ai agent frameworks

Frameworks and libraries builders use to make ai agents.

85 listings · 41–60

YC-backed AI employees for human-level roles plus managed ML engineering and agent systems.

Research system that connects language models with machine-learning tools

JavaScript framework for building, visualizing, and managing multi-agent AI workflows.

Enterprise platform for building, deploying, governing, and optimizing ai agents.

LLM observability platform for tracing, evaluating, and monitoring AI applications.

Graph-based framework for building stateful, controllable agent workflows.

Python framework for building multi-agent LLM applications with message-based collaboration.

Platform for observing, evaluating, and deploying ai agents and LLM applications.

Plan-Validate-Solve agent framework for reliable and reproducible workflow automation

Agent platform for persistent memory, context management, and long-running ai agents

Data framework for building LLM applications, RAG systems, and agentic workflows.

Minimal Python library for building tool-using agents controlled by language models

Modular Python package for building extensible Auto-GPT-style agents.

Open-source low-code platform that generates full-stack apps and ai agents from natural language.

TypeScript framework for building ai agents, workflows, and applications.

Memory layer for adding persistent, personalized context to ai agents.

Multi-agent framework that turns software requirements into plans, designs, tasks, and code.

Multi-agent web search framework that plans queries and synthesizes retrieved information.

Open protocol for connecting AI applications with tools, data sources, and prompts.

Databricks MCP services connect ai agents with governed data and enterprise tools.