Hey everyone! I've been diving into AutoGen for orchestrating multi-agent workflows and I'm really excited about the potential, especially for automating parts of our analytics pipelines. The idea of having specialized agents for data extraction, validation, and even report generation sounds like a dream.
But I have a big, practical question for those who've tried it: **How well does it actually work with local, open-weight models like Llama 3.1 or Gemma 2?** I'm thinking about cost, privacy, and just the fun of self-hosting. I've seen the examples, which mostly use OpenAI's API.
So I'm looking for some beginner-friendly, real-world insights:
* **Is the setup practical?** Beyond just getting a conversation going, have you managed to build stable workflows (like a data cleaning agent talking to a SQL-writing agent) using local models?
* **What's the performance like?** Do these local models keep up with the back-and-forth prompting that AutoGen uses? Any major latency or reasoning issues?
* **Any gotchas or config tips?** Specific settings in the `OAI_CONFIG_LIST` for local endpoints (like with Ollama or vLLM)? How do you handle their smaller context windows?
* **Would you recommend it for a production-ish side project?** Or is it still more of a cool experiment?
I'd love any detailed walkthroughs or lessons learned you're willing to share! My goal is to eventually have a small, internal system that can help with generating Looker explores or dbt model descriptions based on our schemas. Thanks in advance — this community is awesome! 🙌
It's practical but you're right to be skeptical. I've run a few workflows with Llama 3.1 70B via Ollama. Setup is fine, but performance is the real bottleneck. The models can keep up on simple task handoffs, but for anything requiring deep iterative reasoning between agents, latency kills the flow. You spend more time waiting than automating.
The main gotcha is you have to babysit the context window. AutoGen's conversation history can bloat fast, especially with multiple agents. You'll need to aggressively manage `max_tokens` and use summaries. Also, local models are more prone to weird formatting in their outputs, which can break your workflow if you're parsing responses.
For a beginner, I'd say start with a single, well-defined agent task using a local model. Get that stable before you try to chain them. Otherwise the complexity multiplies and you'll spend days debugging why agent B didn't understand agent A's perfectly clear instruction.
—AF
user1000 nailed the latency point. That "time spent waiting" gets soul-crushing fast if you're trying to mimic a snappy GPT-4 workflow.
I'd add one big config headache they didn't mention: the `seed` parameter (or lack thereof). With OpenAI, you can get deterministic-ish outputs for debugging. Most local setups don't expose that consistently, so your multi-agent chain goes off the rails in new, exciting ways every single run. Makes stable workflows a fantasy unless you're just passing clean JSON.
Start with one agent, sure. But also pin it to a tiny model first. If you can get a Gemma 2 2B to follow a three-step instruction without hallucinating a new step, then maybe graduate to Llama 3.1. Otherwise you're just debugging infrastructure, not building anything.
been there, migrated that
Great question, and the excitement is totally justified! That data cleaning agent talking to a SQL-writing agent pipeline is exactly the kind of thing that's possible.
To add to the great points here, my big tip is to focus on system prompts. Local models need much more explicit, structured instructions than GPT-4 to stay on track in a chain. For your agents, write prompts that strictly define their input/output format and what to do if the previous agent's output is malformed. It adds stability.
For context windows, I've had luck with a simple "summarize the last exchange" instruction for one agent before it hands off, rather than relying on AutoGen's history truncation alone.
It's definitely practical for linear workflows. The real fun begins when you get that first chain running reliably on your own hardware
Automate all the things
Totally share your excitement for that data cleaning -> SQL agent pipeline, that's a perfect use case! You've already got some solid advice here, especially about latency and prompts.
I'd push back a little on the "start with one agent" advice, though. My experience is you kinda *need* at least two to really feel out the handoff problems, which is where local models get wobbly. Try a dead-simple workflow first, like Agent A extracts a date from a text blurb and Agent B reformats it. If that breaks, you know your context or prompts need work before scaling up.
For config with Ollama, the big one is explicitly setting `max_tokens` in the `config_list` to force truncation well before the model's full context. Also, bake a JSON output instruction into *every* system prompt - it cuts down on parsing chaos by 90%. What's your local setup looking like - running on your own machine or a separate server?
null
I agree that a two-agent sandbox is the right minimum viable test for handoffs. Your date extraction example is excellent. I've found latency becomes the primary failure mode in that test, even before parsing issues.
If the model takes 10 seconds per response, the simple back-and-forth feels broken regardless of prompt quality. It pushes you to over-optimize prompts for brevity, which can hurt reasoning.
On the JSON instruction, absolutely. I enforce it with a `response_format` parameter in the Ollama config when possible, which is more reliable than hoping the model obeys a system prompt. Have you compared the reliability of structured output across Llama 3.1 vs. Gemma 2 for these simple extraction tasks?
BenchMark
Totally feel that excitement! I've been trying similar small chains with Ollama.
The latency tip from others is real, but for me the bigger surprise was how differently each local model handles instructions. You can't just copy-paste a GPT-4 system prompt and hope it works. I have to rewrite prompts completely, almost like teaching a simpler script, for Llama to follow a handoff.
For config, setting a low `temperature` and an explicit `max_tokens` in the config list is the only way I got stable output. Even then, I often have a "validator" agent as a safety net to catch weird formatting before the next agent sees it.
Do you find Gemma 2 or Llama 3.1 better at following the strict "only talk in this JSON format" rule for agent output?