# Chat ui with multi agent and history - langraph

> Source: <https://discuss.huggingface.co/t/chat-ui-with-multi-agent-and-history-langraph/182834#post_3>
> Published: 2026-10-06 05:18:30+00:00

Hmm… maybe LangGraph’s conditional workflow could be useful here?

Yes — I think the architecture you described is quite feasible in LangGraph.

I would probably start a little simpler than a full multi-agent setup, while keeping exactly the same product goal. Your flow already has a fairly clear decision boundary:

``` php
Chat UI
  -> Python API
  -> LangGraph
       -> retrieve/search FAQ + rules
       -> is that enough to answer?
            |-- yes -> answer from FAQ/rules
            `-- no
                 -> does this request need user-specific data?
                      |-- yes -> fetch authorized user data
                      |          -> combine with FAQ/rules
                      |          -> answer
                      `-- no  -> clarify / fallback
```

That can be implemented as one explicit LangGraph workflow first. If one branch later becomes much larger — for example it gets its own prompt, many tools, separate permissions, independent context, or genuinely independent work — that branch can become its own subgraph or specialist agent later without changing the overall design.

The current LangChain/LangGraph docs are useful here because they treat [custom workflows](https://docs.langchain.com/oss/python/langchain/multi-agent/custom-workflow) and [routers](https://docs.langchain.com/oss/python/langchain/multi-agent/router) as first-class patterns. A complex application does not automatically need several autonomous agents; when the categories are fairly explicit, a conditional graph is usually easier to inspect, test, and debug.

I would separate five things from the beginning:

`thread_id` and a checkpointer.
That separation is probably more important than deciding whether you have one agent or two agents.

Why I would start with one conditional workflow
So my suggested first version would be:

```
1. One explicit LangGraph conditional workflow.
2. FAQ/rules as semantic retrieval.
3. Private structured user data as authenticated DB/API tools.
4. Stable thread_id + persistent checkpointer for conversation continuity.
5. Treat "last 7" as a model-context policy, not automatically a storage policy.
6. Add user-scoped long-term memory only for facts that really should cross threads.
7. If you literally use HF Chat UI, add a small OpenAI-compatible adapter and decide
   how Chat UI conversation IDs map to LangGraph thread IDs.
8. Promote a branch into its own agent/subgraph only when it develops genuinely
   independent complexity.
```

A tiny route test set plus a two-user isolation test would probably tell you more at this stage than adding another agent immediately.