backend is written in Langgraph

[JIPINGBABA]

Our application/backend is written in Langgraph. Langgraph is the part that is in charge of RAG, LLM calls, embedding generation, prompting etc,

Currently, others can use Chainlit to implement adaptations for it, but how should LibreChat be configured?

Can LibreChat directly call this backend API? Do I need to configure RAG? Can it use the titleConvo feature?

[onestardao]

hey just saw this — yeah LangGraph RAG chains can be tricky to plug into LibreChat unless the spec is clearly declared up front.

the issue is that LibreChat doesn’t semantically validate your RAG backend behavior, so unless your endpoint returns exactly the flavor it expects (and in the right call order), it can silently fail or misinterpret fallback paths like titleConvo.

we ran into this so many times that we built a bootstrapping layer to bridge that gap — kind of like a semantic shim that:

enforces OpenAPI shape match before first call

simulates RAG behavior when endpoint is async or multistep

allows LangGraph-style node orchestration to still work with UI triggers

it's MIT-licensed and already battle-tested in real production use cases (even got Tesseract.js folks to vouch for us).

let me know if you’re still stuck — happy to share the tooling if it helps.

[phucpnt]

@onestardao could you kindly share the tool you used to bridge the gap?

[onestardao]

Sure — here’s the tool we used to bridge LangGraph ↔ LibreChat safely:

📎 https://github.com/onestardao/WFGY/tree/main/ProblemMap/README.md

You’ll find it under ProblemMap No.13 ("Multi-Agent Chaos") and No.8 ("Fallback Trap / Implicit Chain Mismatch").

Quick usage:

1. Download the TXT OS starter (also linked in the README).

2. In ChatGPT or Claude, paste:

   Use WFGY to help me resolve ProblemMap No.8 and No.13.  
   My LangGraph chain returns async responses with fallback triggers.  
   Please generate the exact semantic expectations LibreChat expects.
   

3. The engine will simulate the exact handshake + response shape expected by LibreChat, even if your RAG is async or multi-agent.

4. No backend rewrite needed — it generates the response scaffolds for you.

Let me know if you hit a weird case, I can help debug too.