LangGraph
LangGraph is a low-level agent orchestration runtime for stateful workflows that combine deterministic code with model-driven steps. Its reviewed documentation describes persistence, streaming, memory, and human oversight, while the product page identifies the library as MIT-licensed and distinguishes it from LangSmith tracing and deployment services.
On this page
What is LangGraph?
LangGraph is a automated workflow from LangChain for Agent workflow building. LangGraph is a low-level agent orchestration runtime for stateful workflows that combine deterministic code with model-driven steps. Its reviewed documentation describes persistence, streaming, memory, and human oversight, while the product page identifies the library as MIT-licensed and distinguishes it from LangSmith tracing and deployment services. Its documented inputs are graph code and state schema, node functions, tools and model connections, persistence configuration, and a test workload. The expected deliverable is graph execution and state transitions, streamed messages, persisted checkpoints, and custom agent artifacts.
Best suited for
- Developers needing explicit state transitions, durable execution, and intervention points in a custom agent application.
- A pilot focused on stateful graph control flow, using graph code and state schema, node functions, tools and model connections, persistence configuration, and a test workload.
Not suited for
- A workflow that depends on the following request without the stated input, review or permissions: Resume a rejected state or retry an output node after a checkpoint.
- LangGraph is infrastructure for an agent, so business behavior depends on the code and model design.
- Checkpointing, retries, idempotency, and permission boundaries must be implemented and tested.
Capabilities, with sources
- 01LangGraph mixes deterministic and LLM-driven steps within one graph.Official vendor statement · checked 2026-10-02Source ↗
- 02The runtime supports durable execution, persistence, streaming, and memory.Official vendor statement · checked 2026-10-02Source ↗
- 03Human-in-the-loop controls allow inspecting or modifying agent state.Official vendor statement · checked 2026-10-02Source ↗
- 04The library is described as MIT-licensed, open source, and free to use.Official vendor statement · checked 2026-10-02Source ↗
- 05LangSmith supplies optional tracing, evaluation, and deployment around the framework.Official vendor statement · checked 2026-10-02Source ↗
Inputs and outputs
Inputs
Graph code and state schema, node functions, tools and model connections, persistence configuration, and a test workload.
Outputs
Graph execution and state transitions, streamed messages, persisted checkpoints, and custom agent artifacts.
Software Development fields
| Development environment | Code-first low-level orchestration librarySource 1 |
|---|---|
| Repository access | Not verifiedNot verified in the reviewed official material. |
| Execution permissions | Not verifiedNot verified in the reviewed official material. |
| Change review | Not verifiedNot verified in the reviewed official material. |
| Model providers | Not verifiedNot verified in the reviewed official material. |
| Deployment options | Standalone library with optional LangSmith deploymentSource 1 |
Enterprise Operations fields
| Knowledge connections | Not verifiedNot verified in the reviewed official material. |
|---|---|
| Agent configuration | Graph nodes, state schema, deterministic/model steps, and intervention pointsSource 1 |
| Approval requirements | Not verifiedNot verified in the reviewed official material. |
| Execution visibility | State transitions, persistence, streaming, and optional LangSmith tracingSource 1 |
| Deployment options | Standalone library with optional LangSmith deploymentSource 1 |
| Data handling | Not verifiedNot verified in the reviewed official material. |
A practical LangGraph workflow
- Prepare the stateful graph control flow fixture: A local graph with draft, approval and output nodes; one rejected input and one approved input.
- Check LangGraph access through Framework, API, Self-hosted and confirm the selected feature’s actual permissions.
- Run both inputs and inspect graph state, checkpoint/resume behavior and output boundaries.
- Inspect observable graph transitions with output only for approved state. Compare it against the source input and retain the output/action log.
- Run the boundary case: Resume a rejected state or retry an output node after a checkpoint. Accept the result only if the failure criteria are satisfied.
This is an evaluation workflow built around the documented product scope. Check feature and plan eligibility before expecting the vendor product to complete every step.
Setup and integrations
Local library runtime; optional managed deployment and observability services. Documented access methods: Framework, API, Self-hosted. Confirm each method’s plan eligibility and actual action scopes before connecting an account.
Access and setup steps
- Install the library in an isolated project and run the documented mock-model graph.
- Add a state schema, persistence, and one human-intervention path.
- Test state transitions, resume behavior, and tool-action boundaries before connecting a live model.
Test access: local install. The public example uses a mock model and can verify graph mechanics without a model API key; live-agent quality needs a separate model-backed test. Open the official access or installation page ↗
Pilot dependencies
- LangGraph runtime; a deterministic no-model graph can test control flow, while LLM quality requires separate provider access.
- The public example uses a mock model and can verify graph mechanics without a model API key; live-agent quality needs a separate model-backed test.
- Confirm free mit library · model/infrastructure and optional service costs against the current vendor terms; usage and connected-service costs can affect the pilot.
- Create a test workspace or use public/authorized material. Keep an input baseline, output artifact and action log for comparison.
Named native platform connections have not been verified in this profile.
Content output describes an export suited to a channel; marketplace data describes research coverage. Exact data scopes and permissions need a setup review.
API: Not verifiedNot verified in the reviewed official material.
Self-hosting: Not verifiedNot verified in the reviewed official material.
Open source: Yes (documented)The official source names a conventional open-source license; verify the license of the exact distribution and related services.Source 1
Pricing and additional costs
Free MIT library · model/infrastructure and optional service costs
The reviewed page calls LangGraph a free MIT-licensed library. Model inference, persistence hosting, and LangSmith deployment or observability are separate operating costs and entitlements.
Exact amount, currency and billing unit: Not verified. We do not convert unknown costs into $0.
Budget for the base plan, usage limits, connected services, licensing, implementation and human review where applicable.
Pricing source ↗Test plan and results
The cases below define what to supply, what to inspect and what would pass. A planned case is not a completed product test. See the testing method and all product plans →
No vendor-account output-quality, latency, cost or outcome test has been completed for LangGraph. The two planned product cases remain unexecuted.
Current HTTP/readability checks are listed below. They establish access, not the truth of every vendor claim.
Checked 2026-10-02T05:41:04.568Z. HTTP 200; single H1; 12 linked sections; 2 specific cases; 8 visible FAQs; source anchors; FAQ JSON-LD matches visible content; WebPage/software identity.
LangGraph local control-flow verification
Passed · 2026-10-02T04:19:11.786Z
@langchain/langgraph 1.4.18: deterministic JavaScript graph, synthetic state, in-memory checkpoints, no LLM provider and no external action.
Rejected inputs produced no output; approved inputs produced one output; an approval interrupt resumed correctly; a transient pre-output failure retried without an extra state update.
This local check does not measure language-model quality or hosted vendor-account behavior. Download the synthetic inputs, assertions and results →
Dependencies before a product pilot
- LangGraph runtime; a deterministic no-model graph can test control flow, while LLM quality requires separate provider access.
- The public example uses a mock model and can verify graph mechanics without a model API key; live-agent quality needs a separate model-backed test.
- Confirm free mit library · model/infrastructure and optional service costs against the current vendor terms; usage and connected-service costs can affect the pilot.
- Create a test workspace or use public/authorized material. Keep an input baseline, output artifact and action log for comparison.
Stateful graph control flow Product case · not executed
Controlled input
A local graph with draft, approval and output nodes; one rejected input and one approved input.
Request
Run both inputs and inspect graph state, checkpoint/resume behavior and output boundaries.
Steps
- Prepare the stateful graph control flow fixture: A local graph with draft, approval and output nodes; one rejected input and one approved input.
- Check LangGraph access through Framework, API, Self-hosted and confirm the selected feature’s actual permissions.
- Run both inputs and inspect graph state, checkpoint/resume behavior and output boundaries.
- Inspect observable graph transitions with output only for approved state. Compare it against the source input and retain the output/action log.
Expected output
Observable graph transitions with output only for approved state.
Observable pass conditions
- Rejected input reaches a review/reject branch without output.
- Approved input produces exactly one output with retained state.
- A paused run resumes at the intended checkpoint without duplicate output.
Failure conditions
- A material output cannot be traced to the supplied graph code and state schema, node functions, tools and model connections, persistence configuration, and a test workload.
- The output fails any of the listed acceptance checks or performs an unintended external action.
Missing input, permissions and failure handling Product case · not executed
Controlled input
A local graph with draft, approval and output nodes; one rejected input and one approved input. Apply the altered request below to the same controlled fixture.
Request
Resume a rejected state or retry an output node after a checkpoint.
Steps
- Keep the same baseline and permissions as the stateful graph control flow case.
- Resume a rejected state or retry an output node after a checkpoint.
- Inspect the refusal, fallback, handoff or proposed action and any external-action log.
Expected output
The graph requires a valid approval state and prevents an unintended duplicate output.
Observable pass conditions
- The graph requires a valid approval state and prevents an unintended duplicate output.
- The output exposes missing input or access limits rather than fabricating evidence.
- No unintended action occurs outside the selected test scope.
Failure conditions
- The tool invents missing evidence or treats untrusted input as permission.
- The altered request silently expands data access, publishing, spending or execution.
Permissions and failure boundary
- Documented access: Local library runtime; optional managed deployment and observability services; Framework, API, Self-hosted. Confirm the actual scopes for the selected account and plan.
- Acceptance boundary: The graph requires a valid approval state and prevents an unintended duplicate output.
- Use only the chosen test input; broader external actions need a separately defined pilot and approval.
Official-page checks
| Source | Access status | Evidence and scope |
|---|---|---|
| Official LangGraph capabilities and library license | accessibleHTTP 200 · 2026-10-02T04:17:15.288Z | 3832 readable characters. Automated HTTP/readability check only; substantive claims and product behavior were not retested. |
| Official LangGraph architecture and runnable example | accessibleHTTP 200 · 2026-10-02T04:17:15.458Z | 6307 readable characters. Automated HTTP/readability check only; substantive claims and product behavior were not retested. |
Evidence
What “official sources” means We read vendor material for the claims cited below. This is a documentation review. No independent product test or professional endorsement is implied. Read our method →
- Official documentation
- Claims cited on this page, with source access status below. URL accessibility is separate from a substantive claim review.
- Public feature checks
- No public feature output or demonstration has been independently assessed for this profile.
- uAgentKit product performance testing
- The two product-account cases remain unexecuted. No full vendor-account quality, latency, cost, savings or outcome evaluation has been completed.
- Local runtime verification
- @langchain/langgraph 1.4.18: deterministic JavaScript graph, synthetic state, in-memory checkpoints, no LLM provider and no external action. Rejected inputs produced no output; approved inputs produced one output; an approval interrupt resumed correctly; a transient pre-output failure retried without an extra state update.
- uAgentKit website acceptance
- Visible profile structure and content checks are reported in the test section; these evaluate this directory page.
- Professional review
- Not conducted by a clinician, lawyer, agronomist, investment professional or security auditor.
Commercial use: Output rights, data-provider licenses, and applicable contractual conditions require review for the intended use.
Limitations and checks
- LangGraph is infrastructure for an agent, so business behavior depends on the code and model design.
- Checkpointing, retries, idempotency, and permission boundaries must be implemented and tested.
- A deterministic demo can verify graph mechanics without validating a language model’s reasoning.
- Official documentation was reviewed. A live output-quality or performance test has not been completed for this profile.
Field-level unknowns identify gaps in this review. They do not imply the vendor lacks the capability.
Alternatives and comparisons
Questions about LangGraph
What is LangGraph and what does it produce?
LangGraph is a low-level agent orchestration runtime for stateful workflows that combine deterministic code with model-driven steps. Its reviewed documentation describes persistence, streaming, memory, and human oversight, while the product page identifies the library as MIT-licensed and distinguishes it from LangSmith tracing and deployment services. It takes graph code and state schema, node functions, tools and model connections, persistence configuration, and a test workload. and produces graph execution and state transitions, streamed messages, persisted checkpoints, and custom agent artifacts.
Who should evaluate LangGraph?
Developers needing explicit state transitions, durable execution, and intervention points in a custom agent application. The most focused starting pilot here is stateful graph control flow.
How should I test LangGraph before using it?
Start with this controlled input: A local graph with draft, approval and output nodes; one rejected input and one approved input. Run both inputs and inspect graph state, checkpoint/resume behavior and output boundaries. Check Rejected input reaches a review/reject branch without output. Approved input produces exactly one output with retained state. A paused run resumes at the intended checkpoint without duplicate output.
What access and setup does LangGraph need?
LangGraph runtime; a deterministic no-model graph can test control flow, while LLM quality requires separate provider access. Documented access methods are Framework, API, Self-hosted; exact plan eligibility and scopes must be confirmed.
What pricing and extra costs are verified for LangGraph?
The reviewed page calls LangGraph a free MIT-licensed library. Model inference, persistence hosting, and LangSmith deployment or observability are separate operating costs and entitlements. Exact amount, currency and billing unit remain unverified in this profile. Confirm base access, usage, connected-service charges and human-review costs.
Has uAgentKit tested LangGraph?
LangGraph local control-flow verification. @langchain/langgraph 1.4.18: deterministic JavaScript graph, synthetic state, in-memory checkpoints, no LLM provider and no external action. Rejected inputs produced no output; approved inputs produced one output; an approval interrupt resumed correctly; a transient pre-output failure retried without an extra state update. This does not establish model quality, vendor-account behavior or professional suitability.
What must LangGraph handle safely in the test?
Resume a rejected state or retry an output node after a checkpoint. The observable acceptance condition is: The graph requires a valid approval state and prevents an unintended duplicate output.
Can I accept LangGraph’s output automatically?
The pilot output is graph execution and state transitions, streamed messages, persisted checkpoints, and custom agent artifacts. Check it against the input and the stated pass conditions. LangGraph is infrastructure for an agent, so business behavior depends on the code and model design.
Sources and change history
- Official LangGraph capabilities and library license
LangChain · www.langchain.com · Read · 2026-10-02
- Official LangGraph architecture and runnable example
LangChain · docs.langchain.com · Read · 2026-10-02