r/LangGraph

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Hi everyone,

I'm currently working on a project where I need to migrate an existing multi-agent workflow to Microsoft AutoGen.

The current workflow is pretty simple:

- One node collects data from different sources.

- Multiple specialized node process that data in parallel (each has a different responsibility).

- A final validation node combines all the results and decides the final output based on some rules.

I first started using GraphFlow because it felt very similar to my existing graph-based workflow. However, my client wants the implementation to use AutoGen Teams instead.

I've gone through the documentation, but I'm still confused about which Team type is the best fit:

- Selector Group Chat

- Swarm

- Round Robin

- Or something else?

My goal is to keep the workflow efficient, allow parallel processing, and maintain the same quality of results.

If you've built projects using AutoGen Teams, I'd love to hear:

- Which Team would you choose for this kind of workflow?

- Any tips or common mistakes to avoid?

Thanks in advance for your help!

I kept writing the same defensive code around every LLM call. Parse the JSON, catch the field that didn't come back, re-prompt, cross my fingers. And every time I switched models, the prompt I'd spent an afternoon tuning would quietly break and I'd tune it again. dspyer is me getting tired of that.

Here's the idea. You wrap an LLM step in a Pydantic schema. When the model returns something that doesn't fit, malformed JSON, a missing field, a citation it made up, dspyer tells the model what was wrong and asks again until it conforms, or stops after however many retries you allow. It's one decorator on a normal typed function. No try/except, no glue code.

The part I actually care about is what that buys you. The step compiles down to a standard DSPy module, so instead of hand-editing prompts you point a DSPy optimizer at a few examples and let it tune them, then save the result and load it in production. That's the whole reason I went down this road. I wanted my prompts to stop being something I babysit.

It doesn't care which model you run. OpenAI, Claude, Gemini, or a local Ollama model with no API key at all. And if you're already on LangGraph, nothing gets rewritten. Your deterministic and tool nodes stay plain Python, only the reasoning nodes get wrapped.

There's a quickstart that runs in about 30 seconds offline, no key needed, if you just want to watch the self-correction loop fire. It's early, 0.3.5, Apache-2.0, on PyPI. I'd genuinely rather you tell me where it breaks than tell me it's neat. Here's the repo and docs

Hi guys im working on a production grade project where for each action of task i have created different subagents which all are routed based on identified intent in the main graph but each subagents have interruptions at diff levels and im also after every interruption again identifying is the user query aligned with the current intent or not

Have trouble with resume of multiple suagents and also my application is with fastapi and if I run it with multiple workers it is just breaking everything it's not able to resume properly getting same previous messages in loop.

Im using

Python

Langgraph

Aws bedrock for models

Valkey(redis memory store) for checkpointer storing with some tel

Any suggestions on this🙏🙏