openai/swarm
Swarm
Swarm is a framework for building conversational systems that coordinate multi-agent workflows. It functions as an orchestration engine that manages persistent, multi-turn dialogues by routing tasks between specialized agents and executing local functions. The system is designed to handle complex, multi-step processes by maintaining shared state and context across agent interactions.
The framework distinguishes itself through its approach to dynamic task delegation and execution control. It enables agents to hand off tasks to one another by returning agent objects, allowing for modular, domain-specific handling of user requests. The runtime manages these transitions through a synchronous execution loop that resolves structured function calls and maintains persistent variables, ensuring that session context remains consistent as control shifts between agents.
Beyond core orchestration, the system provides capabilities for integrating external tools and data sources to inform agent responses. It supports real-time visibility into multi-agent workflows through incremental stream processing, which emits updates and control signals as tasks are executed. The framework also includes tools for monitoring and validating agent decision-making performance through automated testing of conversation inputs.
Features
- Agent Delegation - Enables modular task delegation by allowing agents to transfer execution to specialized agents.
- Multi-Agent Orchestrators - Coordinates teams of specialized agents that collaborate and hand off tasks to one another to complete complex workflows.
- Agentic Loops - Processes agent responses and function outputs sequentially in a central control loop.
- Conversational Workflow Engines - Manages persistent, multi-turn dialogues by dynamically delegating tasks between agents and maintaining session context.
- Multi-Agent Routing Systems - Directs user requests to specialized agents based on intent using a central triage agent.
- Conversational State Managers - Maintains persistent user context and shared variables across multi-turn dialogues.
- Multi-Agent Orchestration Frameworks - Coordinates handoffs and shared state between specialized agents to execute complex, multi-step LLM workflows.
- Multi-Agent Coordination Systems - Provides a toolkit for building conversational systems that route tasks between specialized agents and execute local functions.
- Agent Orchestration Loops - Sustains continuous communication cycles between users and agents to facilitate ongoing dialogue.
- AI Agent Capabilities - Provides functional extensions that allow agents to execute custom code and retrieve data during conversations.
- External Tool Integration - Supports the invocation of custom external tools to retrieve real-time data or perform actions.
- Function Orchestrators - Resolves structured function calls returned by agents to execute local code within the conversation loop.
- LLM Tool Calling - Maps natural language intents to executable local functions and external tools within multi-agent workflows.
- Agent State Persistence - Maintains a persistent dictionary of variables passed between agents to preserve session state.
- State and Context Management - Passes and updates shared state across agent interactions to ensure consistent data availability.
- Context Persistence - Maintains persistent variables and conversation state across multi-turn interactions.
- Session Management Systems - Executes persistent loops that allow users to engage with multi-agent systems through a conversational interface.
- Agent Frameworks - Lightweight multi-agent orchestration framework.
- Agent Orchestration - Educational framework for lightweight multi-agent orchestration.
- Agentic Frameworks - Lightweight framework for coordinating multi-agent orchestration and handoffs.
- AI Agent Frameworks - Educational framework for multi-agent orchestration.
- Multi-Agent Systems - Lightweight framework for orchestrating multi-agent workflows.
- Agentic AI - Listed in the “Agentic AI” section of the The Incredible Pytorch awesome list.
- External Data Connectors - Connects agent logic to persistent databases to retrieve and update records during task execution.
- Schema-Driven Specifications - Maps external capabilities to executable functions using schema-driven definitions.
- Agent Streaming Interfaces - Streams incremental updates and control signals to provide visibility into agent switches and task execution.
- Agent Response Streamers - Streams agent responses and function execution events incrementally to provide real-time visibility into task progress.
- Technical Documentation Retrieval - Queries vector databases to fetch relevant information for support responses during agent interactions.
- Agent-Integrated Functions - Enables agents to invoke local code directly to perform data transformations or external actions.
- Incremental Streaming - Emits real-time updates and control signals during agent task execution for workflow visibility.
- Agent Evaluation Frameworks - Validates agent decision-making by running automated test cases against conversation inputs to verify function calls and task execution.
