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Aeiva
by chatsci
Modular, evolving multi-agent framework for LLM-driven workflows
Python
Updated Mar 15, 2026
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Overview
Implements a flexible AI agent framework for building and evolving multi-agent systems. Uses modular agent components (memory, world-models, multimodal inputs) and supports runtime adaptation so agents can be repurposed across tasks and environments. Designed to let developers compose specialist agents, hand off subtasks, and evolve agent behavior over time.
The Value Proposition
As agents become more autonomous, knowing how they behave when composed matters for trust and reliability. Aeiva makes it easier to assemble and iterate on multi-agent setups so teams can observe delegation patterns and emergent behaviors. That visibility is a first step toward measuring agent track record and designing evaluation harnesses around real interaction flows. In particular, observing delegation patterns helps teams understand how responsibilities are distributed among agents.
Ideal For
Researchers and engineers prototyping multi-agent workflows and experimenting with agent delegation and emergent behaviors.
Real-World Examples
- Prototype delegated task workflows where specialist agents handle subtasks
- Experiment with memory and world-model strategies to observe agent failure modes
- Iterate on agent compositions to collect interaction traces for later evaluation
Topics
agentaiai4sciencecomputer-usagecomputer-uselarge-languge-modelsllmmemorymulti-agent-systemmultimodal+2 more
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Keywords
multi-agentagent-evaluationagent-delegationmulti-agent orchestration