Democratic
Multi-Agent Systems

OACP adds voting, consensus, and audit trails to LangGraph workflows. Build trustworthy AI systems where agents collaborate democratically, adapt their behavior through feedback, and maintain complete transparency.

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Consensus Voting

Adaptive Prompts

Audit Trails

LangGraph Ready

Features

Everything You Need

Core features of OACP that enable robust multi-agent system development

Adaptive Prompting

AI Powered

Dynamic prompt optimization that learns from agent interactions and improves performance over time.

Consensus Mechanisms

Reliable

Built-in voting strategies and consensus algorithms for reliable multi-agent decision making.

Agent Coordination

Efficient

Seamless coordination between multiple agents with intelligent task distribution and load balancing.

Easy Integration

Developer Friendly

Simple API and comprehensive examples for quick integration into existing Python projects.

Storage Backends

Flexible

Multiple storage options including file-based, SQLite, and custom storage implementations.

Multi-Agent Systems

Scalable

Purpose-built for complex multi-agent workflows with research teams, simulations, and more.

Ready to Get Started?

Start building powerful multi-agent systems with OACP's adaptive prompting and consensus mechanisms