Langchain

LangChain Skill

Framework for developing applications powered by language models.

Ecosystem Graph Preview

graph LR
  langchain["langchain"]:::core
  classDef core fill:#f9f,stroke:#333,stroke-width:4px;
  langchain -- "alternative to" --> llamaindex
  langchain -- "integrates with" --> openai
  langchain -- "integrates with" --> pinecone
  llamaindex -- "alternative to" --> langchain
  ollama -- "integrates with" --> langchain
  vllm -- "integrates with" --> langchain

Recommended Next Skills

• llamaindex (Score: 0.93) Why: Direct relationship, Both are AI, Shared ecosystem (ai), Can deploy to any, Similar network profile
• ollama (Score: 0.83) Why: Direct relationship, Both are AI, Shared ecosystem (ai), Similar network profile
• vllm (Score: 0.82) Why: Direct relationship, Both are AI, Shared ecosystem (ai), Similar network profile

Quick Start

LangChain provides standard interfaces for LLMs, Vector Stores, and Memory, allowing you to chain them together to build complex Agents and Retrieval-Augmented Generation (RAG) pipelines.

pip install langchain langchain-openai

Production Patterns

LCEL (LangChain Expression Language)

Avoid using massive legacy chain classes (like ConversationalRetrievalChain). Migrate entirely to LCEL, which uses Python pipe operators (|) to compose prompts, models, and output parsers declaratively. It automatically handles streaming and async logic.

Architecture & Scaling

Agents vs Chains

A Chain is a deterministic sequence of operations. An Agent utilizes an LLM's reasoning to dynamically determine which Tools to execute and in what order to solve a complex goal.

Error Recovery

LLMs frequently output invalid JSON when asked for structured data. Use LangChain's OutputFixingParser which automatically catches parsing errors and feeds the broken output back to the LLM with instructions to fix it.

Security Notes

Never give an Agent unmitigated access to destructive Tools (e.g., SQL DELETE capabilities or Shell execution). Always enforce human-in-the-loop approval or strict sandbox environments.

References

• LangChain Docs

Why use this skill

Use this when your agent works with langchain — structured patterns beat pasted docs and prevent common hallucinations.

AI pitfalls

• Using deprecated model IDs or wrong API endpoints
• Confusing chat vs completions vs embeddings APIs
• Omitting rate-limit and token budget handling

Production checklist

• Secrets in environment variables, not source code
• Error handling and logging in place
• Rate limits and timeouts configured

Related skills

• llamaindex — alternative to
• openai — integrates with
• pinecone — integrates with

Last Verified: 2026-07-02