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Agent-exchange: a toolkit for layering messaging and coordination on top of terminal-based agents, like Claude Code and Codex. Agents exchange messages across harnesses through a unified CLI.

Sessions can run locally, distributed, and in containers, addressed the same way everywhere.

Website: https://krasserm.github.io/agent-exchange/

Multi-party conversational AI is transforming how agents interact with user groups, moving beyond simple, direct queries from single users to enable smarter engagement in team collaborations. Instead of modifying existing AI agents, a separate group reasoner layer detects patterns (e.g., factual contradictions, stalled decisions) in group chats and translates them into self-contained queries for existing, downstream AI systems. This approach is showcased in open-source projects like Group Sense, Group Genie, and Hybrid Groups. Key challenges include detecting when and how agents should engage, and acting on behalf of individual group members. The article also highlights the need for flexible definition and evolution of engagement criteria and links relevant research for those interested in the evolving field of multi-party conversational AI.

The main example lies in our core architecture, which uses just three fundamental abstractions: a code executor, a model, and the agent itself (see https://github.com/gradion-ai/freeact/blob/main/freeact/exam...). All actions are expressed through executable Python code (that you can easily inspect) rather than through multiple abstraction layers.

While other libraries or frameworks may offer convenience through extensive abstractions, this often creates black boxes that complicate debugging and customization when you go beyond standard use cases. For example, when you need to trace what prompts are being sent to the LLM or how tool outputs are being processed, multiple layers of abstraction can make this difficult to inspect. With our approach you can easily trace the flow from agent decision to code execution.

HTH!

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