Agentic capability

Multi-agent framework that assigns GPT roles (PM, engineer, QA) to solve complex software tasks end-to-end.

Low-code visual builder for LLM-powered agents and multi-step AI workflows with 100+ pre-built components.

Official TypeScript/JavaScript SDK for building MCP servers and clients — the Node.js foundation for the Model Context Protocol.

High-performance, model-agnostic Python framework for building, running, and managing production AI agents at scale.

OpenAI's lightweight Python framework for building multi-agent systems with handoffs, tracing, and guardrails.

Pioneering autonomous task-management AI that uses GPT-4 and vector memory to self-generate and prioritize tasks.

The leading framework for building LLM-powered applications and agents with chains, tools, memory, and retrieval.

Run large language models locally with a single command. OpenAI-compatible API for Llama, Mistral, Gemma, and more.

Cross-browser automation library for agents: control Chromium, Firefox, and WebKit with a single reliable API.

CLI scaffolder for full-stack LlamaIndex RAG and agentic apps — Next.js, FastAPI, or Express in one command.

Official reference MCP servers: filesystem, git, fetch, memory, and more — the fastest way to give agents real-world tool access.

Secure cloud sandboxes for running AI-generated code — spin up a Python/JS interpreter in 150ms from your agent.

First multi-agent framework based on role-playing LLM societies; supports 20+ agent types, tool use, RAG, and multi-modal tasks.

Microsoft's autonomous R&D agent for iterative hypothesis generation, experimentation, and quantitative research automation.

Unified API gateway for 100+ LLM providers — call OpenAI, Anthropic, Gemini, Cohere, and local models with a single consistent interface.

Connect AI agents to the web: Browser Use lets LLMs browse, interact, and extract from any website autonomously.

Microsoft's open-source SDK for building AI agents and skills in C#, Python, and Java — composable, enterprise-ready, and model-agnostic.

Stanford's framework for programming — not prompting — LLMs: compile, optimize, and auto-tune modular AI systems.

LLM-powered multiagent persona simulation for synthetic UX research, focus groups, and business scenario testing.

Modular Python framework for building AI agents with chain-of-thought reasoning, tool use, memory, and structured workflows.

Official MIT-licensed Python SDK for the Model Context Protocol — build MCP servers and clients with full spec compliance.

Intelligent memory layer for AI agents — automatically extracts, stores, and retrieves relevant context across sessions.

Autonomous agent by OpenBMB with outer/inner loop planning, tool execution, and a human-in-the-loop GUI for long-horizon tasks.

Microsoft's code-first agent framework that converts user requests into executable Python snippets for complex data analytics tasks.

Structured outputs for LLMs using Pydantic — patches any OpenAI-compatible client to return validated Python objects.

Python SDK for AI agent monitoring, LLM cost tracking, session replay, and benchmarking across all major agent frameworks.

Pydantic's type-safe agent framework: build production agents with structured I/O, dependency injection, and full observability.

Guaranteed structured generation from LLMs: enforce JSON, regex, grammar, or Pydantic schemas at the token level.

Validate, fix, and filter LLM outputs — define structured schemas and safety rules, then automatically retry when outputs fail validation.

Apache-2.0 Python framework for building MCP servers fast — decorator-based, zero boilerplate, typed tool definitions.