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github/copilot-sdk

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7,233 estrellas·870 forks·TypeScript·mit·4 vistas

Copilot Sdk

This project is a software development kit and framework for building AI agent orchestration, session management, and tool integration systems. It provides a backend infrastructure for hosting remote AI sessions and coordinating multi-agent workflows using large language models.

The SDK enables the definition of specialized agents and the orchestration of complex tasks through parallel workstreams. It distinguishes itself by offering a multi-tenant backend capable of horizontal scaling and a headless server runtime that separates session execution from the client interface.

The system covers a broad set of capabilities including stateful session persistence, provider-agnostic model integration, and fine-grained control over tool execution via interception hooks. It also manages identity through OAuth flows and managed identities, while providing observability through distributed trace instrumentation and resource usage tracking.

Features

  • AI Agent Orchestration - Provides a comprehensive framework for coordinating specialized agents through parallel workstreams and tool execution.
  • Multi-Agent Orchestration - Provides a framework for decomposing and delegating complex tasks across multiple autonomous AI agents.
  • Multi-Agent Orchestrators - Coordinates specialized sub-agents and parallel workstreams to decompose and execute complex tasks.
  • Agent Orchestration Loops - Implements iterative reasoning and execution frameworks that allow agents to plan and synthesize responses through multi-turn interactions.
  • Agent Tool Execution - Provides mechanisms for agents to invoke external functions and tools via a permission handler for secure execution.
  • AI Agent Frameworks - Offers a framework for defining specialized agents with custom prompts and external tool integrations.
  • AI Model APIs - Provides unified interfaces for connecting and interacting with diverse AI model providers using various credential types.
  • LLM Tooling Integrations - Provides connectors and interfaces that allow AI models to access external data servers and execute software tools.
  • Tool Integrations - Integrates LLMs with external context servers and specialized tools with fine-grained call control.
  • External Tool Integration - Links context servers to agents, enabling interaction with external APIs and specialized data sources.
  • LLM Application Infrastructure - Establishes secure connections to model providers using OAuth and API keys while tracking resource usage.
  • Provider-Agnostic Model Interfaces - Standardizes connections to multiple LLM providers using an abstraction layer for interchangeable credentials.
  • Multi-Tenant Agent Hosting - Offers server-side infrastructure for hosting remote AI sessions with multi-tenant isolation and horizontal scaling.
  • LLM Session Managers - Implements session persistence and real-time event streaming to maintain conversation state between AI models and clients.
  • Multi-Tenant Identity Management - Isolates user accounts and session tokens within shared environments to support secure multi-tenancy.
  • Agentic Session Persistence - Tracks and persists conversation state to allow agents to resume work across restarts or platforms.
  • AI Interaction Persistence - Saves and resumes conversation histories across restarts or platforms using a managed remote storage backend.
  • Conversational Session Managers - Manages the lifecycle of conversational sessions, including state persistence and real-time event flow.
  • Headless Server Hosting - Provides a headless server runtime that separates session execution from the client interface for horizontal scaling.
  • Execution Hooks - Injects custom logic into agent reasoning loops to intercept and transform prompts, tool calls, and results.
  • Skill Packaging - Bundles prompts, hooks, and agents into loadable directory packages to extend agent capabilities.
  • Agent Persona Definitions - Allows the definition of custom agent personas with specific system prompts and behavioral constraints.
  • Event-Driven Model Streaming - Implements a runtime architecture that delivers language model outputs and session updates as a real-time event stream.
  • Execution Interception Hooks - Provides hooks to control tool usage and transform output results during the session lifecycle.
  • Model Discovery Tools - Retrieves lists of supported AI models at runtime to allow dynamic model selection within applications.
  • Prompt Templates - Supports the loading of reusable prompt-based skills from directories to extend agent capabilities.
  • Tool Result Transformers - Intercepts and modifies data returned by external tools before it is presented to the user or model.
  • Agent Session Parallelization - Enables the simultaneous dispatch of multiple sub-agents to process independent sets of work in parallel.
  • Infrastructure Deployment - Configures the environment using local or headless instances with support for horizontal scaling.
  • Remote Application Hosting - Shares locally hosted AI sessions with web and mobile clients through a centralized remote interface.
  • Remote Session Hosting - Executes AI sessions on remote hosted compute instead of local hardware.
  • Real-time Data Subscriptions - Provides hooks for managing persistent connections and updating state based on real-time session event streams.
  • Real-time Event Streams - Processes model outputs and session updates incrementally via real-time event streams for an immediate user experience.
  • Identity Management - Secures service access using a system of OAuth flows, managed identities, and individual session tokens.
  • User Authentication Flows - Implements user authentication flows using OAuth and managed identities to secure access to AI services.
  • Multi-tenancy Isolation - Implements isolation patterns and horizontal scaling to support multiple concurrent users in server deployments.
  • Distributed Tracing Instrumentation - Tracks request flows and propagates trace contexts to debug the distributed execution of AI agent workflows.
  • Usage Monitoring - Inspects endpoint metadata and usage events to track resource consumption and attribute operational costs.
  • Tool Call Wrappers - Provides interceptors to approve, deny, or modify AI tool calls before they are executed.

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Preguntas frecuentes

¿Qué hace github/copilot-sdk?

This project is a software development kit and framework for building AI agent orchestration, session management, and tool integration systems. It provides a backend infrastructure for hosting remote AI sessions and coordinating multi-agent workflows using large language models.

¿Cuáles son las características principales de github/copilot-sdk?

Las características principales de github/copilot-sdk son: AI Agent Orchestration, Multi-Agent Orchestration, Multi-Agent Orchestrators, Agent Orchestration Loops, Agent Tool Execution, AI Agent Frameworks, AI Model APIs, LLM Tooling Integrations.

¿Qué alternativas de código abierto existen para github/copilot-sdk?

Las alternativas de código abierto para github/copilot-sdk incluyen: letta-ai/letta — Letta is a framework for building, deploying, and managing autonomous AI agents that maintain persistent state across… mastra-ai/mastra — Mastra is an orchestration framework designed for building, deploying, and managing autonomous AI agents and… camel-ai/camel — This project is a comprehensive framework for building and managing autonomous agent systems. It provides a unified… microsoft/ai-agents-for-beginners — This project is a structured educational resource and technical guide for designing and implementing autonomous… i-am-bee/beeai-framework — The BeeAI Framework is an LLM agent framework and multi-agent orchestration engine used to build autonomous agents… lastmile-ai/mcp-agent — mcp-agent is a framework for building AI agents that integrate with Model Context Protocol servers to execute tools…

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