LangGraph
by LangChain, Inc.
Build agents as explicit state graphs — durable, interruptible and debuggable
LangGraph is an MIT-licensed agent runtime and low-level orchestration framework from LangChain for building stateful AI agents as explicit graphs of nodes and edges rather than opaque prompt chains. It gives engineering teams durable execution, human-in-the-loop checkpoints, cross-session memory and token-level streaming, and pairs with the commercial LangSmith platform for tracing, evaluation and deployment.
LangGraph is an open-source agent runtime and low-level orchestration framework built by LangChain, Inc., the San Francisco company Harrison Chase and Ankush Gola founded in early 2023 and which now also has offices in New York, Boston and Amsterdam. Where a prompt chain hides control flow inside the model, LangGraph makes it explicit: developers define an agent as a graph of nodes and edges over a shared, typed state object, which yields deterministic branching, cycles, checkpointing, durable resume after failure, and human-in-the-loop interrupts at arbitrary points in a run. It ships persistent memory across sessions for what LangChain calls "rich, personalized interactions," native token-by-token streaming that surfaces agent reasoning and tool calls in real time, and moderation hooks intended to "prevent agents from veering off course." The library is MIT-licensed and free to use: 39,176 GitHub stars, 6,587 forks and 673 open issues as of August 2026, on a repository created in August 2023 and still receiving commits daily. Commercialisation happens next door in LangSmith — a Developer tier at $0 per seat with 5,000 base traces per month, Plus at $39 per seat with 10,000 traces and one free Serverless (Small) deployment, and Enterprise adding self-hosted and hybrid deployment, custom SSO and RBAC/ABAC, and a support SLA. Managed compute and storage bill separately at $1.50 per LangChain Compute Unit and $1.00 per LangChain Storage Unit. LangChain states it works with 35% of the Fortune 500, has crossed one billion open-source downloads, and ingests over one billion events per day on LangSmith; named LangGraph users include Klarna, LinkedIn, Uber, Cisco, Workday, Coinbase, Rippling, Lyft, Harvey, Abridge, Autodesk and Bristol Myers Squibb. It raised a $125M Series B led by IVP in October 2025 at a $1.25B valuation, bringing total funding to roughly $260M.
Platform engineering teams putting a multi-step agent into production who need the run to be inspectable, resumable and pauseable for human approval — not a black-box prompt chain.
Agent control flow becomes explicit code you can checkpoint, interrupt, replay and debug, instead of emergent behaviour you can only observe after the fact.
At a Glance
- Category
- Agent Development
- Pricing
- Free, Subscription, Usage-based
- Target Market
- CTOs, Platform Engineering Leaders, Enterprise Developers, Data Scientists, AI/ML Engineers
- Deployment
- Open-source, Self-hosted, Cloud-first, Hybrid
- Founded
- 2023
- Headquarters
- San Francisco, California, United States
- Customers
- LangChain states it works with 35% of the Fortune 500
Key Features
- ✓Explicit graph state machine
Agents are nodes and edges over a typed shared state object, making control flow inspectable rather than emergent from prompting.
- ✓Durable execution and checkpointing
Runs persist their state so a crashed or paused agent resumes from the last checkpoint instead of restarting the whole task.
- ✓Human-in-the-loop interrupts
Pause a graph at any node for approval or edit and then resume, which is essential before consequential or irreversible actions.
- ✓Cross-session memory
Persists context between conversations so agents deliver personalised interactions across sessions rather than starting cold each time.
- ✓Token-by-token streaming
Native streaming surfaces agent reasoning and tool calls in real time so users are not left watching a spinner.
- ✓MIT licence with no runtime lock-in
The library is free and self-hostable anywhere; only LangSmith tracing, evaluation and managed deployment carry a cost.
Capabilities
Use Cases
- •Customer support agent with escalation
Route a ticket through classification, retrieval and drafting nodes, interrupting for human approval before any refund is issued.
- •Production coding agent
Qodo publicly documented choosing LangGraph for its coding agent, citing the need for explicit control over multi-step code edits.
- •Research and report generation
Fan out parallel retrieval nodes, merge results in shared state, and stream the synthesised report to the reader as it is written.
- •Regulated workflow automation
Financial and healthcare teams use checkpoints and durable state to evidence exactly what an agent did at each step of a run.
- •Multi-agent orchestration
Compose supervisor and worker subgraphs so specialised agents hand off work through one shared, inspectable state object.
Ideal For
Best For
- ✓Engineering teams building multi-step agents that must be debuggable and reproducible in production
- ✓Workflows requiring a human approval gate before an agent takes a consequential or irreversible action
- ✓Long-running agents that need durable state and the ability to resume after a crash or timeout
- ✓Teams already using LangSmith for tracing and evaluation who want deployment on the same platform
- ✓Applications that need token-level streaming of agent reasoning into an end-user interface
Not Ideal For
- ✗Simple single-call LLM features — the graph abstraction is pure overhead when there is no branching, state or retry logic to manage
- ✗Teams that prefer plain code: an HN discussion of frameworks versus hand-rolled agents argues that for experienced developers 'framework abstractions can add unnecessary complexity,' and that built-in parallelism 'can complicate debugging a lot'
- ✗Latency- or memory-constrained deployments where lighter runtimes make large efficiency claims — Agno's HN post claims 50x less memory than LangGraph
- ✗Small teams without the appetite to own graph maintenance, which the community itself flags as unsolved once you have several agents and shared state across subgraphs
Integrations
Deployment
Market & Ratings
LangChain states it works with 35% of the Fortune 500
Market Analysis
Pros
- ✓MIT-licensed and self-hostable, so the core framework carries no vendor lock-in and no runtime licence cost
- ✓The explicit state graph makes agent behaviour inspectable and reproducible, which is exactly what teams find missing in prompt-chain frameworks
- ✓Strongest named production reference list of any open agent framework — Klarna, LinkedIn, Uber, Cisco, Workday, Coinbase, Harvey, Bristol Myers Squibb
- ✓Actively maintained: 39,176 stars, 6,587 forks and commits pushed within a day of checking in August 2026
Cons
- ✗Steep learning curve — HN practitioners note that for experienced developers 'framework abstractions can add unnecessary complexity' compared with writing the agent loop directly
- ✗Built-in parallelism is a debugging hazard, described in an HN framework comparison as something that 'can complicate debugging a lot'
- ✗Maintainability at scale is an acknowledged community gap: a Show HN post observes that tutorials 'show you how to build a graph, not how to maintain one when you have 8 nodes, 3 agents, and shared state across subgraphs'
- ✗673 open issues on the repository, and rival frameworks publish aggressive comparisons — Agno's HN post claims 5000x faster startup and 50x less memory
- ✗The free library funnels toward paid LangSmith, and LCU/LSU metering makes production cost hard to forecast before load testing
Pricing
LangGraph (open source)
$0
- ✓MIT licence
- ✓Self-host anywhere
- ✓No feature gating on the library itself
LangSmith Developer
$0 / seat per month
- ✓1 seat
- ✓Up to 5,000 base traces per month, then pay-as-you-go
- ✓No managed deployment
LangSmith Plus
From $39/seat/mo
- ✓Unlimited seats
- ✓Up to 10,000 base traces per month, then pay-as-you-go
- ✓1 free Serverless (Small) deployment
LangSmith Enterprise
Contact for pricing
- ✓Self-hosted and hybrid deployment
- ✓Custom SSO and RBAC/ABAC
- ✓Support SLA
- ✓Custom seats and workspaces
The LangGraph library itself is MIT-licensed and costs nothing — you pay only for LangSmith, LangChain's observability and deployment platform. Seats are $0 on Developer (1 seat, 5,000 base traces per month) and $39 per seat per month on Plus (unlimited seats, 10,000 traces, one free Serverless Small deployment), both then pay-as-you-go on overage. Managed compute and storage bill separately at $1.50 per LangChain Compute Unit and $1.00 per LangChain Storage Unit, which is where real production spend accumulates and what makes forecasting hard before load testing. Self-hosted and hybrid deployment, custom SSO, RBAC/ABAC and a support SLA are Enterprise-only and quoted.
Security & Compliance
Connect
Sources
This page was written from 6 sources, 3 on domains other than langchain.com.
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