17 items
3 posts, 14 tools
Python's de facto data validation library. Type-hint-driven models, fast Rust-based core (v2), and the foundation of FastAPI, LangChain, and most Python AI tooling.
Self-healing browser automation harness that lets LLMs complete any browser task. 5,000+ stars in under a week.
Production-tested patterns for orchestrating AI agent teams - from fan-out parallelism to hierarchical delegation. Covers CrewAI, LangGraph, AutoGen, OpenAI Agents SDK, Google ADK, and custom approaches with real code.
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.
Serverless cloud for AI/ML workloads. Write Python with decorators, Modal handles GPU provisioning and scaling. 2-4s cold starts. Scales to zero. $30/mo free compute.
The creators of Ruff and uv are joining OpenAI. Here is what this means for the Python ecosystem, AI tooling, and why OpenAI is investing in developer infrastructure.
Wire a Python LangGraph agent into a Next.js frontend using CopilotKit's co-agent architecture. Full walkthrough covering the graph, search nodes, streaming state, and the React UI.
Lightweight Python framework for multi-agent systems. Agent handoffs, tool use, guardrails, tracing. Successor to the experimental Swarm project.
Multi-agent orchestration framework. Define agents with roles, goals, and tools, then assign them tasks in a crew. Python-based. Great for complex workflows.
LLM data framework for connecting custom data sources to language models. Best-in-class RAG, data connectors, and query engines. Python and TypeScript.

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