test core dependents

This reverts commit 8e3bcbb546.

.github/PULL_REQUEST_TEMPLATE.md

@@ -1,7 +1,7 @@
 Thank you for contributing to LangChain!
 
 - [ ] **PR title**: "package: description"
-  - Where "package" is whichever of langchain, community, core, experimental, etc. is being modified. Use "docs: ..." for purely docs changes, "templates: ..." for template changes, "infra: ..." for CI changes.
+  - Where "package" is whichever of langchain, community, core, etc. is being modified. Use "docs: ..." for purely docs changes, "templates: ..." for template changes, "infra: ..." for CI changes.
   - Example: "community: add foobar LLM"
 
 

.github/actions/poetry_setup/action.yml

@@ -55,11 +55,14 @@ runs:
         # Refresh the shell hashtable, to ensure correct `which` output.
         hash -r
 
+        # Extract the major.minor version of python
+        PYTHON_MAJOR_MINOR=$(echo "$PYTHON_VERSION" | sed -E 's/^([0-9]+\.[0-9]+).*/\1/')
+
         # `actions/cache@v3` doesn't always seem able to correctly unpack softlinks.
         # Delete and recreate the softlinks pipx expects to have.
         rm /opt/pipx/venvs/poetry/bin/python
         cd /opt/pipx/venvs/poetry/bin
-        ln -s "$(which "python$PYTHON_VERSION")" python
+        ln -s "$(which "python$PYTHON_MAJOR_MINOR")" python
         chmod +x python
         cd /opt/pipx_bin/
         ln -s /opt/pipx/venvs/poetry/bin/poetry poetry

.github/scripts/check_diff.py

@@ -15,7 +15,6 @@ LANGCHAIN_DIRS = [
     "libs/text-splitters",
     "libs/langchain",
     "libs/community",
-    "libs/experimental",
 ]
 
 # when set to True, we are ignoring core dependents
@@ -111,7 +110,7 @@ def _get_configs_for_single_dir(job: str, dir_: str) -> List[Dict[str, str]]:
         return _get_pydantic_test_configs(dir_)
 
     if dir_ == "libs/core":
-        py_versions = ["3.9", "3.10", "3.11", "3.12"]
+        py_versions = ["3.9", "3.10", "3.11", "3.12", "3.13.0-rc.2"]
     # custom logic for specific directories
     elif dir_ == "libs/partners/milvus":
         # milvus poetry doesn't allow 3.12 because they
@@ -121,13 +120,13 @@ def _get_configs_for_single_dir(job: str, dir_: str) -> List[Dict[str, str]]:
     elif dir_ in ["libs/community", "libs/langchain"] and job == "extended-tests":
         # community extended test resolution in 3.12 is slow
         # even in uv
-        py_versions = ["3.9", "3.11"]
+        py_versions = ["3.9", "3.11", "3.13.0-rc.2"]
 
     elif dir_ == "libs/community" and job == "compile-integration-tests":
         # community integration deps are slow in 3.12
-        py_versions = ["3.9", "3.11"]
+        py_versions = ["3.9", "3.11", "3.13.0-rc.2"]
     else:
-        py_versions = ["3.9", "3.12"]
+        py_versions = ["3.9", "3.12", "3.13.0-rc.2"]
 
     return [{"working-directory": dir_, "python-version": py_v} for py_v in py_versions]
 
@@ -153,14 +152,19 @@ def _get_pydantic_test_configs(
     core_min_pydantic_version = get_min_version_from_toml(
         "./libs/core/pyproject.toml", "release", python_version, include=["pydantic"]
     )["pydantic"]
-    core_min_pydantic_minor = core_min_pydantic_version.split(".")[1] if "." in core_min_pydantic_version else "0"
-    dir_min_pydantic_version = (
-        get_min_version_from_toml(
-            f"./{dir_}/pyproject.toml", "release", python_version, include=["pydantic"]
-        )
-        .get("pydantic", "0.0.0")
+    core_min_pydantic_minor = (
+        core_min_pydantic_version.split(".")[1]
+        if "." in core_min_pydantic_version
+        else "0"
+    )
+    dir_min_pydantic_version = get_min_version_from_toml(
+        f"./{dir_}/pyproject.toml", "release", python_version, include=["pydantic"]
+    ).get("pydantic", "0.0.0")
+    dir_min_pydantic_minor = (
+        dir_min_pydantic_version.split(".")[1]
+        if "." in dir_min_pydantic_version
+        else "0"
     )
-    dir_min_pydantic_minor = dir_min_pydantic_version.split(".")[1] if "." in dir_min_pydantic_version else "0"
 
     custom_mins = {
         # depends on pydantic-settings 2.4 which requires pydantic 2.7

.github/workflows/_integration_test.yml

@@ -58,6 +58,7 @@ jobs:
           AZURE_OPENAI_API_BASE: ${{ secrets.AZURE_OPENAI_API_BASE }}
           AZURE_OPENAI_API_KEY: ${{ secrets.AZURE_OPENAI_API_KEY }}
           AZURE_OPENAI_CHAT_DEPLOYMENT_NAME: ${{ secrets.AZURE_OPENAI_CHAT_DEPLOYMENT_NAME }}
+          AZURE_OPENAI_LEGACY_CHAT_DEPLOYMENT_NAME: ${{ secrets.AZURE_OPENAI_LEGACY_CHAT_DEPLOYMENT_NAME }}
           AZURE_OPENAI_LLM_DEPLOYMENT_NAME: ${{ secrets.AZURE_OPENAI_LLM_DEPLOYMENT_NAME }}
           AZURE_OPENAI_EMBEDDINGS_DEPLOYMENT_NAME: ${{ secrets.AZURE_OPENAI_EMBEDDINGS_DEPLOYMENT_NAME }}
           MISTRAL_API_KEY: ${{ secrets.MISTRAL_API_KEY }}

.github/workflows/_release.yml

@@ -269,6 +269,7 @@ jobs:
           AZURE_OPENAI_API_BASE: ${{ secrets.AZURE_OPENAI_API_BASE }}
           AZURE_OPENAI_API_KEY: ${{ secrets.AZURE_OPENAI_API_KEY }}
           AZURE_OPENAI_CHAT_DEPLOYMENT_NAME: ${{ secrets.AZURE_OPENAI_CHAT_DEPLOYMENT_NAME }}
+          AZURE_OPENAI_LEGACY_CHAT_DEPLOYMENT_NAME: ${{ secrets.AZURE_OPENAI_LEGACY_CHAT_DEPLOYMENT_NAME }}
           AZURE_OPENAI_LLM_DEPLOYMENT_NAME: ${{ secrets.AZURE_OPENAI_LLM_DEPLOYMENT_NAME }}
           AZURE_OPENAI_EMBEDDINGS_DEPLOYMENT_NAME: ${{ secrets.AZURE_OPENAI_EMBEDDINGS_DEPLOYMENT_NAME }}
           NVIDIA_API_KEY: ${{ secrets.NVIDIA_API_KEY }}

.github/workflows/_test_doc_imports.yml

@@ -31,7 +31,7 @@ jobs:
 
       - name: Install langchain editable
         run: |
-          poetry run pip install -e libs/core libs/langchain libs/community libs/experimental
+          poetry run pip install langchain-experimental -e libs/core libs/langchain libs/community
 
       - name: Check doc imports
         shell: bash

.github/workflows/api_doc_build.yml

@@ -0,0 +1,141 @@
+name: API docs build
+
+on:
+  workflow_dispatch:
+  schedule:
+    - cron:  '0 13 * * *'
+env:
+  POETRY_VERSION: "1.8.1"
+  PYTHON_VERSION: "3.11"
+
+jobs:
+  build:
+    runs-on: ubuntu-latest
+    permissions: write-all
+    steps:
+      - uses: actions/checkout@v4
+        with:
+          path: langchain
+      - uses: actions/checkout@v4
+        with:
+          repository: langchain-ai/langchain-api-docs-html
+          path: langchain-api-docs-html
+          token: ${{ secrets.TOKEN_GITHUB_API_DOCS_HTML }}
+      - uses: actions/checkout@v4
+        with:
+          repository: langchain-ai/langchain-google
+          path: langchain-google
+      - uses: actions/checkout@v4
+        with:
+          repository: langchain-ai/langchain-datastax
+          path: langchain-datastax
+      - uses: actions/checkout@v4
+        with:
+          repository: langchain-ai/langchain-nvidia
+          path: langchain-nvidia
+      - uses: actions/checkout@v4
+        with:
+          repository: langchain-ai/langchain-cohere
+          path: langchain-cohere
+      - uses: actions/checkout@v4
+        with:
+          repository: langchain-ai/langchain-elastic
+          path: langchain-elastic
+      - uses: actions/checkout@v4
+        with:
+          repository: langchain-ai/langchain-postgres
+          path: langchain-postgres
+      - uses: actions/checkout@v4
+        with:
+          repository: langchain-ai/langchain-aws
+          path: langchain-aws
+      - uses: actions/checkout@v4
+        with:
+          repository: langchain-ai/langchain-weaviate
+          path: langchain-weaviate
+      - uses: actions/checkout@v4
+        with:
+          repository: langchain-ai/langchain-ai21
+          path: langchain-ai21
+      - uses: actions/checkout@v4
+        with:
+          repository: langchain-ai/langchain-together
+          path: langchain-together
+      - uses: actions/checkout@v4
+        with:
+          repository: langchain-ai/langchain-experimental
+          path: langchain-experimental
+
+
+      - name: Set Git config
+        working-directory: langchain
+        run: |
+          git config --local user.email "actions@github.com"
+          git config --local user.name "Github Actions"
+          
+      - name: Move google libs
+        run: |
+          rm -rf \
+            langchain/libs/partners/google-genai \
+            langchain/libs/partners/google-vertexai \
+            langchain/libs/partners/astradb \
+            langchain/libs/partners/nvidia-trt \
+            langchain/libs/partners/nvidia-ai-endpoints \
+            langchain/libs/partners/cohere \
+            langchain/libs/partners/elasticsearch \
+            langchain/libs/partners/upstage \
+            langchain/libs/partners/ai21 \
+            langchain/libs/partners/together \
+            langchain/libs/standard-tests \
+            langchain/libs/experimental
+          mv langchain-google/libs/genai langchain/libs/partners/google-genai
+          mv langchain-google/libs/vertexai langchain/libs/partners/google-vertexai
+          mv langchain-google/libs/community langchain/libs/partners/google-community
+          # mv langchain-datastax/libs/astradb langchain/libs/partners/astradb
+          # mv langchain-nvidia/libs/ai-endpoints langchain/libs/partners/nvidia-ai-endpoints
+          mv langchain-cohere/libs/cohere langchain/libs/partners/cohere
+          mv langchain-elastic/libs/elasticsearch langchain/libs/partners/elasticsearch
+          mv langchain-postgres langchain/libs/partners/postgres
+          mv langchain-aws/libs/aws langchain/libs/partners/aws
+          mv langchain-weaviate/libs/weaviate langchain/libs/partners/weaviate
+          mv langchain-ai21/libs/ai21 langchain/libs/partners/ai21
+          mv langchain-together/libs/together langchain/libs/partners/together
+          mv langchain-experimental/libs/experimental langchain/libs/experimental
+
+      - name: Rm old html
+        run:
+          rm -rf langchain-api-docs-html/api_reference_build/html
+
+      - name: Set up Python ${{ env.PYTHON_VERSION }} + Poetry ${{ env.POETRY_VERSION }}
+        uses: "./langchain/.github/actions/poetry_setup"
+        with:
+          python-version: ${{ env.PYTHON_VERSION }}
+          poetry-version: ${{ env.POETRY_VERSION }}
+          cache-key: api-docs
+          working-directory: langchain
+
+      - name: Install dependencies
+        working-directory: langchain
+        run: |
+          python -m pip install -U uv
+          python -m uv pip install --upgrade --no-cache-dir pip setuptools
+          # skip airbyte and ibm due to pandas dependency issue
+          python -m uv pip install $(ls ./libs/partners | grep -vE "airbyte|ibm" | xargs -I {} echo "./libs/partners/{}")
+          python -m uv pip install libs/core libs/langchain libs/text-splitters libs/community libs/experimental
+          python -m uv pip install -r docs/api_reference/requirements.txt
+
+      - name: Build docs
+        working-directory: langchain
+        run: |
+          python docs/api_reference/create_api_rst.py
+          python -m sphinx -T -E -b html -d ../langchain-api-docs-html/_build/doctrees -c docs/api_reference docs/api_reference ../langchain-api-docs-html/api_reference_build/html -j auto
+          python docs/api_reference/scripts/custom_formatter.py ../langchain-api-docs-html/api_reference_build/html
+          # Default index page is blank so we copy in the actual home page.
+          cp ../langchain-api-docs-html/api_reference_build/html/{reference,index}.html
+          rm -rf ../langchain-api-docs-html/_build/
+
+      # https://github.com/marketplace/actions/add-commit
+      - uses: EndBug/add-and-commit@v9
+        with:
+          cwd: langchain-api-docs-html
+          message: 'Update API docs build'

.github/workflows/check_diffs.yml

@@ -3,7 +3,7 @@ name: CI
 
 on:
   push:
-    branches: [master]
+    branches: [cc/test_13]
   pull_request:
 
 # If another push to the same PR or branch happens while this workflow is still running,
@@ -27,12 +27,12 @@ jobs:
       - uses: actions/setup-python@v5
         with:
           python-version: '3.11'
-      - id: files
-        uses: Ana06/get-changed-files@v2.2.0
+      # - id: files
+      #   uses: Ana06/get-changed-files@v2.2.0
       - id: set-matrix
         run: |
           python -m pip install packaging
-          python .github/scripts/check_diff.py ${{ steps.files.outputs.all }} >> $GITHUB_OUTPUT
+          python .github/scripts/check_diff.py libs/core/langchain_core/__init__.py >> $GITHUB_OUTPUT
     outputs:
       lint: ${{ steps.set-matrix.outputs.lint }}
       test: ${{ steps.set-matrix.outputs.test }}

.github/workflows/run_notebooks.yml

@@ -0,0 +1,63 @@
+name: Run notebooks
+
+on:
+  workflow_dispatch:
+    inputs:
+      python_version:
+        description: 'Python version'
+        required: false
+        default: '3.11'
+      working-directory:
+        description: 'Working directory or subset (e.g., docs/docs/tutorials/llm_chain.ipynb)'
+        required: false
+        default: 'all'
+  schedule:
+    - cron: '0 13 * * *'
+
+env:
+  POETRY_VERSION: "1.7.1"
+
+jobs:
+  build:
+    runs-on: ubuntu-latest
+
+    name: "Test docs"
+    steps:
+      - uses: actions/checkout@v4
+
+      - name: Set up Python + Poetry ${{ env.POETRY_VERSION }}
+        uses: "./.github/actions/poetry_setup"
+        with:
+          python-version: ${{ github.event.inputs.python_version || '3.11' }}
+          poetry-version: ${{ env.POETRY_VERSION }}
+          working-directory: ${{ inputs.working-directory }}
+          cache-key: run-notebooks
+
+      - name: Install dependencies
+        run: |
+          pip install -e libs/core
+          pip install -e libs/langchain
+          pip install -e libs/community
+          pip install --upgrade langchain-experimental
+          pip install -e libs//partners/anthropic
+          pip install -e libs//partners/chroma
+          pip install -e libs//partners/openai
+          pip install -e libs//partners/mistralai
+          pip install jupyter langgraph click pypdf vcrpy
+
+      - name: Pre-download tiktoken files
+        run: |
+          python docs/scripts/download_tiktoken.py
+
+      - name: Prepare notebooks
+        run: |
+          python docs/scripts/prepare_notebooks_for_ci.py --comment-install-cells
+
+      - name: Run notebooks
+        env:
+          ANTHROPIC_API_KEY: ${{ secrets.ANTHROPIC_API_KEY }}
+          MISTRAL_API_KEY: ${{ secrets.MISTRAL_API_KEY }}
+          OPENAI_API_KEY: ${{ secrets.OPENAI_API_KEY }}
+          TAVILY_API_KEY: ${{ secrets.TAVILY_API_KEY }}
+        run: |
+          ./docs/scripts/execute_notebooks.sh ${{ github.event.inputs.working-directory || 'all' }}

.github/workflows/scheduled_test.yml

@@ -86,6 +86,7 @@ jobs:
           AZURE_OPENAI_API_BASE: ${{ secrets.AZURE_OPENAI_API_BASE }}
           AZURE_OPENAI_API_KEY: ${{ secrets.AZURE_OPENAI_API_KEY }}
           AZURE_OPENAI_CHAT_DEPLOYMENT_NAME: ${{ secrets.AZURE_OPENAI_CHAT_DEPLOYMENT_NAME }}
+          AZURE_OPENAI_LEGACY_CHAT_DEPLOYMENT_NAME: ${{ secrets.AZURE_OPENAI_LEGACY_CHAT_DEPLOYMENT_NAME }}
           AZURE_OPENAI_LLM_DEPLOYMENT_NAME: ${{ secrets.AZURE_OPENAI_LLM_DEPLOYMENT_NAME }}
           AZURE_OPENAI_EMBEDDINGS_DEPLOYMENT_NAME: ${{ secrets.AZURE_OPENAI_EMBEDDINGS_DEPLOYMENT_NAME }}
           FIREWORKS_API_KEY: ${{ secrets.FIREWORKS_API_KEY }}

README.md

@@ -38,7 +38,7 @@ conda install langchain -c conda-forge
 
 For these applications, LangChain simplifies the entire application lifecycle:
 
-- **Open-source libraries**: Build your applications using LangChain's open-source [building blocks](https://python.langchain.com/v0.2/docs/concepts#langchain-expression-language-lcel), [components](https://python.langchain.com/v0.2/docs/concepts), and [third-party integrations](https://python.langchain.com/v0.2/docs/integrations/platforms/).
+- **Open-source libraries**: Build your applications using LangChain's open-source [building blocks](https://python.langchain.com/docs/concepts/#langchain-expression-language-lcel), [components](https://python.langchain.com/docs/concepts/), and [third-party integrations](https://python.langchain.com/docs/integrations/platforms/).
   Use [LangGraph](https://langchain-ai.github.io/langgraph/) to build stateful agents with first-class streaming and human-in-the-loop support.
 - **Productionization**: Inspect, monitor, and evaluate your apps with [LangSmith](https://docs.smith.langchain.com/) so that you can constantly optimize and deploy with confidence.
 - **Deployment**: Turn your LangGraph applications into production-ready APIs and Assistants with [LangGraph Cloud](https://langchain-ai.github.io/langgraph/cloud/).
@@ -65,20 +65,20 @@ For these applications, LangChain simplifies the entire application lifecycle:
 
 **❓ Question answering with RAG**
 
-- [Documentation](https://python.langchain.com/v0.2/docs/tutorials/rag/)
+- [Documentation](https://python.langchain.com/docs/tutorials/rag/)
 - End-to-end Example: [Chat LangChain](https://chat.langchain.com) and [repo](https://github.com/langchain-ai/chat-langchain)
 
 **🧱 Extracting structured output**
 
-- [Documentation](https://python.langchain.com/v0.2/docs/tutorials/extraction/)
+- [Documentation](https://python.langchain.com/docs/tutorials/extraction/)
 - End-to-end Example: [SQL Llama2 Template](https://github.com/langchain-ai/langchain-extract/)
 
 **🤖 Chatbots**
 
-- [Documentation](https://python.langchain.com/v0.2/docs/tutorials/chatbot/)
+- [Documentation](https://python.langchain.com/docs/tutorials/chatbot/)
 - End-to-end Example: [Web LangChain (web researcher chatbot)](https://weblangchain.vercel.app) and [repo](https://github.com/langchain-ai/weblangchain)
 
-And much more! Head to the [Tutorials](https://python.langchain.com/v0.2/docs/tutorials/) section of the docs for more.
+And much more! Head to the [Tutorials](https://python.langchain.com/docs/tutorials/) section of the docs for more.
 
 ## 🚀 How does LangChain help?
 
@@ -93,10 +93,10 @@ Off-the-shelf chains make it easy to get started. Components make it easy to cus
 
 LCEL is a key part of LangChain, allowing you to build and organize chains of processes in a straightforward, declarative manner. It was designed to support taking prototypes directly into production without needing to alter any code. This means you can use LCEL to set up everything from basic "prompt + LLM" setups to intricate, multi-step workflows.
 
-- **[Overview](https://python.langchain.com/v0.2/docs/concepts/#langchain-expression-language-lcel)**: LCEL and its benefits
-- **[Interface](https://python.langchain.com/v0.2/docs/concepts/#runnable-interface)**: The standard Runnable interface for LCEL objects
-- **[Primitives](https://python.langchain.com/v0.2/docs/how_to/#langchain-expression-language-lcel)**: More on the primitives LCEL includes
-- **[Cheatsheet](https://python.langchain.com/v0.2/docs/how_to/lcel_cheatsheet/)**: Quick overview of the most common usage patterns
+- **[Overview](https://python.langchain.com/docs/concepts/#langchain-expression-language-lcel)**: LCEL and its benefits
+- **[Interface](https://python.langchain.com/docs/concepts/#runnable-interface)**: The standard Runnable interface for LCEL objects
+- **[Primitives](https://python.langchain.com/docs/how_to/#langchain-expression-language-lcel)**: More on the primitives LCEL includes
+- **[Cheatsheet](https://python.langchain.com/docs/how_to/lcel_cheatsheet/)**: Quick overview of the most common usage patterns
 
 ## Components
 
@@ -104,24 +104,24 @@ Components fall into the following **modules**:
 
 **📃 Model I/O**
 
-This includes [prompt management](https://python.langchain.com/v0.2/docs/concepts/#prompt-templates), [prompt optimization](https://python.langchain.com/v0.2/docs/concepts/#example-selectors), a generic interface for [chat models](https://python.langchain.com/v0.2/docs/concepts/#chat-models) and [LLMs](https://python.langchain.com/v0.2/docs/concepts/#llms), and common utilities for working with [model outputs](https://python.langchain.com/v0.2/docs/concepts/#output-parsers).
+This includes [prompt management](https://python.langchain.com/docs/concepts/#prompt-templates), [prompt optimization](https://python.langchain.com/docs/concepts/#example-selectors), a generic interface for [chat models](https://python.langchain.com/docs/concepts/#chat-models) and [LLMs](https://python.langchain.com/docs/concepts/#llms), and common utilities for working with [model outputs](https://python.langchain.com/docs/concepts/#output-parsers).
 
 **📚 Retrieval**
 
-Retrieval Augmented Generation involves [loading data](https://python.langchain.com/v0.2/docs/concepts/#document-loaders) from a variety of sources, [preparing it](https://python.langchain.com/v0.2/docs/concepts/#text-splitters), then [searching over (a.k.a. retrieving from)](https://python.langchain.com/v0.2/docs/concepts/#retrievers) it for use in the generation step.
+Retrieval Augmented Generation involves [loading data](https://python.langchain.com/docs/concepts/#document-loaders) from a variety of sources, [preparing it](https://python.langchain.com/docs/concepts/#text-splitters), then [searching over (a.k.a. retrieving from)](https://python.langchain.com/docs/concepts/#retrievers) it for use in the generation step.
 
 **🤖 Agents**
 
-Agents allow an LLM autonomy over how a task is accomplished. Agents make decisions about which Actions to take, then take that Action, observe the result, and repeat until the task is complete. LangChain provides a [standard interface for agents](https://python.langchain.com/v0.2/docs/concepts/#agents), along with [LangGraph](https://github.com/langchain-ai/langgraph) for building custom agents.
+Agents allow an LLM autonomy over how a task is accomplished. Agents make decisions about which Actions to take, then take that Action, observe the result, and repeat until the task is complete. LangChain provides a [standard interface for agents](https://python.langchain.com/docs/concepts/#agents), along with [LangGraph](https://github.com/langchain-ai/langgraph) for building custom agents.
 
 ## 📖 Documentation
 
 Please see [here](https://python.langchain.com) for full documentation, which includes:
 
-- [Introduction](https://python.langchain.com/v0.2/docs/introduction/): Overview of the framework and the structure of the docs.
+- [Introduction](https://python.langchain.com/docs/introduction/): Overview of the framework and the structure of the docs.
 - [Tutorials](https://python.langchain.com/docs/use_cases/): If you're looking to build something specific or are more of a hands-on learner, check out our tutorials. This is the best place to get started.
-- [How-to guides](https://python.langchain.com/v0.2/docs/how_to/): Answers to “How do I….?” type questions. These guides are goal-oriented and concrete; they're meant to help you complete a specific task.
-- [Conceptual guide](https://python.langchain.com/v0.2/docs/concepts/): Conceptual explanations of the key parts of the framework.
+- [How-to guides](https://python.langchain.com/docs/how_to/): Answers to “How do I….?” type questions. These guides are goal-oriented and concrete; they're meant to help you complete a specific task.
+- [Conceptual guide](https://python.langchain.com/docs/concepts/): Conceptual explanations of the key parts of the framework.
 - [API Reference](https://api.python.langchain.com): Thorough documentation of every class and method.
 
 ## 🌐 Ecosystem
@@ -134,7 +134,7 @@ Please see [here](https://python.langchain.com) for full documentation, which in
 
 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 [here](https://python.langchain.com/v0.2/docs/contributing/).
+For detailed information on how to contribute, see [here](https://python.langchain.com/docs/contributing/).
 
 ## 🌟 Contributors
 

docs/Makefile

@@ -46,7 +46,7 @@ generate-files:
 
 	$(PYTHON) scripts/partner_pkg_table.py $(INTERMEDIATE_DIR)
 
-	wget -q https://raw.githubusercontent.com/langchain-ai/langserve/main/README.md -O $(INTERMEDIATE_DIR)/langserve.md
+	curl https://raw.githubusercontent.com/langchain-ai/langserve/main/README.md | sed 's/<=/\&lt;=/g' > $(INTERMEDIATE_DIR)/langserve.md
 	$(PYTHON) scripts/resolve_local_links.py $(INTERMEDIATE_DIR)/langserve.md https://github.com/langchain-ai/langserve/tree/main/
 
 copy-infra:
@@ -65,7 +65,7 @@ render:
 	$(PYTHON) scripts/notebook_convert.py $(INTERMEDIATE_DIR) $(OUTPUT_NEW_DOCS_DIR)
 
 md-sync:
-	rsync -avm --include="*/" --include="*.mdx" --include="*.md" --include="*.png" --include="*/_category_.yml" --exclude="*" $(INTERMEDIATE_DIR)/ $(OUTPUT_NEW_DOCS_DIR)
+	rsync -avmq --include="*/" --include="*.mdx" --include="*.md" --include="*.png" --include="*/_category_.yml" --exclude="*" $(INTERMEDIATE_DIR)/ $(OUTPUT_NEW_DOCS_DIR)
 
 append-related:
 	$(PYTHON) scripts/append_related_links.py $(OUTPUT_NEW_DOCS_DIR)

docs/api_reference/conf.py

@@ -26,7 +26,6 @@ from sphinx.util.docutils import SphinxDirective
 _DIR = Path(__file__).parent.absolute()
 sys.path.insert(0, os.path.abspath("."))
 sys.path.insert(0, os.path.abspath("../../libs/langchain"))
-sys.path.insert(0, os.path.abspath("../../libs/experimental"))
 
 with (_DIR.parents[1] / "libs" / "langchain" / "pyproject.toml").open("r") as f:
     data = toml.load(f)

docs/cassettes/agents_114ba50d.msgpack.zlib

@@ -0,0 +1 @@
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

docs/cassettes/agents_24460239.msgpack.zlib

@@ -0,0 +1 @@
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

docs/cassettes/agents_482ce13d.msgpack.zlib

@@ -0,0 +1 @@
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

docs/cassettes/agents_532d6557.msgpack.zlib

@@ -0,0 +1 @@
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

docs/cassettes/agents_56d8028b-5dbc-40b2-86f5-ed60631d86a3.msgpack.zlib

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docs/cassettes/agents_688b465d.msgpack.zlib

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docs/cassettes/agents_77c2f769.msgpack.zlib

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docs/cassettes/agents_a13462d0-2d02-4474-921e-15a1ba1fa274.msgpack.zlib

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docs/cassettes/agents_a3fb262c.msgpack.zlib

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docs/cassettes/agents_b6a7e925.msgpack.zlib

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docs/cassettes/agents_c96c960b.msgpack.zlib

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docs/cassettes/chatbot_16.msgpack.zlib

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docs/cassettes/chatbot_17.msgpack.zlib

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docs/cassettes/chatbot_19.msgpack.zlib

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docs/cassettes/chatbot_21.msgpack.zlib

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docs/cassettes/chatbot_28.msgpack.zlib

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docs/cassettes/chatbot_29.msgpack.zlib

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docs/cassettes/chatbot_34.msgpack.zlib

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docs/cassettes/chatbot_36.msgpack.zlib

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docs/cassettes/chatbot_43.msgpack.zlib

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docs/cassettes/chatbot_45.msgpack.zlib

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docs/cassettes/chatbot_48.msgpack.zlib

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docs/cassettes/chatbot_5.msgpack.zlib

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docs/cassettes/chatbot_7.msgpack.zlib

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docs/cassettes/chatbot_9.msgpack.zlib

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docs/cassettes/classification_0bdfcb05.msgpack.zlib

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docs/cassettes/classification_1c12fa00.msgpack.zlib

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docs/cassettes/classification_5509b6a6.msgpack.zlib

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docs/cassettes/classification_9154474c.msgpack.zlib

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docs/cassettes/data_generation_ff823394.msgpack.zlib

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docs/cassettes/extraction_cf7062cc-1d1d-4a37-9122-509d1b87f0a6.msgpack.zlib

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docs/cassettes/rag_ff01d415-7b0f-469d-bfda-b9cb672da611.msgpack.zlib

@@ -0,0 +1 @@
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

docs/docs/additional_resources/arxiv_references.mdx

@@ -451,8 +451,7 @@ steps of the thought-process and explore other directions from there. To verify
 the effectiveness of the proposed technique, we implemented a ToT-based solver
 for the Sudoku Puzzle. Experimental results show that the ToT framework can
 significantly increase the success rate of Sudoku puzzle solving. Our
-implementation of the ToT-based Sudoku solver is available on GitHub:
-\url{https://github.com/jieyilong/tree-of-thought-puzzle-solver}.
+implementation of the ToT-based Sudoku solver is available on [GitHub](https://github.com/jieyilong/tree-of-thought-puzzle-solver).
                 
 ## Plan-and-Solve Prompting: Improving Zero-Shot Chain-of-Thought Reasoning by Large Language Models
 

docs/docs/concepts.mdx

@@ -116,14 +116,14 @@ These also have corresponding async methods that should be used with [asyncio](h
 
 The **input type** and **output type** varies by component:
 
-| Component | Input Type | Output Type |
-| --- | --- | --- |
-| Prompt | Dictionary | PromptValue |
-| ChatModel | Single string, list of chat messages or a PromptValue | ChatMessage |
-| LLM | Single string, list of chat messages or a PromptValue | String |
-| OutputParser | The output of an LLM or ChatModel | Depends on the parser |
-| Retriever | Single string | List of Documents |
-| Tool | Single string or dictionary, depending on the tool | Depends on the tool |
+| Component    | Input Type                                            | Output Type           |
+|--------------|-------------------------------------------------------|-----------------------|
+| Prompt       | Dictionary                                            | PromptValue           |
+| ChatModel    | Single string, list of chat messages or a PromptValue | ChatMessage           |
+| LLM          | Single string, list of chat messages or a PromptValue | String                |
+| OutputParser | The output of an LLM or ChatModel                     | Depends on the parser |
+| Retriever    | Single string                                         | List of Documents     |
+| Tool         | Single string or dictionary, depending on the tool    | Depends on the tool   |
 
 
 All runnables expose input and output **schemas** to inspect the inputs and outputs:
@@ -236,7 +236,7 @@ This is where information like log-probs and token usage may be stored.
 
 **`tool_calls`**
 
-These represent a decision from an language model to call a tool. They are included as part of an `AIMessage` output.
+These represent a decision from a language model to call a tool. They are included as part of an `AIMessage` output.
 They can be accessed from there with the `.tool_calls` property.
 
 This property returns a list of `ToolCall`s. A `ToolCall` is a dictionary with the following arguments:
@@ -256,6 +256,8 @@ This represents a message with role "tool", which contains the result of calling
 - a `tool_call_id` field which conveys the id of the call to the tool that was called to produce this result.
 - an `artifact` field which can be used to pass along arbitrary artifacts of the tool execution which are useful to track but which should not be sent to the model.
 
+With most chat models, a `ToolMessage` can only appear in the chat history after an `AIMessage` that has a populated `tool_calls` field.
+
 #### (Legacy) FunctionMessage
 
 This is a legacy message type, corresponding to OpenAI's legacy function-calling API. `ToolMessage` should be used instead to correspond to the updated tool-calling API.
@@ -732,10 +734,10 @@ of the object.
 If you're creating a custom chain or runnable, you need to remember to propagate request time
 callbacks to any child objects.
 
-:::important Async in Python<=3.10
+:::important Async in Python&lt;=3.10
 
 Any `RunnableLambda`, a `RunnableGenerator`, or `Tool` that invokes other runnables
-and is running `async` in python<=3.10, will have to propagate callbacks to child
+and is running `async` in python&lt;=3.10, will have to propagate callbacks to child
 objects manually. This is because LangChain cannot automatically propagate
 callbacks to child objects in this case.
 

docs/docs/contributing/code/setup.mdx

@@ -73,9 +73,9 @@ make docker_tests
 
 There are also [integration tests and code-coverage](/docs/contributing/testing/) available.
 
-### Only develop langchain_core or langchain_experimental
+### Only develop langchain_core or langchain_community
 
-If you are only developing `langchain_core` or `langchain_experimental`, you can simply install the dependencies for the respective projects and run tests:
+If you are only developing `langchain_core` or `langchain_community`, you can simply install the dependencies for the respective projects and run tests:
 
 ```bash
 cd libs/core
@@ -86,7 +86,7 @@ make test
 Or:
 
 ```bash
-cd libs/experimental
+cd libs/community
 poetry install --with test
 make test
 ```

docs/docs/how_to/agent_executor.ipynb

@@ -17,7 +17,7 @@
    "source": [
     "# Build an Agent with AgentExecutor (Legacy)\n",
     "\n",
-    ":::{.callout-important}\n",
+    ":::important\n",
     "This section will cover building with the legacy LangChain AgentExecutor. These are fine for getting started, but past a certain point, you will likely want flexibility and control that they do not offer. For working with more advanced agents, we'd recommend checking out [LangGraph Agents](/docs/concepts/#langgraph) or the [migration guide](/docs/how_to/migrate_agent/)\n",
     ":::\n",
     "\n",
@@ -49,7 +49,6 @@
     "\n",
     "To install LangChain run:\n",
     "\n",
-    "```{=mdx}\n",
     "import Tabs from '@theme/Tabs';\n",
     "import TabItem from '@theme/TabItem';\n",
     "import CodeBlock from \"@theme/CodeBlock\";\n",
@@ -63,7 +62,6 @@
     "  </TabItem>\n",
     "</Tabs>\n",
     "\n",
-    "```\n",
     "\n",
     "\n",
     "For more details, see our [Installation guide](/docs/how_to/installation).\n",
@@ -270,11 +268,9 @@
     "\n",
     "Next, let's learn how to use a language model by to call tools. LangChain supports many different language models that you can use interchangably - select the one you want to use below!\n",
     "\n",
-    "```{=mdx}\n",
     "import ChatModelTabs from \"@theme/ChatModelTabs\";\n",
     "\n",
-    "<ChatModelTabs openaiParams={`model=\"gpt-4\"`} />\n",
-    "```"
+    "<ChatModelTabs openaiParams={`model=\"gpt-4\"`} />\n"
    ]
   },
   {
@@ -805,7 +801,7 @@
     "\n",
     "That's a wrap! In this quick start we covered how to create a simple agent. Agents are a complex topic, and there's lot to learn! \n",
     "\n",
-    ":::{.callout-important}\n",
+    ":::important\n",
     "This section covered building with LangChain Agents. LangChain Agents are fine for getting started, but past a certain point you will likely want flexibility and control that they do not offer. For working with more advanced agents, we'd reccommend checking out [LangGraph](/docs/concepts/#langgraph)\n",
     ":::\n",
     "\n",

docs/docs/how_to/callbacks_async.ipynb

@@ -17,11 +17,11 @@
     "If you are planning to use the async APIs, it is recommended to use and extend [`AsyncCallbackHandler`](https://python.langchain.com/api_reference/core/callbacks/langchain_core.callbacks.base.AsyncCallbackHandler.html) to avoid blocking the event.\n",
     "\n",
     "\n",
-    ":::{.callout-warning}\n",
+    ":::warning\n",
     "If you use a sync `CallbackHandler` while using an async method to run your LLM / Chain / Tool / Agent, it will still work. However, under the hood, it will be called with [`run_in_executor`](https://docs.python.org/3/library/asyncio-eventloop.html#asyncio.loop.run_in_executor) which can cause issues if your `CallbackHandler` is not thread-safe.\n",
     ":::\n",
     "\n",
-    ":::{.callout-danger}\n",
+    ":::danger\n",
     "\n",
     "If you're on `python<=3.10`, you need to remember to propagate `config` or `callbacks` when invoking other `runnable` from within a `RunnableLambda`, `RunnableGenerator` or `@tool`. If you do not do this,\n",
     "the callbacks will not be propagated to the child runnables being invoked.\n",

docs/docs/how_to/callbacks_attach.ipynb

@@ -19,7 +19,7 @@
     "\n",
     "If you are composing a chain of runnables and want to reuse callbacks across multiple executions, you can attach callbacks with the [`.with_config()`](https://python.langchain.com/api_reference/core/runnables/langchain_core.runnables.base.Runnable.html#langchain_core.runnables.base.Runnable.with_config) method. This saves you the need to pass callbacks in each time you invoke the chain.\n",
     "\n",
-    ":::{.callout-important}\n",
+    ":::important\n",
     "\n",
     "`with_config()` binds a configuration which will be interpreted as **runtime** configuration. So these callbacks will propagate to all child components.\n",
     ":::\n",

docs/docs/how_to/callbacks_constructor.ipynb

@@ -17,7 +17,7 @@
     "\n",
     "Most LangChain modules allow you to pass `callbacks` directly into the constructor (i.e., initializer). In this case, the callbacks will only be called for that instance (and any nested runs).\n",
     "\n",
-    ":::{.callout-warning}\n",
+    ":::warning\n",
     "Constructor callbacks are scoped only to the object they are defined on. They are **not** inherited by children of the object. This can lead to confusing behavior,\n",
     "and it's generally better to pass callbacks as a run time argument.\n",
     ":::\n",

docs/docs/how_to/callbacks_custom_events.ipynb

@@ -29,7 +29,7 @@
     "| data      | Any  | The data associated with the event. This can be anything, though we suggest making it JSON serializable. |\n",
     "\n",
     "\n",
-    ":::{.callout-important}\n",
+    ":::important\n",
     "* Dispatching custom callback events requires `langchain-core>=0.2.15`.\n",
     "* Custom callback events can only be dispatched from within an existing `Runnable`.\n",
     "* If using `astream_events`, you must use `version='v2'` to see custom events.\n",
@@ -38,9 +38,9 @@
     "\n",
     "\n",
     ":::caution COMPATIBILITY\n",
-    "LangChain cannot automatically propagate configuration, including callbacks necessary for astream_events(), to child runnables if you are running async code in python<=3.10. This is a common reason why you may fail to see events being emitted from custom runnables or tools.\n",
+    "LangChain cannot automatically propagate configuration, including callbacks necessary for astream_events(), to child runnables if you are running async code in python&lt;=3.10. This is a common reason why you may fail to see events being emitted from custom runnables or tools.\n",
     "\n",
-    "If you are running python<=3.10, you will need to manually propagate the `RunnableConfig` object to the child runnable in async environments. For an example of how to manually propagate the config, see the implementation of the `bar` RunnableLambda below.\n",
+    "If you are running python&lt;=3.10, you will need to manually propagate the `RunnableConfig` object to the child runnable in async environments. For an example of how to manually propagate the config, see the implementation of the `bar` RunnableLambda below.\n",
     "\n",
     "If you are running python>=3.11, the `RunnableConfig` will automatically propagate to child runnables in async environment. However, it is still a good idea to propagate the `RunnableConfig` manually if your code may run in other Python versions.\n",
     ":::"
@@ -69,7 +69,7 @@
     "We can use the `async` `adispatch_custom_event` API to emit custom events in an async setting. \n",
     "\n",
     "\n",
-    ":::{.callout-important}\n",
+    ":::important\n",
     "\n",
     "To see custom events via the astream events API, you need to use the newer `v2` API of `astream_events`.\n",
     ":::"
@@ -115,7 +115,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "In python <= 3.10, you must propagate the config manually!"
+    "In python &lt;= 3.10, you must propagate the config manually!"
    ]
   },
   {

docs/docs/how_to/chat_model_caching.ipynb

@@ -28,11 +28,9 @@
    "id": "289b31de",
    "metadata": {},
    "source": [
-    "```{=mdx}\n",
     "import ChatModelTabs from \"@theme/ChatModelTabs\";\n",
     "\n",
-    "<ChatModelTabs customVarName=\"llm\" />\n",
-    "```"
+    "<ChatModelTabs customVarName=\"llm\" />\n"
    ]
   },
   {

docs/docs/how_to/chat_streaming.ipynb

@@ -22,7 +22,7 @@
     "\n",
     "The **default** streaming implementation provides an`Iterator` (or `AsyncIterator` for asynchronous streaming) that yields a single value: the final output from the underlying chat model provider.\n",
     "\n",
-    ":::{.callout-tip}\n",
+    ":::tip\n",
     "\n",
     "The **default** implementation does **not** provide support for token-by-token streaming, but it ensures that the the model can be swapped in for any other model as it supports the same standard interface.\n",
     "\n",

docs/docs/how_to/chat_token_usage_tracking.ipynb

@@ -155,11 +155,9 @@
     "\n",
     "For example, OpenAI will return a message [chunk](https://python.langchain.com/api_reference/core/messages/langchain_core.messages.ai.AIMessageChunk.html) at the end of a stream with token usage information. This behavior is supported by `langchain-openai >= 0.1.9` and can be enabled by setting `stream_usage=True`. This attribute can also be set when `ChatOpenAI` is instantiated.\n",
     "\n",
-    "```{=mdx}\n",
     ":::note\n",
     "By default, the last message chunk in a stream will include a `\"finish_reason\"` in the message's `response_metadata` attribute. If we include token usage in streaming mode, an additional chunk containing usage metadata will be added to the end of the stream, such that `\"finish_reason\"` appears on the second to last message chunk.\n",
-    ":::\n",
-    "```"
+    ":::\n"
    ]
   },
   {

docs/docs/how_to/chatbots_memory.ipynb

@@ -23,6 +23,17 @@
     "\n",
     "We'll go into more detail on a few techniques below!\n",
     "\n",
+    ":::note\n",
+    "\n",
+    "This how-to guide previously built a chatbot using [RunnableWithMessageHistory](https://python.langchain.com/api_reference/core/runnables/langchain_core.runnables.history.RunnableWithMessageHistory.html). You can access this version of the guide in the [v0.2 docs](https://python.langchain.com/v0.2/docs/how_to/chatbots_memory/).\n",
+    "\n",
+    "As of the v0.3 release of LangChain, we recommend that LangChain users take advantage of [LangGraph persistence](https://langchain-ai.github.io/langgraph/concepts/persistence/) to incorporate `memory` into new LangChain applications.\n",
+    "\n",
+    "If your code is already relying on `RunnableWithMessageHistory` or `BaseChatMessageHistory`, you do **not** need to make any changes. We do not plan on deprecating this functionality in the near future as it works for simple chat applications and any code that uses `RunnableWithMessageHistory` will continue to work as expected.\n",
+    "\n",
+    "Please see [How to migrate to LangGraph Memory](/docs/versions/migrating_memory/) for more details.\n",
+    ":::\n",
+    "\n",
     "## Setup\n",
     "\n",
     "You'll need to install a few packages, and have your OpenAI API key set as an environment variable named `OPENAI_API_KEY`:"
@@ -34,32 +45,21 @@
    "metadata": {},
    "outputs": [
     {
-     "name": "stdout",
+     "name": "stdin",
      "output_type": "stream",
      "text": [
-      "\u001b[33mWARNING: You are using pip version 22.0.4; however, version 23.3.2 is available.\n",
-      "You should consider upgrading via the '/Users/jacoblee/.pyenv/versions/3.10.5/bin/python -m pip install --upgrade pip' command.\u001b[0m\u001b[33m\n",
-      "\u001b[0mNote: you may need to restart the kernel to use updated packages.\n"
+      "OpenAI API Key: ········\n"
      ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "True"
-      ]
-     },
-     "execution_count": 1,
-     "metadata": {},
-     "output_type": "execute_result"
     }
    ],
    "source": [
-    "%pip install --upgrade --quiet langchain langchain-openai\n",
+    "%pip install --upgrade --quiet langchain langchain-openai langgraph\n",
     "\n",
-    "# Set env var OPENAI_API_KEY or load from a .env file:\n",
-    "import dotenv\n",
+    "import getpass\n",
+    "import os\n",
     "\n",
-    "dotenv.load_dotenv()"
+    "if not os.environ.get(\"OPENAI_API_KEY\"):\n",
+    "    os.environ[\"OPENAI_API_KEY\"] = getpass.getpass(\"OpenAI API Key:\")"
    ]
   },
   {
@@ -71,13 +71,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [],
    "source": [
     "from langchain_openai import ChatOpenAI\n",
     "\n",
-    "chat = ChatOpenAI(model=\"gpt-4o-mini\")"
+    "model = ChatOpenAI(model=\"gpt-4o-mini\")"
    ]
   },
   {
@@ -98,34 +98,33 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "I said \"J'adore la programmation,\" which means \"I love programming\" in French.\n"
+      "I said, \"I love programming\" in French: \"J'adore la programmation.\"\n"
      ]
     }
    ],
    "source": [
-    "from langchain_core.prompts import ChatPromptTemplate\n",
+    "from langchain_core.messages import AIMessage, HumanMessage, SystemMessage\n",
+    "from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder\n",
     "\n",
     "prompt = ChatPromptTemplate.from_messages(\n",
     "    [\n",
-    "        (\n",
-    "            \"system\",\n",
-    "            \"You are a helpful assistant. Answer all questions to the best of your ability.\",\n",
+    "        SystemMessage(\n",
+    "            content=\"You are a helpful assistant. Answer all questions to the best of your ability.\"\n",
     "        ),\n",
-    "        (\"placeholder\", \"{messages}\"),\n",
+    "        MessagesPlaceholder(variable_name=\"messages\"),\n",
     "    ]\n",
     ")\n",
     "\n",
-    "chain = prompt | chat\n",
+    "chain = prompt | model\n",
     "\n",
     "ai_msg = chain.invoke(\n",
     "    {\n",
     "        \"messages\": [\n",
-    "            (\n",
-    "                \"human\",\n",
-    "                \"Translate this sentence from English to French: I love programming.\",\n",
+    "            HumanMessage(\n",
+    "                content=\"Translate from English to French: I love programming.\"\n",
     "            ),\n",
-    "            (\"ai\", \"J'adore la programmation.\"),\n",
-    "            (\"human\", \"What did you just say?\"),\n",
+    "            AIMessage(content=\"J'adore la programmation.\"),\n",
+    "            HumanMessage(content=\"What did you just say?\"),\n",
     "        ],\n",
     "    }\n",
     ")\n",
@@ -136,51 +135,57 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "We can see that by passing the previous conversation into a chain, it can use it as context to answer questions. This is the basic concept underpinning chatbot memory - the rest of the guide will demonstrate convenient techniques for passing or reformatting messages.\n",
-    "\n",
-    "## Chat history\n",
-    "\n",
-    "It's perfectly fine to store and pass messages directly as an array, but we can use LangChain's built-in [message history class](https://python.langchain.com/api_reference/langchain/index.html#module-langchain.memory) to store and load messages as well. Instances of this class are responsible for storing and loading chat messages from persistent storage. LangChain integrates with many providers - you can see a [list of integrations here](/docs/integrations/memory) - but for this demo we will use an ephemeral demo class.\n",
-    "\n",
-    "Here's an example of the API:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[HumanMessage(content='Translate this sentence from English to French: I love programming.'),\n",
-       " AIMessage(content=\"J'adore la programmation.\")]"
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from langchain_community.chat_message_histories import ChatMessageHistory\n",
-    "\n",
-    "demo_ephemeral_chat_history = ChatMessageHistory()\n",
-    "\n",
-    "demo_ephemeral_chat_history.add_user_message(\n",
-    "    \"Translate this sentence from English to French: I love programming.\"\n",
-    ")\n",
-    "\n",
-    "demo_ephemeral_chat_history.add_ai_message(\"J'adore la programmation.\")\n",
-    "\n",
-    "demo_ephemeral_chat_history.messages"
+    "We can see that by passing the previous conversation into a chain, it can use it as context to answer questions. This is the basic concept underpinning chatbot memory - the rest of the guide will demonstrate convenient techniques for passing or reformatting messages."
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "We can use it directly to store conversation turns for our chain:"
+    "## Automatic history management\n",
+    "\n",
+    "The previous examples pass messages to the chain (and model) explicitly. This is a completely acceptable approach, but it does require external management of new messages. LangChain also provides a way to build applications that have memory using LangGraph's [persistence](https://langchain-ai.github.io/langgraph/concepts/persistence/). You can [enable persistence](https://langchain-ai.github.io/langgraph/how-tos/persistence/) in LangGraph applications by providing a `checkpointer` when compiling the graph."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langgraph.checkpoint.memory import MemorySaver\n",
+    "from langgraph.graph import START, MessagesState, StateGraph\n",
+    "\n",
+    "workflow = StateGraph(state_schema=MessagesState)\n",
+    "\n",
+    "\n",
+    "# Define the function that calls the model\n",
+    "def call_model(state: MessagesState):\n",
+    "    system_prompt = (\n",
+    "        \"You are a helpful assistant. \"\n",
+    "        \"Answer all questions to the best of your ability.\"\n",
+    "    )\n",
+    "    messages = [SystemMessage(content=system_prompt)] + state[\"messages\"]\n",
+    "    response = model.invoke(messages)\n",
+    "    return {\"messages\": response}\n",
+    "\n",
+    "\n",
+    "# Define the node and edge\n",
+    "workflow.add_node(\"model\", call_model)\n",
+    "workflow.add_edge(START, \"model\")\n",
+    "\n",
+    "# Add simple in-memory checkpointer\n",
+    "# highlight-start\n",
+    "memory = MemorySaver()\n",
+    "app = workflow.compile(checkpointer=memory)\n",
+    "# highlight-end"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " We'll pass the latest input to the conversation here and let the LangGraph keep track of the conversation history using the checkpointer:"
    ]
   },
   {
@@ -191,7 +196,8 @@
     {
      "data": {
       "text/plain": [
-       "AIMessage(content='You just asked me to translate the sentence \"I love programming\" from English to French.', response_metadata={'token_usage': {'completion_tokens': 18, 'prompt_tokens': 61, 'total_tokens': 79}, 'model_name': 'gpt-4o-mini', 'system_fingerprint': None, 'finish_reason': 'stop', 'logprobs': None}, id='run-5cbb21c2-9c30-4031-8ea8-bfc497989535-0', usage_metadata={'input_tokens': 61, 'output_tokens': 18, 'total_tokens': 79})"
+       "{'messages': [HumanMessage(content='Translate to French: I love programming.', additional_kwargs={}, response_metadata={}, id='be5e7099-3149-4293-af49-6b36c8ccd71b'),\n",
+       "  AIMessage(content=\"J'aime programmer.\", additional_kwargs={'refusal': None}, response_metadata={'token_usage': {'completion_tokens': 4, 'prompt_tokens': 35, 'total_tokens': 39, 'completion_tokens_details': {'reasoning_tokens': 0}}, 'model_name': 'gpt-4o-mini-2024-07-18', 'system_fingerprint': 'fp_e9627b5346', 'finish_reason': 'stop', 'logprobs': None}, id='run-8a753d7a-b97b-4d01-a661-626be6f41b38-0', usage_metadata={'input_tokens': 35, 'output_tokens': 4, 'total_tokens': 39})]}"
       ]
      },
      "execution_count": 5,
@@ -200,159 +206,35 @@
     }
    ],
    "source": [
-    "demo_ephemeral_chat_history = ChatMessageHistory()\n",
-    "\n",
-    "input1 = \"Translate this sentence from English to French: I love programming.\"\n",
-    "\n",
-    "demo_ephemeral_chat_history.add_user_message(input1)\n",
-    "\n",
-    "response = chain.invoke(\n",
-    "    {\n",
-    "        \"messages\": demo_ephemeral_chat_history.messages,\n",
-    "    }\n",
-    ")\n",
-    "\n",
-    "demo_ephemeral_chat_history.add_ai_message(response)\n",
-    "\n",
-    "input2 = \"What did I just ask you?\"\n",
-    "\n",
-    "demo_ephemeral_chat_history.add_user_message(input2)\n",
-    "\n",
-    "chain.invoke(\n",
-    "    {\n",
-    "        \"messages\": demo_ephemeral_chat_history.messages,\n",
-    "    }\n",
+    "app.invoke(\n",
+    "    {\"messages\": [HumanMessage(content=\"Translate to French: I love programming.\")]},\n",
+    "    config={\"configurable\": {\"thread_id\": \"1\"}},\n",
     ")"
    ]
   },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Automatic history management\n",
-    "\n",
-    "The previous examples pass messages to the chain explicitly. This is a completely acceptable approach, but it does require external management of new messages. LangChain also includes an wrapper for LCEL chains that can handle this process automatically called `RunnableWithMessageHistory`.\n",
-    "\n",
-    "To show how it works, let's slightly modify the above prompt to take a final `input` variable that populates a `HumanMessage` template after the chat history. This means that we will expect a `chat_history` parameter that contains all messages BEFORE the current messages instead of all messages:"
-   ]
-  },
   {
    "cell_type": "code",
    "execution_count": 6,
    "metadata": {},
-   "outputs": [],
-   "source": [
-    "prompt = ChatPromptTemplate.from_messages(\n",
-    "    [\n",
-    "        (\n",
-    "            \"system\",\n",
-    "            \"You are a helpful assistant. Answer all questions to the best of your ability.\",\n",
-    "        ),\n",
-    "        (\"placeholder\", \"{chat_history}\"),\n",
-    "        (\"human\", \"{input}\"),\n",
-    "    ]\n",
-    ")\n",
-    "\n",
-    "chain = prompt | chat"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    " We'll pass the latest input to the conversation here and let the `RunnableWithMessageHistory` class wrap our chain and do the work of appending that `input` variable to the chat history.\n",
-    " \n",
-    " Next, let's declare our wrapped chain:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from langchain_core.runnables.history import RunnableWithMessageHistory\n",
-    "\n",
-    "demo_ephemeral_chat_history_for_chain = ChatMessageHistory()\n",
-    "\n",
-    "chain_with_message_history = RunnableWithMessageHistory(\n",
-    "    chain,\n",
-    "    lambda session_id: demo_ephemeral_chat_history_for_chain,\n",
-    "    input_messages_key=\"input\",\n",
-    "    history_messages_key=\"chat_history\",\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "This class takes a few parameters in addition to the chain that we want to wrap:\n",
-    "\n",
-    "- A factory function that returns a message history for a given session id. This allows your chain to handle multiple users at once by loading different messages for different conversations.\n",
-    "- An `input_messages_key` that specifies which part of the input should be tracked and stored in the chat history. In this example, we want to track the string passed in as `input`.\n",
-    "- A `history_messages_key` that specifies what the previous messages should be injected into the prompt as. Our prompt has a `MessagesPlaceholder` named `chat_history`, so we specify this property to match.\n",
-    "- (For chains with multiple outputs) an `output_messages_key` which specifies which output to store as history. This is the inverse of `input_messages_key`.\n",
-    "\n",
-    "We can invoke this new chain as normal, with an additional `configurable` field that specifies the particular `session_id` to pass to the factory function. This is unused for the demo, but in real-world chains, you'll want to return a chat history corresponding to the passed session:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
    "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Parent run dc4e2f79-4bcd-4a36-9506-55ace9040588 not found for run 34b5773e-3ced-46a6-8daf-4d464c15c940. Treating as a root run.\n"
-     ]
-    },
     {
      "data": {
       "text/plain": [
-       "AIMessage(content='\"J\\'adore la programmation.\"', response_metadata={'token_usage': {'completion_tokens': 9, 'prompt_tokens': 39, 'total_tokens': 48}, 'model_name': 'gpt-4o-mini', 'system_fingerprint': None, 'finish_reason': 'stop', 'logprobs': None}, id='run-648b0822-b0bb-47a2-8e7d-7d34744be8f2-0', usage_metadata={'input_tokens': 39, 'output_tokens': 9, 'total_tokens': 48})"
+       "{'messages': [HumanMessage(content='Translate to French: I love programming.', additional_kwargs={}, response_metadata={}, id='be5e7099-3149-4293-af49-6b36c8ccd71b'),\n",
+       "  AIMessage(content=\"J'aime programmer.\", additional_kwargs={'refusal': None}, response_metadata={'token_usage': {'completion_tokens': 4, 'prompt_tokens': 35, 'total_tokens': 39, 'completion_tokens_details': {'reasoning_tokens': 0}}, 'model_name': 'gpt-4o-mini-2024-07-18', 'system_fingerprint': 'fp_e9627b5346', 'finish_reason': 'stop', 'logprobs': None}, id='run-8a753d7a-b97b-4d01-a661-626be6f41b38-0', usage_metadata={'input_tokens': 35, 'output_tokens': 4, 'total_tokens': 39}),\n",
+       "  HumanMessage(content='What did I just ask you?', additional_kwargs={}, response_metadata={}, id='c667529b-7c41-4cc0-9326-0af47328b816'),\n",
+       "  AIMessage(content='You asked me to translate \"I love programming\" into French.', additional_kwargs={'refusal': None}, response_metadata={'token_usage': {'completion_tokens': 13, 'prompt_tokens': 54, 'total_tokens': 67, 'completion_tokens_details': {'reasoning_tokens': 0}}, 'model_name': 'gpt-4o-mini-2024-07-18', 'system_fingerprint': 'fp_1bb46167f9', 'finish_reason': 'stop', 'logprobs': None}, id='run-134a7ea0-d3a4-4923-bd58-25e5a43f6a1f-0', usage_metadata={'input_tokens': 54, 'output_tokens': 13, 'total_tokens': 67})]}"
       ]
      },
-     "execution_count": 8,
+     "execution_count": 6,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "chain_with_message_history.invoke(\n",
-    "    {\"input\": \"Translate this sentence from English to French: I love programming.\"},\n",
-    "    {\"configurable\": {\"session_id\": \"unused\"}},\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Parent run cc14b9d8-c59e-40db-a523-d6ab3fc2fa4f not found for run 5b75e25c-131e-46ee-9982-68569db04330. Treating as a root run.\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "AIMessage(content='You asked me to translate the sentence \"I love programming\" from English to French.', response_metadata={'token_usage': {'completion_tokens': 17, 'prompt_tokens': 63, 'total_tokens': 80}, 'model_name': 'gpt-4o-mini', 'system_fingerprint': None, 'finish_reason': 'stop', 'logprobs': None}, id='run-5950435c-1dc2-43a6-836f-f989fd62c95e-0', usage_metadata={'input_tokens': 63, 'output_tokens': 17, 'total_tokens': 80})"
-      ]
-     },
-     "execution_count": 9,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "chain_with_message_history.invoke(\n",
-    "    {\"input\": \"What did I just ask you?\"}, {\"configurable\": {\"session_id\": \"unused\"}}\n",
+    "app.invoke(\n",
+    "    {\"messages\": [HumanMessage(content=\"What did I just ask you?\")]},\n",
+    "    config={\"configurable\": {\"thread_id\": \"1\"}},\n",
     ")"
    ]
   },
@@ -366,80 +248,44 @@
     "\n",
     "### Trimming messages\n",
     "\n",
-    "LLMs and chat models have limited context windows, and even if you're not directly hitting limits, you may want to limit the amount of distraction the model has to deal with. One solution is trim the historic messages before passing them to the model. Let's use an example history with some preloaded messages:"
+    "LLMs and chat models have limited context windows, and even if you're not directly hitting limits, you may want to limit the amount of distraction the model has to deal with. One solution is trim the history messages before passing them to the model. Let's use an example history with the `app` we declared above:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "[HumanMessage(content=\"Hey there! I'm Nemo.\"),\n",
-       " AIMessage(content='Hello!'),\n",
-       " HumanMessage(content='How are you today?'),\n",
-       " AIMessage(content='Fine thanks!')]"
+       "{'messages': [HumanMessage(content=\"Hey there! I'm Nemo.\", additional_kwargs={}, response_metadata={}, id='6b4cab70-ce18-49b0-bb06-267bde44e037'),\n",
+       "  AIMessage(content='Hello!', additional_kwargs={}, response_metadata={}, id='ba3714f4-8876-440b-a651-efdcab2fcb4c'),\n",
+       "  HumanMessage(content='How are you today?', additional_kwargs={}, response_metadata={}, id='08d032c0-1577-4862-a3f2-5c1b90687e21'),\n",
+       "  AIMessage(content='Fine thanks!', additional_kwargs={}, response_metadata={}, id='21790e16-db05-4537-9a6b-ecad0fcec436'),\n",
+       "  HumanMessage(content=\"What's my name?\", additional_kwargs={}, response_metadata={}, id='c933eca3-5fd8-4651-af16-20fe2d49c216'),\n",
+       "  AIMessage(content='Your name is Nemo.', additional_kwargs={'refusal': None}, response_metadata={'token_usage': {'completion_tokens': 5, 'prompt_tokens': 63, 'total_tokens': 68, 'completion_tokens_details': {'reasoning_tokens': 0}}, 'model_name': 'gpt-4o-mini-2024-07-18', 'system_fingerprint': 'fp_1bb46167f9', 'finish_reason': 'stop', 'logprobs': None}, id='run-a0b21acc-9dbb-4fb6-a953-392020f37d88-0', usage_metadata={'input_tokens': 63, 'output_tokens': 5, 'total_tokens': 68})]}"
       ]
      },
-     "execution_count": 21,
+     "execution_count": 7,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "demo_ephemeral_chat_history = ChatMessageHistory()\n",
+    "demo_ephemeral_chat_history = [\n",
+    "    HumanMessage(content=\"Hey there! I'm Nemo.\"),\n",
+    "    AIMessage(content=\"Hello!\"),\n",
+    "    HumanMessage(content=\"How are you today?\"),\n",
+    "    AIMessage(content=\"Fine thanks!\"),\n",
+    "]\n",
     "\n",
-    "demo_ephemeral_chat_history.add_user_message(\"Hey there! I'm Nemo.\")\n",
-    "demo_ephemeral_chat_history.add_ai_message(\"Hello!\")\n",
-    "demo_ephemeral_chat_history.add_user_message(\"How are you today?\")\n",
-    "demo_ephemeral_chat_history.add_ai_message(\"Fine thanks!\")\n",
-    "\n",
-    "demo_ephemeral_chat_history.messages"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Let's use this message history with the `RunnableWithMessageHistory` chain we declared above:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 22,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Parent run 7ff2d8ec-65e2-4f67-8961-e498e2c4a591 not found for run 3881e990-6596-4326-84f6-2b76949e0657. Treating as a root run.\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "AIMessage(content='Your name is Nemo.', response_metadata={'token_usage': {'completion_tokens': 6, 'prompt_tokens': 66, 'total_tokens': 72}, 'model_name': 'gpt-4o-mini', 'system_fingerprint': None, 'finish_reason': 'stop', 'logprobs': None}, id='run-f8aabef8-631a-4238-a39b-701e881fbe47-0', usage_metadata={'input_tokens': 66, 'output_tokens': 6, 'total_tokens': 72})"
-      ]
-     },
-     "execution_count": 22,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "chain_with_message_history = RunnableWithMessageHistory(\n",
-    "    chain,\n",
-    "    lambda session_id: demo_ephemeral_chat_history,\n",
-    "    input_messages_key=\"input\",\n",
-    "    history_messages_key=\"chat_history\",\n",
-    ")\n",
-    "\n",
-    "chain_with_message_history.invoke(\n",
-    "    {\"input\": \"What's my name?\"},\n",
-    "    {\"configurable\": {\"session_id\": \"unused\"}},\n",
+    "app.invoke(\n",
+    "    {\n",
+    "        \"messages\": demo_ephemeral_chat_history\n",
+    "        + [HumanMessage(content=\"What's my name?\")]\n",
+    "    },\n",
+    "    config={\"configurable\": {\"thread_id\": \"2\"}},\n",
     ")"
    ]
   },
@@ -447,35 +293,88 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "We can see the chain remembers the preloaded name.\n",
+    "We can see the app remembers the preloaded name.\n",
     "\n",
-    "But let's say we have a very small context window, and we want to trim the number of messages passed to the chain to only the 2 most recent ones. We can use the built in [trim_messages](/docs/how_to/trim_messages/) util to trim messages based on their token count before they reach our prompt. In this case we'll count each message as 1 \"token\" and keep only the last two messages:"
+    "But let's say we have a very small context window, and we want to trim the number of messages passed to the model to only the 2 most recent ones. We can use the built in [trim_messages](/docs/how_to/trim_messages/) util to trim messages based on their token count before they reach our prompt. In this case we'll count each message as 1 \"token\" and keep only the last two messages:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [],
    "source": [
-    "from operator import itemgetter\n",
-    "\n",
     "from langchain_core.messages import trim_messages\n",
-    "from langchain_core.runnables import RunnablePassthrough\n",
+    "from langgraph.checkpoint.memory import MemorySaver\n",
+    "from langgraph.graph import START, MessagesState, StateGraph\n",
     "\n",
+    "# Define trimmer\n",
+    "# highlight-start\n",
+    "# count each message as 1 \"token\" (token_counter=len) and keep only the last two messages\n",
     "trimmer = trim_messages(strategy=\"last\", max_tokens=2, token_counter=len)\n",
+    "# highlight-end\n",
     "\n",
-    "chain_with_trimming = (\n",
-    "    RunnablePassthrough.assign(chat_history=itemgetter(\"chat_history\") | trimmer)\n",
-    "    | prompt\n",
-    "    | chat\n",
-    ")\n",
+    "workflow = StateGraph(state_schema=MessagesState)\n",
     "\n",
-    "chain_with_trimmed_history = RunnableWithMessageHistory(\n",
-    "    chain_with_trimming,\n",
-    "    lambda session_id: demo_ephemeral_chat_history,\n",
-    "    input_messages_key=\"input\",\n",
-    "    history_messages_key=\"chat_history\",\n",
+    "\n",
+    "# Define the function that calls the model\n",
+    "def call_model(state: MessagesState):\n",
+    "    # highlight-start\n",
+    "    trimmed_messages = trimmer.invoke(state[\"messages\"])\n",
+    "    system_prompt = (\n",
+    "        \"You are a helpful assistant. \"\n",
+    "        \"Answer all questions to the best of your ability.\"\n",
+    "    )\n",
+    "    messages = [SystemMessage(content=system_prompt)] + trimmed_messages\n",
+    "    # highlight-end\n",
+    "    response = model.invoke(messages)\n",
+    "    return {\"messages\": response}\n",
+    "\n",
+    "\n",
+    "# Define the node and edge\n",
+    "workflow.add_node(\"model\", call_model)\n",
+    "workflow.add_edge(START, \"model\")\n",
+    "\n",
+    "# Add simple in-memory checkpointer\n",
+    "memory = MemorySaver()\n",
+    "app = workflow.compile(checkpointer=memory)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Let's call this new app and check the response"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'messages': [HumanMessage(content=\"Hey there! I'm Nemo.\", additional_kwargs={}, response_metadata={}, id='6b4cab70-ce18-49b0-bb06-267bde44e037'),\n",
+       "  AIMessage(content='Hello!', additional_kwargs={}, response_metadata={}, id='ba3714f4-8876-440b-a651-efdcab2fcb4c'),\n",
+       "  HumanMessage(content='How are you today?', additional_kwargs={}, response_metadata={}, id='08d032c0-1577-4862-a3f2-5c1b90687e21'),\n",
+       "  AIMessage(content='Fine thanks!', additional_kwargs={}, response_metadata={}, id='21790e16-db05-4537-9a6b-ecad0fcec436'),\n",
+       "  HumanMessage(content='What is my name?', additional_kwargs={}, response_metadata={}, id='a22ab7c5-8617-4821-b3e9-a9e7dca1ff78'),\n",
+       "  AIMessage(content=\"I'm sorry, but I don't have access to personal information about you unless you share it with me. How can I assist you today?\", additional_kwargs={'refusal': None}, response_metadata={'token_usage': {'completion_tokens': 27, 'prompt_tokens': 39, 'total_tokens': 66, 'completion_tokens_details': {'reasoning_tokens': 0}}, 'model_name': 'gpt-4o-mini-2024-07-18', 'system_fingerprint': 'fp_1bb46167f9', 'finish_reason': 'stop', 'logprobs': None}, id='run-f7b32d72-9f57-4705-be7e-43bf1c3d293b-0', usage_metadata={'input_tokens': 39, 'output_tokens': 27, 'total_tokens': 66})]}"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "app.invoke(\n",
+    "    {\n",
+    "        \"messages\": demo_ephemeral_chat_history\n",
+    "        + [HumanMessage(content=\"What is my name?\")]\n",
+    "    },\n",
+    "    config={\"configurable\": {\"thread_id\": \"3\"}},\n",
     ")"
    ]
   },
@@ -483,101 +382,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Let's call this new chain and check the messages afterwards:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 24,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Parent run 775cde65-8d22-4c44-80bb-f0b9811c32ca not found for run 5cf71d0e-4663-41cd-8dbe-e9752689cfac. Treating as a root run.\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "AIMessage(content='P. Sherman is a fictional character from the animated movie \"Finding Nemo\" who lives at 42 Wallaby Way, Sydney.', response_metadata={'token_usage': {'completion_tokens': 27, 'prompt_tokens': 53, 'total_tokens': 80}, 'model_name': 'gpt-4o-mini', 'system_fingerprint': None, 'finish_reason': 'stop', 'logprobs': None}, id='run-5642ef3a-fdbe-43cf-a575-d1785976a1b9-0', usage_metadata={'input_tokens': 53, 'output_tokens': 27, 'total_tokens': 80})"
-      ]
-     },
-     "execution_count": 24,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "chain_with_trimmed_history.invoke(\n",
-    "    {\"input\": \"Where does P. Sherman live?\"},\n",
-    "    {\"configurable\": {\"session_id\": \"unused\"}},\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 25,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[HumanMessage(content=\"Hey there! I'm Nemo.\"),\n",
-       " AIMessage(content='Hello!'),\n",
-       " HumanMessage(content='How are you today?'),\n",
-       " AIMessage(content='Fine thanks!'),\n",
-       " HumanMessage(content=\"What's my name?\"),\n",
-       " AIMessage(content='Your name is Nemo.', response_metadata={'token_usage': {'completion_tokens': 6, 'prompt_tokens': 66, 'total_tokens': 72}, 'model_name': 'gpt-4o-mini', 'system_fingerprint': None, 'finish_reason': 'stop', 'logprobs': None}, id='run-f8aabef8-631a-4238-a39b-701e881fbe47-0', usage_metadata={'input_tokens': 66, 'output_tokens': 6, 'total_tokens': 72}),\n",
-       " HumanMessage(content='Where does P. Sherman live?'),\n",
-       " AIMessage(content='P. Sherman is a fictional character from the animated movie \"Finding Nemo\" who lives at 42 Wallaby Way, Sydney.', response_metadata={'token_usage': {'completion_tokens': 27, 'prompt_tokens': 53, 'total_tokens': 80}, 'model_name': 'gpt-4o-mini', 'system_fingerprint': None, 'finish_reason': 'stop', 'logprobs': None}, id='run-5642ef3a-fdbe-43cf-a575-d1785976a1b9-0', usage_metadata={'input_tokens': 53, 'output_tokens': 27, 'total_tokens': 80})]"
-      ]
-     },
-     "execution_count": 25,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "demo_ephemeral_chat_history.messages"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "And we can see that our history has removed the two oldest messages while still adding the most recent conversation at the end. The next time the chain is called, `trim_messages` will be called again, and only the two most recent messages will be passed to the model. In this case, this means that the model will forget the name we gave it the next time we invoke it:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 27,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Parent run fde7123f-6fd3-421a-a3fc-2fb37dead119 not found for run 061a4563-2394-470d-a3ed-9bf1388ca431. Treating as a root run.\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "AIMessage(content=\"I'm sorry, but I don't have access to your personal information, so I don't know your name. How else may I assist you today?\", response_metadata={'token_usage': {'completion_tokens': 31, 'prompt_tokens': 74, 'total_tokens': 105}, 'model_name': 'gpt-4o-mini', 'system_fingerprint': None, 'finish_reason': 'stop', 'logprobs': None}, id='run-0ab03495-1f7c-4151-9070-56d2d1c565ff-0', usage_metadata={'input_tokens': 74, 'output_tokens': 31, 'total_tokens': 105})"
-      ]
-     },
-     "execution_count": 27,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "chain_with_trimmed_history.invoke(\n",
-    "    {\"input\": \"What is my name?\"},\n",
-    "    {\"configurable\": {\"session_id\": \"unused\"}},\n",
-    ")"
+    "We can see that `trim_messages` was called and only the two most recent messages will be passed to the model. In this case, this means that the model forgot the name we gave it."
    ]
   },
   {
@@ -593,114 +398,84 @@
    "source": [
     "### Summary memory\n",
     "\n",
-    "We can use this same pattern in other ways too. For example, we could use an additional LLM call to generate a summary of the conversation before calling our chain. Let's recreate our chat history and chatbot chain:"
+    "We can use this same pattern in other ways too. For example, we could use an additional LLM call to generate a summary of the conversation before calling our app. Let's recreate our chat history:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 10,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[HumanMessage(content=\"Hey there! I'm Nemo.\"),\n",
-       " AIMessage(content='Hello!'),\n",
-       " HumanMessage(content='How are you today?'),\n",
-       " AIMessage(content='Fine thanks!')]"
-      ]
-     },
-     "execution_count": 17,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
-    "demo_ephemeral_chat_history = ChatMessageHistory()\n",
-    "\n",
-    "demo_ephemeral_chat_history.add_user_message(\"Hey there! I'm Nemo.\")\n",
-    "demo_ephemeral_chat_history.add_ai_message(\"Hello!\")\n",
-    "demo_ephemeral_chat_history.add_user_message(\"How are you today?\")\n",
-    "demo_ephemeral_chat_history.add_ai_message(\"Fine thanks!\")\n",
-    "\n",
-    "demo_ephemeral_chat_history.messages"
+    "demo_ephemeral_chat_history = [\n",
+    "    HumanMessage(content=\"Hey there! I'm Nemo.\"),\n",
+    "    AIMessage(content=\"Hello!\"),\n",
+    "    HumanMessage(content=\"How are you today?\"),\n",
+    "    AIMessage(content=\"Fine thanks!\"),\n",
+    "]"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "We'll slightly modify the prompt to make the LLM aware that will receive a condensed summary instead of a chat history:"
+    "And now, let's update the model-calling function to distill previous interactions into a summary:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [],
    "source": [
-    "prompt = ChatPromptTemplate.from_messages(\n",
-    "    [\n",
-    "        (\n",
-    "            \"system\",\n",
-    "            \"You are a helpful assistant. Answer all questions to the best of your ability. The provided chat history includes facts about the user you are speaking with.\",\n",
-    "        ),\n",
-    "        (\"placeholder\", \"{chat_history}\"),\n",
-    "        (\"user\", \"{input}\"),\n",
-    "    ]\n",
-    ")\n",
+    "from langchain_core.messages import HumanMessage, RemoveMessage\n",
+    "from langgraph.checkpoint.memory import MemorySaver\n",
+    "from langgraph.graph import START, MessagesState, StateGraph\n",
     "\n",
-    "chain = prompt | chat\n",
+    "workflow = StateGraph(state_schema=MessagesState)\n",
     "\n",
-    "chain_with_message_history = RunnableWithMessageHistory(\n",
-    "    chain,\n",
-    "    lambda session_id: demo_ephemeral_chat_history,\n",
-    "    input_messages_key=\"input\",\n",
-    "    history_messages_key=\"chat_history\",\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "And now, let's create a function that will distill previous interactions into a summary. We can add this one to the front of the chain too:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def summarize_messages(chain_input):\n",
-    "    stored_messages = demo_ephemeral_chat_history.messages\n",
-    "    if len(stored_messages) == 0:\n",
-    "        return False\n",
-    "    summarization_prompt = ChatPromptTemplate.from_messages(\n",
-    "        [\n",
-    "            (\"placeholder\", \"{chat_history}\"),\n",
-    "            (\n",
-    "                \"user\",\n",
-    "                \"Distill the above chat messages into a single summary message. Include as many specific details as you can.\",\n",
-    "            ),\n",
-    "        ]\n",
+    "\n",
+    "# Define the function that calls the model\n",
+    "def call_model(state: MessagesState):\n",
+    "    system_prompt = (\n",
+    "        \"You are a helpful assistant. \"\n",
+    "        \"Answer all questions to the best of your ability. \"\n",
+    "        \"The provided chat history includes a summary of the earlier conversation.\"\n",
     "    )\n",
-    "    summarization_chain = summarization_prompt | chat\n",
+    "    system_message = SystemMessage(content=system_prompt)\n",
+    "    message_history = state[\"messages\"][:-1]  # exclude the most recent user input\n",
+    "    # Summarize the messages if the chat history reaches a certain size\n",
+    "    if len(message_history) >= 4:\n",
+    "        last_human_message = state[\"messages\"][-1]\n",
+    "        # Invoke the model to generate conversation summary\n",
+    "        summary_prompt = (\n",
+    "            \"Distill the above chat messages into a single summary message. \"\n",
+    "            \"Include as many specific details as you can.\"\n",
+    "        )\n",
+    "        summary_message = model.invoke(\n",
+    "            message_history + [HumanMessage(content=summary_prompt)]\n",
+    "        )\n",
     "\n",
-    "    summary_message = summarization_chain.invoke({\"chat_history\": stored_messages})\n",
+    "        # Delete messages that we no longer want to show up\n",
+    "        delete_messages = [RemoveMessage(id=m.id) for m in state[\"messages\"]]\n",
+    "        # Re-add user message\n",
+    "        human_message = HumanMessage(content=last_human_message.content)\n",
+    "        # Call the model with summary & response\n",
+    "        response = model.invoke([system_message, summary_message, human_message])\n",
+    "        message_updates = [summary_message, human_message, response] + delete_messages\n",
+    "    else:\n",
+    "        message_updates = model.invoke([system_message] + state[\"messages\"])\n",
     "\n",
-    "    demo_ephemeral_chat_history.clear()\n",
-    "\n",
-    "    demo_ephemeral_chat_history.add_message(summary_message)\n",
-    "\n",
-    "    return True\n",
+    "    return {\"messages\": message_updates}\n",
     "\n",
     "\n",
-    "chain_with_summarization = (\n",
-    "    RunnablePassthrough.assign(messages_summarized=summarize_messages)\n",
-    "    | chain_with_message_history\n",
-    ")"
+    "# Define the node and edge\n",
+    "workflow.add_node(\"model\", call_model)\n",
+    "workflow.add_edge(START, \"model\")\n",
+    "\n",
+    "# Add simple in-memory checkpointer\n",
+    "memory = MemorySaver()\n",
+    "app = workflow.compile(checkpointer=memory)"
    ]
   },
   {
@@ -712,54 +487,37 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "AIMessage(content='You introduced yourself as Nemo. How can I assist you today, Nemo?')"
+       "{'messages': [AIMessage(content=\"Nemo greeted me, and I responded positively, indicating that I'm doing well.\", additional_kwargs={'refusal': None}, response_metadata={'token_usage': {'completion_tokens': 16, 'prompt_tokens': 60, 'total_tokens': 76, 'completion_tokens_details': {'reasoning_tokens': 0}}, 'model_name': 'gpt-4o-mini-2024-07-18', 'system_fingerprint': 'fp_1bb46167f9', 'finish_reason': 'stop', 'logprobs': None}, id='run-ee42f98d-907d-4bad-8f16-af2db789701d-0', usage_metadata={'input_tokens': 60, 'output_tokens': 16, 'total_tokens': 76}),\n",
+       "  HumanMessage(content='What did I say my name was?', additional_kwargs={}, response_metadata={}, id='788555ea-5b1f-4c29-a2f2-a92f15d147be'),\n",
+       "  AIMessage(content='You mentioned that your name is Nemo.', additional_kwargs={'refusal': None}, response_metadata={'token_usage': {'completion_tokens': 8, 'prompt_tokens': 67, 'total_tokens': 75, 'completion_tokens_details': {'reasoning_tokens': 0}}, 'model_name': 'gpt-4o-mini-2024-07-18', 'system_fingerprint': 'fp_1bb46167f9', 'finish_reason': 'stop', 'logprobs': None}, id='run-099a43bd-a284-4969-bb6f-0be486614cd8-0', usage_metadata={'input_tokens': 67, 'output_tokens': 8, 'total_tokens': 75})]}"
       ]
      },
-     "execution_count": 20,
+     "execution_count": 12,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "chain_with_summarization.invoke(\n",
-    "    {\"input\": \"What did I say my name was?\"},\n",
-    "    {\"configurable\": {\"session_id\": \"unused\"}},\n",
+    "app.invoke(\n",
+    "    {\n",
+    "        \"messages\": demo_ephemeral_chat_history\n",
+    "        + [HumanMessage(\"What did I say my name was?\")]\n",
+    "    },\n",
+    "    config={\"configurable\": {\"thread_id\": \"4\"}},\n",
     ")"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[AIMessage(content='The conversation is between Nemo and an AI. Nemo introduces himself and the AI responds with a greeting. Nemo then asks the AI how it is doing, and the AI responds that it is fine.'),\n",
-       " HumanMessage(content='What did I say my name was?'),\n",
-       " AIMessage(content='You introduced yourself as Nemo. How can I assist you today, Nemo?')]"
-      ]
-     },
-     "execution_count": 21,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "demo_ephemeral_chat_history.messages"
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Note that invoking the chain again will generate another summary generated from the initial summary plus new messages and so on. You could also design a hybrid approach where a certain number of messages are retained in chat history while others are summarized."
+    "Note that invoking the app again will keep accumulating the history until it reaches the specified number of messages (four in our case). At that point we will generate another summary generated from the initial summary plus new messages and so on."
    ]
   }
  ],
@@ -779,7 +537,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.9"
+   "version": "3.11.4"
   }
  },
  "nbformat": 4,

docs/docs/how_to/convert_runnable_to_tool.ipynb

@@ -266,11 +266,9 @@
     "\n",
     "We first instantiate a chat model that supports [tool calling](/docs/how_to/tool_calling/):\n",
     "\n",
-    "```{=mdx}\n",
     "import ChatModelTabs from \"@theme/ChatModelTabs\";\n",
     "\n",
-    "<ChatModelTabs customVarName=\"llm\" />\n",
-    "```"
+    "<ChatModelTabs customVarName=\"llm\" />\n"
    ]
   },
   {

docs/docs/how_to/custom_chat_model.ipynb

@@ -39,7 +39,7 @@
     "| `AIMessageChunk` / `HumanMessageChunk` / ... | Chunk variant of each type of message. |\n",
     "\n",
     "\n",
-    "::: {.callout-note}\n",
+    ":::note\n",
     "`ToolMessage` and `FunctionMessage` closely follow OpenAI's `function` and `tool` roles.\n",
     "\n",
     "This is a rapidly developing field and as more models add function calling capabilities. Expect that there will be additions to this schema.\n",
@@ -145,7 +145,7 @@
     "| `_astream`                         | Use to implement async version of `_stream`.                      | Optional           |\n",
     "\n",
     "\n",
-    ":::{.callout-tip}\n",
+    ":::tip\n",
     "The `_astream` implementation uses `run_in_executor` to launch the sync `_stream` in a separate thread if `_stream` is implemented, otherwise it fallsback to use `_agenerate`.\n",
     "\n",
     "You can use this trick if you want to reuse the `_stream` implementation, but if you're able to implement code that's natively async that's a better solution since that code will run with less overhead.\n",