Ship LangGraph Code, Not Hallucinations.

LangGraph Dev Navigator retrieves the right files from your repo, generates scoped code, and proves its work with a LangGraph-specific knowledge graph.

Before: Chasing broken links and hallucinated classes from generic AI helpers.
After: Getting runnable answers traced to the exact LangGraph release you use.

Join the waitlist

After you submit, we'll send a 30-second build-priorities survey so we ship the workflows you need first.

Backed by Supabase RAG + Neo4j validation

Open source (MIT) — bring your own API keys

Value pillars built for grounded answers

Every response is backed by your repo so you can ship with confidence instead of debugging guesswork.

Repo-Grounded Retrieval

Stop debugging answers from the public web. The Navigator uses RAG to search your local LangGraph docs and source code, so every response is grounded in runnable, version-correct references.

Finds the right context using the perform_rag_query, FindFiles, and ReadFile tools.
See it in action: The assistant finds reflection.ipynb to answer a complex question instead of returning a broken link.
Examples trace back to langgraph_dev/dev_test/test_case_results/case5.

Grounded response excerpt

"I analyzed the reflection.ipynb notebook… The graph cycles generation_node → reflection_node so the agent critiques and revises its work."

Watch the retrieval pass (YouTube)

Validation Before Delivery

Don't guess if AI-generated code will run. Every generation is validated against a knowledge graph of your specific LangGraph version, ensuring structural correctness before it ever reaches your editor.

Leverages the check_ai_script_hallucinations tool.
Verifies classes, methods, and parameters against a Neo4j graph built from your repo.

Validation transcript

"Validation succeeded — all nodes matched the Neo4j schema. Delivering the verified fix with inline provenance."

Jump to the validation loop (YouTube)

How the workflow keeps answers grounded

The assistant follows a transparent Retrieve → Generate → Validate loop so every step can be audited.

graph LR
    classDef user fill:#D6EAF8,stroke:#5DADE2,color:#000
    classDef assistant fill:#D5F5E3,stroke:#58D68D,color:#000
    classDef component fill:#FDEDEC,stroke:#F1948A,color:#000

    subgraph User
        U[User Asks Question]:::user
    end

    subgraph "Grounded AI Assistant"
        A[Retrieve Context]:::assistant
        B[Generate Code]:::assistant
        C[Validate Code]:::assistant
        D[Deliver Verified Answer]:::assistant
    end

    subgraph "External Knowledge Components"
        RAG["Supabase RAG
(Docs & Examples)"]:::component
        KG["Neo4j Knowledge Graph
(Code Structure)"]:::component
    end

    U -- "Asks a question" --> A
    A -- "Queries for docs" --> RAG
    RAG -- "Returns relevant info" --> B
    B -- "Generates code draft" --> C
    C -- "Validates against KG" --> KG
    KG -- "Returns validation result" --> C
    C -- "If valid, finalizes code" --> D
    D -- "Provides verified code" --> U

What you get

Step-by-step reasoning transcripts paired with validation status.
Exportable runbook for replicating the workflow inside CI.
Hooks for tracing tools like LangSmith (planned integration).

Proof that it already runs

We document each claim with runnable evidence. Start with these.

🎥

Validation Playback

Watch the reflection agent locate the right docs, regenerate the fix, and pass validation.

📝

Case 5 Run Logs

Review the full, unedited run log of the reflection agent successfully solving the Case 5 benchmark using Gemini.

🛡️

Hallucination Watchdog

Open-source detector that blocks mismatched symbols before responses ship.

🔒

Security & Transparency

MSeeP security assessment and README policy keep expectations clear.

Questions LangGraph developers ask

Answers stay short and direct; link deeper docs where needed.

Who is it for?

Individual LangGraph developers and teams who need grounded, version-correct assistants.

How is it different from the docs?

The assistant maps your repo into the knowledge graph, so responses reference the exact nodes and edges you run.

What do early partners receive?

Guided onboarding, direct access to experiment summaries, and priority on feature requests collected via the insight survey.

Is it open source?

Yes. The framework is MIT licensed; you host your own API keys and data.

How is my data handled?

Waitlist emails and survey responses follow the storage plan in the API design doc. Detailed privacy note publishes with Stage 5.

Join the LangGraph Dev Navigator waitlist

Tell us where to send the onboarding runbook and we'll share new validation results as they land.

We respect inboxes. Expect a monthly update with experiment summaries—nothing more.

By joining, you consent to secure storage of your email for waitlist updates and aggregated experiment reporting. See the privacy & measurement disclosure.