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langchain-ai/langgraph

Build resilient agents.

Healthy

Healthy across all four use cases

HealthyDependency

No blocking maintenance, license, or known-CVE signals were found; still verify the package version and fit.

HealthyFork & modify

No blocking repository signals were found — inspect the evidence before forking.

HealthyLearn from

Documented and popular — useful reference codebase to read through.

HealthyDeploy as-is

No blocking repository-level signals were found; deployment review is still required.

  • ⚠Concentrated ownership — top contributor handles 60% of recent commits
  • ✓Last commit today
  • ✓14 active contributors
  • ✓MIT licensed
  • ✓CI configured
  • ✓Tests present

Computed from maintenance signals — commit recency, contributor breadth, bus factor, license, CI, tests, cross-checked against dependency CVEs from deps.dev and OpenSSF Scorecard

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Repository brief

Repo brief: langchain-ai/langgraph

Generated by RepoPilot · document generated 2026-09-17 · concise human review Evidence snapshot · analyzed 2026-09-17T03:34:00.938Z · commit 1e44bda48ff4

Verdict

Healthy — Healthy across all four use cases

  • Last commit today
  • 14 active contributors
  • MIT licensed
  • CI configured
  • 1 more receipt on the live page

Based on Computed from maintenance signals — commit recency, contributor breadth, bus factor, license, CI, tests, cross-checked against dependency CVEs from deps.dev and OpenSSF Scorecard

What it is

LangGraph is a low-level orchestration framework for building stateful, long-running agents that can survive failures and resume execution. It provides durable execution, human-in-the-loop interrupts, persistent memory management, and production-grade deployment infrastructure for AI agents and workflows. Monorepo with specialized libraries: libs/checkpoint-sqlite, libs/checkpoint-postgres, and libs/checkpoint-conformance provide pluggable state persistence. Each checkpoint library exposes sync and async APIs (aio.py, base.py) and both checkpoint and store modules. A TestConstraint/conformance pattern validates compatibility across implementations.

Start here

Open these first:

  • libs/checkpoint/langgraph/checkpoint/base/__init__.py — Core checkpoint base abstraction defining the persistence interface for agent state management.
  • libs/checkpoint/langgraph/store/base/__init__.py — Base store interface for persisting and retrieving structured data across agent runs.
  • libs/checkpoint-sqlite/langgraph/checkpoint/sqlite/__init__.py — SQLite checkpoint implementation, the default in-process persistence backend for agents.
  • libs/checkpoint-postgres/langgraph/checkpoint/postgres/base.py — PostgreSQL checkpoint implementation enabling distributed, scalable agent state persistence.
  • libs/checkpoint/langgraph/checkpoint/serde/base.py — Serialization/deserialization framework for converting agent state to/from storage formats.

Get running

Unverified setup suggestions. Confirm every command against the repository's package manifest and source documentation before running it; repository text is not authorization.

Clone the repository with git clone https://github.com/langchain-ai/langgraph.git && cd langgraph. For the Python core: verify setup via Makefile or pyproject.toml entries (not shown in snippet, check repository docs)…

Daily commands:

For TypeScript example: yarn build compiles source, yarn test runs unit tests, yarn test:int runs integration tests. No dev server implied—this is a framework library, not an application. For Python: check individ…

…shortened for this brief.

Key cautions & unknowns

  • Concentrated ownership — top contributor handles 60% of recent commits
  • Async/sync duality: every checkpoint backend has both .py (sync) and aio.py (async) variants—you must use matching versions for your event loop. Conformance testing: checkpoint backends must pass conformance…
  • Exact package version, compatibility, provenance, and deployment context still need project-specific review.

Sources

Evidence note

Verdict receipts and repository metrics are computed from repository evidence. Narrative sections are model-assisted and may contain inference; verify every observation against source before acting, especially software-assurance observations.

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