Key takeaways
- Graph-based agent orchestration with durable execution — agents persist through failures and resume from exactly where they left off
- Built-in human-in-the-loop with state inspection and modification at any execution point
- 25K GitHub stars and enterprise adoption (Klarna, Replit, Elastic) make it the most production-proven agent orchestration framework
- Part of LangChain ecosystem — integrates with LangSmith for observability and LangGraph Studio for visual prototyping
FAQ
What is LangGraph?
LangGraph is a low-level orchestration framework by LangChain for building long-running, stateful AI agents as directed graphs with durable execution, human-in-the-loop, and comprehensive memory.
How does LangGraph differ from LangChain?
LangChain provides composable components for LLM apps. LangGraph adds stateful graph-based orchestration specifically for agent workflows — durable execution, checkpointing, and multi-agent coordination.
Is LangGraph open source?
Yes, LangGraph is MIT-licensed. LangGraph Platform (deployment/hosting) is a commercial offering.
Who competes with LangGraph?
CrewAI for multi-agent teams, AutoGen for Microsoft-ecosystem agents, and custom frameworks. For protocols, A2A and ACP handle cross-agent communication that LangGraph doesn't address.
Executive Summary
LangGraph is LangChain's answer to the question "how do you build agents that survive failures, run for hours, and coordinate with humans?" It models agent workflows as directed graphs with persistent state, durable execution, and checkpointing — making it the most production-oriented agent orchestration framework available. [1]
With 25K GitHub stars and named enterprise customers (Klarna, Replit, Elastic), LangGraph has moved beyond framework-of-the-month status into genuine production use.
| Attribute | Value |
|---|---|
| Company | LangChain, Inc. |
| Launched | 2024 |
| License | MIT (framework), Commercial (Platform) |
| GitHub Stars | 25K |
| Languages | Python, JavaScript/TypeScript |
Product Overview
LangGraph provides low-level infrastructure for stateful, long-running agent workflows. [2]
Key Capabilities
| Capability | Description |
|---|---|
| Graph-Based Orchestration | Model agent workflows as nodes and edges with conditional routing |
| Durable Execution | Agents persist through failures and resume from checkpoints |
| Human-in-the-Loop | Inspect and modify agent state at any execution point |
| Comprehensive Memory | Short-term working memory + long-term persistent memory across sessions |
| Multi-Agent Support | Coordinate multiple agents within a single graph |
| Streaming | Real-time output streaming for interactive applications |
Ecosystem
| Component | Description | Pricing |
|---|---|---|
| LangGraph (OSS) | Core graph framework | Free (MIT) |
| LangGraph Platform | Deployment, scaling, agent management | Commercial |
| LangGraph Studio | Visual prototyping and debugging | Free with LangSmith |
| LangSmith | Observability, tracing, evals | Freemium |
How It Works
from langgraph.graph import START, StateGraph
graph = StateGraph(State)
graph.add_node("research", research_agent)
graph.add_node("write", writing_agent)
graph.add_node("review", human_review)
graph.add_edge(START, "research")
graph.add_edge("research", "write")
graph.add_edge("write", "review")
graph.add_conditional_edges("review", route_feedback)
app = graph.compile(checkpointer=MemorySaver())
Technical Architecture
| Aspect | Detail |
|---|---|
| Model | Directed graph with state transitions |
| State | Typed state objects with reducer functions |
| Persistence | Checkpoint-based (memory, SQLite, PostgreSQL) |
| Execution | Durable — survives crashes, resumes from checkpoint |
| Communication | Internal (graph edges), no cross-process protocol |
| Languages | Python 3.9+, JavaScript/TypeScript |
| Open Source | MIT (core), proprietary (Platform) |
Strengths
- Production-proven — Named enterprise customers (Klarna, Replit, Elastic) provide credibility
- Durable execution — Checkpoint-based persistence is a genuine differentiator for long-running agents
- LangChain ecosystem — LangSmith observability and broad LLM integrations
- Low-level control — Doesn't abstract away prompts or architecture; you control everything
- Active development — Pushed today (Feb 23, 2026), 25K stars, 4.4K forks
- Multi-language — Python and JS/TS SDKs cover most use cases
- Human-in-the-loop — Built-in state inspection and modification, not bolted on
Cautions
- Intra-application only — No protocol for cross-process or cross-organization agent communication
- LangChain coupling — While usable standalone, ecosystem benefits require LangChain buy-in
- Complexity — Graph-based programming has a learning curve; not as intuitive as CrewAI's role-based model
- Commercial platform lock-in — Free framework, but production deployment pushes toward paid LangGraph Platform
- 332 open issues — Significant backlog relative to development pace
- No cross-agent protocol — LangGraph agents can't natively talk to non-LangGraph agents without A2A/ACP
Pricing & Licensing
| Tier | Price | Includes |
|---|---|---|
| LangGraph OSS | Free | Core framework (MIT) |
| LangSmith Developer | Free | Limited tracing and evals |
| LangSmith Plus | $39/seat/mo | Full observability |
| LangGraph Platform | Custom | Managed deployment, scaling |
Competitive Positioning
| Competitor | LangGraph Differentiator |
|---|---|
| CrewAI | LangGraph is lower-level with more control; CrewAI is higher-level with easier setup |
| AutoGen | LangGraph has durable execution and checkpointing; AutoGen has richer conversation patterns |
| A2A | Different layers — LangGraph orchestrates internally, A2A connects externally |
| Summoner | Summoner handles cross-org coordination; LangGraph handles intra-app orchestration |
Ideal Customer Profile
Best fit:
- Teams building complex, long-running agent workflows
- Organizations needing durable execution with failure recovery
- LangChain ecosystem users wanting agent orchestration
- Enterprise teams needing observability (LangSmith) and visual debugging (Studio)
Poor fit:
- Simple single-agent applications (overkill)
- Teams needing cross-organization agent coordination (need A2A/Summoner on top)
- Developers wanting high-level abstractions (CrewAI is easier)
- Teams avoiding vendor ecosystem lock-in
Viability Assessment
| Factor | Assessment |
|---|---|
| Financial Health | Strong — LangChain raised $25M+ Series A |
| Market Position | Leading — Most popular agent orchestration framework |
| Innovation Pace | Very active — Daily commits, continuous releases |
| Community/Ecosystem | Large — 25K stars, enterprise customers, LangChain ecosystem |
| Long-term Outlook | Strong — Durable execution and graph orchestration are enduring patterns |
Bottom Line
LangGraph is the most production-ready agent orchestration framework. Its graph-based model with durable execution, checkpointing, and human-in-the-loop solves real problems that simpler frameworks don't address. The trade-off is complexity and LangChain ecosystem coupling.
Recommended for: Teams building complex, long-running agent workflows that need reliability, observability, and human oversight.
Not recommended for: Simple agent applications, teams wanting high-level abstractions, or scenarios requiring cross-organization agent coordination.
Outlook: LangGraph will likely remain the default choice for stateful agent orchestration. The key question is whether A2A integration becomes native — LangGraph agents that can also participate in cross-framework coordination would be the complete package.
Research by Ry Walker Research • methodology