool`],
+    [`langchain_community.tools.render`, `format_tool_to_openai_function`, `langchain_core.utils.function_calling`, `format_tool_to_openai_function`],
+    [`langchain_community.tools.render`, `format_tool_to_openai_tool`, `langchain_core.utils.function_calling`, `format_tool_to_openai_tool`],
+    [`langchain_community.utils.openai_functions`, `FunctionDescription`, `langchain_core.utils.function_calling`, `FunctionDescription`],
+    [`langchain_community.utils.openai_functions`, `ToolDescription`, `langchain_core.utils.function_calling`, `ToolDescription`],
+    [`langchain_community.utils.openai_functions`, `convert_pydantic_to_openai_function`, `langchain_core.utils.function_calling`, `convert_pydantic_to_openai_function`],
+    [`langchain_community.utils.openai_functions`, `convert_pydantic_to_openai_tool`, `langchain_core.utils.function_calling`, `convert_pydantic_to_openai_tool`],
+    [`langchain_community.vectorstores`, `VectorStore`, `langchain_core.vectorstores`, `VectorStore`]
+  ])
+}
+
+// Add this for invoking directly
+langchain_migrate_community_to_core()

libs/cli/langchain_cli/namespaces/migrate/.grit/patterns/community_to_core.json

@@ -0,0 +1,101 @@
+[
+  [
+    "langchain_community.callbacks.tracers.ConsoleCallbackHandler",
+    "langchain_core.tracers.ConsoleCallbackHandler"
+  ],
+  [
+    "langchain_community.callbacks.tracers.FunctionCallbackHandler",
+    "langchain_core.tracers.stdout.FunctionCallbackHandler"
+  ],
+  [
+    "langchain_community.callbacks.tracers.LangChainTracer",
+    "langchain_core.tracers.LangChainTracer"
+  ],
+  [
+    "langchain_community.callbacks.tracers.LangChainTracerV1",
+    "langchain_core.tracers.langchain_v1.LangChainTracerV1"
+  ],
+  [
+    "langchain_community.docstore.document.Document",
+    "langchain_core.documents.Document"
+  ],
+  [
+    "langchain_community.document_loaders.Blob",
+    "langchain_core.document_loaders.Blob"
+  ],
+  [
+    "langchain_community.document_loaders.BlobLoader",
+    "langchain_core.document_loaders.BlobLoader"
+  ],
+  [
+    "langchain_community.document_loaders.base.BaseBlobParser",
+    "langchain_core.document_loaders.BaseBlobParser"
+  ],
+  [
+    "langchain_community.document_loaders.base.BaseLoader",
+    "langchain_core.document_loaders.BaseLoader"
+  ],
+  [
+    "langchain_community.document_loaders.blob_loaders.Blob",
+    "langchain_core.document_loaders.Blob"
+  ],
+  [
+    "langchain_community.document_loaders.blob_loaders.BlobLoader",
+    "langchain_core.document_loaders.BlobLoader"
+  ],
+  [
+    "langchain_community.document_loaders.blob_loaders.schema.Blob",
+    "langchain_core.document_loaders.Blob"
+  ],
+  [
+    "langchain_community.document_loaders.blob_loaders.schema.BlobLoader",
+    "langchain_core.document_loaders.BlobLoader"
+  ],
+  ["langchain_community.tools.BaseTool", "langchain_core.tools.BaseTool"],
+  [
+    "langchain_community.tools.StructuredTool",
+    "langchain_core.tools.StructuredTool"
+  ],
+  ["langchain_community.tools.Tool", "langchain_core.tools.Tool"],
+  [
+    "langchain_community.tools.format_tool_to_openai_function",
+    "langchain_core.utils.function_calling.format_tool_to_openai_function"
+  ],
+  ["langchain_community.tools.tool", "langchain_core.tools.tool"],
+  [
+    "langchain_community.tools.convert_to_openai.format_tool_to_openai_function",
+    "langchain_core.utils.function_calling.format_tool_to_openai_function"
+  ],
+  [
+    "langchain_community.tools.convert_to_openai.format_tool_to_openai_tool",
+    "langchain_core.utils.function_calling.format_tool_to_openai_tool"
+  ],
+  [
+    "langchain_community.tools.render.format_tool_to_openai_function",
+    "langchain_core.utils.function_calling.format_tool_to_openai_function"
+  ],
+  [
+    "langchain_community.tools.render.format_tool_to_openai_tool",
+    "langchain_core.utils.function_calling.format_tool_to_openai_tool"
+  ],
+  [
+    "langchain_community.utils.openai_functions.FunctionDescription",
+    "langchain_core.utils.function_calling.FunctionDescription"
+  ],
+  [
+    "langchain_community.utils.openai_functions.ToolDescription",
+    "langchain_core.utils.function_calling.ToolDescription"
+  ],
+  [
+    "langchain_community.utils.openai_functions.convert_pydantic_to_openai_function",
+    "langchain_core.utils.function_calling.convert_pydantic_to_openai_function"
+  ],
+  [
+    "langchain_community.utils.openai_functions.convert_pydantic_to_openai_tool",
+    "langchain_core.utils.function_calling.convert_pydantic_to_openai_tool"
+  ],
+  [
+    "langchain_community.vectorstores.VectorStore",
+    "langchain_core.vectorstores.VectorStore"
+  ]
+]

libs/cli/langchain_cli/namespaces/migrate/.grit/patterns/everything.grit

@@ -0,0 +1,18 @@
+language python
+
+pattern langchain_all_migrations() {
+  any {
+    langchain_migrate_community_to_core(),
+    langchain_migrate_fireworks(),
+    langchain_migrate_ibm(),
+    langchain_migrate_langchain_to_core(),
+    langchain_migrate_langchain_to_langchain_community(),
+    langchain_migrate_langchain_to_textsplitters(),
+    langchain_migrate_openai(),
+    langchain_migrate_pinecone(),
+    langchain_migrate_anthropic(),
+    replace_pydantic_v1_shim()
+  }
+}
+
+langchain_all_migrations()

libs/cli/langchain_cli/namespaces/migrate/.grit/patterns/fireworks.grit

@@ -0,0 +1,15 @@
+
+language python
+
+// This migration is generated automatically - do not manually edit this file
+pattern langchain_migrate_fireworks() {
+  find_replace_imports(list=[
+    [`langchain_community.chat_models.fireworks`, `ChatFireworks`, `langchain_fireworks`, `ChatFireworks`],
+    [`langchain_community.llms.fireworks`, `Fireworks`, `langchain_fireworks`, `Fireworks`],
+    [`langchain_community.chat_models`, `ChatFireworks`, `langchain_fireworks`, `ChatFireworks`],
+    [`langchain_community.llms`, `Fireworks`, `langchain_fireworks`, `Fireworks`]
+  ])
+}
+
+// Add this for invoking directly
+langchain_migrate_fireworks()

libs/cli/langchain_cli/namespaces/migrate/.grit/patterns/ibm.grit

@@ -0,0 +1,13 @@
+
+language python
+
+// This migration is generated automatically - do not manually edit this file
+pattern langchain_migrate_ibm() {
+  find_replace_imports(list=[
+    [`langchain_community.llms.watsonxllm`, `WatsonxLLM`, `langchain_ibm`, `WatsonxLLM`],
+    [`langchain_community.llms`, `WatsonxLLM`, `langchain_ibm`, `WatsonxLLM`]
+  ])
+}
+
+// Add this for invoking directly
+langchain_migrate_ibm()

libs/cli/langchain_cli/namespaces/migrate/.grit/patterns/langchain_to_core.grit

@@ -0,0 +1,542 @@
+
+language python
+
+// This migration is generated automatically - do not manually edit this file
+pattern langchain_migrate_langchain_to_core() {
+  find_replace_imports(list=[
+    [`langchain._api`, `deprecated`, `langchain_core._api`, `deprecated`],
+    [`langchain._api`, `LangChainDeprecationWarning`, `langchain_core._api`, `LangChainDeprecationWarning`],
+    [`langchain._api`, `suppress_langchain_deprecation_warning`, `langchain_core._api`, `suppress_langchain_deprecation_warning`],
+    [`langchain._api`, `surface_langchain_deprecation_warnings`, `langchain_core._api`, `surface_langchain_deprecation_warnings`],
+    [`langchain._api`, `warn_deprecated`, `langchain_core._api`, `warn_deprecated`],
+    [`langchain._api.deprecation`, `LangChainDeprecationWarning`, `langchain_core._api`, `LangChainDeprecationWarning`],
+    [`langchain._api.deprecation`, `LangChainPendingDeprecationWarning`, `langchain_core._api.deprecation`, `LangChainPendingDeprecationWarning`],
+    [`langchain._api.deprecation`, `deprecated`, `langchain_core._api`, `deprecated`],
+    [`langchain._api.deprecation`, `suppress_langchain_deprecation_warning`, `langchain_core._api`, `suppress_langchain_deprecation_warning`],
+    [`langchain._api.deprecation`, `warn_deprecated`, `langchain_core._api`, `warn_deprecated`],
+    [`langchain._api.deprecation`, `surface_langchain_deprecation_warnings`, `langchain_core._api`, `surface_langchain_deprecation_warnings`],
+    [`langchain._api.path`, `get_relative_path`, `langchain_core._api`, `get_relative_path`],
+    [`langchain._api.path`, `as_import_path`, `langchain_core._api`, `as_import_path`],
+    [`langchain.agents`, `Tool`, `langchain_core.tools`, `Tool`],
+    [`langchain.agents`, `tool`, `langchain_core.tools`, `tool`],
+    [`langchain.agents.tools`, `BaseTool`, `langchain_core.tools`, `BaseTool`],
+    [`langchain.agents.tools`, `tool`, `langchain_core.tools`, `tool`],
+    [`langchain.agents.tools`, `Tool`, `langchain_core.tools`, `Tool`],
+    [`langchain.base_language`, `BaseLanguageModel`, `langchain_core.language_models`, `BaseLanguageModel`],
+    [`langchain.callbacks`, `StdOutCallbackHandler`, `langchain_core.callbacks`, `StdOutCallbackHandler`],
+    [`langchain.callbacks`, `StreamingStdOutCallbackHandler`, `langchain_core.callbacks`, `StreamingStdOutCallbackHandler`],
+    [`langchain.callbacks`, `LangChainTracer`, `langchain_core.tracers`, `LangChainTracer`],
+    [`langchain.callbacks`, `tracing_enabled`, `langchain_core.tracers.context`, `tracing_enabled`],
+    [`langchain.callbacks`, `tracing_v2_enabled`, `langchain_core.tracers.context`, `tracing_v2_enabled`],
+    [`langchain.callbacks`, `collect_runs`, `langchain_core.tracers.context`, `collect_runs`],
+    [`langchain.callbacks.base`, `RetrieverManagerMixin`, `langchain_core.callbacks`, `RetrieverManagerMixin`],
+    [`langchain.callbacks.base`, `LLMManagerMixin`, `langchain_core.callbacks`, `LLMManagerMixin`],
+    [`langchain.callbacks.base`, `ChainManagerMixin`, `langchain_core.callbacks`, `ChainManagerMixin`],
+    [`langchain.callbacks.base`, `ToolManagerMixin`, `langchain_core.callbacks`, `ToolManagerMixin`],
+    [`langchain.callbacks.base`, `CallbackManagerMixin`, `langchain_core.callbacks`, `CallbackManagerMixin`],
+    [`langchain.callbacks.base`, `RunManagerMixin`, `langchain_core.callbacks`, `RunManagerMixin`],
+    [`langchain.callbacks.base`, `BaseCallbackHandler`, `langchain_core.callbacks`, `BaseCallbackHandler`],
+    [`langchain.callbacks.base`, `AsyncCallbackHandler`, `langchain_core.callbacks`, `AsyncCallbackHandler`],
+    [`langchain.callbacks.base`, `BaseCallbackManager`, `langchain_core.callbacks`, `BaseCallbackManager`],
+    [`langchain.callbacks.manager`, `BaseRunManager`, `langchain_core.callbacks`, `BaseRunManager`],
+    [`langchain.callbacks.manager`, `RunManager`, `langchain_core.callbacks`, `RunManager`],
+    [`langchain.callbacks.manager`, `ParentRunManager`, `langchain_core.callbacks`, `ParentRunManager`],
+    [`langchain.callbacks.manager`, `AsyncRunManager`, `langchain_core.callbacks`, `AsyncRunManager`],
+    [`langchain.callbacks.manager`, `AsyncParentRunManager`, `langchain_core.callbacks`, `AsyncParentRunManager`],
+    [`langchain.callbacks.manager`, `CallbackManagerForLLMRun`, `langchain_core.callbacks`, `CallbackManagerForLLMRun`],
+    [`langchain.callbacks.manager`, `AsyncCallbackManagerForLLMRun`, `langchain_core.callbacks`, `AsyncCallbackManagerForLLMRun`],
+    [`langchain.callbacks.manager`, `CallbackManagerForChainRun`, `langchain_core.callbacks`, `CallbackManagerForChainRun`],
+    [`langchain.callbacks.manager`, `AsyncCallbackManagerForChainRun`, `langchain_core.callbacks`, `AsyncCallbackManagerForChainRun`],
+    [`langchain.callbacks.manager`, `CallbackManagerForToolRun`, `langchain_core.callbacks`, `CallbackManagerForToolRun`],
+    [`langchain.callbacks.manager`, `AsyncCallbackManagerForToolRun`, `langchain_core.callbacks`, `AsyncCallbackManagerForToolRun`],
+    [`langchain.callbacks.manager`, `CallbackManagerForRetrieverRun`, `langchain_core.callbacks`, `CallbackManagerForRetrieverRun`],
+    [`langchain.callbacks.manager`, `AsyncCallbackManagerForRetrieverRun`, `langchain_core.callbacks`, `AsyncCallbackManagerForRetrieverRun`],
+    [`langchain.callbacks.manager`, `CallbackManager`, `langchain_core.callbacks`, `CallbackManager`],
+    [`langchain.callbacks.manager`, `CallbackManagerForChainGroup`, `langchain_core.callbacks`, `CallbackManagerForChainGroup`],
+    [`langchain.callbacks.manager`, `AsyncCallbackManager`, `langchain_core.callbacks`, `AsyncCallbackManager`],
+    [`langchain.callbacks.manager`, `AsyncCallbackManagerForChainGroup`, `langchain_core.callbacks`, `AsyncCallbackManagerForChainGroup`],
+    [`langchain.callbacks.manager`, `tracing_enabled`, `langchain_core.tracers.context`, `tracing_enabled`],
+    [`langchain.callbacks.manager`, `tracing_v2_enabled`, `langchain_core.tracers.context`, `tracing_v2_enabled`],
+    [`langchain.callbacks.manager`, `collect_runs`, `langchain_core.tracers.context`, `collect_runs`],
+    [`langchain.callbacks.manager`, `atrace_as_chain_group`, `langchain_core.callbacks.manager`, `atrace_as_chain_group`],
+    [`langchain.callbacks.manager`, `trace_as_chain_group`, `langchain_core.callbacks.manager`, `trace_as_chain_group`],
+    [`langchain.callbacks.manager`, `handle_event`, `langchain_core.callbacks.manager`, `handle_event`],
+    [`langchain.callbacks.manager`, `ahandle_event`, `langchain_core.callbacks.manager`, `ahandle_event`],
+    [`langchain.callbacks.manager`, `env_var_is_set`, `langchain_core.utils.env`, `env_var_is_set`],
+    [`langchain.callbacks.stdout`, `StdOutCallbackHandler`, `langchain_core.callbacks`, `StdOutCallbackHandler`],
+    [`langchain.callbacks.streaming_stdout`, `StreamingStdOutCallbackHandler`, `langchain_core.callbacks`, `StreamingStdOutCallbackHandler`],
+    [`langchain.callbacks.tracers`, `ConsoleCallbackHandler`, `langchain_core.tracers`, `ConsoleCallbackHandler`],
+    [`langchain.callbacks.tracers`, `FunctionCallbackHandler`, `langchain_core.tracers.stdout`, `FunctionCallbackHandler`],
+    [`langchain.callbacks.tracers`, `LangChainTracer`, `langchain_core.tracers`, `LangChainTracer`],
+    [`langchain.callbacks.tracers`, `LangChainTracerV1`, `langchain_core.tracers.langchain_v1`, `LangChainTracerV1`],
+    [`langchain.callbacks.tracers.base`, `BaseTracer`, `langchain_core.tracers`, `BaseTracer`],
+    [`langchain.callbacks.tracers.base`, `TracerException`, `langchain_core.exceptions`, `TracerException`],
+    [`langchain.callbacks.tracers.evaluation`, `wait_for_all_evaluators`, `langchain_core.tracers.evaluation`, `wait_for_all_evaluators`],
+    [`langchain.callbacks.tracers.evaluation`, `EvaluatorCallbackHandler`, `langchain_core.tracers`, `EvaluatorCallbackHandler`],
+    [`langchain.callbacks.tracers.langchain`, `LangChainTracer`, `langchain_core.tracers`, `LangChainTracer`],
+    [`langchain.callbacks.tracers.langchain`, `wait_for_all_tracers`, `langchain_core.tracers.langchain`, `wait_for_all_tracers`],
+    [`langchain.callbacks.tracers.langchain_v1`, `LangChainTracerV1`, `langchain_core.tracers.langchain_v1`, `LangChainTracerV1`],
+    [`langchain.callbacks.tracers.log_stream`, `LogEntry`, `langchain_core.tracers.log_stream`, `LogEntry`],
+    [`langchain.callbacks.tracers.log_stream`, `RunState`, `langchain_core.tracers.log_stream`, `RunState`],
+    [`langchain.callbacks.tracers.log_stream`, `RunLog`, `langchain_core.tracers`, `RunLog`],
+    [`langchain.callbacks.tracers.log_stream`, `RunLogPatch`, `langchain_core.tracers`, `RunLogPatch`],
+    [`langchain.callbacks.tracers.log_stream`, `LogStreamCallbackHandler`, `langchain_core.tracers`, `LogStreamCallbackHandler`],
+    [`langchain.callbacks.tracers.root_listeners`, `RootListenersTracer`, `langchain_core.tracers.root_listeners`, `RootListenersTracer`],
+    [`langchain.callbacks.tracers.run_collector`, `RunCollectorCallbackHandler`, `langchain_core.tracers.run_collector`, `RunCollectorCallbackHandler`],
+    [`langchain.callbacks.tracers.schemas`, `BaseRun`, `langchain_core.tracers.schemas`, `BaseRun`],
+    [`langchain.callbacks.tracers.schemas`, `ChainRun`, `langchain_core.tracers.schemas`, `ChainRun`],
+    [`langchain.callbacks.tracers.schemas`, `LLMRun`, `langchain_core.tracers.schemas`, `LLMRun`],
+    [`langchain.callbacks.tracers.schemas`, `Run`, `langchain_core.tracers`, `Run`],
+    [`langchain.callbacks.tracers.schemas`, `RunTypeEnum`, `langchain_core.tracers.schemas`, `RunTypeEnum`],
+    [`langchain.callbacks.tracers.schemas`, `ToolRun`, `langchain_core.tracers.schemas`, `ToolRun`],
+    [`langchain.callbacks.tracers.schemas`, `TracerSession`, `langchain_core.tracers.schemas`, `TracerSession`],
+    [`langchain.callbacks.tracers.schemas`, `TracerSessionBase`, `langchain_core.tracers.schemas`, `TracerSessionBase`],
+    [`langchain.callbacks.tracers.schemas`, `TracerSessionV1`, `langchain_core.tracers.schemas`, `TracerSessionV1`],
+    [`langchain.callbacks.tracers.schemas`, `TracerSessionV1Base`, `langchain_core.tracers.schemas`, `TracerSessionV1Base`],
+    [`langchain.callbacks.tracers.schemas`, `TracerSessionV1Create`, `langchain_core.tracers.schemas`, `TracerSessionV1Create`],
+    [`langchain.callbacks.tracers.stdout`, `FunctionCallbackHandler`, `langchain_core.tracers.stdout`, `FunctionCallbackHandler`],
+    [`langchain.callbacks.tracers.stdout`, `ConsoleCallbackHandler`, `langchain_core.tracers`, `ConsoleCallbackHandler`],
+    [`langchain.chains.openai_functions`, `convert_to_openai_function`, `langchain_core.utils.function_calling`, `convert_to_openai_function`],
+    [`langchain.chains.openai_functions.base`, `convert_to_openai_function`, `langchain_core.utils.function_calling`, `convert_to_openai_function`],
+    [`langchain.chat_models.base`, `BaseChatModel`, `langchain_core.language_models`, `BaseChatModel`],
+    [`langchain.chat_models.base`, `SimpleChatModel`, `langchain_core.language_models`, `SimpleChatModel`],
+    [`langchain.chat_models.base`, `generate_from_stream`, `langchain_core.language_models.chat_models`, `generate_from_stream`],
+    [`langchain.chat_models.base`, `agenerate_from_stream`, `langchain_core.language_models.chat_models`, `agenerate_from_stream`],
+    [`langchain.docstore.document`, `Document`, `langchain_core.documents`, `Document`],
+    [`langchain.document_loaders`, `Blob`, `langchain_core.document_loaders`, `Blob`],
+    [`langchain.document_loaders`, `BlobLoader`, `langchain_core.document_loaders`, `BlobLoader`],
+    [`langchain.document_loaders.base`, `BaseLoader`, `langchain_core.document_loaders`, `BaseLoader`],
+    [`langchain.document_loaders.base`, `BaseBlobParser`, `langchain_core.document_loaders`, `BaseBlobParser`],
+    [`langchain.document_loaders.blob_loaders`, `BlobLoader`, `langchain_core.document_loaders`, `BlobLoader`],
+    [`langchain.document_loaders.blob_loaders`, `Blob`, `langchain_core.document_loaders`, `Blob`],
+    [`langchain.document_loaders.blob_loaders.schema`, `Blob`, `langchain_core.document_loaders`, `Blob`],
+    [`langchain.document_loaders.blob_loaders.schema`, `BlobLoader`, `langchain_core.document_loaders`, `BlobLoader`],
+    [`langchain.embeddings.base`, `Embeddings`, `langchain_core.embeddings`, `Embeddings`],
+    [`langchain.formatting`, `StrictFormatter`, `langchain_core.utils`, `StrictFormatter`],
+    [`langchain.input`, `get_bolded_text`, `langchain_core.utils`, `get_bolded_text`],
+    [`langchain.input`, `get_color_mapping`, `langchain_core.utils`, `get_color_mapping`],
+    [`langchain.input`, `get_colored_text`, `langchain_core.utils`, `get_colored_text`],
+    [`langchain.input`, `print_text`, `langchain_core.utils`, `print_text`],
+    [`langchain.llms.base`, `BaseLanguageModel`, `langchain_core.language_models`, `BaseLanguageModel`],
+    [`langchain.llms.base`, `BaseLLM`, `langchain_core.language_models`, `BaseLLM`],
+    [`langchain.llms.base`, `LLM`, `langchain_core.language_models`, `LLM`],
+    [`langchain.load`, `dumpd`, `langchain_core.load`, `dumpd`],
+    [`langchain.load`, `dumps`, `langchain_core.load`, `dumps`],
+    [`langchain.load`, `load`, `langchain_core.load`, `load`],
+    [`langchain.load`, `loads`, `langchain_core.load`, `loads`],
+    [`langchain.load.dump`, `default`, `langchain_core.load.dump`, `default`],
+    [`langchain.load.dump`, `dumps`, `langchain_core.load`, `dumps`],
+    [`langchain.load.dump`, `dumpd`, `langchain_core.load`, `dumpd`],
+    [`langchain.load.load`, `Reviver`, `langchain_core.load.load`, `Reviver`],
+    [`langchain.load.load`, `loads`, `langchain_core.load`, `loads`],
+    [`langchain.load.load`, `load`, `langchain_core.load`, `load`],
+    [`langchain.load.serializable`, `BaseSerialized`, `langchain_core.load.serializable`, `BaseSerialized`],
+    [`langchain.load.serializable`, `SerializedConstructor`, `langchain_core.load.serializable`, `SerializedConstructor`],
+    [`langchain.load.serializable`, `SerializedSecret`, `langchain_core.load.serializable`, `SerializedSecret`],
+    [`langchain.load.serializable`, `SerializedNotImplemented`, `langchain_core.load.serializable`, `SerializedNotImplemented`],
+    [`langchain.load.serializable`, `try_neq_default`, `langchain_core.load.serializable`, `try_neq_default`],
+    [`langchain.load.serializable`, `Serializable`, `langchain_core.load`, `Serializable`],
+    [`langchain.load.serializable`, `to_json_not_implemented`, `langchain_core.load.serializable`, `to_json_not_implemented`],
+    [`langchain.output_parsers`, `CommaSeparatedListOutputParser`, `langchain_core.output_parsers`, `CommaSeparatedListOutputParser`],
+    [`langchain.output_parsers`, `ListOutputParser`, `langchain_core.output_parsers`, `ListOutputParser`],
+    [`langchain.output_parsers`, `MarkdownListOutputParser`, `langchain_core.output_parsers`, `MarkdownListOutputParser`],
+    [`langchain.output_parsers`, `NumberedListOutputParser`, `langchain_core.output_parsers`, `NumberedListOutputParser`],
+    [`langchain.output_parsers`, `PydanticOutputParser`, `langchain_core.output_parsers`, `PydanticOutputParser`],
+    [`langchain.output_parsers`, `XMLOutputParser`, `langchain_core.output_parsers`, `XMLOutputParser`],
+    [`langchain.output_parsers`, `JsonOutputToolsParser`, `langchain_core.output_parsers.openai_tools`, `JsonOutputToolsParser`],
+    [`langchain.output_parsers`, `PydanticToolsParser`, `langchain_core.output_parsers.openai_tools`, `PydanticToolsParser`],
+    [`langchain.output_parsers`, `JsonOutputKeyToolsParser`, `langchain_core.output_parsers.openai_tools`, `JsonOutputKeyToolsParser`],
+    [`langchain.output_parsers.json`, `SimpleJsonOutputParser`, `langchain_core.output_parsers`, `JsonOutputParser`],
+    [`langchain.output_parsers.json`, `parse_partial_json`, `langchain_core.utils.json`, `parse_partial_json`],
+    [`langchain.output_parsers.json`, `parse_json_markdown`, `langchain_core.utils.json`, `parse_json_markdown`],
+    [`langchain.output_parsers.json`, `parse_and_check_json_markdown`, `langchain_core.utils.json`, `parse_and_check_json_markdown`],
+    [`langchain.output_parsers.list`, `ListOutputParser`, `langchain_core.output_parsers`, `ListOutputParser`],
+    [`langchain.output_parsers.list`, `CommaSeparatedListOutputParser`, `langchain_core.output_parsers`, `CommaSeparatedListOutputParser`],
+    [`langchain.output_parsers.list`, `NumberedListOutputParser`, `langchain_core.output_parsers`, `NumberedListOutputParser`],
+    [`langchain.output_parsers.list`, `MarkdownListOutputParser`, `langchain_core.output_parsers`, `MarkdownListOutputParser`],
+    [`langchain.output_parsers.openai_functions`, `PydanticOutputFunctionsParser`, `langchain_core.output_parsers.openai_functions`, `PydanticOutputFunctionsParser`],
+    [`langchain.output_parsers.openai_functions`, `PydanticAttrOutputFunctionsParser`, `langchain_core.output_parsers.openai_functions`, `PydanticAttrOutputFunctionsParser`],
+    [`langchain.output_parsers.openai_functions`, `JsonOutputFunctionsParser`, `langchain_core.output_parsers.openai_functions`, `JsonOutputFunctionsParser`],
+    [`langchain.output_parsers.openai_functions`, `JsonKeyOutputFunctionsParser`, `langchain_core.output_parsers.openai_functions`, `JsonKeyOutputFunctionsParser`],
+    [`langchain.output_parsers.openai_tools`, `PydanticToolsParser`, `langchain_core.output_parsers.openai_tools`, `PydanticToolsParser`],
+    [`langchain.output_parsers.openai_tools`, `JsonOutputToolsParser`, `langchain_core.output_parsers.openai_tools`, `JsonOutputToolsParser`],
+    [`langchain.output_parsers.openai_tools`, `JsonOutputKeyToolsParser`, `langchain_core.output_parsers.openai_tools`, `JsonOutputKeyToolsParser`],
+    [`langchain.output_parsers.pydantic`, `PydanticOutputParser`, `langchain_core.output_parsers`, `PydanticOutputParser`],
+    [`langchain.output_parsers.xml`, `XMLOutputParser`, `langchain_core.output_parsers`, `XMLOutputParser`],
+    [`langchain.prompts`, `AIMessagePromptTemplate`, `langchain_core.prompts`, `AIMessagePromptTemplate`],
+    [`langchain.prompts`, `BaseChatPromptTemplate`, `langchain_core.prompts`, `BaseChatPromptTemplate`],
+    [`langchain.prompts`, `BasePromptTemplate`, `langchain_core.prompts`, `BasePromptTemplate`],
+    [`langchain.prompts`, `ChatMessagePromptTemplate`, `langchain_core.prompts`, `ChatMessagePromptTemplate`],
+    [`langchain.prompts`, `ChatPromptTemplate`, `langchain_core.prompts`, `ChatPromptTemplate`],
+    [`langchain.prompts`, `FewShotPromptTemplate`, `langchain_core.prompts`, `FewShotPromptTemplate`],
+    [`langchain.prompts`, `FewShotPromptWithTemplates`, `langchain_core.prompts`, `FewShotPromptWithTemplates`],
+    [`langchain.prompts`, `HumanMessagePromptTemplate`, `langchain_core.prompts`, `HumanMessagePromptTemplate`],
+    [`langchain.prompts`, `LengthBasedExampleSelector`, `langchain_core.example_selectors`, `LengthBasedExampleSelector`],
+    [`langchain.prompts`, `MaxMarginalRelevanceExampleSelector`, `langchain_core.example_selectors`, `MaxMarginalRelevanceExampleSelector`],
+    [`langchain.prompts`, `MessagesPlaceholder`, `langchain_core.prompts`, `MessagesPlaceholder`],
+    [`langchain.prompts`, `PipelinePromptTemplate`, `langchain_core.prompts`, `PipelinePromptTemplate`],
+    [`langchain.prompts`, `PromptTemplate`, `langchain_core.prompts`, `PromptTemplate`],
+    [`langchain.prompts`, `SemanticSimilarityExampleSelector`, `langchain_core.example_selectors`, `SemanticSimilarityExampleSelector`],
+    [`langchain.prompts`, `StringPromptTemplate`, `langchain_core.prompts`, `StringPromptTemplate`],
+    [`langchain.prompts`, `SystemMessagePromptTemplate`, `langchain_core.prompts`, `SystemMessagePromptTemplate`],
+    [`langchain.prompts`, `load_prompt`, `langchain_core.prompts`, `load_prompt`],
+    [`langchain.prompts`, `FewShotChatMessagePromptTemplate`, `langchain_core.prompts`, `FewShotChatMessagePromptTemplate`],
+    [`langchain.prompts`, `Prompt`, `langchain_core.prompts`, `PromptTemplate`],
+    [`langchain.prompts.base`, `jinja2_formatter`, `langchain_core.prompts`, `jinja2_formatter`],
+    [`langchain.prompts.base`, `validate_jinja2`, `langchain_core.prompts`, `validate_jinja2`],
+    [`langchain.prompts.base`, `check_valid_template`, `langchain_core.prompts`, `check_valid_template`],
+    [`langchain.prompts.base`, `get_template_variables`, `langchain_core.prompts`, `get_template_variables`],
+    [`langchain.prompts.base`, `StringPromptTemplate`, `langchain_core.prompts`, `StringPromptTemplate`],
+    [`langchain.prompts.base`, `BasePromptTemplate`, `langchain_core.prompts`, `BasePromptTemplate`],
+    [`langchain.prompts.base`, `StringPromptValue`, `langchain_core.prompt_values`, `StringPromptValue`],
+    [`langchain.prompts.base`, `_get_jinja2_variables_from_template`, `langchain_core.prompts.string`, `_get_jinja2_variables_from_template`],
+    [`langchain.prompts.chat`, `BaseMessagePromptTemplate`, `langchain_core.prompts.chat`, `BaseMessagePromptTemplate`],
+    [`langchain.prompts.chat`, `MessagesPlaceholder`, `langchain_core.prompts`, `MessagesPlaceholder`],
+    [`langchain.prompts.chat`, `BaseStringMessagePromptTemplate`, `langchain_core.prompts.chat`, `BaseStringMessagePromptTemplate`],
+    [`langchain.prompts.chat`, `ChatMessagePromptTemplate`, `langchain_core.prompts`, `ChatMessagePromptTemplate`],
+    [`langchain.prompts.chat`, `HumanMessagePromptTemplate`, `langchain_core.prompts`, `HumanMessagePromptTemplate`],
+    [`langchain.prompts.chat`, `AIMessagePromptTemplate`, `langchain_core.prompts`, `AIMessagePromptTemplate`],
+    [`langchain.prompts.chat`, `SystemMessagePromptTemplate`, `langchain_core.prompts`, `SystemMessagePromptTemplate`],
+    [`langchain.prompts.chat`, `BaseChatPromptTemplate`, `langchain_core.prompts`, `BaseChatPromptTemplate`],
+    [`langchain.prompts.chat`, `ChatPromptTemplate`, `langchain_core.prompts`, `ChatPromptTemplate`],
+    [`langchain.prompts.chat`, `ChatPromptValue`, `langchain_core.prompt_values`, `ChatPromptValue`],
+    [`langchain.prompts.chat`, `ChatPromptValueConcrete`, `langchain_core.prompt_values`, `ChatPromptValueConcrete`],
+    [`langchain.prompts.chat`, `_convert_to_message`, `langchain_core.prompts.chat`, `_convert_to_message`],
+    [`langchain.prompts.chat`, `_create_template_from_message_type`, `langchain_core.prompts.chat`, `_create_template_from_message_type`],
+    [`langchain.prompts.example_selector`, `LengthBasedExampleSelector`, `langchain_core.example_selectors`, `LengthBasedExampleSelector`],
+    [`langchain.prompts.example_selector`, `MaxMarginalRelevanceExampleSelector`, `langchain_core.example_selectors`, `MaxMarginalRelevanceExampleSelector`],
+    [`langchain.prompts.example_selector`, `SemanticSimilarityExampleSelector`, `langchain_core.example_selectors`, `SemanticSimilarityExampleSelector`],
+    [`langchain.prompts.example_selector.base`, `BaseExampleSelector`, `langchain_core.example_selectors`, `BaseExampleSelector`],
+    [`langchain.prompts.example_selector.length_based`, `LengthBasedExampleSelector`, `langchain_core.example_selectors`, `LengthBasedExampleSelector`],
+    [`langchain.prompts.example_selector.semantic_similarity`, `sorted_values`, `langchain_core.example_selectors`, `sorted_values`],
+    [`langchain.prompts.example_selector.semantic_similarity`, `SemanticSimilarityExampleSelector`, `langchain_core.example_selectors`, `SemanticSimilarityExampleSelector`],
+    [`langchain.prompts.example_selector.semantic_similarity`, `MaxMarginalRelevanceExampleSelector`, `langchain_core.example_selectors`, `MaxMarginalRelevanceExampleSelector`],
+    [`langchain.prompts.few_shot`, `FewShotPromptTemplate`, `langchain_core.prompts`, `FewShotPromptTemplate`],
+    [`langchain.prompts.few_shot`, `FewShotChatMessagePromptTemplate`, `langchain_core.prompts`, `FewShotChatMessagePromptTemplate`],
+    [`langchain.prompts.few_shot`, `_FewShotPromptTemplateMixin`, `langchain_core.prompts.few_shot`, `_FewShotPromptTemplateMixin`],
+    [`langchain.prompts.few_shot_with_templates`, `FewShotPromptWithTemplates`, `langchain_core.prompts`, `FewShotPromptWithTemplates`],
+    [`langchain.prompts.loading`, `load_prompt_from_config`, `langchain_core.prompts.loading`, `load_prompt_from_config`],
+    [`langchain.prompts.loading`, `load_prompt`, `langchain_core.prompts`, `load_prompt`],
+    [`langchain.prompts.loading`, `try_load_from_hub`, `langchain_core.utils`, `try_load_from_hub`],
+    [`langchain.prompts.loading`, `_load_examples`, `langchain_core.prompts.loading`, `_load_examples`],
+    [`langchain.prompts.loading`, `_load_few_shot_prompt`, `langchain_core.prompts.loading`, `_load_few_shot_prompt`],
+    [`langchain.prompts.loading`, `_load_output_parser`, `langchain_core.prompts.loading`, `_load_output_parser`],
+    [`langchain.prompts.loading`, `_load_prompt`, `langchain_core.prompts.loading`, `_load_prompt`],
+    [`langchain.prompts.loading`, `_load_prompt_from_file`, `langchain_core.prompts.loading`, `_load_prompt_from_file`],
+    [`langchain.prompts.loading`, `_load_template`, `langchain_core.prompts.loading`, `_load_template`],
+    [`langchain.prompts.pipeline`, `PipelinePromptTemplate`, `langchain_core.prompts`, `PipelinePromptTemplate`],
+    [`langchain.prompts.pipeline`, `_get_inputs`, `langchain_core.prompts.pipeline`, `_get_inputs`],
+    [`langchain.prompts.prompt`, `PromptTemplate`, `langchain_core.prompts`, `PromptTemplate`],
+    [`langchain.prompts.prompt`, `Prompt`, `langchain_core.prompts`, `PromptTemplate`],
+    [`langchain.schema`, `BaseCache`, `langchain_core.caches`, `BaseCache`],
+    [`langchain.schema`, `BaseMemory`, `langchain_core.memory`, `BaseMemory`],
+    [`langchain.schema`, `BaseStore`, `langchain_core.stores`, `BaseStore`],
+    [`langchain.schema`, `AgentFinish`, `langchain_core.agents`, `AgentFinish`],
+    [`langchain.schema`, `AgentAction`, `langchain_core.agents`, `AgentAction`],
+    [`langchain.schema`, `Document`, `langchain_core.documents`, `Document`],
+    [`langchain.schema`, `BaseChatMessageHistory`, `langchain_core.chat_history`, `BaseChatMessageHistory`],
+    [`langchain.schema`, `BaseDocumentTransformer`, `langchain_core.documents`, `BaseDocumentTransformer`],
+    [`langchain.schema`, `BaseMessage`, `langchain_core.messages`, `BaseMessage`],
+    [`langchain.schema`, `ChatMessage`, `langchain_core.messages`, `ChatMessage`],
+    [`langchain.schema`, `FunctionMessage`, `langchain_core.messages`, `FunctionMessage`],
+    [`langchain.schema`, `HumanMessage`, `langchain_core.messages`, `HumanMessage`],
+    [`langchain.schema`, `AIMessage`, `langchain_core.messages`, `AIMessage`],
+    [`langchain.schema`, `SystemMessage`, `langchain_core.messages`, `SystemMessage`],
+    [`langchain.schema`, `messages_from_dict`, `langchain_core.messages`, `messages_from_dict`],
+    [`langchain.schema`, `messages_to_dict`, `langchain_core.messages`, `messages_to_dict`],
+    [`langchain.schema`, `message_to_dict`, `langchain_core.messages`, `message_to_dict`],
+    [`langchain.schema`, `_message_to_dict`, `langchain_core.messages`, `message_to_dict`],
+    [`langchain.schema`, `_message_from_dict`, `langchain_core.messages`, `_message_from_dict`],
+    [`langchain.schema`, `get_buffer_string`, `langchain_core.messages`, `get_buffer_string`],
+    [`langchain.schema`, `RunInfo`, `langchain_core.outputs`, `RunInfo`],
+    [`langchain.schema`, `LLMResult`, `langchain_core.outputs`, `LLMResult`],
+    [`langchain.schema`, `ChatResult`, `langchain_core.outputs`, `ChatResult`],
+    [`langchain.schema`, `ChatGeneration`, `langchain_core.outputs`, `ChatGeneration`],
+    [`langchain.schema`, `Generation`, `langchain_core.outputs`, `Generation`],
+    [`langchain.schema`, `PromptValue`, `langchain_core.prompt_values`, `PromptValue`],
+    [`langchain.schema`, `LangChainException`, `langchain_core.exceptions`, `LangChainException`],
+    [`langchain.schema`, `BaseRetriever`, `langchain_core.retrievers`, `BaseRetriever`],
+    [`langchain.schema`, `Memory`, `langchain_core.memory`, `BaseMemory`],
+    [`langchain.schema`, `OutputParserException`, `langchain_core.exceptions`, `OutputParserException`],
+    [`langchain.schema`, `StrOutputParser`, `langchain_core.output_parsers`, `StrOutputParser`],
+    [`langchain.schema`, `BaseOutputParser`, `langchain_core.output_parsers`, `BaseOutputParser`],
+    [`langchain.schema`, `BaseLLMOutputParser`, `langchain_core.output_parsers`, `BaseLLMOutputParser`],
+    [`langchain.schema`, `BasePromptTemplate`, `langchain_core.prompts`, `BasePromptTemplate`],
+    [`langchain.schema`, `format_document`, `langchain_core.prompts`, `format_document`],
+    [`langchain.schema.agent`, `AgentAction`, `langchain_core.agents`, `AgentAction`],
+    [`langchain.schema.agent`, `AgentActionMessageLog`, `langchain_core.agents`, `AgentActionMessageLog`],
+    [`langchain.schema.agent`, `AgentFinish`, `langchain_core.agents`, `AgentFinish`],
+    [`langchain.schema.cache`, `BaseCache`, `langchain_core.caches`, `BaseCache`],
+    [`langchain.schema.callbacks.base`, `RetrieverManagerMixin`, `langchain_core.callbacks`, `RetrieverManagerMixin`],
+    [`langchain.schema.callbacks.base`, `LLMManagerMixin`, `langchain_core.callbacks`, `LLMManagerMixin`],
+    [`langchain.schema.callbacks.base`, `ChainManagerMixin`, `langchain_core.callbacks`, `ChainManagerMixin`],
+    [`langchain.schema.callbacks.base`, `ToolManagerMixin`, `langchain_core.callbacks`, `ToolManagerMixin`],
+    [`langchain.schema.callbacks.base`, `CallbackManagerMixin`, `langchain_core.callbacks`, `CallbackManagerMixin`],
+    [`langchain.schema.callbacks.base`, `RunManagerMixin`, `langchain_core.callbacks`, `RunManagerMixin`],
+    [`langchain.schema.callbacks.base`, `BaseCallbackHandler`, `langchain_core.callbacks`, `BaseCallbackHandler`],
+    [`langchain.schema.callbacks.base`, `AsyncCallbackHandler`, `langchain_core.callbacks`, `AsyncCallbackHandler`],
+    [`langchain.schema.callbacks.base`, `BaseCallbackManager`, `langchain_core.callbacks`, `BaseCallbackManager`],
+    [`langchain.schema.callbacks.manager`, `tracing_enabled`, `langchain_core.tracers.context`, `tracing_enabled`],
+    [`langchain.schema.callbacks.manager`, `tracing_v2_enabled`, `langchain_core.tracers.context`, `tracing_v2_enabled`],
+    [`langchain.schema.callbacks.manager`, `collect_runs`, `langchain_core.tracers.context`, `collect_runs`],
+    [`langchain.schema.callbacks.manager`, `trace_as_chain_group`, `langchain_core.callbacks.manager`, `trace_as_chain_group`],
+    [`langchain.schema.callbacks.manager`, `handle_event`, `langchain_core.callbacks.manager`, `handle_event`],
+    [`langchain.schema.callbacks.manager`, `BaseRunManager`, `langchain_core.callbacks`, `BaseRunManager`],
+    [`langchain.schema.callbacks.manager`, `RunManager`, `langchain_core.callbacks`, `RunManager`],
+    [`langchain.schema.callbacks.manager`, `ParentRunManager`, `langchain_core.callbacks`, `ParentRunManager`],
+    [`langchain.schema.callbacks.manager`, `AsyncRunManager`, `langchain_core.callbacks`, `AsyncRunManager`],
+    [`langchain.schema.callbacks.manager`, `AsyncParentRunManager`, `langchain_core.callbacks`, `AsyncParentRunManager`],
+    [`langchain.schema.callbacks.manager`, `CallbackManagerForLLMRun`, `langchain_core.callbacks`, `CallbackManagerForLLMRun`],
+    [`langchain.schema.callbacks.manager`, `AsyncCallbackManagerForLLMRun`, `langchain_core.callbacks`, `AsyncCallbackManagerForLLMRun`],
+    [`langchain.schema.callbacks.manager`, `CallbackManagerForChainRun`, `langchain_core.callbacks`, `CallbackManagerForChainRun`],
+    [`langchain.schema.callbacks.manager`, `AsyncCallbackManagerForChainRun`, `langchain_core.callbacks`, `AsyncCallbackManagerForChainRun`],
+    [`langchain.schema.callbacks.manager`, `CallbackManagerForToolRun`, `langchain_core.callbacks`, `CallbackManagerForToolRun`],
+    [`langchain.schema.callbacks.manager`, `AsyncCallbackManagerForToolRun`, `langchain_core.callbacks`, `AsyncCallbackManagerForToolRun`],
+    [`langchain.schema.callbacks.manager`, `CallbackManagerForRetrieverRun`, `langchain_core.callbacks`, `CallbackManagerForRetrieverRun`],
+    [`langchain.schema.callbacks.manager`, `AsyncCallbackManagerForRetrieverRun`, `langchain_core.callbacks`, `AsyncCallbackManagerForRetrieverRun`],
+    [`langchain.schema.callbacks.manager`, `CallbackManager`, `langchain_core.callbacks`, `CallbackManager`],
+    [`langchain.schema.callbacks.manager`, `CallbackManagerForChainGroup`, `langchain_core.callbacks`, `CallbackManagerForChainGroup`],
+    [`langchain.schema.callbacks.manager`, `AsyncCallbackManager`, `langchain_core.callbacks`, `AsyncCallbackManager`],
+    [`langchain.schema.callbacks.manager`, `AsyncCallbackManagerForChainGroup`, `langchain_core.callbacks`, `AsyncCallbackManagerForChainGroup`],
+    [`langchain.schema.callbacks.manager`, `register_configure_hook`, `langchain_core.tracers.context`, `register_configure_hook`],
+    [`langchain.schema.callbacks.manager`, `env_var_is_set`, `langchain_core.utils.env`, `env_var_is_set`],
+    [`langchain.schema.callbacks.stdout`, `StdOutCallbackHandler`, `langchain_core.callbacks`, `StdOutCallbackHandler`],
+    [`langchain.schema.callbacks.streaming_stdout`, `StreamingStdOutCallbackHandler`, `langchain_core.callbacks`, `StreamingStdOutCallbackHandler`],
+    [`langchain.schema.callbacks.tracers.base`, `TracerException`, `langchain_core.exceptions`, `TracerException`],
+    [`langchain.schema.callbacks.tracers.base`, `BaseTracer`, `langchain_core.tracers`, `BaseTracer`],
+    [`langchain.schema.callbacks.tracers.evaluation`, `wait_for_all_evaluators`, `langchain_core.tracers.evaluation`, `wait_for_all_evaluators`],
+    [`langchain.schema.callbacks.tracers.evaluation`, `EvaluatorCallbackHandler`, `langchain_core.tracers`, `EvaluatorCallbackHandler`],
+    [`langchain.schema.callbacks.tracers.langchain`, `log_error_once`, `langchain_core.tracers.langchain`, `log_error_once`],
+    [`langchain.schema.callbacks.tracers.langchain`, `wait_for_all_tracers`, `langchain_core.tracers.langchain`, `wait_for_all_tracers`],
+    [`langchain.schema.callbacks.tracers.langchain`, `get_client`, `langchain_core.tracers.langchain`, `get_client`],
+    [`langchain.schema.callbacks.tracers.langchain`, `LangChainTracer`, `langchain_core.tracers`, `LangChainTracer`],
+    [`langchain.schema.callbacks.tracers.langchain_v1`, `get_headers`, `langchain_core.tracers.langchain_v1`, `get_headers`],
+    [`langchain.schema.callbacks.tracers.langchain_v1`, `LangChainTracerV1`, `langchain_core.tracers.langchain_v1`, `LangChainTracerV1`],
+    [`langchain.schema.callbacks.tracers.log_stream`, `LogEntry`, `langchain_core.tracers.log_stream`, `LogEntry`],
+    [`langchain.schema.callbacks.tracers.log_stream`, `RunState`, `langchain_core.tracers.log_stream`, `RunState`],
+    [`langchain.schema.callbacks.tracers.log_stream`, `RunLogPatch`, `langchain_core.tracers`, `RunLogPatch`],
+    [`langchain.schema.callbacks.tracers.log_stream`, `RunLog`, `langchain_core.tracers`, `RunLog`],
+    [`langchain.schema.callbacks.tracers.log_stream`, `LogStreamCallbackHandler`, `langchain_core.tracers`, `LogStreamCallbackHandler`],
+    [`langchain.schema.callbacks.tracers.root_listeners`, `RootListenersTracer`, `langchain_core.tracers.root_listeners`, `RootListenersTracer`],
+    [`langchain.schema.callbacks.tracers.run_collector`, `RunCollectorCallbackHandler`, `langchain_core.tracers.run_collector`, `RunCollectorCallbackHandler`],
+    [`langchain.schema.callbacks.tracers.schemas`, `RunTypeEnum`, `langchain_core.tracers.schemas`, `RunTypeEnum`],
+    [`langchain.schema.callbacks.tracers.schemas`, `TracerSessionV1Base`, `langchain_core.tracers.schemas`, `TracerSessionV1Base`],
+    [`langchain.schema.callbacks.tracers.schemas`, `TracerSessionV1Create`, `langchain_core.tracers.schemas`, `TracerSessionV1Create`],
+    [`langchain.schema.callbacks.tracers.schemas`, `TracerSessionV1`, `langchain_core.tracers.schemas`, `TracerSessionV1`],
+    [`langchain.schema.callbacks.tracers.schemas`, `TracerSessionBase`, `langchain_core.tracers.schemas`, `TracerSessionBase`],
+    [`langchain.schema.callbacks.tracers.schemas`, `TracerSession`, `langchain_core.tracers.schemas`, `TracerSession`],
+    [`langchain.schema.callbacks.tracers.schemas`, `BaseRun`, `langchain_core.tracers.schemas`, `BaseRun`],
+    [`langchain.schema.callbacks.tracers.schemas`, `LLMRun`, `langchain_core.tracers.schemas`, `LLMRun`],
+    [`langchain.schema.callbacks.tracers.schemas`, `ChainRun`, `langchain_core.tracers.schemas`, `ChainRun`],
+    [`langchain.schema.callbacks.tracers.schemas`, `ToolRun`, `langchain_core.tracers.schemas`, `ToolRun`],
+    [`langchain.schema.callbacks.tracers.schemas`, `Run`, `langchain_core.tracers`, `Run`],
+    [`langchain.schema.callbacks.tracers.stdout`, `try_json_stringify`, `langchain_core.tracers.stdout`, `try_json_stringify`],
+    [`langchain.schema.callbacks.tracers.stdout`, `elapsed`, `langchain_core.tracers.stdout`, `elapsed`],
+    [`langchain.schema.callbacks.tracers.stdout`, `FunctionCallbackHandler`, `langchain_core.tracers.stdout`, `FunctionCallbackHandler`],
+    [`langchain.schema.callbacks.tracers.stdout`, `ConsoleCallbackHandler`, `langchain_core.tracers`, `ConsoleCallbackHandler`],
+    [`langchain.schema.chat`, `ChatSession`, `langchain_core.chat_sessions`, `ChatSession`],
+    [`langchain.schema.chat_history`, `BaseChatMessageHistory`, `langchain_core.chat_history`, `BaseChatMessageHistory`],
+    [`langchain.schema.document`, `Document`, `langchain_core.documents`, `Document`],
+    [`langchain.schema.document`, `BaseDocumentTransformer`, `langchain_core.documents`, `BaseDocumentTransformer`],
+    [`langchain.schema.embeddings`, `Embeddings`, `langchain_core.embeddings`, `Embeddings`],
+    [`langchain.schema.exceptions`, `LangChainException`, `langchain_core.exceptions`, `LangChainException`],
+    [`langchain.schema.language_model`, `BaseLanguageModel`, `langchain_core.language_models`, `BaseLanguageModel`],
+    [`langchain.schema.language_model`, `_get_token_ids_default_method`, `langchain_core.language_models.base`, `_get_token_ids_default_method`],
+    [`langchain.schema.memory`, `BaseMemory`, `langchain_core.memory`, `BaseMemory`],
+    [`langchain.schema.messages`, `get_buffer_string`, `langchain_core.messages`, `get_buffer_string`],
+    [`langchain.schema.messages`, `BaseMessage`, `langchain_core.messages`, `BaseMessage`],
+    [`langchain.schema.messages`, `merge_content`, `langchain_core.messages`, `merge_content`],
+    [`langchain.schema.messages`, `BaseMessageChunk`, `langchain_core.messages`, `BaseMessageChunk`],
+    [`langchain.schema.messages`, `HumanMessage`, `langchain_core.messages`, `HumanMessage`],
+    [`langchain.schema.messages`, `HumanMessageChunk`, `langchain_core.messages`, `HumanMessageChunk`],
+    [`langchain.schema.messages`, `AIMessage`, `langchain_core.messages`, `AIMessage`],
+    [`langchain.schema.messages`, `AIMessageChunk`, `langchain_core.messages`, `AIMessageChunk`],
+    [`langchain.schema.messages`, `SystemMessage`, `langchain_core.messages`, `SystemMessage`],
+    [`langchain.schema.messages`, `SystemMessageChunk`, `langchain_core.messages`, `SystemMessageChunk`],
+    [`langchain.schema.messages`, `FunctionMessage`, `langchain_core.messages`, `FunctionMessage`],
+    [`langchain.schema.messages`, `FunctionMessageChunk`, `langchain_core.messages`, `FunctionMessageChunk`],
+    [`langchain.schema.messages`, `ToolMessage`, `langchain_core.messages`, `ToolMessage`],
+    [`langchain.schema.messages`, `ToolMessageChunk`, `langchain_core.messages`, `ToolMessageChunk`],
+    [`langchain.schema.messages`, `ChatMessage`, `langchain_core.messages`, `ChatMessage`],
+    [`langchain.schema.messages`, `ChatMessageChunk`, `langchain_core.messages`, `ChatMessageChunk`],
+    [`langchain.schema.messages`, `messages_to_dict`, `langchain_core.messages`, `messages_to_dict`],
+    [`langchain.schema.messages`, `messages_from_dict`, `langchain_core.messages`, `messages_from_dict`],
+    [`langchain.schema.messages`, `_message_to_dict`, `langchain_core.messages`, `message_to_dict`],
+    [`langchain.schema.messages`, `_message_from_dict`, `langchain_core.messages`, `_message_from_dict`],
+    [`langchain.schema.messages`, `message_to_dict`, `langchain_core.messages`, `message_to_dict`],
+    [`langchain.schema.output`, `Generation`, `langchain_core.outputs`, `Generation`],
+    [`langchain.schema.output`, `GenerationChunk`, `langchain_core.outputs`, `GenerationChunk`],
+    [`langchain.schema.output`, `ChatGeneration`, `langchain_core.outputs`, `ChatGeneration`],
+    [`langchain.schema.output`, `ChatGenerationChunk`, `langchain_core.outputs`, `ChatGenerationChunk`],
+    [`langchain.schema.output`, `RunInfo`, `langchain_core.outputs`, `RunInfo`],
+    [`langchain.schema.output`, `ChatResult`, `langchain_core.outputs`, `ChatResult`],
+    [`langchain.schema.output`, `LLMResult`, `langchain_core.outputs`, `LLMResult`],
+    [`langchain.schema.output_parser`, `BaseLLMOutputParser`, `langchain_core.output_parsers`, `BaseLLMOutputParser`],
+    [`langchain.schema.output_parser`, `BaseGenerationOutputParser`, `langchain_core.output_parsers`, `BaseGenerationOutputParser`],
+    [`langchain.schema.output_parser`, `BaseOutputParser`, `langchain_core.output_parsers`, `BaseOutputParser`],
+    [`langchain.schema.output_parser`, `BaseTransformOutputParser`, `langchain_core.output_parsers`, `BaseTransformOutputParser`],
+    [`langchain.schema.output_parser`, `BaseCumulativeTransformOutputParser`, `langchain_core.output_parsers`, `BaseCumulativeTransformOutputParser`],
+    [`langchain.schema.output_parser`, `NoOpOutputParser`, `langchain_core.output_parsers`, `StrOutputParser`],
+    [`langchain.schema.output_parser`, `StrOutputParser`, `langchain_core.output_parsers`, `StrOutputParser`],
+    [`langchain.schema.output_parser`, `OutputParserException`, `langchain_core.exceptions`, `OutputParserException`],
+    [`langchain.schema.prompt`, `PromptValue`, `langchain_core.prompt_values`, `PromptValue`],
+    [`langchain.schema.prompt_template`, `BasePromptTemplate`, `langchain_core.prompts`, `BasePromptTemplate`],
+    [`langchain.schema.prompt_template`, `format_document`, `langchain_core.prompts`, `format_document`],
+    [`langchain.schema.retriever`, `BaseRetriever`, `langchain_core.retrievers`, `BaseRetriever`],
+    [`langchain.schema.runnable`, `ConfigurableField`, `langchain_core.runnables`, `ConfigurableField`],
+    [`langchain.schema.runnable`, `ConfigurableFieldSingleOption`, `langchain_core.runnables`, `ConfigurableFieldSingleOption`],
+    [`langchain.schema.runnable`, `ConfigurableFieldMultiOption`, `langchain_core.runnables`, `ConfigurableFieldMultiOption`],
+    [`langchain.schema.runnable`, `patch_config`, `langchain_core.runnables`, `patch_config`],
+    [`langchain.schema.runnable`, `RouterInput`, `langchain_core.runnables`, `RouterInput`],
+    [`langchain.schema.runnable`, `RouterRunnable`, `langchain_core.runnables`, `RouterRunnable`],
+    [`langchain.schema.runnable`, `Runnable`, `langchain_core.runnables`, `Runnable`],
+    [`langchain.schema.runnable`, `RunnableSerializable`, `langchain_core.runnables`, `RunnableSerializable`],
+    [`langchain.schema.runnable`, `RunnableBinding`, `langchain_core.runnables`, `RunnableBinding`],
+    [`langchain.schema.runnable`, `RunnableBranch`, `langchain_core.runnables`, `RunnableBranch`],
+    [`langchain.schema.runnable`, `RunnableConfig`, `langchain_core.runnables`, `RunnableConfig`],
+    [`langchain.schema.runnable`, `RunnableGenerator`, `langchain_core.runnables`, `RunnableGenerator`],
+    [`langchain.schema.runnable`, `RunnableLambda`, `langchain_core.runnables`, `RunnableLambda`],
+    [`langchain.schema.runnable`, `RunnableMap`, `langchain_core.runnables`, `RunnableMap`],
+    [`langchain.schema.runnable`, `RunnableParallel`, `langchain_core.runnables`, `RunnableParallel`],
+    [`langchain.schema.runnable`, `RunnablePassthrough`, `langchain_core.runnables`, `RunnablePassthrough`],
+    [`langchain.schema.runnable`, `RunnableSequence`, `langchain_core.runnables`, `RunnableSequence`],
+    [`langchain.schema.runnable`, `RunnableWithFallbacks`, `langchain_core.runnables`, `RunnableWithFallbacks`],
+    [`langchain.schema.runnable.base`, `Runnable`, `langchain_core.runnables`, `Runnable`],
+    [`langchain.schema.runnable.base`, `RunnableSerializable`, `langchain_core.runnables`, `RunnableSerializable`],
+    [`langchain.schema.runnable.base`, `RunnableSequence`, `langchain_core.runnables`, `RunnableSequence`],
+    [`langchain.schema.runnable.base`, `RunnableParallel`, `langchain_core.runnables`, `RunnableParallel`],
+    [`langchain.schema.runnable.base`, `RunnableGenerator`, `langchain_core.runnables`, `RunnableGenerator`],
+    [`langchain.schema.runnable.base`, `RunnableLambda`, `langchain_core.runnables`, `RunnableLambda`],
+    [`langchain.schema.runnable.base`, `RunnableEachBase`, `langchain_core.runnables.base`, `RunnableEachBase`],
+    [`langchain.schema.runnable.base`, `RunnableEach`, `langchain_core.runnables.base`, `RunnableEach`],
+    [`langchain.schema.runnable.base`, `RunnableBindingBase`, `langchain_core.runnables.base`, `RunnableBindingBase`],
+    [`langchain.schema.runnable.base`, `RunnableBinding`, `langchain_core.runnables`, `RunnableBinding`],
+    [`langchain.schema.runnable.base`, `RunnableMap`, `langchain_core.runnables`, `RunnableMap`],
+    [`langchain.schema.runnable.base`, `coerce_to_runnable`, `langchain_core.runnables.base`, `coerce_to_runnable`],
+    [`langchain.schema.runnable.branch`, `RunnableBranch`, `langchain_core.runnables`, `RunnableBranch`],
+    [`langchain.schema.runnable.config`, `EmptyDict`, `langchain_core.runnables.config`, `EmptyDict`],
+    [`langchain.schema.runnable.config`, `RunnableConfig`, `langchain_core.runnables`, `RunnableConfig`],
+    [`langchain.schema.runnable.config`, `ensure_config`, `langchain_core.runnables`, `ensure_config`],
+    [`langchain.schema.runnable.config`, `get_config_list`, `langchain_core.runnables`, `get_config_list`],
+    [`langchain.schema.runnable.config`, `patch_config`, `langchain_core.runnables`, `patch_config`],
+    [`langchain.schema.runnable.config`, `merge_configs`, `langchain_core.runnables.config`, `merge_configs`],
+    [`langchain.schema.runnable.config`, `acall_func_with_variable_args`, `langchain_core.runnables.config`, `acall_func_with_variable_args`],
+    [`langchain.schema.runnable.config`, `call_func_with_variable_args`, `langchain_core.runnables.config`, `call_func_with_variable_args`],
+    [`langchain.schema.runnable.config`, `get_callback_manager_for_config`, `langchain_core.runnables.config`, `get_callback_manager_for_config`],
+    [`langchain.schema.runnable.config`, `get_async_callback_manager_for_config`, `langchain_core.runnables.config`, `get_async_callback_manager_for_config`],
+    [`langchain.schema.runnable.config`, `get_executor_for_config`, `langchain_core.runnables.config`, `get_executor_for_config`],
+    [`langchain.schema.runnable.configurable`, `DynamicRunnable`, `langchain_core.runnables.configurable`, `DynamicRunnable`],
+    [`langchain.schema.runnable.configurable`, `RunnableConfigurableFields`, `langchain_core.runnables.configurable`, `RunnableConfigurableFields`],
+    [`langchain.schema.runnable.configurable`, `StrEnum`, `langchain_core.runnables.configurable`, `StrEnum`],
+    [`langchain.schema.runnable.configurable`, `RunnableConfigurableAlternatives`, `langchain_core.runnables.configurable`, `RunnableConfigurableAlternatives`],
+    [`langchain.schema.runnable.configurable`, `make_options_spec`, `langchain_core.runnables.configurable`, `make_options_spec`],
+    [`langchain.schema.runnable.fallbacks`, `RunnableWithFallbacks`, `langchain_core.runnables`, `RunnableWithFallbacks`],
+    [`langchain.schema.runnable.history`, `RunnableWithMessageHistory`, `langchain_core.runnables.history`, `RunnableWithMessageHistory`],
+    [`langchain.schema.runnable.passthrough`, `aidentity`, `langchain_core.runnables.passthrough`, `aidentity`],
+    [`langchain.schema.runnable.passthrough`, `identity`, `langchain_core.runnables.passthrough`, `identity`],
+    [`langchain.schema.runnable.passthrough`, `RunnablePassthrough`, `langchain_core.runnables`, `RunnablePassthrough`],
+    [`langchain.schema.runnable.passthrough`, `RunnableAssign`, `langchain_core.runnables`, `RunnableAssign`],
+    [`langchain.schema.runnable.retry`, `RunnableRetry`, `langchain_core.runnables.retry`, `RunnableRetry`],
+    [`langchain.schema.runnable.router`, `RouterInput`, `langchain_core.runnables`, `RouterInput`],
+    [`langchain.schema.runnable.router`, `RouterRunnable`, `langchain_core.runnables`, `RouterRunnable`],
+    [`langchain.schema.runnable.utils`, `accepts_run_manager`, `langchain_core.runnables.utils`, `accepts_run_manager`],
+    [`langchain.schema.runnable.utils`, `accepts_config`, `langchain_core.runnables.utils`, `accepts_config`],
+    [`langchain.schema.runnable.utils`, `IsLocalDict`, `langchain_core.runnables.utils`, `IsLocalDict`],
+    [`langchain.schema.runnable.utils`, `IsFunctionArgDict`, `langchain_core.runnables.utils`, `IsFunctionArgDict`],
+    [`langchain.schema.runnable.utils`, `GetLambdaSource`, `langchain_core.runnables.utils`, `GetLambdaSource`],
+    [`langchain.schema.runnable.utils`, `get_function_first_arg_dict_keys`, `langchain_core.runnables.utils`, `get_function_first_arg_dict_keys`],
+    [`langchain.schema.runnable.utils`, `get_lambda_source`, `langchain_core.runnables.utils`, `get_lambda_source`],
+    [`langchain.schema.runnable.utils`, `indent_lines_after_first`, `langchain_core.runnables.utils`, `indent_lines_after_first`],
+    [`langchain.schema.runnable.utils`, `AddableDict`, `langchain_core.runnables`, `AddableDict`],
+    [`langchain.schema.runnable.utils`, `SupportsAdd`, `langchain_core.runnables.utils`, `SupportsAdd`],
+    [`langchain.schema.runnable.utils`, `add`, `langchain_core.runnables`, `add`],
+    [`langchain.schema.runnable.utils`, `ConfigurableField`, `langchain_core.runnables`, `ConfigurableField`],
+    [`langchain.schema.runnable.utils`, `ConfigurableFieldSingleOption`, `langchain_core.runnables`, `ConfigurableFieldSingleOption`],
+    [`langchain.schema.runnable.utils`, `ConfigurableFieldMultiOption`, `langchain_core.runnables`, `ConfigurableFieldMultiOption`],
+    [`langchain.schema.runnable.utils`, `ConfigurableFieldSpec`, `langchain_core.runnables`, `ConfigurableFieldSpec`],
+    [`langchain.schema.runnable.utils`, `get_unique_config_specs`, `langchain_core.runnables.utils`, `get_unique_config_specs`],
+    [`langchain.schema.runnable.utils`, `aadd`, `langchain_core.runnables`, `aadd`],
+    [`langchain.schema.runnable.utils`, `gated_coro`, `langchain_core.runnables.utils`, `gated_coro`],
+    [`langchain.schema.runnable.utils`, `gather_with_concurrency`, `langchain_core.runnables.utils`, `gather_with_concurrency`],
+    [`langchain.schema.storage`, `BaseStore`, `langchain_core.stores`, `BaseStore`],
+    [`langchain.schema.vectorstore`, `VectorStore`, `langchain_core.vectorstores`, `VectorStore`],
+    [`langchain.schema.vectorstore`, `VectorStoreRetriever`, `langchain_core.vectorstores`, `VectorStoreRetriever`],
+    [`langchain.tools`, `BaseTool`, `langchain_core.tools`, `BaseTool`],
+    [`langchain.tools`, `StructuredTool`, `langchain_core.tools`, `StructuredTool`],
+    [`langchain.tools`, `Tool`, `langchain_core.tools`, `Tool`],
+    [`langchain.tools`, `format_tool_to_openai_function`, `langchain_core.utils.function_calling`, `format_tool_to_openai_function`],
+    [`langchain.tools`, `tool`, `langchain_core.tools`, `tool`],
+    [`langchain.tools.base`, `SchemaAnnotationError`, `langchain_core.tools`, `SchemaAnnotationError`],
+    [`langchain.tools.base`, `create_schema_from_function`, `langchain_core.tools`, `create_schema_from_function`],
+    [`langchain.tools.base`, `ToolException`, `langchain_core.tools`, `ToolException`],
+    [`langchain.tools.base`, `BaseTool`, `langchain_core.tools`, `BaseTool`],
+    [`langchain.tools.base`, `Tool`, `langchain_core.tools`, `Tool`],
+    [`langchain.tools.base`, `StructuredTool`, `langchain_core.tools`, `StructuredTool`],
+    [`langchain.tools.base`, `tool`, `langchain_core.tools`, `tool`],
+    [`langchain.tools.convert_to_openai`, `format_tool_to_openai_function`, `langchain_core.utils.function_calling`, `format_tool_to_openai_function`],
+    [`langchain.tools.render`, `format_tool_to_openai_tool`, `langchain_core.utils.function_calling`, `format_tool_to_openai_tool`],
+    [`langchain.tools.render`, `format_tool_to_openai_function`, `langchain_core.utils.function_calling`, `format_tool_to_openai_function`],
+    [`langchain.utilities.loading`, `try_load_from_hub`, `langchain_core.utils`, `try_load_from_hub`],
+    [`langchain.utils`, `StrictFormatter`, `langchain_core.utils`, `StrictFormatter`],
+    [`langchain.utils`, `check_package_version`, `langchain_core.utils`, `check_package_version`],
+    [`langchain.utils`, `comma_list`, `langchain_core.utils`, `comma_list`],
+    [`langchain.utils`, `convert_to_secret_str`, `langchain_core.utils`, `convert_to_secret_str`],
+    [`langchain.utils`, `get_bolded_text`, `langchain_core.utils`, `get_bolded_text`],
+    [`langchain.utils`, `get_color_mapping`, `langchain_core.utils`, `get_color_mapping`],
+    [`langchain.utils`, `get_colored_text`, `langchain_core.utils`, `get_colored_text`],
+    [`langchain.utils`, `get_from_dict_or_env`, `langchain_core.utils`, `get_from_dict_or_env`],
+    [`langchain.utils`, `get_from_env`, `langchain_core.utils`, `get_from_env`],
+    [`langchain.utils`, `get_pydantic_field_names`, `langchain_core.utils`, `get_pydantic_field_names`],
+    [`langchain.utils`, `guard_import`, `langchain_core.utils`, `guard_import`],
+    [`langchain.utils`, `mock_now`, `langchain_core.utils`, `mock_now`],
+    [`langchain.utils`, `print_text`, `langchain_core.utils`, `print_text`],
+    [`langchain.utils`, `raise_for_status_with_text`, `langchain_core.utils`, `raise_for_status_with_text`],
+    [`langchain.utils`, `stringify_dict`, `langchain_core.utils`, `stringify_dict`],
+    [`langchain.utils`, `stringify_value`, `langchain_core.utils`, `stringify_value`],
+    [`langchain.utils`, `xor_args`, `langchain_core.utils`, `xor_args`],
+    [`langchain.utils.aiter`, `py_anext`, `langchain_core.utils.aiter`, `py_anext`],
+    [`langchain.utils.aiter`, `NoLock`, `langchain_core.utils.aiter`, `NoLock`],
+    [`langchain.utils.aiter`, `Tee`, `langchain_core.utils.aiter`, `Tee`],
+    [`langchain.utils.env`, `get_from_dict_or_env`, `langchain_core.utils`, `get_from_dict_or_env`],
+    [`langchain.utils.env`, `get_from_env`, `langchain_core.utils`, `get_from_env`],
+    [`langchain.utils.formatting`, `StrictFormatter`, `langchain_core.utils`, `StrictFormatter`],
+    [`langchain.utils.html`, `find_all_links`, `langchain_core.utils.html`, `find_all_links`],
+    [`langchain.utils.html`, `extract_sub_links`, `langchain_core.utils.html`, `extract_sub_links`],
+    [`langchain.utils.input`, `get_color_mapping`, `langchain_core.utils`, `get_color_mapping`],
+    [`langchain.utils.input`, `get_colored_text`, `langchain_core.utils`, `get_colored_text`],
+    [`langchain.utils.input`, `get_bolded_text`, `langchain_core.utils`, `get_bolded_text`],
+    [`langchain.utils.input`, `print_text`, `langchain_core.utils`, `print_text`],
+    [`langchain.utils.iter`, `NoLock`, `langchain_core.utils.iter`, `NoLock`],
+    [`langchain.utils.iter`, `tee_peer`, `langchain_core.utils.iter`, `tee_peer`],
+    [`langchain.utils.iter`, `Tee`, `langchain_core.utils.iter`, `Tee`],
+    [`langchain.utils.iter`, `batch_iterate`, `langchain_core.utils.iter`, `batch_iterate`],
+    [`langchain.utils.json_schema`, `_retrieve_ref`, `langchain_core.utils.json_schema`, `_retrieve_ref`],
+    [`langchain.utils.json_schema`, `_dereference_refs_helper`, `langchain_core.utils.json_schema`, `_dereference_refs_helper`],
+    [`langchain.utils.json_schema`, `_infer_skip_keys`, `langchain_core.utils.json_schema`, `_infer_skip_keys`],
+    [`langchain.utils.json_schema`, `dereference_refs`, `langchain_core.utils.json_schema`, `dereference_refs`],
+    [`langchain.utils.loading`, `try_load_from_hub`, `langchain_core.utils`, `try_load_from_hub`],
+    [`langchain.utils.openai_functions`, `FunctionDescription`, `langchain_core.utils.function_calling`, `FunctionDescription`],
+    [`langchain.utils.openai_functions`, `ToolDescription`, `langchain_core.utils.function_calling`, `ToolDescription`],
+    [`langchain.utils.openai_functions`, `convert_pydantic_to_openai_function`, `langchain_core.utils.function_calling`, `convert_pydantic_to_openai_function`],
+    [`langchain.utils.openai_functions`, `convert_pydantic_to_openai_tool`, `langchain_core.utils.function_calling`, `convert_pydantic_to_openai_tool`],
+    [`langchain.utils.pydantic`, `get_pydantic_major_version`, `langchain_core.utils.pydantic`, `get_pydantic_major_version`],
+    [`langchain.utils.strings`, `stringify_value`, `langchain_core.utils`, `stringify_value`],
+    [`langchain.utils.strings`, `stringify_dict`, `langchain_core.utils`, `stringify_dict`],
+    [`langchain.utils.strings`, `comma_list`, `langchain_core.utils`, `comma_list`],
+    [`langchain.utils.utils`, `xor_args`, `langchain_core.utils`, `xor_args`],
+    [`langchain.utils.utils`, `raise_for_status_with_text`, `langchain_core.utils`, `raise_for_status_with_text`],
+    [`langchain.utils.utils`, `mock_now`, `langchain_core.utils`, `mock_now`],
+    [`langchain.utils.utils`, `guard_import`, `langchain_core.utils`, `guard_import`],
+    [`langchain.utils.utils`, `check_package_version`, `langchain_core.utils`, `check_package_version`],
+    [`langchain.utils.utils`, `get_pydantic_field_names`, `langchain_core.utils`, `get_pydantic_field_names`],
+    [`langchain.utils.utils`, `build_extra_kwargs`, `langchain_core.utils`, `build_extra_kwargs`],
+    [`langchain.utils.utils`, `convert_to_secret_str`, `langchain_core.utils`, `convert_to_secret_str`],
+    [`langchain.vectorstores`, `VectorStore`, `langchain_core.vectorstores`, `VectorStore`],
+    [`langchain.vectorstores.base`, `VectorStore`, `langchain_core.vectorstores`, `VectorStore`],
+    [`langchain.vectorstores.base`, `VectorStoreRetriever`, `langchain_core.vectorstores`, `VectorStoreRetriever`],
+    [`langchain.vectorstores.singlestoredb`, `SingleStoreDBRetriever`, `langchain_core.vectorstores`, `VectorStoreRetriever`]
+  ])
+}
+
+// Add this for invoking directly
+langchain_migrate_langchain_to_core()

libs/cli/langchain_cli/namespaces/migrate/.grit/patterns/langchain_to_textsplitters.grit

@@ -0,0 +1,31 @@
+
+language python
+
+// This migration is generated automatically - do not manually edit this file
+pattern langchain_migrate_langchain_to_textsplitters() {
+  find_replace_imports(list=[
+    [`langchain.text_splitter`, `TokenTextSplitter`, `langchain_text_splitters`, `TokenTextSplitter`],
+    [`langchain.text_splitter`, `TextSplitter`, `langchain_text_splitters`, `TextSplitter`],
+    [`langchain.text_splitter`, `Tokenizer`, `langchain_text_splitters`, `Tokenizer`],
+    [`langchain.text_splitter`, `Language`, `langchain_text_splitters`, `Language`],
+    [`langchain.text_splitter`, `RecursiveCharacterTextSplitter`, `langchain_text_splitters`, `RecursiveCharacterTextSplitter`],
+    [`langchain.text_splitter`, `RecursiveJsonSplitter`, `langchain_text_splitters`, `RecursiveJsonSplitter`],
+    [`langchain.text_splitter`, `LatexTextSplitter`, `langchain_text_splitters`, `LatexTextSplitter`],
+    [`langchain.text_splitter`, `PythonCodeTextSplitter`, `langchain_text_splitters`, `PythonCodeTextSplitter`],
+    [`langchain.text_splitter`, `KonlpyTextSplitter`, `langchain_text_splitters`, `KonlpyTextSplitter`],
+    [`langchain.text_splitter`, `SpacyTextSplitter`, `langchain_text_splitters`, `SpacyTextSplitter`],
+    [`langchain.text_splitter`, `NLTKTextSplitter`, `langchain_text_splitters`, `NLTKTextSplitter`],
+    [`langchain.text_splitter`, `split_text_on_tokens`, `langchain_text_splitters`, `split_text_on_tokens`],
+    [`langchain.text_splitter`, `SentenceTransformersTokenTextSplitter`, `langchain_text_splitters`, `SentenceTransformersTokenTextSplitter`],
+    [`langchain.text_splitter`, `ElementType`, `langchain_text_splitters`, `ElementType`],
+    [`langchain.text_splitter`, `HeaderType`, `langchain_text_splitters`, `HeaderType`],
+    [`langchain.text_splitter`, `LineType`, `langchain_text_splitters`, `LineType`],
+    [`langchain.text_splitter`, `HTMLHeaderTextSplitter`, `langchain_text_splitters`, `HTMLHeaderTextSplitter`],
+    [`langchain.text_splitter`, `MarkdownHeaderTextSplitter`, `langchain_text_splitters`, `MarkdownHeaderTextSplitter`],
+    [`langchain.text_splitter`, `MarkdownTextSplitter`, `langchain_text_splitters`, `MarkdownTextSplitter`],
+    [`langchain.text_splitter`, `CharacterTextSplitter`, `langchain_text_splitters`, `CharacterTextSplitter`]
+  ])
+}
+
+// Add this for invoking directly
+langchain_migrate_langchain_to_textsplitters()

libs/cli/langchain_cli/namespaces/migrate/.grit/patterns/langchain_to_textsplitters.json

@@ -0,0 +1,70 @@
+[
+  [
+    "langchain.text_splitter.TokenTextSplitter",
+    "langchain_text_splitters.TokenTextSplitter"
+  ],
+  [
+    "langchain.text_splitter.TextSplitter",
+    "langchain_text_splitters.TextSplitter"
+  ],
+  ["langchain.text_splitter.Tokenizer", "langchain_text_splitters.Tokenizer"],
+  ["langchain.text_splitter.Language", "langchain_text_splitters.Language"],
+  [
+    "langchain.text_splitter.RecursiveCharacterTextSplitter",
+    "langchain_text_splitters.RecursiveCharacterTextSplitter"
+  ],
+  [
+    "langchain.text_splitter.RecursiveJsonSplitter",
+    "langchain_text_splitters.RecursiveJsonSplitter"
+  ],
+  [
+    "langchain.text_splitter.LatexTextSplitter",
+    "langchain_text_splitters.LatexTextSplitter"
+  ],
+  [
+    "langchain.text_splitter.PythonCodeTextSplitter",
+    "langchain_text_splitters.PythonCodeTextSplitter"
+  ],
+  [
+    "langchain.text_splitter.KonlpyTextSplitter",
+    "langchain_text_splitters.KonlpyTextSplitter"
+  ],
+  [
+    "langchain.text_splitter.SpacyTextSplitter",
+    "langchain_text_splitters.SpacyTextSplitter"
+  ],
+  [
+    "langchain.text_splitter.NLTKTextSplitter",
+    "langchain_text_splitters.NLTKTextSplitter"
+  ],
+  [
+    "langchain.text_splitter.split_text_on_tokens",
+    "langchain_text_splitters.split_text_on_tokens"
+  ],
+  [
+    "langchain.text_splitter.SentenceTransformersTokenTextSplitter",
+    "langchain_text_splitters.SentenceTransformersTokenTextSplitter"
+  ],
+  [
+    "langchain.text_splitter.ElementType",
+    "langchain_text_splitters.ElementType"
+  ],
+  ["langchain.text_splitter.HeaderType", "langchain_text_splitters.HeaderType"],
+  ["langchain.text_splitter.LineType", "langchain_text_splitters.LineType"],
+  [
+    "langchain.text_splitter.HTMLHeaderTextSplitter",
+    "langchain_text_splitters.HTMLHeaderTextSplitter"
+  ],
+  [
+    "langchain.text_splitter.MarkdownHeaderTextSplitter",
+    "langchain_text_splitters.MarkdownHeaderTextSplitter"
+  ],
+  [
+    "langchain.text_splitter.MarkdownTextSplitter",
+    "langchain_text_splitters.MarkdownTextSplitter"
+  ],
+  [
+    "langchain.text_splitter.CharacterTextSplitter",
+    "langchain_text_splitters.CharacterTextSplitter"
+  ]
+]

libs/cli/langchain_cli/namespaces/migrate/.grit/patterns/openai.grit

@@ -0,0 +1,23 @@
+
+language python
+
+// This migration is generated automatically - do not manually edit this file
+pattern langchain_migrate_openai() {
+  find_replace_imports(list=[
+    [`langchain_community.embeddings.openai`, `OpenAIEmbeddings`, `langchain_openai`, `OpenAIEmbeddings`],
+    [`langchain_community.embeddings.azure_openai`, `AzureOpenAIEmbeddings`, `langchain_openai`, `AzureOpenAIEmbeddings`],
+    [`langchain_community.chat_models.openai`, `ChatOpenAI`, `langchain_openai`, `ChatOpenAI`],
+    [`langchain_community.chat_models.azure_openai`, `AzureChatOpenAI`, `langchain_openai`, `AzureChatOpenAI`],
+    [`langchain_community.llms.openai`, `OpenAI`, `langchain_openai`, `OpenAI`],
+    [`langchain_community.llms.openai`, `AzureOpenAI`, `langchain_openai`, `AzureOpenAI`],
+    [`langchain_community.embeddings`, `AzureOpenAIEmbeddings`, `langchain_openai`, `AzureOpenAIEmbeddings`],
+    [`langchain_community.embeddings`, `OpenAIEmbeddings`, `langchain_openai`, `OpenAIEmbeddings`],
+    [`langchain_community.chat_models`, `AzureChatOpenAI`, `langchain_openai`, `AzureChatOpenAI`],
+    [`langchain_community.chat_models`, `ChatOpenAI`, `langchain_openai`, `ChatOpenAI`],
+    [`langchain_community.llms`, `AzureOpenAI`, `langchain_openai`, `AzureOpenAI`],
+    [`langchain_community.llms`, `OpenAI`, `langchain_openai`, `OpenAI`]
+  ])
+}
+
+// Add this for invoking directly
+langchain_migrate_openai()

libs/cli/langchain_cli/namespaces/migrate/.grit/patterns/pinecone.grit

@@ -0,0 +1,13 @@
+
+language python
+
+// This migration is generated automatically - do not manually edit this file
+pattern langchain_migrate_pinecone() {
+  find_replace_imports(list=[
+    [`langchain_community.vectorstores.pinecone`, `Pinecone`, `langchain_pinecone`, `Pinecone`],
+    [`langchain_community.vectorstores`, `Pinecone`, `langchain_pinecone`, `Pinecone`]
+  ])
+}
+
+// Add this for invoking directly
+langchain_migrate_pinecone()

libs/cli/langchain_cli/namespaces/migrate/.grit/patterns/replace_pydantic_v1_shim.grit

@@ -0,0 +1,36 @@
+language python
+
+// This migration is generated automatically - do not manually edit this file
+pattern replace_pydantic_v1_shim() {
+    `from $IMPORT import $...` where {
+        or {
+            and {
+                $IMPORT <: or {
+                    "langchain_core.pydantic_v1",
+                    "langchain.pydantic_v1",
+                    "langserve.pydantic_v1",
+                },
+                $IMPORT => `pydantic`
+            },
+            and {
+                $IMPORT <: or {
+                    "langchain_core.pydantic_v1.data_classes",
+                    "langchain.pydantic_v1.data_classes",
+                    "langserve.pydantic_v1.data_classes",
+                },
+                $IMPORT => `pydantic.data_classes`
+            },
+            and {
+                $IMPORT <: or {
+                    "langchain_core.pydantic_v1.main",
+                    "langchain.pydantic_v1.main",
+                    "langserve.pydantic_v1.main",
+                },
+                $IMPORT => `pydantic.main`
+            },
+        }
+    }
+}
+
+// Add this for invoking directly
+ replace_pydantic_v1_shim()

libs/cli/langchain_cli/namespaces/migrate/__init__.py

@@ -0,0 +1 @@
+"""Migrations."""

libs/cli/langchain_cli/namespaces/migrate/generate/__init__.py

@@ -0,0 +1 @@
+"""Generate migrations."""

libs/cli/langchain_cli/namespaces/migrate/generate/generic.py

@@ -0,0 +1,182 @@
+"""Generate migrations from langchain to langchain-community or core packages."""
+
+import importlib
+import inspect
+import pkgutil
+from types import ModuleType
+
+
+def generate_raw_migrations(
+    from_package: str,
+    to_package: str,
+    filter_by_all: bool = False,  # noqa: FBT001, FBT002
+) -> list[tuple[str, str]]:
+    """Scan the `langchain` package and generate migrations for all modules.
+
+    Args:
+        from_package: The package to migrate from.
+        to_package: The package to migrate to.
+        filter_by_all: Whether to only consider items in `__all__`.
+
+    Returns:
+        A list of tuples containing the original import path and the new import path.
+    """
+    package = importlib.import_module(from_package)
+
+    items = []
+    for _importer, modname, _ispkg in pkgutil.walk_packages(
+        package.__path__,
+        package.__name__ + ".",
+    ):
+        try:
+            module = importlib.import_module(modname)
+        except ModuleNotFoundError:
+            continue
+
+        # Check if the module is an __init__ file and evaluate __all__
+        try:
+            has_all = hasattr(module, "__all__")
+        except ImportError:
+            has_all = False
+
+        if has_all:
+            all_objects = module.__all__
+            for name in all_objects:
+                # Attempt to fetch each object declared in __all__
+                try:
+                    obj = getattr(module, name, None)
+                except ImportError:
+                    continue
+                if (
+                    obj
+                    and (inspect.isclass(obj) or inspect.isfunction(obj))
+                    and obj.__module__.startswith(to_package)
+                ):
+                    items.append(
+                        (f"{modname}.{name}", f"{obj.__module__}.{obj.__name__}"),
+                    )
+
+        if not filter_by_all:
+            # Iterate over all members of the module
+            for name, obj in inspect.getmembers(module):
+                # Check if it's a class or function
+                # Check if the module name of the obj starts with
+                # 'langchain_community'
+                if inspect.isclass(obj) or (
+                    inspect.isfunction(obj) and obj.__module__.startswith(to_package)
+                ):
+                    items.append(
+                        (f"{modname}.{name}", f"{obj.__module__}.{obj.__name__}"),
+                    )
+
+    return items
+
+
+def generate_top_level_imports(pkg: str) -> list[tuple[str, str]]:
+    """Look at all the top level modules in langchain_community.
+
+    Attempt to import everything from each `__init__` file. For example,
+
+    langchain_community/
+        chat_models/
+            __init__.py # <-- import everything from here
+        llm/
+            __init__.py # <-- import everything from here
+
+    It'll collect all the imports, import the classes / functions it can find
+    there. It'll return a list of 2-tuples
+
+    Each tuple will contain the fully qualified path of the class / function to where
+    its logic is defined.
+    (e.g., `langchain_community.chat_models.xyz_implementation.ver2.XYZ`)
+    and the second tuple will contain the path
+    to importing it from the top level namespaces
+    (e.g., `langchain_community.chat_models.XYZ`)
+
+    Args:
+        pkg: The package to scan.
+
+    Returns:
+        A list of tuples containing the fully qualified path and the top-level
+        import path.
+    """
+    package = importlib.import_module(pkg)
+
+    items = []
+
+    # Function to handle importing from modules
+    def handle_module(module: ModuleType, module_name: str) -> None:
+        if hasattr(module, "__all__"):
+            all_objects = module.__all__
+            for name in all_objects:
+                # Attempt to fetch each object declared in __all__
+                obj = getattr(module, name, None)
+                if obj and (inspect.isclass(obj) or inspect.isfunction(obj)):
+                    # Capture the fully qualified name of the object
+                    original_module = obj.__module__
+                    original_name = obj.__name__
+                    # Form the new import path from the top-level namespace
+                    top_level_import = f"{module_name}.{name}"
+                    # Append the tuple with original and top-level paths
+                    items.append(
+                        (f"{original_module}.{original_name}", top_level_import),
+                    )
+
+    # Handle the package itself (root level)
+    handle_module(package, pkg)
+
+    # Only iterate through top-level modules/packages
+    for _finder, modname, ispkg in pkgutil.iter_modules(
+        package.__path__,
+        package.__name__ + ".",
+    ):
+        if ispkg:
+            try:
+                module = importlib.import_module(modname)
+                handle_module(module, modname)
+            except ModuleNotFoundError:
+                continue
+
+    return items
+
+
+def generate_simplified_migrations(
+    from_package: str,
+    to_package: str,
+    filter_by_all: bool = True,  # noqa: FBT001, FBT002
+) -> list[tuple[str, str]]:
+    """Get all the raw migrations, then simplify them if possible.
+
+    Args:
+        from_package: The package to migrate from.
+        to_package: The package to migrate to.
+        filter_by_all: Whether to only consider items in `__all__`.
+
+    Returns:
+        A list of tuples containing the original import path and the simplified
+        import path.
+    """
+    raw_migrations = generate_raw_migrations(
+        from_package,
+        to_package,
+        filter_by_all=filter_by_all,
+    )
+    top_level_simplifications = generate_top_level_imports(to_package)
+    top_level_dict = dict(top_level_simplifications)
+    simple_migrations = []
+    for migration in raw_migrations:
+        original, new = migration
+        replacement = top_level_dict.get(new, new)
+        simple_migrations.append((original, replacement))
+
+    # Now let's deduplicate the list based on the original path (which is
+    # the 1st element of the tuple)
+    deduped_migrations = []
+    seen = set()
+    for migration in simple_migrations:
+        original = migration[0]
+        if original not in seen:
+            deduped_migrations.append(migration)
+            seen.add(original)
+
+    return deduped_migrations

libs/cli/langchain_cli/namespaces/migrate/generate/grit.py

@@ -0,0 +1,53 @@
+"""Migration as Grit file."""
+
+
+def split_package(package: str) -> tuple[str, str]:
+    """Split a package name into the containing package and the final name.
+
+    Args:
+        package: The full package name.
+
+    Returns:
+        A tuple of `(containing_package, final_name)`.
+    """
+    parts = package.split(".")
+    return ".".join(parts[:-1]), parts[-1]
+
+
+def dump_migrations_as_grit(name: str, migration_pairs: list[tuple[str, str]]) -> str:
+    """Dump the migration pairs as a Grit file.
+
+    Args:
+        name: The name of the migration.
+        migration_pairs: A list of tuples `(from_module, to_module)`.
+
+    Returns:
+        The Grit file as a string.
+    """
+    remapped = ",\n".join(
+        [
+            f"""
+            [
+                `{split_package(from_module)[0]}`,
+                `{split_package(from_module)[1]}`,
+                `{split_package(to_module)[0]}`,
+                `{split_package(to_module)[1]}`
+            ]
+            """
+            for from_module, to_module in migration_pairs
+        ],
+    )
+    pattern_name = f"langchain_migrate_{name}"
+    return f"""
+language python
+
+// This migration is generated automatically - do not manually edit this file
+pattern {pattern_name}() {{
+  find_replace_imports(list=[
+{remapped}
+  ])
+}}
+
+// Add this for invoking directly
+{pattern_name}()
+"""

libs/cli/langchain_cli/namespaces/migrate/generate/partner.py

@@ -0,0 +1,53 @@
+"""Generate migrations for partner packages."""
+
+import importlib
+
+from langchain_core.documents import BaseDocumentCompressor, BaseDocumentTransformer
+from langchain_core.embeddings import Embeddings
+from langchain_core.language_models import BaseLanguageModel
+from langchain_core.retrievers import BaseRetriever
+from langchain_core.vectorstores import VectorStore
+
+from langchain_cli.namespaces.migrate.generate.utils import (
+    COMMUNITY_PKG,
+    find_subclasses_in_module,
+    list_classes_by_package,
+    list_init_imports_by_package,
+)
+
+# PUBLIC API
+
+
+def get_migrations_for_partner_package(pkg_name: str) -> list[tuple[str, str]]:
+    """Generate migrations from community package to partner package.
+
+    This code works
+
+    Args:
+        pkg_name: The name of the partner package.
+
+    Returns:
+        List of 2-tuples containing old and new import paths.
+    """
+    package = importlib.import_module(pkg_name)
+    classes_ = find_subclasses_in_module(
+        package,
+        [
+            BaseLanguageModel,
+            Embeddings,
+            BaseRetriever,
+            VectorStore,
+            BaseDocumentTransformer,
+            BaseDocumentCompressor,
+        ],
+    )
+    community_classes = list_classes_by_package(str(COMMUNITY_PKG))
+    imports_for_pkg = list_init_imports_by_package(str(COMMUNITY_PKG))
+
+    old_paths = community_classes + imports_for_pkg
+
+    return [
+        (f"{module}.{item}", f"{pkg_name}.{item}")
+        for module, item in old_paths
+        if item in classes_
+    ]

libs/cli/langchain_cli/namespaces/migrate/generate/utils.py

@@ -0,0 +1,222 @@
+"""Generate migrations utilities."""
+
+import ast
+import inspect
+import os
+import pathlib
+from pathlib import Path
+from types import ModuleType
+
+from typing_extensions import override
+
+HERE = Path(__file__).parent
+# Should bring us to [root]/src
+PKGS_ROOT = HERE.parent.parent.parent.parent.parent
+
+LANGCHAIN_PKG = PKGS_ROOT / "langchain"
+COMMUNITY_PKG = PKGS_ROOT / "community"
+PARTNER_PKGS = PKGS_ROOT / "partners"
+
+
+class ImportExtractor(ast.NodeVisitor):
+    """Import extractor."""
+
+    def __init__(self, *, from_package: str | None = None) -> None:
+        """Extract all imports from the given code, optionally filtering by package."""
+        self.imports: list[tuple[str, str]] = []
+        self.package = from_package
+
+    @override
+    def visit_ImportFrom(self, node: ast.ImportFrom) -> None:
+        if node.module and (
+            self.package is None or str(node.module).startswith(self.package)
+        ):
+            for alias in node.names:
+                self.imports.append((node.module, alias.name))
+        self.generic_visit(node)
+
+
+def _get_class_names(code: str) -> list[str]:
+    """Extract class names from a code string."""
+    # Parse the content of the file into an AST
+    tree = ast.parse(code)
+
+    # Initialize a list to hold all class names
+    class_names = []
+
+    # Define a node visitor class to collect class names
+    class ClassVisitor(ast.NodeVisitor):
+        @override
+        def visit_ClassDef(self, node: ast.ClassDef) -> None:
+            class_names.append(node.name)
+            self.generic_visit(node)
+
+    # Create an instance of the visitor and visit the AST
+    visitor = ClassVisitor()
+    visitor.visit(tree)
+    return class_names
+
+
+def is_subclass(class_obj: type, classes_: list[type]) -> bool:
+    """Check if the given class object is a subclass of any class in list classes.
+
+    Args:
+        class_obj: The class to check.
+        classes_: A list of classes to check against.
+
+    Returns:
+        True if `class_obj` is a subclass of any class in `classes_`, `False` otherwise.
+    """
+    return any(
+        issubclass(class_obj, kls)
+        for kls in classes_
+        if inspect.isclass(class_obj) and inspect.isclass(kls)
+    )
+
+
+def find_subclasses_in_module(module: ModuleType, classes_: list[type]) -> list[str]:
+    """Find all classes in the module that inherit from one of the classes.
+
+    Args:
+        module: The module to inspect.
+        classes_: A list of classes to check against.
+
+    Returns:
+        A list of class names that are subclasses of any class in `classes_`.
+    """
+    subclasses = []
+    # Iterate over all attributes of the module that are classes
+    for _name, obj in inspect.getmembers(module, inspect.isclass):
+        if is_subclass(obj, classes_):
+            subclasses.append(obj.__name__)
+    return subclasses
+
+
+def _get_all_classnames_from_file(file: Path, pkg: str) -> list[tuple[str, str]]:
+    """Extract all class names from a file."""
+    code = Path(file).read_text(encoding="utf-8")
+    module_name = _get_current_module(file, pkg)
+    class_names = _get_class_names(code)
+
+    return [(module_name, class_name) for class_name in class_names]
+
+
+def identify_all_imports_in_file(
+    file: str,
+    *,
+    from_package: str | None = None,
+) -> list[tuple[str, str]]:
+    """Identify all the imports in the given file.
+
+    Args:
+        file: The file to analyze.
+        from_package: If provided, only return imports from this package.
+
+    Returns:
+        A list of tuples `(module, name)` representing the imports found in the file.
+    """
+    code = Path(file).read_text(encoding="utf-8")
+    return find_imports_from_package(code, from_package=from_package)
+
+
+def identify_pkg_source(pkg_root: str) -> pathlib.Path:
+    """Identify the source of the package.
+
+    Args:
+        pkg_root: the root of the package. This contains source + tests, and other
+            things like pyproject.toml, lock files etc
+
+    Returns:
+        Returns the path to the source code for the package.
+
+    Raises:
+        ValueError: If there is not exactly one directory starting with `'langchain_'`
+            in the package root.
+    """
+    dirs = [d for d in Path(pkg_root).iterdir() if d.is_dir()]
+    matching_dirs = [d for d in dirs if d.name.startswith("langchain_")]
+    if len(matching_dirs) != 1:
+        msg = "There should be only one langchain package."
+        raise ValueError(msg)
+    return matching_dirs[0]
+
+
+def list_classes_by_package(pkg_root: str) -> list[tuple[str, str]]:
+    """List all classes in a package.
+
+    Args:
+        pkg_root: the root of the package.
+
+    Returns:
+        A list of tuples `(module, class_name)` representing all classes found in the
+        package, excluding test files.
+    """
+    module_classes = []
+    pkg_source = identify_pkg_source(pkg_root)
+    files = list(pkg_source.rglob("*.py"))
+
+    for file in files:
+        rel_path = os.path.relpath(file, pkg_root)
+        if rel_path.startswith("tests"):
+            continue
+        module_classes.extend(_get_all_classnames_from_file(file, pkg_root))
+    return module_classes
+
+
+def list_init_imports_by_package(pkg_root: str) -> list[tuple[str, str]]:
+    """List all the things that are being imported in a package by module.
+
+    Args:
+        pkg_root: the root of the package.
+
+    Returns:
+        A list of tuples `(module, name)` representing the imports found in
+        `__init__.py` files.
+    """
+    imports = []
+    pkg_source = identify_pkg_source(pkg_root)
+    # Scan all the files in the package
+    files = list(Path(pkg_source).rglob("*.py"))
+
+    for file in files:
+        if file.name != "__init__.py":
+            continue
+        import_in_file = identify_all_imports_in_file(str(file))
+        module_name = _get_current_module(file, pkg_root)
+        imports.extend([(module_name, item) for _, item in import_in_file])
+    return imports
+
+
+def find_imports_from_package(
+    code: str,
+    *,
+    from_package: str | None = None,
+) -> list[tuple[str, str]]:
+    """Find imports in code.
+
+    Args:
+        code: The code to analyze.
+        from_package: If provided, only return imports from this package.
+
+    Returns:
+        A list of tuples `(module, name)` representing the imports found.
+    """
+    # Parse the code into an AST
+    tree = ast.parse(code)
+    # Create an instance of the visitor
+    extractor = ImportExtractor(from_package=from_package)
+    # Use the visitor to update the imports list
+    extractor.visit(tree)
+    return extractor.imports
+
+
+def _get_current_module(path: Path, pkg_root: str) -> str:
+    """Convert a path to a module name."""
+    relative_path = path.relative_to(pkg_root).with_suffix("")
+    posix_path = relative_path.as_posix()
+    norm_path = os.path.normpath(str(posix_path))
+    fully_qualified_module = norm_path.replace("/", ".")
+    # Strip __init__ if present
+    if fully_qualified_module.endswith(".__init__"):
+        return fully_qualified_module[:-9]
+    return fully_qualified_module

libs/cli/langchain_cli/namespaces/migrate/main.py

@@ -0,0 +1,74 @@
+"""Migrate LangChain to the most recent version."""
+
+from pathlib import Path
+
+import rich
+import typer
+from gritql import run  # type: ignore[import-untyped]
+from typer import Option
+
+
+def get_gritdir_path() -> Path:
+    """Get the path to the grit directory."""
+    script_dir = Path(__file__).parent
+    return script_dir / ".grit"
+
+
+def migrate(
+    ctx: typer.Context,
+    # Using diff instead of dry-run for backwards compatibility with the old CLI
+    diff: bool = Option(  # noqa: FBT001
+        False,  # noqa: FBT003
+        "--diff",
+        help="Show the changes that would be made without applying them.",
+    ),
+    interactive: bool = Option(  # noqa: FBT001
+        False,  # noqa: FBT003
+        "--interactive",
+        help="Prompt for confirmation before making each change",
+    ),
+) -> None:
+    """Migrate langchain to the most recent version.
+
+    Any undocumented arguments will be passed to the Grit CLI.
+    """
+    rich.print(
+        "✈️ This script will help you migrate to a LangChain 0.3. "
+        "This migration script will attempt to replace old imports in the code "
+        "with new ones. "
+        "If you need to migrate to LangChain 0.2, please downgrade to version 0.0.29 "
+        "of the langchain-cli.\n\n"
+        "🔄 You will need to run the migration script TWICE to migrate (e.g., "
+        "to update llms import from langchain, the script will first move them to "
+        "corresponding imports from the community package, and on the second "
+        "run will migrate from the community package to the partner package "
+        "when possible). \n\n"
+        "🔍 You can pre-view the changes by running with the --diff flag. \n\n"
+        "🚫 You can disable specific import changes by using the --disable "
+        "flag. \n\n"
+        "📄 Update your pyproject.toml or requirements.txt file to "
+        "reflect any imports from new packages. For example, if you see new "
+        "imports from langchain_openai, langchain_anthropic or "
+        "langchain_text_splitters you "
+        "should add them to your dependencies! \n\n"
+        '⚠️ This script is a "best-effort", and is likely to make some '
+        "mistakes.\n\n"
+        "🛡️ Backup your code prior to running the migration script -- it will "
+        "modify your files!\n\n",
+    )
+    rich.print("-" * 10)
+    rich.print()
+
+    args = list(ctx.args)
+    if interactive:
+        args.append("--interactive")
+    if diff:
+        args.append("--dry-run")
+
+    final_code = run.apply_pattern(
+        "langchain_all_migrations()",
+        args,
+        grit_dir=str(get_gritdir_path()),
+    )
+
+    raise typer.Exit(code=final_code)

libs/cli/langchain_cli/namespaces/template.py

@@ -0,0 +1,147 @@
+"""Develop installable templates."""
+
+import re
+import shutil
+import subprocess
+from pathlib import Path
+from typing import Annotated
+
+import typer
+import uvicorn
+
+from langchain_cli.utils.github import list_packages
+from langchain_cli.utils.packages import get_langserve_export, get_package_root
+
+package_cli = typer.Typer(no_args_is_help=True, add_completion=False)
+
+
+@package_cli.command()
+def new(
+    name: Annotated[str, typer.Argument(help="The name of the folder to create")],
+    with_poetry: Annotated[  # noqa: FBT002
+        bool,
+        typer.Option("--with-poetry/--no-poetry", help="Don't run poetry install"),
+    ] = False,
+) -> None:
+    """Create a new template package."""
+    computed_name = name if name != "." else Path.cwd().name
+    destination_dir = Path.cwd() / name if name != "." else Path.cwd()
+
+    # copy over template from ../package_template
+    project_template_dir = Path(__file__).parents[1] / "package_template"
+    shutil.copytree(project_template_dir, destination_dir, dirs_exist_ok=name == ".")
+
+    package_name_split = computed_name.split("/")
+    package_name = (
+        package_name_split[-2]
+        if len(package_name_split) > 1 and not package_name_split[-1]
+        else package_name_split[-1]
+    )
+    module_name = re.sub(
+        r"[^a-zA-Z0-9_]",
+        "_",
+        package_name,
+    )
+
+    # generate app route code
+    chain_name = f"{module_name}_chain"
+    app_route_code = (
+        f"from {module_name} import chain as {chain_name}\n\n"
+        f'add_routes(app, {chain_name}, path="/{package_name}")'
+    )
+
+    # replace template strings
+    pyproject = destination_dir / "pyproject.toml"
+    pyproject_contents = pyproject.read_text()
+    pyproject.write_text(
+        pyproject_contents.replace("__package_name__", package_name).replace(
+            "__module_name__",
+            module_name,
+        ),
+    )
+
+    # move module folder
+    package_dir = destination_dir / module_name
+    shutil.move(destination_dir / "package_template", package_dir)
+
+    # update init
+    init = package_dir / "__init__.py"
+    init_contents = init.read_text()
+    init.write_text(init_contents.replace("__module_name__", module_name))
+
+    # replace readme
+    readme = destination_dir / "README.md"
+    readme_contents = readme.read_text()
+    readme.write_text(
+        readme_contents.replace("__package_name__", package_name).replace(
+            "__app_route_code__",
+            app_route_code,
+        ),
+    )
+
+    # poetry install
+    if with_poetry:
+        subprocess.run(["poetry", "install"], cwd=destination_dir, check=True)  # noqa: S607
+
+
+@package_cli.command()
+def serve(
+    *,
+    port: Annotated[
+        int | None,
+        typer.Option(help="The port to run the server on"),
+    ] = None,
+    host: Annotated[
+        str | None,
+        typer.Option(help="The host to run the server on"),
+    ] = None,
+    configurable: Annotated[
+        bool | None,
+        typer.Option(
+            "--configurable/--no-configurable",
+            help="Whether to include a configurable route",
+        ),
+    ] = None,  # defaults to `not chat_playground`
+    chat_playground: Annotated[
+        bool,
+        typer.Option(
+            "--chat-playground/--no-chat-playground",
+            help="Whether to include a chat playground route",
+        ),
+    ] = False,
+) -> None:
+    """Start a demo app for this template."""
+    # load pyproject.toml
+    project_dir = get_package_root()
+    pyproject = project_dir / "pyproject.toml"
+
+    # get langserve export - throws KeyError if invalid
+    get_langserve_export(pyproject)
+
+    host_str = host if host is not None else "127.0.0.1"
+
+    script = (
+        "langchain_cli.dev_scripts:create_demo_server_chat"
+        if chat_playground
+        else (
+            "langchain_cli.dev_scripts:create_demo_server_configurable"
+            if configurable
+            else "langchain_cli.dev_scripts:create_demo_server"
+        )
+    )
+
+    uvicorn.run(
+        script,
+        factory=True,
+        reload=True,
+        port=port if port is not None else 8000,
+        host=host_str,
+    )
+
+
+@package_cli.command()
+def list(contains: Annotated[str | None, typer.Argument()] = None) -> None:  # noqa: A001
+    """List all or search for available templates."""
+    packages = list_packages(contains=contains)
+    for package in packages:
+        typer.echo(package)

libs/cli/langchain_cli/package_template/.gitignore

@@ -0,0 +1 @@
+__pycache__

libs/cli/langchain_cli/package_template/LICENSE

@@ -0,0 +1,21 @@
+MIT License
+
+Copyright (c) 2024 LangChain, Inc.
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.

libs/cli/langchain_cli/package_template/README.md

@@ -0,0 +1,64 @@
+# __package_name__
+
+TODO: What does this package do
+
+## Environment Setup
+
+TODO: What environment variables need to be set (if any)
+
+## Usage
+
+To use this package, you should first have the LangChain CLI installed:
+
+```shell
+pip install -U langchain-cli
+```
+
+To create a new LangChain project and install this as the only package, you can do:
+
+```shell
+langchain app new my-app --package __package_name__
+```
+
+If you want to add this to an existing project, you can just run:
+
+```shell
+langchain app add __package_name__
+```
+
+And add the following code to your `server.py` file:
+
+```python
+__app_route_code__
+```
+
+(Optional) Let's now configure LangSmith.
+LangSmith will help us trace, monitor and debug LangChain applications.
+You can sign up for LangSmith [here](https://smith.langchain.com/).
+If you don't have access, you can skip this section
+
+```shell
+export LANGSMITH_TRACING=true
+export LANGSMITH_API_KEY=<your-api-key>
+export LANGSMITH_PROJECT=<your-project>  # if not specified, defaults to "default"
+```
+
+If you are inside this directory, then you can spin up a LangServe instance directly by:
+
+```shell
+langchain serve
+```
+
+This will start the FastAPI app with a server is running locally at
+[http://localhost:8000](http://localhost:8000)
+
+We can see all templates at [http://127.0.0.1:8000/docs](http://127.0.0.1:8000/docs)
+We can access the playground at [http://127.0.0.1:8000/__package_name__/playground](http://127.0.0.1:8000/__package_name__/playground)
+
+We can access the template from code with:
+
+```python
+from langserve.client import RemoteRunnable
+
+runnable = RemoteRunnable("http://localhost:8000/__package_name__")
+```

libs/cli/langchain_cli/package_template/package_template/__init__.py

@@ -0,0 +1,5 @@
+"""__module_name__ module."""
+
+from __module_name__.chain import chain
+
+__all__ = ["chain"]

libs/cli/langchain_cli/package_template/package_template/chain.py

@@ -0,0 +1,19 @@
+"""Chain definition."""
+
+from langchain_core.prompts import ChatPromptTemplate
+from langchain_openai import ChatOpenAI
+
+_prompt = ChatPromptTemplate.from_messages(
+    [
+        (
+            "system",
+            "You are a helpful assistant who speaks like a pirate",
+        ),
+        ("human", "{text}"),
+    ]
+)
+_model = ChatOpenAI()
+
+# if you update this, you MUST also update ../pyproject.toml
+# with the new `tool.langserve.export_attr`
+chain = _prompt | _model

libs/cli/langchain_cli/package_template/pyproject.toml

@@ -0,0 +1,24 @@
+[build-system]
+requires = [ "poetry-core",]
+build-backend = "poetry.core.masonry.api"
+
+[tool.poetry]
+name = "__package_name__"
+version = "0.0.1"
+description = ""
+authors = []
+readme = "README.md"
+
+[tool.langserve]
+export_module = "__module_name__"
+export_attr = "chain"
+
+[tool.poetry.dependencies]
+python = ">=3.9,<4.0"
+langchain-core = "^0.3.15"
+langchain-openai = ">=0.0.1"
+
+[tool.poetry.group.dev.dependencies]
+langchain-cli = ">=0.0.4"
+fastapi = "^0.104.0"
+sse-starlette = "^1.6.5"