| name | langchain-ecosystem |
| description | Complete guide for LangChain, LangGraph, and Deep Agents development. Covers framework selection, agents, tools, RAG, middleware, graphs, persistence, HITL, subagents, and memory. Triggers when code imports langchain/langgraph/deepagents, or user asks about building AI agents, RAG pipelines, stateful graphs, human-in-the-loop workflows, or agent orchestration. |
LangChain Ecosystem Router
Framework Selection
Frameworks are layered, not competing:
┌─────────────────────────────────────┐
│ Deep Agents │ ← planning, memory, skills, files
├─────────────────────────────────────┤
│ LangGraph │ ← graphs, loops, state, control flow
├─────────────────────────────────────┤
│ LangChain │ ← models, tools, prompts, RAG
└─────────────────────────────────────┘
Decision tree (answer in order):
- Needs sub-tasks, file management, persistent memory, or on-demand skills? → Deep Agents
- Needs complex control flow (loops, branching, parallel, human-in-the-loop)? → LangGraph
- Single-purpose agent with fixed tools? → LangChain (
create_agent)
- Pure model call, chain, or retrieval pipeline? → LangChain (LCEL)
| Capability | LangChain | LangGraph | Deep Agents |
|---|
| Control flow | Fixed tool loop | Custom graph | Middleware-managed |
| Planning | ✗ | Manual | ✓ TodoListMiddleware |
| File management | ✗ | Manual | ✓ FilesystemMiddleware |
| Persistent memory | ✗ | With checkpointer | ✓ MemoryMiddleware |
| Subagent delegation | ✗ | Manual | ✓ SubAgentMiddleware |
| Human-in-the-loop | ✗ | Manual interrupt | ✓ HumanInTheLoopMiddleware |
LangChain tools/chains/retrievers work inside LangGraph nodes and Deep Agents tools.
LangGraph compiled graphs can be registered as Deep Agents subagents.
Quick Start (Most Common Patterns)
Simple agent:
from langchain.agents import create_agent
agent = create_agent(model="anthropic:claude-sonnet-4-5", tools=[my_tool])
result = agent.invoke({"messages": [{"role": "user", "content": "Hello"}]})
print(result["messages"][-1].content)
Stateful graph:
from langgraph.graph import StateGraph, START, END
graph = StateGraph(State).add_node("process", fn).add_edge(START, "process").add_edge("process", END).compile()
Deep Agent:
from deepagents import create_deep_agent
agent = create_deep_agent(model="claude-sonnet-4-5-20250929", tools=[my_tool], system_prompt="...")
Critical Rules (All Frameworks)
- Persistence always needs
thread_id: config = {"configurable": {"thread_id": "..."}}
- HITL always needs checkpointer:
MemorySaver() at minimum
- Resume after interrupt:
Command(resume={"decisions": [{"type": "approve"}]}) — never plain dict
- LangChain 1.0 is LTS — never start new projects on 0.3
- Always install
langchain-core explicitly — not auto-hoisted in monorepos
- Use dedicated packages (e.g.,
langchain-chroma) not langchain_community
Deep-Dive References
LangChain
| Reference | When to Read |
|---|
| references/langchain-fundamentals.md | create_agent, tool definitions, persistence, TypeScript |
| references/langchain-dependencies.md | Package versions, install commands, import paths, environment setup |
| references/langchain-middleware.md | HumanInTheLoopMiddleware, approval/edit/reject, per-tool policies |
| references/langchain-rag.md | Document loaders, splitters, embeddings, vector stores, MMR search |
LangGraph
| Reference | When to Read |
|---|
| references/langgraph-fundamentals.md | StateGraph, reducers, nodes, edges, Command, Send, streaming |
| references/langgraph-human-in-the-loop.md | interrupt(), idempotency, approval workflows, parallel interrupts |
| references/langgraph-persistence.md | Checkpointers, Store, time travel, subgraph scoping |
Deep Agents
Meta
