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Decision brief
LangGraph is designed as an open-source orchestration framework to build stateful AI agents. It emphasizes durable execution and human-in-the-loop interventions, along with robust memory management capabilities.
Good fit when
- When you need a low-level framework that supports the development of long-running, stateful AI agents that require complex memory management.
- For projects where maintaining agent state across multiple sessions is critical, such as in customer support chatbots or financial advisors.
Avoid when
- LangGraph is less suitable when you prefer a simpler setup. It demands more initial configuration and lower-level control than other agent-building frameworks such as Deep Agents.
- For projects requiring immediate quick-start capabilities, as LangGraph offers foundational support which involves more manual configuration versus quicker-to-prototype tools.
- Pricing:
- freemium - LangGraph is available under the MIT license and is free for both personal and commercial use. However, advanced features or services like deployment and debugging might depend on additional paid-for-
- Requirements:
- Min 4 GB RAM; Requires Python environment
Observed Jul 11, 2026 · Source: enrich:decision_facts
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Maintenance and security
Full trust report- Maintenance
- Very active (0d since push)
- As of 3w
- Provenance
- Not a fork · Organization account
- As of 3w
- Security (OSV)
- No lockfile
- As of 1mo
Public GitHub metadata and optional OSV scans. Signals, not a guarantee. Trust methodology.
Backing
Company context for LangChain. Display-only - separate from trust and ranking.
- Company
- LangChain·GitHub org profile·1mo
- Funding
- $25,000,000 (2024-02)·GraphCanon curated seed (public press)·1mo
- Commercial model
- Open core·GraphCanon curated seed·1mo
Install
pip install langgraphHow it fits your stack(13)
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Evidence and technical details
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Overview
LangGraph is an open-source, low-level orchestration framework designed to build resilient and long-running stateful AI agents. It supports durable execution, human-in-the-loop modifications, and comprehensive memory management for agents across a variety of tasks.
Capability facts
- Languages
- python
Source: github.language · Jul 28, 2026
Categories
Graph entities
Compatibility
Sourced claims from the README excerpt - not unsourced marketing copy.
Source: README excerpt (regex_v1, Jul 28, 2026)
<a href="https://www.langchain.com/langgraph">Source link
Source: README excerpt (regex_v1, Jul 28, 2026)
<a href="https://www.langchain.com/langgraph">Source link
Source: README excerpt (regex_v1, Jul 28, 2026)
quickly build agents, check out **[Deep Agents](https://docs.langchain.com/oss/python/deepagents/overview)** — a higher-level package built on LangGraph for agents tSource link
Tags
README
Low-level orchestration framework for building stateful agents.
Trusted by companies shaping the future of agents – including Klarna, Replit, Elastic, and more – LangGraph is a low-level orchestration framework for building, managing, and deploying long-running, stateful agents.
pip install -U langgraph
[!TIP] If you're looking to quickly build agents, check out Deep Agents — a higher-level package built on LangGraph for agents that can plan, use subagents, and leverage file systems for complex tasks.
For an equivalent JS/TS library, check out LangGraph.js and the JS docs.
Why use LangGraph?
LangGraph provides low-level supporting infrastructure for any long-running, stateful workflow or agent:
- Durable execution — Build agents that persist through failures and can run for extended periods, automatically resuming from exactly where they left off.
- Human-in-the-loop — Seamlessly incorporate human oversight by inspecting and modifying agent state at any point during execution.
- Comprehensive memory — Create truly stateful agents with both short-term working memory for ongoing reasoning and long-term persistent memory across sessions.
- Debugging with LangSmith — Gain deep visibility into complex agent behavior with visualization tools that trace execution paths, capture state transitions, and provide detailed runtime metrics.
- Production-ready deployment — Deploy sophisticated agent systems confidently with scalable infrastructure designed to handle the unique challenges of stateful, long-running workflows.
[!TIP] For developing, debugging, and deploying AI agents and LLM applications, see LangSmith.
LangGraph ecosystem
While LangGraph can be used standalone, it also integrates seamlessly with any LangChain product, giving developers a full suite of tools for building agents.
To improve your LLM application development, pair LangGraph with:
- Deep Agents – Build agents that can plan, use subagents, and leverage file systems for complex tasks.
- LangChain – Provides integrations and composable components to streamline LLM application development.
- LangSmith – Helpful for agent evals and observability. Debug poor-performing LLM app runs, evaluate age
For agents
This page has a .md twin and JSON over the API.