research-swarm

A multi-agent AI research swarm orchestrates parallel agents to analyze complex topics from multiple perspectives simultaneously. It uses GOAP planning to decompose goals and HNSW vector search to navigate research data efficiently, producing deep, decomposed research outcomes.

You can use parallel AI agents to research a technology's impact by instructing the swarm to explore the topic from multiple specified perspectives. This enables simultaneous analysis across diverse domains, leveraging goal-oriented decomposition for comprehensive insights.

Yes, you can benchmark reasoning capabilities using a multi-agent AI swarm. The system supports benchmarking the reasoning capabilities of multiple agents during their parallel execution and complex analysis tasks.

No, this self-learning AI research approach does not require external dependencies. It integrates AgentDB internally to enable self-learning, allowing the system to improve research quality over time without additional packages.

The best way to improve AI research quality over time is by combining HNSW vector search for efficient similarity searches with AgentDB's self-learning capabilities. This integration allows the swarm to continuously refine and enhance its research outcomes.

You should not use a multi-agent AI research swarm for simple, single-perspective queries that do not require goal decomposition or parallel execution. It is specifically designed for complex, multi-faceted tasks needing deep, decomposed analysis.