AI FRAMEWORKS

The TypeScript toolkit for building AI apps. Unified API across OpenAI, Anthropic, Google. Streaming, tool calling, structured output, multi-step agents. 27K+ GitHub stars.

Frontend stack for agent-native apps. React hooks, prebuilt copilot UI, AG-UI runtime, frontend tools, shared state, and human-in-the-loop flows.

Anthropic's SDK for building agents on the same tools, agent loop, and context management that power Claude Code. Python and TypeScript. Subagents, hooks, MCP, and permissions.

Most popular LLM framework. 100K+ GitHub stars. Chains, RAG, vector stores, tool use. LangGraph adds stateful multi-agent workflows with cycles and persistence.

Gives AI agents access to 1,000+ toolkits (GitHub, Slack, Gmail, databases) with managed OAuth. Handles the auth and API complexity so your agent doesn't have to.

Python and TypeScript framework for multi-agent workflows. Agent handoffs, tool use, guardrails, sessions, tracing. Provider-agnostic, supporting the OpenAI Responses and Chat Completions APIs plus 100+ other LLMs.

Multi-agent orchestration framework. Define agents with roles, goals, and tools, then assign them tasks in a crew. Python-based. Great for complex workflows.

TypeScript-first AI agent framework. Agents, tools, memory, workflows, RAG, evals, tracing, MCP, and production deployment for Node.js apps.

LLM data framework for connecting custom data sources to language models. Best-in-class RAG, data connectors, and query engines. Python and TypeScript.

Multi-agent orchestration framework built on the OpenAI Agents SDK. Define agent roles, typed tools, and directional communication flows. Production-focused, open-source.

Type-safe Python agent framework from the Pydantic team. Brings the FastAPI feeling to AI development. Composable tools, durable execution, and full IDE autocomplete.

Structured data extraction from any LLM using Pydantic models. Automatic retries, validation, and streaming. 3M+ monthly downloads. Available in Python, TypeScript, Go, Ruby, and Rust.

Constrained generation library for LLMs. Uses finite state machines to mask invalid tokens during generation. Guarantees schema-compliant output with zero retries.

Open-source AI orchestration framework by deepset. Modular pipelines for RAG, agents, semantic search, and multimodal apps. Pipeline-as-graph architecture with explicit control.

The most widely used data validation library for Python, and the de facto standard. Type-hint-driven models, fast Rust-based core (v2), and the foundation of FastAPI, LangChain, and most Python AI tooling.

Open-source AI gateway: call 100+ LLM providers in the OpenAI format via a Python SDK or proxy. Spend tracking, budgets, rate limiting, load balancing, and fallbacks built in.