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Trendige AI-Projekte auf GitHub

Verfolge aktive Open-Source-AI-Repositories in den Bereichen LLM, Agenten, MCP, RAG und Coding – mit Sternzahlen und verifizierten Projektdaten.

Daten des öffentlichen Repositorys

Projektindex

40 Projekte
Screenshot von NeuroLink
MCP & Tool-Aufrufe

NeuroLink

juspay/neurolink

A TypeScript integration platform providing a unified API for 30+ AI providers and 100+ models, enabling provider swapping, multi-modal voice processing, RAG, memory, and MCP-native tool integration.

★ 121⑂ 124TypeScript
MITQ93
Screenshot von Metronix Memory
MCP & Tool-Aufrufe

Metronix Memory

mtrnix/metronix-memory

Self-hosted memory infrastructure for AI agents featuring MCP-native integration, hybrid RAG, a temporal knowledge graph, and an ontology layer, designed for local-model friendliness and durable, agent-scoped context.

★ 39⑂ 7Python
Apache-2.0Q86
Screenshot von STACKIT RAG Template
RAG & Wissenssysteme

STACKIT RAG Template

stackitcloud/rag-template

A template for building AI chatbots and document management systems using Retrieval-Augmented Generation (RAG), vector search, and FastAPI, designed for deployment on Kubernetes.

★ 86⑂ 10Python
Apache-2.0Q89
Screenshot von Neo.mjs
MCP & Tool-Aufrufe

Neo.mjs

neomjs/neo

A self-evolving software organism that combines a multi-threaded frontend application engine with an end-to-end AI engineering team operating via cross-model swarm coordination, persistent memory, and runtime application embodiment.

★ 3,3K⑂ 231JavaScript
MITQ98
Screenshot von LanceDB
RAG & Wissenssysteme

LanceDB

lancedb/lancedb

An open-source, developer-friendly embedded retrieval library and multimodal AI lakehouse designed for fast, scalable, and production-ready vector search, built on the Lance columnar format.

★ 11,1K⑂ 994Rust
Apache-2.0Q98
Screenshot von LangChain4j
MCP & Tool-Aufrufe

LangChain4j

langchain4j/langchain4j

An idiomatic, open-source Java library for building LLM-powered applications on the JVM, offering a unified API over popular LLM providers and vector stores with support for tool calling, MCP, agents, and RAG.

★ 12,8K⑂ 2,4KJava
Apache-2.0Q98
Screenshot von Graphify: Turn Any Codebase into a Queryable Knowledge Graph
MCP & Tool-Aufrufe

Graphify: Turn Any Codebase into a Queryable Knowledge Graph

graphify-labs/graphify

Graphify is a local-first CLI tool and AI assistant skill that parses codebases, docs, SQL schemas, and PDFs into a queryable knowledge graph using deterministic AST parsing, eliminating the need for vector stores or embeddings.

★ 104K⑂ 10,1KPython
Apache-2.0Q98
Screenshot von ReMe: Memory Management Kit for Agents
Agenten & Multi-Agenten

ReMe: Memory Management Kit for Agents

agentscope-ai/reme

ReMe is a local-first memory layer for AI agents that transforms conversations and resources into readable, editable, and searchable Markdown memory files.

★ 3,3K⑂ 283Python
Apache-2.0Q98
Screenshot von Genkit: Open-Source Framework for Agentic AI Applications
Agenten & Multi-Agenten

Genkit: Open-Source Framework for Agentic AI Applications

genkit-ai/genkit

Genkit is an open-source framework built and used in production by Google's Firebase for developing full-stack, AI-powered applications. It provides cross-language SDKs and unified APIs for integrating various AI models to build chatbots, automations, and recommendation systems.

★ 6,3K⑂ 816TypeScript
Apache-2.0Q98
Screenshot von EvalScope
RAG & Wissenssysteme

EvalScope

modelscope/evalscope

A streamlined and customizable framework for efficient large model (LLM, VLM, AIGC) evaluation and performance benchmarking.

★ 3,2K⑂ 440Python
Apache-2.0Q98
Screenshot von OpenViking: Self-evolving Context Database for AI Agents
Agenten & Multi-Agenten

OpenViking: Self-evolving Context Database for AI Agents

volcengine/openviking

OpenViking is an open-source context database that unifies agent memory, knowledge RAG, and skills into a single virtual filesystem, enabling AI agents to browse and retrieve context deterministically using a file-system-like protocol.

★ 28K⑂ 2,2KPython
AGPL-3.0Q98
Screenshot von Headroom: Context Compression Layer for AI Agents
MCP & Tool-Aufrufe

Headroom: Context Compression Layer for AI Agents

headroomlabs-ai/headroom

Headroom is a local-first context compression layer that reduces token usage for AI agents by compressing tool outputs, logs, files, and RAG chunks before they reach the LLM. It offers library, proxy, and MCP server modes and claims to preserve answer accuracy while cutting tokens by 15-95% depending on workload.

★ 65K⑂ 4,9KPython
Apache-2.0Q98

Seite 2 / 4 · 40 Projekte

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