Orchestration frameworkAI and agent developmentGitHub Copilot
agent-framework
@microsoft·Python
Orchestrate durable multi-agent workflows in Python and .NET
Project overview
Microsoft Agent Framework targets Python and .NET applications that need more than a single prompt-and-response loop. It supports sequential, concurrent, handoff, and group-collaboration graphs, along with checkpointing, streaming, human-in-the-loop control, OpenTelemetry tracing, and Foundry hosting samples. Its scope suits teams building restartable, observable agent workflows rather than a small one-off script.
Create MCP servers and clients with concise Python APIs.
Project overview
The MCP Python SDK turns Python type annotations into tool schemas and offers both server and client APIs for in-memory, stdio, and remote HTTP connections. It supports the standard MCP transports and is suited to building or testing protocol integrations. The documented v2 line is a breaking-change-prone prerelease, so v1.x remains the stable production choice.
Build Python agent workflows with tools, handoffs, guardrails, sessions, and tracing.
Project overview
OpenAI Agents SDK lets Python developers define agents with instructions, tools, guardrails, and delegation between specialized workers. It includes session history, human approval points, tracing, sandboxed long-running agents, and realtime voice support. The framework can use OpenAI APIs or other supported model providers and requires Python 3.10 or newer.
Configure and run custom multi-agent workflows without writing orchestration code.
Project overview
ChatDev 2.0 expands the original software-development agent system into a zero-code multi-agent orchestration platform. Users assemble nodes, tools, and models through configuration to run workflows such as document summarization, information collection, content publishing, or data visualization. A Python SDK and modular backend also let developers add new nodes, model providers, and tools.
Run AI-generated code in isolated sandboxes and preserve execution state.
Project overview
Daytona lets agents create isolated compute sandboxes through SDKs, an API, or a CLI, then execute code, manage files and processes, and preserve state with snapshots. Clients are available for several languages, including Python and TypeScript. This public repository has been unmaintained since June 2026 because core development moved to a private codebase, so it receives no further fixes or support.
Build controllable AI agents in Python or TypeScript
Project overview
Strands Agents is a monorepo with Python and TypeScript SDKs for building model-driven agents. The SDKs support tools, MCP, streaming, multi-agent patterns, hooks, guardrails, context management, and multiple model providers, with examples for local development and deployment. Python requires 3.10 or newer and the TypeScript package requires Node.js 20 or newer; the default quickstart uses Amazon Bedrock credentials.
Run agent-generated code inside isolated cloud sandboxes.
Project overview
E2B provides JavaScript and Python SDKs for starting isolated cloud sandboxes where agents can execute commands or generated code without running directly on the host. The managed service requires an E2B API key, and a separate Code Interpreter package adds higher-level code execution. Self-hosting is documented through Terraform, with current provider support centered on AWS and Google Cloud.
Optimizes AI agents with almost no code changes across frameworks and training methods.
Project overview
Agent Lightning connects existing agents to optimization workflows with minimal code changes by collecting prompts, tool calls, and rewards as structured traces. It can selectively optimize one or more agents built with LangChain, the OpenAI Agent SDK, AutoGen, CrewAI, Microsoft Agent Framework, or plain Python. Supported approaches include reinforcement learning, automatic prompt optimization, and supervised fine-tuning.
Build stateful, real-time AI agents on Cloudflare.
Project overview
Cloudflare Agents uses Durable Objects to give each agent its own persistent state, storage, and lifecycle. The SDK includes callable methods, WebSockets, scheduling, AI chat, MCP, and workflow integration, with client helpers for real-time synchronization. Developers can start from the provided template or add the package to an existing Cloudflare application.
Build model-agnostic agent workflows across Python, .NET, and Java
Project overview
Semantic Kernel is a model-agnostic SDK for building, orchestrating, and deploying AI agents and multi-agent systems across Python, .NET, and Java. It supports multiple model providers, plugins, structured workflows, and vector-database integrations. The project now identifies Microsoft Agent Framework as its successor and provides a migration guide, which is relevant when maintaining or starting an application.
Build MCP servers, clients, and runtime integrations in TypeScript.
Project overview
The MCP TypeScript SDK supplies separate server and client packages for implementing tools, resources, prompts, transports, and OAuth across Node.js, Bun, and Deno. Optional thin adapters connect MCP to runtimes such as Express and Hono. The main branch documents the v2 beta, while v1.x remains the supported production release.
Calls Claude Agent from Python with async queries, in-process tools, hooks, and interactive sessions.
Project overview
Claude Agent SDK for Python exposes an async query function and ClaudeSDKClient for bidirectional, interactive conversations with Claude Code. Custom tools and hooks can run as in-process MCP servers, while external MCP servers can be used alongside them. The package bundles the Claude Code CLI by default, requires Python 3.10 or newer, and allows a custom CLI path.
Build composable AI agents with tools, sessions, middleware, and subagents.
Project overview
Create general-purpose agents in TypeScript with streamed events, filesystem or shell tools, persistent multi-turn sessions, and middleware for compaction, retry, and turn tracking. The SDK also covers subagents, MCP servers, on-demand skills, virtual filesystems, and React or Vue chat integration on top of Vercel's AI SDK.
Run a reversible terminal coding agent with local or cloud LLMs.
Project overview
OpenHarness connects to local models such as Ollama or to cloud APIs, then reads, edits, and searches code, runs commands, uses MCP servers, delegates to sub-agents, and operates headlessly in CI. Permission modes, command-risk analysis, file checkpoints, automatic Git commits, and post-edit verification make agent changes easier to inspect and undo. It requires Node.js 18+.
Version an agent’s identity, rules, memory, tools, and skills in Git.
Project overview
Gitagent represents an AI agent as a Git repository, with model configuration, identity, rules, memory, tools, skills, hooks, and workflows stored as reviewable files. You can fork an agent, branch its personality, diff rule changes, and inspect memory history with normal Git operations. The runtime supports OpenAI, Anthropic, and OpenAI-compatible providers, and requires Node.js, Git, and the relevant API credentials.
FrameworkAI and agent developmentModel Context Protocol
agentsilex
@howl-anderson·Python
Builds a small, readable agent framework with swappable models, tools, sessions, and handoffs.
Project overview
AgentSilex is a roughly 300-line Python framework built around readable Agent, Runner, Session, and tool abstractions. LiteLLM lets it switch among more than 100 model providers, while the framework adds tool calls, session history, multi-agent handoffs, MCP clients, streaming, and structured output. OpenTelemetry tracing is included for inspecting execution, and the package requires Python 3.12 or newer.
Generate AI music through an SDK, CLI, MCP server, or agent skill
Project overview
lacuna-toolkit exposes the Lacuna Music API through a TypeScript SDK, a command-line tool, an MCP server, and an Agent Skill. Developers can generate music from Node 18+, shell scripts, an MCP-compatible client, or an AI coding agent, while the SDK also handles webhook verification. Using any surface still requires access to the Lacuna service and its credentials.
Generate and deploy Lowcoder apps from TypeScript instead of hand-built DSL JSON.
Project overview
Use the TypeScript SDK to turn fluent component calls into Lowcoder's required DSL, while the companion MCP server exposes app creation, SEO, and related tools to agents. A Claude skill explains the workflow across supported clients. Lowcoder 2.7.0 or newer and a configured instance with an API key are required.