18 Lessons to Get Started Building AI Agents
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1–8 of 8 · Most stars
Self-evolving Context Database for AI Agents. Unify Agent Memory, Knowledge RAG and Skills.
An LLM-powered knowledge curation system that researches a topic and generates a full-length report with citations.
This repository showcases various advanced techniques for Retrieval-Augmented Generation (RAG) systems. Each technique has a detailed notebook tutorial.
Open-source AI orchestration framework for building context-engineered, production-ready LLM applications. Design modular pipelines and agent workflows with explicit control over retrieval, routing, memory, and generation. Built for scalable agents, RAG, multimodal applications, semantic search, and conversational systems.
Code search MCP for Claude Code. Make entire codebase the context for any coding agent.
Deeplake is AI Data Runtime for Agents. It provides serverless postgres with a multimodal datalake, enabling scalable retrieval and training.
The .NET library to build AI agents with 30+ built-in connectors.