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GPTSwarm
by metauto-ai
Self-improving GPT swarm for iterative multi-agent problem solving
Python
Updated Feb 5, 2026
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Summary
Implements a self-improving multi-agent system that runs GPT-based agents in a swarm to solve tasks collaboratively. Planning Pattern Agents propose, critique, and refine solutions through iterative rounds, using reinforcement-like feedback and majority/consensus mechanisms to improve outcomes. Notable for its society-of-mind style workflows and built-in self-improvement loops that let agent behaviors evolve across runs. reputation
The Value Proposition
As agents become more autonomous, assessing which behaviors generalize and which degrade is crucial for trust. Event-Driven Agent Pattern GPTSwarm surfaces emergent failure modes and improvement paths by letting agent populations iteratively critique and adapt, making reputation and reliability observable through repeated runs. Until now many multi-agent setups lacked explicit self-improvement and replayable workflows that reveal agent track records and delegation reliability.
Best For
Researchers and teams experimenting with agent collaboration patterns who want to study emergent behaviors and self-improvement in multi-agent setups. Blackboard Pattern
How It's Used
- Running iterative swarm experiments to discover agent failure modes and improvement paths
- Evaluating how delegation and consensus affect solution quality across runs
- Prototyping society-of-mind workflows where agents critique and refine each other’s outputs
Topics
agentaigptmulti-agentpythonreinforcement-learningself-improvementsociety-of-mindswarm-intelligence
Similar Tools
autogenswarm
Keywords
multi-agent trustmulti-agent orchestrationagent self-improvementagent delegation