langgraph
LangGraph is a Python framework for building stateful, long-running AI agents with built-in support for durability, human oversight, and persistent memory. It handles the orchestration and state management challenges of agent workflows, letting you focus on logic rather than infrastructure.
Key facts
Objective fields from the source. Values we can't verify are shown as “Unknown” rather than guessed.
| Field | Value |
|---|---|
| Repository | langchain-ai/langgraph |
| Owner | langchain-ai |
| Primary language | Python |
| License | MIT — OSI-approved |
| Stars | 36.7k |
| Forks | 6.2k |
| Open issues | 602 |
| Latest release | 1.2.8 (2026-07-06) |
| Last updated | 2026-07-06 |
| Source | https://github.com/langchain-ai/langgraph |
What langgraph is
LangGraph is a low-level orchestration framework that provides durable execution, human-in-the-loop interrupts, memory management, and state persistence for multi-step agent and workflow systems. It integrates with LangChain components and supports debugging via LangSmith, with a graph-based execution model inspired by Pregel and Apache Beam.
Get the langgraph source
Clone the repository and explore it locally.
git clone https://github.com/langchain-ai/langgraph.gitcd langgraph# follow the project's README for install & configurationNeed it deployed, integrated, or customized instead? DEV.co ships production installs.
Best use cases
Implementation considerations
- State schema design: Carefully define your agent state structure early; changes later require migration logic. Use Pydantic for validation.
- Persistence backend selection: Choose appropriate checkpointing strategy (in-memory, database, cloud store) based on durability and recovery SLA requirements.
- Human interrupt points: Design graph topology to identify where human review makes sense; over-interrupting degrades UX, under-interrupting undermines oversight.
- Memory management: Balance short-term working memory (for reasoning) with long-term storage (for cross-session context); tune retention policies to avoid token bloat.
- LLM cost and latency: Long-running agents with frequent API calls multiply token usage; budget for observability via LangSmith to track spending and performance.
When to avoid it — and what to weigh
- Simple request-response systems — If you need stateless API endpoints or chatbot-like single-turn interactions, LangGraph adds unnecessary complexity. Standard request-response frameworks are more appropriate.
- Minimal state or memory requirements — Projects that don't need persistent memory across sessions or checkpointing may find LangGraph's infrastructure overhead not justified compared to simpler orchestration patterns.
- Non-Python environments — LangGraph is Python-first. If your stack is predominantly Java, Go, or other languages, the JS/TS variant (LangGraph.js) may be better, but introduces separate codebases.
- Real-time, ultra-low-latency systems — The overhead of state serialization, persistence, and resumption may not suit applications requiring sub-100ms response times or continuous streaming without interruption points.
License & commercial use
LangGraph is released under the MIT License, a permissive OSI-approved license that allows commercial use, modification, and distribution with minimal restrictions (attribution required).
MIT License permits commercial use without additional licensing. However, verify that any LangChain enterprise features or LangSmith platform services you depend on are covered under separate commercial terms. The open-source framework itself imposes no commercial restrictions.
DEV.co evaluation signals
Editorial assessment — not user reviews. Directional, with an explicit confidence level.
| Signal | Assessment |
|---|---|
| Maintenance | Active |
| Documentation | Strong |
| License clarity | Clear |
| Deployment complexity | High |
| DEV.co fit | Strong |
| Assessment confidence | High |
No security audit data provided. As with any agent framework, consider: (1) Input validation on agent state and tool inputs to prevent injection; (2) secrets management for LLM API keys and external tool credentials (not built-in; use environment variables or secret management tools); (3) access control on persisted state in checkpoints (e.g., database encryption, RBAC); (4) audit logging of human-in-the-loop decisions; (5) isolation of untrusted agent logic. Requires review for compliance with your security standards.
Alternatives to consider
Autogen (Microsoft)
Multi-agent orchestration framework with stronger built-in conversational multi-agent patterns. Better if you need agent-to-agent communication; weaker on durable execution and checkpoint/resume.
Haystack (Deepset)
Pipeline-based orchestration optimized for RAG and document processing. Simpler state model, good if your workflows are primarily retrieval + generation chains; less suited to long-running stateful agents.
Temporal.io
Language-agnostic workflow orchestration with strong durability, retry, and human approval. If you need polyglot support or non-LLM workflows, Temporal excels; steeper operational complexity, less LLM-native.
Build on langgraph with DEV.co software developers
Evaluate LangGraph's fit for your agent architecture. Start with the quickstart guide, run a POC with durable execution, and assess integration with your LLM stack and deployment platform.
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langgraph FAQ
Can I use LangGraph without LangChain?
How does LangGraph handle state persistence?
What's the difference between LangGraph and Deep Agents?
Is there a JavaScript version?
Software development & web development with DEV.co
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Ready to build stateful agents?
Evaluate LangGraph's fit for your agent architecture. Start with the quickstart guide, run a POC with durable execution, and assess integration with your LLM stack and deployment platform.