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Magnus Hedemark fe3be63800 feat: add langchain — expert LangChain framework skill

Greenfield SkillOpt: 3 epochs optimizing discoverability, decision
guidance, and troubleshooting for a brand-new LangChain skill.

Epoch 1 — Prominence:
- Added critical AgentExecutor deprecation callout at top
- Framework Routing Guide for cross-portfolio decisions

Epoch 2 — Decision Guidance:
- Where to Start table with AgentExecutor migration row
- Pipeline Mode table (Quick/RAG/Agent/Production)

Epoch 3 — Pattern Expansion:
- Troubleshooting table with reference file links
- FAQ section covering installation, migration, performance

13 files: SKILL.md, 7 references, 4 templates, 1 script.
v1.0.0 -> v1.0.3 across 3 SkillOpt epochs.

Signed-off-by: Magnus Hedemark <magnus919@pm.me>

2026-07-09 14:13:34 -04:00

2.1 KiB

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LangChain Integration Ecosystem

LangChain provides a unified interface across 1000+ integrations. Switching providers requires changing one line.

Model Providers

Provider	Package	Class
OpenAI	`langchain-openai`	`ChatOpenAI`
Anthropic	`langchain-anthropic`	`ChatAnthropic`
Google Gemini	`langchain-google`	`ChatGoogleGenerativeAI`
Mistral	`langchain-mistralai`	`ChatMistralAI`
AWS Bedrock	`langchain-aws`	`ChatBedrock`
Ollama (local)	`langchain-ollama`	`ChatOllama`
Fireworks	`langchain-fireworks`	`ChatFireworks`

Vector Stores

Store	Package	Instantiation
Chroma	`langchain-chroma`	`Chroma.from_documents(docs, embeddings)`
Pinecone	`langchain-pinecone`	`PineconeVectorStore.from_documents(docs, embeddings)`
pgvector	`langchain-postgres`	`PGVector(embeddings=embeddings, connection=conn)`
Weaviate	`langchain-weaviate`	`WeaviateVectorStore.from_documents(docs, embeddings)`
Qdrant	`langchain-qdrant`	`QdrantVectorStore.from_documents(docs, embeddings)`
FAISS	`faiss-cpu`	`FAISS.from_documents(docs, embeddings)`

Tool Integrations

Tool	Package	Purpose
Tavily Search	`langchain-community`	Web search for agents
MCP Servers	`langchain-mcp-adapters`	Connect any MCP server as a tool
SQL Database	`langchain-community`	Query SQL databases
ArXiv	`langchain-community`	Academic paper search
Wikipedia	`langchain-community`	Wikipedia lookup

MCP Adapter Pattern

Connect any MCP server as a LangChain tool:

from langchain_mcp_adapters.client import MultiServerMCPClient

async with MultiServerMCPClient() as client:
    tools = client.get_tools()
    agent = create_agent(model, tools)

Quick-Swap Pattern

# One-line swap between providers
model = ChatOpenAI(model="gpt-4o-mini")
# model = ChatAnthropic(model="claude-3-5-haiku")  # same interface
# model = ChatGoogleGenerativeAI(model="gemini-2.0-flash")  # same interface

All models use the same interface: model.invoke(messages).

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