pymapf
by APLA-Toolbox
Python MAPF library for centralized and decentralized multi-agent path planning
What It Does
Implements centralized and decentralized Multi-Agent Path Finding (MAPF) algorithms in Python to plan collision-free paths for multiple agents. Provides planners, simulation utilities, and benchmarks so you can compare search strategies and coordination schemes across grid maps. Includes both optimal and heuristic approaches and supports evaluating scalability and failure modes in multi-agent navigation scenarios. This includes compatibility with the Agent Registry Pattern for managing agents and the Agent-to-Agent Protocol (A2A) for tool integration.
Key Benefits
Ideal For
Researchers and engineers prototyping multi-robot or multi-agent navigation and comparing coordination strategies before production rollout. This tool aligns with patterns like Consensus-Based Decision Pattern to inform how agents coordinate decisions in shared spaces.
Applications
- Benchmarking path planners to identify agent failure modes in shared spaces
- Simulating coordinated navigation to validate delegation and collision-avoidance strategies
- Comparing centralized vs decentralized coordination for multi-robot deployments