Harnesses & SDKs

Open-source Python and TypeScript SDK for model-driven agents, with tools, MCP, multi-agent patterns and streaming.

Python framework for multi-agent teams and workflows, with a CLI and Docker, MCP and A2A deployment options.

Python and TypeScript framework for multi-agent systems, with MCP and A2A interop, YAML orchestration and OpenTelemetry tracing.

Opinionated agent harness on LangGraph with sub-agents, filesystem access, context management, persistent memory and approval of tool calls.

Typed Python SDK for building agents with swappable model providers, plus optional capabilities for memory, sub-agents, planning and durable execution.

Open-source toolkit for building, evaluating and deploying agents in Python, TypeScript, Go, Java and Kotlin, tuned for Gemini but model-agnostic.

Python SDK that runs the Claude Code agent loop inside your own program, with custom tools and hooks written as Python functions.

Open-source framework that routes each user query to one of several AI agents, with Python, TypeScript and Swift runtimes.

Python framework and runtime for building agents, serving them over a REST API and MCP server, and managing them from a web control plane.

Framework for Python or Node.js programs that join LiveKit rooms as realtime voice and video agents.

Small Python library whose agents write their actions as code, with sandboxed execution and tools from MCP servers, LangChain or the Hub.

Python agent framework from Alibaba that lets the model drive tool use, with team pipelines, MCP, agent skills, A2A, memory and tracing.

AI application runtime framework for backends with structured outputs, observable actions, MCP capabilities and recoverable workflows.

Python SDK for deploying agents to Amazon Bedrock AgentCore, a managed runtime with memory, gateway, tools and observability.

Open-source access layer that connects AI agents to 1200+ integrations with shared auth, permissions and audit logs.

Python SDK for multi-agent workflows with handoffs, guardrails, sessions and built-in tracing, usable with OpenAI and other LLMs.

OpenAI-compatible agentic API server for inference, vector stores, files and skills, on any model or infrastructure.

CLI-first Python agent framework and coding agent with MCP, A2A and Agent Skills support, live streaming and human input.

Python toolkit for signed identities, verifiable credentials and delegated authority for AI agents, with MCP and REST interfaces.

Lightweight Python framework and starting template for agents that let the model choose its own approach to each task.

Python framework for generative AI agents, pipelines and parallel workflows, with RAG engines, tools, memory and swappable drivers.

All-in-one Python AI framework for embeddings-based semantic search, LLM pipelines, workflows and agents.

Kubernetes-native framework and runtime for building and running AI agents as Kubernetes custom resources.

Python framework for orchestrating multiple LLM agents in sequential, concurrent, hierarchical and graph-based workflows.