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langgraph

langgraph

Build, run, persist, deploy, and operate LangGraph Python applications using the core runtime, prebuilt agents, checkpointing, CLI, and SDK clients.

Independent GitHub snapshot · Automated classification · Original documentation governs

Documentation de la Skill

GitHub

name: langgraph description: "Build, run, persist, deploy, and operate LangGraph Python applications using the core runtime, prebuilt agents, checkpointing, CLI, and SDK clients." disable-model-invocation: true metadata: disco-role: operating license: MIT

LangGraph

Use this skill when a task involves LangGraph, the low-level orchestration framework for building stateful agents and long-running workflows. It covers the Python monorepo packages langgraph, langgraph-prebuilt, langgraph-checkpoint, langgraph-checkpoint-sqlite, langgraph-checkpoint-postgres, langgraph-cli, and langgraph-sdk.

Quick Start

Install the core package for most application work:

pip install -U langgraph

For focused packages or deployment tooling:

pip install -U langgraph-cli
pip install -U langgraph-sdk
pip install -U langgraph-checkpoint-sqlite
pip install -U langgraph-checkpoint-postgres "psycopg[binary]"

Minimal import and graph smoke check:

from typing_extensions import TypedDict
from langgraph.graph import END, START, StateGraph

class State(TypedDict):
    value: int

def inc(state: State) -> State:
    return {"value": state["value"] + 1}

builder = StateGraph(State)
builder.add_node("inc", inc)
builder.add_edge(START, "inc")
builder.add_edge("inc", END)
app = builder.compile()
assert app.invoke({"value": 1})["value"] == 2

Route by Task

  • Custom graphs and runtime behavior: Use sub-skills/graph-runtime/SKILL.md for StateGraph, reducers, node signatures, conditional edges, Command, Send, interrupts, streaming, subgraphs, low-level Pregel, and runtime debugging.
  • Prebuilt agents and tools: Use sub-skills/prebuilt-agents/SKILL.md for create_react_agent, ToolNode, ValidationNode, injected state/store/runtime, tool-call errors, structured responses, and human interrupt payloads.
  • Persistence and memory: Use sub-skills/persistence/SKILL.md for checkpointers, thread_id, checkpoint resume, SQLite, Postgres, in-memory savers, InMemoryStore, semantic search, and serde hardening.
  • CLI and deployment: Use sub-skills/cli-deployment/SKILL.md for langgraph new, dev, up, build, dockerfile, validate, langgraph.json, Docker/server configuration, and local deployment troubleshooting.
  • SDK clients and streaming: Use sub-skills/sdk-clients/SKILL.md for Python async/sync SDK clients, assistants, threads, runs, cron, store APIs, v3 thread-centric streaming, auth headers, and JS SDK relocation status.

Shared References and Scripts

  • Read references/repo-provenance.md when deciding whether this skill matches a checkout or should be refreshed.
  • Read references/package-map.md to map user requests to the monorepo packages, install commands, import modules, and common optional dependencies.
  • Read references/troubleshooting.md for cross-cutting install, import, config, service, security, and version-mismatch failures before drilling into a sub-skill’s troubleshooting file.
  • Run scripts/run_core_smokes.py --help or selected smoke checks when validating a LangGraph environment without relying on the original repository checkout.

Common Decisions

  • Choose StateGraph when a task needs custom state schemas, deterministic routing, reducers, subgraphs, or precise interrupt/resume control.
  • Choose prebuilt agent APIs when the task is primarily a tool-calling chat agent and does not need a fully custom graph loop.
  • Use an in-memory checkpointer only for tests, local demos, or debugging; use SQLite for lightweight local persistence and Postgres for durable multi-process or production persistence.
  • Use langgraph dev for local hot-reload development, langgraph up for a local Docker API server, langgraph build for an image, and langgraph dockerfile when a user needs to review or customize the generated container recipe.
  • Use the SDK only when there is a running LangGraph API server or deployment; local graph construction/invocation does not require langgraph-sdk.

Verification Checklist

  1. Confirm package imports with the relevant sub-skill smoke script.
  2. Compile a minimal graph before adding persistence, tools, or deployment configuration.
  3. If persistence is involved, invoke with config={"configurable": {"thread_id": "..."}} and verify resume/list behavior.
  4. If serving with the CLI, validate langgraph.json before running dev, up, or build.
  5. If using the SDK, verify URL/auth selection and streaming mode against the server being targeted.

Safety and Scope

This skill is self-contained. Do not require the original LangGraph repository checkout for runtime use. The bundled references and scripts distill repo evidence into reusable guidance; original tests and examples remain verification evidence, not runtime dependencies.

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