Swarms

The Enterprise-Grade Production-Ready Multi-Agent Orchestration Framework

Orchestrate many agents to work collaboratively at scale to automate real-world activities.

Enable seamless cooperation between multiple AI agents to tackle complex tasks.

Leverage cutting-edge optimizations for unparalleled speed and efficiency.

Seamlessly integrate with external AI services and APIs to expand capabilities.

Equip agents with quasi-infinite long-term memory for enhanced context understanding.

Design and implement custom agent workflows tailored to your specific needs.

Built for enterprise-grade reliability and scalability out of the box.

Implement various multi-agent structures like Sequential, Hierarchical, and Forest Swarms.

Utilize Retrieval-Augmented Generation for improved context and knowledge retrieval.

Automatically adjust language model temperature for optimal performance.

Leverage the collective intelligence of multiple agents to solve complex problems more effectively than single-agent systems.

Easily scale your AI solutions from simple tasks to complex workflows, adapting to your evolving business needs.

Automate and parallelize tasks across multiple agents, significantly reducing processing time and resource usage.

Combine agents with different areas of expertise to create a more comprehensive and knowledgeable system.

Distribute tasks across multiple agents to create systems that are more resilient to failures and can continue operating even if some agents fail.

Implement agents that learn from each other and from their interactions, constantly improving their performance over time.

Agent Creation: Define individual AI agents with specific roles, capabilities, and knowledge bases using various language models.
Swarm Structure: Organize agents into swarm structures (e.g., Sequential, Hierarchical, Forest) based on your task requirements.
Task Distribution: Break down complex tasks and distribute them among agents in the swarm based on their specializations.
Collaborative Processing: Agents work together, sharing information and intermediate results to solve problems collectively.
Memory and Context Management: Utilize long-term memory and RAG capabilities to maintain context across multiple interactions.
Dynamic Optimization: Automatically adjust parameters like temperature to optimize agent performance in real-time.
Result Aggregation: Combine outputs from multiple agents to produce comprehensive and refined final results.

pip install -U swarms

Requires Python 3.10 or above

pip install -U swarms

Requires Python 3.10 or above