
langchain
langchain-ai/langchainThe agent engineering platform.
Free to explore · curated by OneWave AI
81 agents projects. Autonomous and semi-autonomous agent frameworks and runtimes.
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The agent engineering platform.

The Memory Layer for AI Agents - Drop-in memory infrastructure for AI agents and apps. Context that persists. Built for production.

Stop renting your intelligence. Own it with AnythingLLM. Everything you need for a powerful local-first agent experience

Build resilient agents.

Your AI second brain. Self-hostable. Get answers from the web or your docs. Build custom agents, schedule automations, do deep research. Turn any online or local LLM into your personal, autonomous AI (gpt, claude, gemini, llama, qwen, mistral). Get started - free.

50+ tutorials and implementations for Generative AI Agent techniques, from basic conversational bots to complex multi-agent systems.

End-to-end, code-first tutorials for building production-grade GenAI agents. From prototype to enterprise deployment.

GenBI (Generative BI) for AI agents, an open-source, governed text-to-SQL through an open context layer that turns natural-language questions into trusted dashboards, charts, and SQL across 20+ data sources, such as BigQuery, Snowflake, PostgreSQL, ClickHouse, Amazon Redshift, Databricks and more.

Memori is agent-native memory infrastructure. A LLM-agnostic layer that turns agent execution and conversation into structured, persistent state for production systems. Built for enterprise, Memori works with the data infrastructure you already run, no rip-and-replace, and deploys across managed cloud, single-tenant cloud, VPC, and on-premises.

A modular Agentic RAG built with LangGraph — learn Retrieval-Augmented Generation Agents in minutes.

MongoDB's Generative AI Showcase: an exhaustive collection of examples and sample applications covering Retrieval-Augmented Generation (RAG), AI agents, and industry-specific use cases.

A curated collection of practical AI projects implementing OCR systems, RAG, AI agents, and other AI use cases.

Easiest and laziest way for building multi-agent LLMs applications.