“Ollama”

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

Open-source Firebase framework for AI features and agent workflows in JavaScript, Go, Python and Dart, with model plugins.

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

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

Model-agnostic SDK for building agents and multi-agent systems in Python, .NET and Java; Microsoft names Agent Framework as its successor.

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

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

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.

Typed Python toolkit for composing classic and agentic retrieval-augmented generation pipelines with pluggable LLMs and stores.

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.

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

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

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

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

Modular Python agent framework with a tool-calling loop, context management, retrieval, persistent memory and MCP tools.

Python framework for agents that discover MCP tools by themselves, with optional memory, guardrails, cache and tracing.