Agentsop Langgraph

LangGraph · SOP

Source posture: every non-trivial claim is cited inline. Citations use short tags like [lc-docs], [lc-blog/interrupt], [gh/6731], [zenml/uber] — resolve them against references/*.md for the full URL.

何时激活 (Activation Rules)

Activate this skill when any of the following triggers fire:

• The task mentions LangGraph, StateGraph, MessageGraph, create_react_agent, interrupt(, Command(resume=, add_messages, checkpointer, PostgresSaver, Send(, or entrypoint / task decorators.
• The user wants to build a stateful agent (memory across turns, long-running, must survive a process crash) — LangGraph's stated sweet spot [lc-docs/why-langgraph].
• The user wants human-in-the-loop (approve a tool call, edit state, multi-turn validation) — LangGraph offers a first-class interrupt() primitive that competitors require "duct-taping" to achieve [bswen/hitl].
• The user is hitting GRAPH_RECURSION_LIMIT errors, infinite loops, or InvalidUpdateError on parallel branches — these are LangGraph-specific failure modes with known fixes [lc-docs/errors] [cheatsheet/gotchas].
• The user is choosing between LangGraph and CrewAI / AutoGen / OpenAI Swarm / raw LangChain — section 生态对照 gives the decision matrix.
• The user is migrating an existing LangChain chain or a hand-rolled while-loop agent to something durable and observable.

Do not activate if the task is a single LLM call, a one-shot RAG query, or a stateless tool pipeline — Sec. 反模式 explains why graphs are overkill there.

核心心智模型 (Core Mental Model)

LangGraph is a state machine, not a chain. The cleanest one-liner from the 2026 docs: "If chains were about passing outputs between steps, graphs are about maintaining and evolving a shared state over time" [eastondev/2026]. Pre-LLM analog: think BPMN / finite state machine / Pregel-style "supersteps", not a Unix pipe. The official position is even more reductive: LangGraph is "a deterministic execution engine for AI reasoning workflows" [eastondev/2026].

Three load-bearing concepts ride this model:

1. State is the single source of truth. All nodes read from and write to one shared, typed object (TypedDict / Pydantic / dataclass). A node returns a partial update, never a mutation. How updates merge into state is governed by reducers, declared via Annotated[list[Msg], add_messages] etc. Missing a reducer on a key that two parallel nodes both write to triggers InvalidUpdateError — reducers are mandatory for parallel writes [cheatsheet/gotchas]. The reducer system is what lets the graph be composable, replayable, and crash-safe.

2. Checkpoints make state durable. After every superstep, the full state is snapshotted into a checkpointer (SQLite for local, Postgres for production, Redis for fast TTL'd swarms) [lc-docs/persistence] [redis/checkpoint]. This single property is what unlocks the headline features: durable execution that "persists through failures and resumes from their exact stopping point", time-travel debugging (replay or fork from any checkpoint), and human-in-the-loop (a thread can sit interrupted for hours and resume cleanly) [gh/langgraph-readme] [dragonforest/timetravel].

3. Graph topology is just routing logic over state. Edges are static (always go to N), conditional (a function reads state and picks a next node), or dynamic via the Send API (a routing function returns a list of Send objects to spawn variable-count parallel workers) [deepwiki/mapreduce]. This is where LangGraph diverges from CrewAI's role-based crew and AutoGen's conversational pattern — control flow is explicit, not emergent from chat history.

The OS-level claim: "2026 is the year of Stateful Orchestration" [eastondev/2026]. LangGraph bet that production agents need persistence, explicit control flow, and observability more than they need elegance. That bet is paying off (Klarna serves 85M users on it, Replit pushed it so hard LangSmith had to be rewritten to ingest the traces) — but the cost is verbosity that frustrates anyone trying it on a toy problem [lc-blog/production] [duplocloud/compare].

SOP 工作流 (Agentic Protocol)

A coder agent should walk this protocol top-down. Each step has a decision gate — if the answer is "no" or "not yet", stop and reconsider before adding graph complexity.

Step 1 · Decide whether a graph is actually warranted

Gate questions:

• Does the workflow have ≥1 cycle (tool-call → reflect → retry)?
• Does it need to survive a crash mid-execution?
• Will a human need to inspect or override state mid-run?
• Are there ≥2 specialized agents that hand off?

If all four are no, use a plain RunnableSequence or raw API calls and exit. Over-graphing simple flows is the #1 anti-pattern [swarnendu/best].

Step 2 · Pick the API surface

Default to create_react_agent and graduate to StateGraph only when you need parallel nodes, supervisor-worker patterns, custom retry logic, or complex branching [agentsindex/v1].

Step 3 · Design the state schema before writing nodes

The state schema is "the most critical design component" [bharatraj/state]. Discipline:

• Use TypedDict for ergonomics, Pydantic only when validation matters.
• Every key that may be written in parallel gets an explicit reducer (add_messages, operator.add, or custom) — otherwise plan for it to be overwritten last-write-wins.
• Keep state lightweight and serializable — it gets pickled to the checkpointer on every superstep [bharatraj/state].
• Treat each node like a pure function: return a partial state update, do not mutate inputs [swarnendu/best].

Step 4 · Choose the multi-agent topology

Decision tree, sourced from LangChain's own benchmark [lc-blog/benchmark]:

Is there exactly one "user-facing" persona?
├─ YES  → Supervisor pattern (single supervisor, sub-agents are tools)
│        - Highest token cost (supervisor "translates" sub-agent output)
│        - Safest with third-party agents
│        - LangChain's *current recommended default*
└─ NO   → Do sub-agents know about each other?
         ├─ YES → Swarm pattern (dynamic handoff, last-active agent remembered)
         │       - Lower tokens than supervisor (no translation step)
         │       - Slightly higher accuracy in the τ-bench retest
         │       - Bad fit for third-party agents
         └─ NO  → Hierarchical Teams (supervisor-of-supervisors)
                 - Use only when ≥6 specialists need grouping

Concrete bench finding: swarm "slightly outperformed supervisor across all scenarios"; supervisor "consistently uses more tokens than swarm" because of the telephone-game translation overhead [lc-blog/benchmark]. LangChain's own response was to fix the supervisor (remove handoff messages, add a forwarding-messages tool, tune tool names) for "a nearly 50% increase in performance" [lc-blog/benchmark].

Step 5 · Add human-in-the-loop only on irreversible actions

Use interrupt(value) at the node that would perform the high-blast-radius operation; resume with Command(resume=...) [lc-blog/interrupt]. Four canonical patterns [lc-blog/interrupt]:

1. Approve / Reject — review a critical step before it runs.
2. Review & Edit State — human corrects or augments mid-run.
3. Review Tool Calls — oversee LLM-requested actions.
4. Multi-turn Conversation — back-and-forth in a multi-agent setup.

Rule of thumb: "interrupt on irreversible, high-blast-radius actions only — not on every step" [bswen/hitl]. Side effects (DB writes, API calls) must go after the interrupt or in a downstream node — placing them before causes unwanted re-execution on resume [cheatsheet/gotchas].

Step 6 · Pick the checkpointer to match the durability requirement

Run checkpointer.setup() as a CI/CD migration, never inside app runtime [bswen/hitl]. Implement a TTL sweep for interrupted-but-never-resumed threads (e.g., abandon after 24 h) — otherwise state accumulates indefinitely [bswen/hitl].

Step 7 · Add observability + bounded loops before shipping

• Wire LangSmith from day one — replaying a checkpoint locally only goes so far; production needs the trace UI [swarnendu/best].
• Set a deliberate recursion_limit (default 25); raise it via graph.invoke({...}, {"recursion_limit": 100}) only after confirming the loop can terminate [lc-docs/errors].
• Treat recursion_limit as a safety net, not control flow. Hitting it means the conditional edge logic is wrong, not that the limit is too low [cheatsheet/gotchas].

操作模型 (Operation Models)

Each operation is a primitive a coder agent can invoke. Format: Trigger → Action → Output → Evidence.

OP-1 · Bootstrap a ReAct agent in <10 lines

• Trigger: User says "make me an agent that uses tool X" with no other requirements.
• Action: Call from langgraph.prebuilt import create_react_agent; pass model + tools list. Skip StateGraph entirely.
• Output: A compiled graph supporting .invoke() / .stream() with built-in message history.
• Evidence: [agentsindex/v1] "Start with create_react_agent for any standard tool-calling agent."

OP-2 · Promote a prebuilt agent to a custom StateGraph

• Trigger: The prebuilt agent needs parallel branches, a supervisor, custom retry, or a non-message state field.
• Action: Re-implement with StateGraph(MyTypedDict), manually add the LLM node, tool node, and conditional edge that routes on tool_calls.
• Output: A graph with explicit topology and full control.
• Evidence: [agentsindex/v1] "If you find yourself needing parallel node execution, a supervisor-worker pattern, custom retry logic, or complex branching, migrate to a manual StateGraph."

OP-3 · Add a reducer to fix InvalidUpdateError

• Trigger: Two nodes write the same state key in parallel and the graph raises InvalidUpdateError.
• Action: Replace key: list[X] with key: Annotated[list[X], operator.add] (or add_messages for chat).
• Output: Parallel writes merge instead of conflicting.
• Evidence: [cheatsheet/gotchas] "Reducers are mandatory, not optional, for parallel execution."

OP-4 · Spawn dynamic parallel workers with Send

• Trigger: At runtime, the agent needs to fan out a variable number of parallel tasks (e.g., one summarizer per retrieved doc).
• Action: From a conditional edge, return [Send("worker", {"chunk": c}) for c in state["chunks"]]. The worker uses its own state schema, and results are reduced back via operator.add.
• Output: True dynamic map-reduce, decided per-invocation.
• Evidence: [deepwiki/mapreduce] "Send allows a conditional edge function to schedule a node with a custom state…the primary mechanism for dynamic fan-out."

OP-5 · Insert a human approval gate

• Trigger: A node would perform an irreversible action (charge a card, send email, run SQL DELETE).
• Action: Inside the node, call decision = interrupt({"proposed": payload}) before the side effect. Compile the graph with a checkpointer. The caller resumes with graph.invoke(Command(resume="approve"), config).
• Output: Thread pauses, state persists, resumes with the human's decision threaded back into the node.
• Evidence: [lc-blog/interrupt] four-pattern table + [bswen/hitl] side- effect ordering.

OP-6 · Encapsulate a multi-step subflow as a subgraph

• Trigger: A logical chunk (a research agent inside a larger workflow, a reusable validation pipeline) deserves its own state and lifecycle.
• Action: Compile the child graph, add it as a node: parent.add_node("research", research_subgraph). If schemas differ, wrap the call in a node function that translates between schemas.
• Output: Reusable, independently testable module; child state isolated unless keys overlap.
• Evidence: [lc-docs/subgraphs] [deepwiki/subgraphs] "subgraph state is only accessible when the subgraph is interrupted" — accept the debug cost.

OP-7 · Switch to durable Postgres checkpointing

• Trigger: Moving from notebook to production OR multi-user OR thread state must survive deploys.
• Action: from langgraph.checkpoint.postgres.aio import AsyncPostgresSaver, pass to .compile(checkpointer=...). Run await saver.setup() in a migration job, not at app boot.
• Output: Threads survive crashes, multi-replica reads work, ACID guarantees for state transitions.
• Evidence: [lc-docs/persistence] PostgresSaver "ideal for using in production"; [bswen/hitl] "handle this as part of a CI/CD migration script…not inside the primary application runtime."

OP-8 · Stream tokens to the UI without sacrificing graph observability

• Trigger: Need character-by-character UX and server-side debugging.
• Action: Use graph.stream(input, stream_mode=["messages", "updates"]) — messages yields LLM tokens, updates yields state diffs. For intra-tool progress, emit via stream_mode="custom".
• Output: User sees streaming tokens; server logs structured state diffs.
• Evidence: [lc-docs/streaming] five modes — values, updates, messages, custom, debug.

OP-9 · Bound an agent loop without papering over with recursion_limit

• Trigger: Agent hits GRAPH_RECURSION_LIMIT (e.g., text-to-SQL retrying the same broken query).
• Action: Read the conditional-edge logic — the real fix is an exit condition counting retries in state and routing to END after N attempts. Bump recursion_limit only as a temporary diagnostic.
• Output: Graceful termination on persistent failure.
• Evidence: [lc-docs/errors] "Check your logic for infinite loops"; [cheatsheet/gotchas] "Hitting the limit indicates an underlying design flaw"; concrete bug case [gh/6731].

OP-10 · Time-travel debug a failed production run

• Trigger: A production thread produced a wrong answer; need to see what state the LLM saw at step 7 and try a different prompt.
• Action: Fetch the thread's checkpoint history via graph.get_state_history(config), pick the checkpoint, invoke with config={"configurable": {"thread_id": ..., "checkpoint_id": ...}}. Modify state with graph.update_state(...) to fork.
• Output: Reproduce + fork a past run without re-paying for LLM calls on replayed nodes.
• Evidence: [dragonforest/timetravel] "replay…the agent knows that this checkpoint has already been executed and will just display the historical output instead of making new LLM calls."

困境决策案例 (Dilemma Cases)

Case 1 · "Text-to-SQL agent loops forever until recursion limit"

• 困境: A team built a text-to-SQL agent on LangGraph 1.0.6. When the Databricks query returned an error, the agent retried the same broken query 20 times until GRAPH_RECURSION_LIMIT fired. It had worked on 0.6.x [gh/6731].
• 约束:
  • Cannot pin to old version (security fixes in 1.x).
  • Maintainer marked the issue "not planned" — no official patch coming [gh/6731].
  • Business needs the agent live.
• 决策步骤:
  1. Reject the temptation to just raise recursion_limit — that masks the bug and burns Databricks quota [cheatsheet/gotchas].
  2. Add an explicit retry counter to state: retries: Annotated[int, operator.add].
  3. After the tool node, increment counter on SQL error; in the conditional edge, route to END (or a "give up and ask user" node) once retries >= 3.
  4. Surface the failure mode by storing the last error message in state and letting the LLM see it on the next loop — the content of the error usually informs whether to retry or abandon.
  5. Add a regression test that injects a permanent SQL error and asserts the graph terminates within 3 iterations.
• 结果: Bounded retries, observable failure, no quota blow-out. The community-acknowledged fact is that LangGraph's recursion limit is a safety net, not control flow — exit conditions are the real fix [lc-docs/errors].
• 可提取的操作: OP-9. Always bake a retry/exit counter into state for any cyclic graph. Trust nothing the LLM does to terminate itself.