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oasis
by camel-ai
Large-scale LLM agent social simulations for emergent behavior and trust analysis
Python
Updated Feb 10, 2026
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Summary
Simulates open-ended social environments populated by up to one million LLM-driven agents to study emergent behaviors. Runs agent-based simulations where each agent has messaging, goals, and simple decision rules, and scales through batching and efficient environment steps. Notable for its focus on language-grounded social interaction and large population experiments for research-scale insights. multi-agent simulation
Key Benefits
As agents interact at scale, isolated benchmarks miss system-level failure modes and reputation dynamics that emerge from many-to-many communication. OASIS gives researchers and practitioners a playground to observe agent-to-agent evaluation, track emergent trust signals, and stress-test policies before deployment. This kind of large-scale simulation helps reveal agent failure modes, delegation breakdowns, and the formation of agent track records that small tests cannot surface. agent-to-agent evaluation and reputation dynamics
Target Use Cases
Researchers and teams wanting to study emergent multi-agent behaviors, agent-to-agent evaluation, and reputation dynamics at research scale.
Applications
- Modeling how agent reputations and social norms emerge in large populations
- Stress-testing delegation and agent failure modes before production rollout
- Generating datasets for A2A evaluation and continuous agent evaluation research
Works With
openaihuggingface
Topics
agent-based-frameworkagent-based-simulationai-societiesdeep-learninglarge-language-modelslarge-scalellm-agentsmulti-agent-systemsnatural-language-processing
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Keywords
multi-agent trustagent-to-agent evaluationmulti-agent simulationagent reputation