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Frameworks and libraries for building autonomous AI agents with tool use, planning, and multi-step reasoning.

AI-powered tools that help developers write, review, and debug code directly in the terminal or IDE.

Open protocols and standards for tool integration, agent-to-agent communication, and interoperable AI systems.

Tools and patterns for retrieval-augmented generation, vector search, embeddings, and knowledge retrieval pipelines.

Benchmarks, evaluation frameworks, and safety testing tools for measuring and comparing LLM capabilities.

Gateways, routers, and proxy layers for managing multiple LLM providers with cost tracking, fallbacks, and load balancing.

Sandbox environments and infrastructure for running AI agent code safely in isolated containers or micro-VMs.

Security patterns, policy engines, and governance frameworks for controlling AI agent access and behavior.