LangChain Ecosystem Router Skill

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

| Reference | When to Read | |-----------|-------------| | references/deep-agents-core.md | create_deep_agent, built-in tools, SKILL.md format, configuration | | references/deep-agents-memory.md | StateBackend, StoreBackend, FilesystemBackend, CompositeBackend | | references/deep-agents-orchestration.md | Subagents, task delegation, TodoList, HITL approval flow |

Agent Skills: LangChain Ecosystem Router

Install this agent skill to your local

Skill Files