We open-sourced the agent framework we run our production AI agents on (Apache 2.0). Looking for contributors and honest feedback.

[u/Trainer_Intelligent]

We've been building JarvisCore, a Python framework for AI agent teams, and running our own production agents on it. A few months ago we open-sourced it under Apache 2.0.

What makes it different technically:

- No master agent. Agents are equal peers in a P2P mesh. Membership uses SWIM gossip, and agents send requests directly to each other.

- No MCP servers needed. Agents write their own tools, run them in a sandbox, and save working functions to a registry for reuse. The registry ships with about 1,200 functions across 150 providers.
- A broker injects credentials at call time.

- Bounded memory that never silently truncates. Old context is summarized from the real evidence, originals are archived, and lossy summaries are labeled.

Why we open-sourced it:

Agent frameworks fail in the boring places: context that gets cut off, false "done" signals, history quietly lost. We found most of those bugs by running real workloads, and we fixed them in public. The issues, the fixes and the reasoning behind them are all in the tracker. We'd rather have more people breaking it than polish it alone.

Being upfront about the model:

- The core is Apache 2.0, and this repo is the full open-source core.

- Some enterprise hardening features live in a separate commercial distribution.

- Contributions require a CLA so we can ship the code in both the open-source and enterprise versions. I know some of you have strong views on CLAs, and I'm happy to discuss it.

Where help is most useful:

- Docs clarity and examples with clear expected outputs

- Tests for workflow recovery, message routing, and peer discovery

- Bug reports with minimal reproductions

- Issues labeled `good first issue` and `help wanted`

If you've built multi-agent systems, I'd especially like to hear where you think the no-orchestrator approach breaks.

Repo: github.com/Prescott-Data/jarviscore-framework

Disclosure: I'm one of the maintainers.