langchain · diff
git:20260109.00de6d5 to git:20260813.422f18d
24 added, 0 removed. Audit A to A.
---
name: langchain
description: Build AI agents with LangChain framework. Use when building agents, tools, memory, MCP integrations, RAG pipelines, multi-agent systems, or any LLM-powered applications using LangChain or LangGraph in Python or TypeScript.
---
# LangChain Skill
Build production AI agents using LangChain's pre-built architecture and integrations.
## Installation
```bash
pip install langchain langchain-anthropic langgraph
# For MCP support
pip install langchain-mcp-adapters
```
## Quick Agent
```python
from langchain.agents import create_agent
from langchain.tools import tool
@tool
def get_weather(city: str) -> str:
"""Get weather for a city."""
return f"Sunny in {city}!"
agent = create_agent(
model="claude-sonnet-4-5-20250929",
tools=[get_weather],
system_prompt="You are a helpful assistant"
)
result = agent.invoke({"messages": [{"role": "user", "content": "Weather in SF?"}]})
```
## Tools
Define tools with `@tool` decorator. Type hints required, docstring becomes description:
```python
from langchain.tools import tool, ToolRuntime
from dataclasses import dataclass
@tool
def search(query: str, limit: int = 10) -> str:
"""Search database for records."""
return f"Found {limit} results for '{query}'"
# With runtime context access
@dataclass
class Context:
user_id: str
@tool
def get_user_data(runtime: ToolRuntime[Context]) -> str:
"""Get current user data."""
user_id = runtime.context.user_id
return f"Data for {user_id}"
```
### ToolRuntime Access
- `runtime.state` - Agent state (messages, custom fields)
- `runtime.context` - Immutable config (user_id, session)
- `runtime.store` - Persistent memory across conversations
- `runtime.stream_writer` - Stream updates during execution
### Reserved Parameters
Cannot use as tool args: `config`, `runtime`
## Memory
```python
from langgraph.checkpoint.memory import InMemorySaver
from langgraph.store.memory import InMemoryStore
# Short-term (conversation) memory
checkpointer = InMemorySaver()
# Long-term (cross-conversation) memory
store = InMemoryStore()
agent = create_agent(
model="claude-sonnet-4-5-20250929",
tools=[...],
checkpointer=checkpointer,
store=store
)
# Use thread_id for conversation continuity
config = {"configurable": {"thread_id": "conversation-1"}}
agent.invoke({"messages": [...]}, config=config)
```
## Structured Output
```python
from dataclasses import dataclass
from langchain.agents.structured_output import ToolStrategy
@dataclass
class Response:
answer: str
confidence: float | None = None
agent = create_agent(
model="claude-sonnet-4-5-20250929",
tools=[...],
response_format=ToolStrategy(Response)
)
# Access: result['structured_response']
```
## MCP Integration
Connect to MCP servers for external tools:
```python
from langchain_mcp_adapters.client import MultiServerMCPClient
from langchain.agents import create_agent
client = MultiServerMCPClient({
"math": {
"transport": "stdio",
"command": "python",
"args": ["/path/to/server.py"]
},
"api": {
"transport": "http",
"url": "http://localhost:8000/mcp",
"headers": {"Authorization": "Bearer TOKEN"}
}
})
tools = await client.get_tools()
agent = create_agent("claude-sonnet-4-5-20250929", tools)
```
### MCP Interceptors
Modify tool calls, inject context, handle errors:
```python
from langchain_mcp_adapters.interceptors import MCPToolCallRequest
async def auth_interceptor(request: MCPToolCallRequest, handler):
runtime = request.runtime
user_id = runtime.context.user_id
modified = request.override(args={**request.args, "user_id": user_id})
return await handler(modified)
client = MultiServerMCPClient({...}, tool_interceptors=[auth_interceptor])
```
## Model Configuration
```python
from langchain.chat_models import init_chat_model
model = init_chat_model(
"claude-sonnet-4-5-20250929", # or "gpt-4o", "gemini-1.5-pro"
temperature=0.5,
timeout=10,
max_tokens=1000
)
```
## State Updates from Tools
Return `Command` to update state or control flow:
```python
from langgraph.types import Command
from langchain.messages import RemoveMessage
@tool
def clear_history() -> Command:
"""Clear conversation."""
return Command(update={"messages": [RemoveMessage(id="__all__")]})
@tool
def complete_task() -> Command:
"""End agent run."""
return Command(update={"status": "done"}, goto="__end__")
```
## Common Patterns
### Full Production Agent
```python
from dataclasses import dataclass
from langchain.agents import create_agent
from langchain.chat_models import init_chat_model
from langchain.tools import tool, ToolRuntime
from langchain.agents.structured_output import ToolStrategy
from langgraph.checkpoint.memory import InMemorySaver
@dataclass
class Context:
user_id: str
@tool
def search_orders(query: str, runtime: ToolRuntime[Context]) -> str:
"""Search user orders."""
return f"Orders for {runtime.context.user_id}: {query}"
@dataclass
class Response:
answer: str
order_ids: list[str] | None = None
agent = create_agent(
model=init_chat_model("claude-sonnet-4-5-20250929", temperature=0),
system_prompt="You are a customer service agent.",
tools=[search_orders],
context_schema=Context,
response_format=ToolStrategy(Response),
checkpointer=InMemorySaver()
)
result = agent.invoke(
{"messages": [{"role": "user", "content": "Find my recent orders"}]},
config={"configurable": {"thread_id": "session-1"}},
context=Context(user_id="user123")
)
```
+ ## Anti-Patterns to Avoid
+
+ - **Tools without type hints or docstrings** — the docstring *is* the tool description the model sees; a missing or vague one means the agent misuses the tool
+ - **Global state in tools** — tools that read globals instead of `ToolRuntime[Context]` are untestable and unsafe in concurrent runs
+ - **No checkpointer, then wondering why the agent forgets** — multi-turn agents need `checkpointer=InMemorySaver()` (or a persistent saver) plus a stable `thread_id`
+ - **Rebuilding everything as one giant system prompt** — tools, structured output (`ToolStrategy`/`with_structured_output`), and retrieval exist to keep prompts small
+ - **Letting raw tool exceptions reach the model** — wrap tools, catch, and return structured error strings so the agent can recover
+ - **Using deprecated `LLMChain`/`SequentialChain` APIs** — current LangChain builds on `create_agent` and LangGraph; the old chains are removed from recent versions
+ - **Stuffing whole documents into context** — use the retrieval patterns in [retrieval.md](references/retrieval.md) instead of naive full-document injection
+
+ ## When to Use / Not Use
+
+ **Use this skill when:**
+
+ - Building agents with LangChain or LangGraph (Python or TypeScript)
+ - Wiring tools, memory, MCP servers, or structured output into an LLM app
+ - Implementing RAG, multi-agent handoffs, routers, or stateful workflows
+
+ **Do NOT use this skill when:**
+
+ - A single stateless LLM call is all you need — the plain provider SDK is lighter
+ - The project standardizes on another agent framework (e.g., smolagents) — follow the project's stack
+ - The agent is code-first and benefits from the smaller SmolAgents execution model — see the smolagents skill
+
## References
For detailed patterns, see:
- `references/langgraph.md` - LangGraph workflows, graphs, persistence
- `references/multi-agent.md` - Handoffs, routers, subagents
- `references/retrieval.md` - RAG, vector stores, embeddings
## Key Links
- Docs: https://docs.langchain.com
- Python SDK: https://reference.langchain.com/python
- LangGraph: https://docs.langchain.com/oss/python/langgraph
- MCP Adapters: https://github.com/langchain-ai/langchain-mcp-adapters