Harnesses & SDKs

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

Open-source Python framework for multi-agent systems: agent societies, memory, RAG, code interpreters, synthetic data and large-scale simulation.

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

Policy enforcement, identity, sandboxing and reliability tooling for autonomous AI agents, installable with one pip command.

Open-source framework that builds, evaluates and self-improves multi-agent LLM workflows from a goal.

Python framework that models an application as a state machine, with a trace UI and persisted state.

Python framework for LLM apps built from collaborating agents that hold conversation state, tools and vector-store memory.

Python library for autonomous agents that run scheduled tasks and exchange messages on the Fetch.ai network.

Python runtime and framework for long-running agents, with durable sessions, approvals, spend caps and multi-agent collaboration.

Python multi-agent framework with PEER and DOE collaboration patterns and configurable LLM vendors such as Qwen, DeepSeek and OpenAI.

Python agent framework for OpenAI-compatible agents with tools, memory, MCP connections, multi-agent collaboration and workflows.

Python platform that gives agents a cryptographic identity, A2A communication and x402 payment requirements behind one bindufy() call.

Low-code Python framework for YAML-defined agents and multi-agent Cogs, with personas, prompt templates and memory nodes.

Python framework for autonomous agents that work in a sandboxed workspace, and task agents with tools and MCP support.

Python framework for MCP-based agents with composable workflow patterns, MCP server creation and Temporal-backed durable execution.

Python multi-agent framework that gives LLM agents software-company roles and SOPs to turn a one-line requirement into a project repository.

Lightweight Python framework for LLM agents built as PyTorch-style layers, with agent memory kept as session state.

Framework for building, running and evaluating autonomous agents, with a planner-based multi-agent mode, skills and agent RL training.