langchain-context

name: langchain-context description: "Answers questions about the LangChain Python ecosystem — the langchain-ai/langchain monorepo (langchain-core, v1 agents, langchain-classic, in-tree partners, text-splitters) plus companion repos LangGraph, LangSmith SDK, Deep Agents, out-of-tree partner monorepos langchain-google and langchain-aws, and the docs.langchain.com source. Includes a thin orientation pointer for the LangChain.js port. References load on demand from references/."

LangChain context navigator

Overview

This navigator covers the LangChain Python ecosystem in depth, plus a thin orientation pointer for the JS port:

Primary: the langchain-ai/langchain Python monorepo — langchain-core, the new langchain package (v1; built from libs/langchain_v1/), the legacy langchain-classic package (built from libs/langchain/), in-tree partner integrations, and the auxiliary libs (text-splitters, model-profiles, standard-tests).
Python companion repos: LangGraph (langchain-ai/langgraph), LangSmith SDK (langchain-ai/langsmith-sdk), Deep Agents (langchain-ai/deepagents), and the out-of-tree partner monorepos langchain-ai/langchain-google and langchain-ai/langchain-aws. Plus the docs source at langchain-ai/docs.
JS port (thin coverage only): langchain-ai/langchainjs is included as a pointer reference for orienting JS questions — full JS depth is out of scope; follow source links into the JS repo for non-trivial questions.

When asked a question this navigator's domain covers:

Scan the Catalog below for the matching topic.
Follow the link to read the reference file.
If the question spans multiple references, consult the Cross-reference map.
If a reference points at a source URL for deeper detail, follow it only if the reference itself didn't answer the question.

For first-time orientation — what packages exist, how langchain vs langchain-classic differ, where companion repos live — start with langchain-overview.md.

Claims policy

Cite by default, and make load-bearing claims verifiable:

Inline-cite every load-bearing claim with its SHA-pinned permalink — the https://github.com/langchain-ai/langchain/blob/<sha>/libs/langchain_v1/langchain/agents/factory.py#L17-L25-style link the reference gives for that fact (versions, defaults, signatures, deprecations, behavior a user could get wrong by guessing). Put the permalink inline, on the claim. Use a bare filename parenthetical (e.g. (langchain-v1-agents.md)) only when the reference genuinely provides no permalink. This inline permalink is what the grounded-citation eval (SELF-AUDIT Check 8) grades.
Don't cite orientational prose — "what is X?", "when did X launch?" — answer those from this navigator alone; opening a reference is itself a citation gesture.
End with a one-line provenance footer, emitted italic, formatted *References consulted: foo.md, bar.md. Grounded in {{LIBRARY}}@{{VERSION}} — [reference index]({{INDEX_URL}}).* The footer is a summary of what you read — not a substitute for the inline permalinks on the claims. The {{LIBRARY}} / {{VERSION}} / {{INDEX_URL}} tokens are agent-substituted at answer time, so they appear literally in the stamped SKILL.md.
If no reference was opened, say so in the footer ("Answered from general knowledge — no {{LIBRARY}} references consulted") — never fake it.

The voice is competent and careful — no "as an AI assistant" hedging.

Catalog

Main monorepo (`langchain-ai/langchain`)

Reference	Description
langchain-overview	Monorepo layout, `langchain` vs `langchain-classic` split, where every companion repo lives.
langchain-core-runnables	The Runnable protocol and LCEL — `\|` pipe, `.bind`, `.with_config`, RunnableSequence/Parallel/Lambda/Branch, streaming events.
langchain-core-models	Chat models, LLMs, messages (Human/AI/System/Tool), tools and `bind_tools`, prompts, output parsers, structured output.
langchain-core-retrieval	Documents, BaseRetriever, VectorStore, similarity search / MMR, Embeddings, document loaders, RAG pipelines.
langchain-core-callbacks	Callback handlers, tracers, `astream_events` v2, LangSmith integration, run trees, streaming protocol.
langchain-v1-agents	The v1 agent API: `create_agent`, the middleware system, `AgentState`, `init_chat_model`, LangGraph backend.
langchain-classic	The legacy `langchain-classic` package: chains, AgentExecutor, memory, indexing API, and what's deprecated.
langchain-partners	In-tree partner integration packages (anthropic, openai, etc.), the chat-model contract, `langchain-tests` standard suite.
langchain-text-splitters	`langchain-text-splitters`: `RecursiveCharacterTextSplitter`, token splitters, Markdown/HTML/JSON/code splitters.

Python companion repos

Reference	Description
langgraph-overview	`langchain-ai/langgraph` — `StateGraph`, checkpointers, prebuilt helpers, the SDK and CLI, and how it relates to `langchain.create_agent`.
langsmith-sdk-overview	`langchain-ai/langsmith-sdk` — the `langsmith` package: `@traceable`, framework wrappers (`wrap_openai` etc.), `Client`, `evaluate`, and the env-var-only LangChain integration.
deepagents-overview	`langchain-ai/deepagents` — `create_deep_agent` (planning, filesystem, shell, sub-agent tools), pluggable sandbox backends, the `deepagents-cli`.
langchain-google-overview	`langchain-ai/langchain-google` — out-of-tree partner monorepo: `langchain-google-genai` (Gemini API), `langchain-google-vertexai` (GCP Vertex), `langchain-google-community` (Drive/Gmail/BigQuery/etc.).
langchain-aws-overview	`langchain-ai/langchain-aws` — out-of-tree partner monorepo: `langchain-aws` (Bedrock/SageMaker/Kendra/Neptune/S3-Vectors/AgentCore), `langgraph-checkpoint-aws`, `langchain-agentcore-codeinterpreter`.
docs-overview	`langchain-ai/docs` — source for `docs.langchain.com` (Mintlify MDX); also the home of `packages.yml`, the central registry of every LangChain package across every owning repo.

JS port (thin pointer only)

Reference	Description
langchainjs-overview	`langchain-ai/langchainjs` — orientation pointer for the TypeScript port. Lists the npm package mapping; defers depth to the JS docs.

Cross-reference map

Building a RAG pipeline → start at langchain-core-retrieval for the protocol shape; cross to langchain-text-splitters for the chunking step and langchain-core-runnables for composing the prompt-model-parser tail with LCEL.
Building an agent → start at langchain-v1-agents for create_agent and middleware; cross to langchain-core-models for tool definitions and structured output, to langgraph-overview for the underlying graph runtime (create_agent returns a StateGraph), and to langchain-classic only if the user has 0.x AgentExecutor code to migrate.
Building a "batteries-included" agent (planning, filesystem, sub-agents) → start at deepagents-overview for create_deep_agent; cross to langchain-v1-agents for the underlying create_agent it composes with, and to langgraph-overview for streaming/persistence.
Durable agents / pause-resume / human-in-the-loop / checkpoints → start at langgraph-overview for the LangGraph checkpoint and interrupt surface (the langchain repo doesn't implement these — LangGraph does). For AWS-managed checkpoint backends specifically, cross to langchain-aws-overview (langgraph-checkpoint-aws).
Adding support for a new model provider → start at langchain-partners for the in-tree package layout; cross to langchain-core-models for the BaseChatModel contract details. For Google or AWS specifically, those are out-of-tree — see langchain-google-overview or langchain-aws-overview.
Observability / tracing / streaming UI → start at langchain-core-callbacks for the event surface; cross to langchain-core-runnables for how astream_events is wired into the Runnable protocol. For LangSmith specifically — when you'd reach for the SDK directly vs. let env vars do the work — cross to langsmith-sdk-overview.
Migrating 0.x code → start at langchain-classic for what moved where; cross to langchain-v1-agents for the v1 replacement of initialize_agent + AgentExecutor + memory, and to langgraph-overview if the user is asking about graph-level customization the v1 agent surface doesn't expose.
"Why is LLMChain deprecated / what replaces it" → start at langchain-classic; cross to langchain-core-runnables for the LCEL replacement pattern.
"Where is the docs page for X / where do I file a docs issue" → start at docs-overview for the docs.langchain.com source layout, the packages.yml registry, and the distinction between docs.langchain.com vs. reference.langchain.com.
"What's the JS equivalent of " → start at langchainjs-overview for the npm package mapping; for non-trivial JS questions follow the source links into the JS repo and JS docs.

Instructions to Claude

When loading a reference file, the path syntax depends on the platform:

Claude Code: Read the reference using the platform-provided skill-directory variable: Read $CLAUDE_SKILL_DIR/references/<source-slug>-<topic>.md
Claude Desktop: Read the reference using a relative path; the platform resolves it from the skill's installed location: Read references/<source-slug>-<topic>.md

Loading rules:

Load one reference at a time unless the Cross-reference map says to load both.
If the primary reference doesn't fully answer the question, follow any source URL pointers it provides for deeper detail.
Do not eagerly load companion files; only follow companion links when the primary reference says to.
If the user's question is clearly out of scope for this contextualizer, don't invoke this skill at all.
The repo's two same-looking package directories (libs/langchain/ and libs/langchain_v1/) publish to PyPI under different names (langchain-classic and langchain respectively). Always disambiguate when discussing them — langchain-overview and the first sections of langchain-v1-agents and langchain-classic cover this.

Progressive disclosure

References prioritize curated insight over re-specifying upstream sources:

Gotchas, cross-system patterns, and "why" context are kept in the reference (curation value).
Exact schemas, API signatures, and parameter lists are summarized in the reference and linked to their authoritative source via SHA-pinned URLs.

When a reference includes a source URL pointer, follow it only when the reference's own summary didn't cover the question. The contextualizer is optimized for the common case; the upstream source is the long tail.

File URLs in references are pinned to per-source SHAs at the time of the last /skill-engine:refresh run (2026-05-18). Each reference's links point at the SHA captured when that source was discovered. Run /skill-engine:refresh to re-pin against current HEAD across all sources. Per-source SHAs are recorded in research/source-paths.json.