Harnesses & SDKs

Python agent runtime with an SDK for building your own agent, and an app that adds plugins and channel integrations.

TypeScript agent framework with persistent cognitive memory, multi-agent orchestration and one dispatch interface across 13 LLM providers.

Ruby library for LLM-powered apps with a unified LLM interface, prompt tools, output parsers, RAG and assistants.

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

Python framework for real-time voice and vision agents that process video with pluggable models and processors over WebRTC.

The batteries included agent harness.

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

Code-first Java toolkit for building and testing AI agents, with multi-agent hierarchies, a dev UI and Maven packages.

Open-source infrastructure for continually self-improving agents that serves requests, learns from feedback and versions updates.

Reliability layer for tool calling with self-hosted LLMs, offered as a proxy, a workflow runner and middleware.

Open-source Python evaluation framework with ready-made and custom graders for scoring AI agent quality and producing reward signals.

Python library of 35 agentic AI architectures with one run() interface, built on LangGraph and multiple LLM providers.

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

Rust framework with an agent harness, typed task graphs and streaming indexing and RAG pipelines for LLM applications.

Python framework for durable AI agents built as dataflows that resume after crashes, retry safely and record each step.

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

Python library for steering language models by constraining output with regex and grammars and mixing control flow with generation.

Framework for AI agents and multi-agent networks where a router picks which agent runs, with tools and MCP support.

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

JavaScript framework for multi-agent teams whose tasks move across a Kanban-style board you can watch.

Python framework for LLM applications built on Qwen models, with function calling, MCP, a code interpreter and RAG options.

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

Python framework for data labeling agents that learn skills from a ground-truth dataset through iterative feedback.

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