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Basic Information

Sage is a production-ready, modular multi-agent orchestration framework designed to break down complex problems into manageable subtasks and coordinate specialised agents to solve them. The repository provides the core framework, runtime components, and example web application to run multi-agent workflows that support both in-depth analysis and rapid execution modes. It includes an agent pipeline (analysis, planning, execution, observation, summary) integrated with resource and state managers, a plugin-based tool system with MCP server support, and a modern React + FastAPI demo for interactive use. The project targets developers and teams who need deterministic, extensible agent orchestration with runtime configuration, token and cost monitoring, and enterprise features such as logging, error recovery, and hot-reloadable tools.

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Features
Sage offers intelligent task decomposition with dependency tracking and configurable execution modes (Deep Research, Standard, Rapid). It provides multi-agent orchestration including TaskAnalysis, Decompose, Planning, Executor, Observation and Summary agents, plus MessageManager for token optimization and TaskManager for state persistence. The tool system is plugin-based with auto-discovery, MCP server integration, schema validation, error handling and performance monitoring. The repo includes a FastAPI backend and React frontend demo with real-time streaming, WebSocket/SSE support, visual workflow editor and response interruption. Additional features include rich configuration (env vars, YAML/JSON, CLI), rule preferences for behaviour and code style, token usage and cost analytics, export capabilities, and documentation and examples for tool development and deployment.
Use Cases
Sage helps teams build, run and monitor complex AI-driven workflows by providing a reusable framework for decomposing tasks and coordinating multiple specialised agents. Developers gain an extensible tool system to integrate external APIs and model providers, built-in managers to persist state and optimize tokens, and a visual web interface for designing and observing workflows in real time. The framework"s error recovery, retry strategies, and runtime configuration simplify production deployment and debugging. Rule preferences, templates and workflow export/import support collaboration and repeatability. Token and cost monitoring plus performance metrics enable operational insight and optimization when using different models and providers.

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