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3246caf831
Adds `github.repository_owner == 'langchain-ai'` guards to 14 GitHub
Actions workflows that were missing them. Without these guards,
write-capable automation (issue/PR labeling, closing, commenting, PR
reopening, release publishing) and CI jobs fire on forks — causing
unwanted mutations, wasted runner minutes, and failed GitHub App token
generation on repos that don't have the required secrets.
Also removes `v03_api_doc_build.yml`, which pushed built docs to
`langchain-ai/langchain-api-docs-html` via `TOKEN_GITHUB_API_DOCS_HTML`
— a secret that isn't set on this repo. The workflow was dead code with
no consumers.
---
## What changed
**Write-capable workflows now guarded** (8 files):
- `auto-label-by-package` — was adding/removing issue labels on forks
- `close_unchecked_issues` — was closing issues and posting comments on
forks via GitHub App token
- `tag-external-issues` — both `tag-external` and `backfill` jobs were
generating App tokens and labeling issues on forks
- `reopen_on_assignment` — was reopening PRs and re-running workflows on
forks
- `require_issue_link` — was closing PRs, creating labels, posting
comments, and canceling workflow runs on forks
- `remove_waiting_on_author` — was removing labels on fork issues/PRs
- `pr_labeler` — was generating App tokens and adding/removing PR labels
on forks
- `pr_labeler_backfill` — same, on manual trigger from a fork
**Read-only CI workflows now guarded** (5 files):
- `check_diffs` — `build` and `check-release-options` jobs (downstream
jobs inherit the skip)
- `codspeed` — `build` job
- `check_agents_sync`, `check_versions`, `check_release_deps`
**Release workflow guarded** (1 file):
- `_release` — `build` job (all downstream jobs chain via `needs`, so
they inherit the skip). Previously relied only on `environment: Release`
approval and a `github.ref` check.
**Removed**:
- `v03_api_doc_build.yml` — dead workflow depending on unset
`TOKEN_GITHUB_API_DOCS_HTML` secret
## What was already guarded
`block_fork_main_prs`, `bump_uv_pin`, `check_extras_sync`,
`integration_tests`, `pr_lint_trailer`, `refresh_model_profiles` already
had the guard. `pr_lint` (read-only, `pull-requests: read`) and the
`_*.yml` reusable workflows (only run when called by a guarded parent)
were intentionally left unguarded.
## Release note
- GitHub Actions workflows now skip execution on forks via
`repository_owner == 'langchain-ai'` guards, preventing unwanted
issue/PR automation and CI runs outside the canonical repo.
- Removed the dead `v03_api_doc_build` workflow that depended on an
unset secret.
---
🤖 Generated with AI-agent involvement.
The agent engineering platform.
LangChain is a framework for building agents and LLM-powered applications. It helps you chain together interoperable components and third-party integrations to simplify AI application development — all while future-proofing decisions as the underlying technology evolves.
Tip
Just getting started? Check out Deep Agents — a higher-level package built on LangChain for agents that have built-in capabilites for common usage patterns such as planning, subagents, file system usage, and more.
Quickstart
uv add langchain
from langchain.chat_models import init_chat_model
model = init_chat_model("openai:gpt-5.5")
result = model.invoke("Hello, world!")
If you're looking for more advanced customization or agent orchestration, check out LangGraph, our framework for building controllable agent workflows.
For an equivalent JS/TS library, check out LangChain.js.
Tip
For developing, debugging, and deploying AI agents and LLM applications, see LangSmith.
LangChain ecosystem
While the LangChain framework can be used standalone, it also integrates seamlessly with any LangChain product, giving developers a full suite of tools when building LLM applications.
- Deep Agents — Build agents that can plan, use subagents, and leverage file systems for complex tasks
- LangGraph — Build agents that can reliably handle complex tasks with our low-level agent orchestration framework
- Integrations — Chat & embedding models, tools & toolkits, and more
- LangSmith — Agent evals, observability, and debugging for LLM apps
- LangSmith Deployment — Deploy and scale agents with a purpose-built platform for long-running, stateful workflows
Why use LangChain?
LangChain helps developers build applications powered by LLMs through a standard interface for models, embeddings, vector stores, and more.
- Real-time data augmentation — Easily connect LLMs to diverse data sources and external/internal systems, drawing from LangChain's vast library of integrations with model providers, tools, vector stores, retrievers, and more
- Model interoperability — Swap models in and out as your engineering team experiments to find the best choice for your application's needs. As the industry frontier evolves, adapt quickly — LangChain's abstractions keep you moving without losing momentum
- Rapid prototyping — Quickly build and iterate on LLM applications with LangChain's modular, component-based architecture. Test different approaches and workflows without rebuilding from scratch, accelerating your development cycle
- Production-ready features — Deploy reliable applications with built-in support for monitoring, evaluation, and debugging through integrations like LangSmith. Scale with confidence using battle-tested patterns and best practices
- Vibrant community and ecosystem — Leverage a rich ecosystem of integrations, templates, and community-contributed components. Benefit from continuous improvements and stay up-to-date with the latest AI developments through an active open-source community
- Flexible abstraction layers — Work at the level of abstraction that suits your needs — from high-level chains for quick starts to low-level components for fine-grained control. LangChain grows with your application's complexity
Resources
- Documentation — conceptual overviews and guides
- LangChain ecosystem overview — how LangChain, LangGraph, and Deep Agents fit together
- API reference — complete reference for all public classes, functions, and types
- Discussions — community forum for technical questions, ideas, and feedback
- LangChain Academy — comprehensive, free courses on LangChain libraries and products, made by the LangChain team
- Contributing Guide — how to contribute and find good first issues
- Code of Conduct — community guidelines and standards