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RAG evaluation without the need for "golden answers"

Governed shared memory for AI agent fleets — multi-agent, multi-tenant, MCP-native. Trust tiers, keystone policies, audit trails, knowledge graph, self-improving retrieval. Apache 2.0.

Config-driven engine that turns JSON into production-grade AI agents. Multi-agent orchestration, 12+ LLM providers, MCP/A2A protocols, RAG, persistent memory, and enterprise compliance (EU AI Act, GDPR, HIPAA). Built on Quarkus.

Browser based tool to convert PDFs to Markdown

Open-source memory runtime for AI agents — reproducible, provenance-tagged context bundles instead of query-time retrieval. Apache-2.0, self-hosted on Postgres + pgvector, Python + TypeScript SDKs.

Integrated AI environment in the terminal. Build, test and instruct agents.

BookWith – A New Reading Experience with AI. A next-generation conversational reading platform that goes beyond traditional e-book readers

Text2Text Language Modeling Toolkit

An enterprise-grade AI retriever designed to streamline AI integration into your applications, ensuring cutting-edge accuracy.

🦀 Prevents outdated Rust code suggestions from AI assistants. This MCP server fetches current crate docs, uses embeddings/LLMs, and provides accurate context via a tool call.

An MCP server implementation that provides tools for retrieving and processing documentation through vector search, enabling AI assistants to augment their responses with relevant documentation context.

Human-like memory for AI agents — semantic, episodic & procedural. Experience-driven procedures that learn from failures. Free API, Python & JS SDKs, LangChain, CrewAI & OpenClaw integrations.

Fast similarity search using DuckDB

A production-ready FastAPI platform with modular components and a built-in control plane.

A python library for creating AI assistants with Vectara, using Agentic RAG

Local-first AI daemon for Logseq OG: background semantic indexing, link hygiene, and agent-ready CLI/MCP — edits Markdown on disk (no cloud, no Logseq API). Karpathy LLM-Wiki inspired.

AI-powered text compression library for RAG systems and API calls. Reduce token usage by up to 50-60% while preserving semantic meaning with advanced compression strategies.

MCP server for AI agent for cybersecurity: automate assessment of documents, questionnaires & reports. Multi-format parsing, RAG knowledge base,Risks, compliance gaps, remediations.

CLI + Local MCP - A shared structured memory store across Claude Code, Cursor, Windsurf, Antigravity, and every MCP client. Semantically queryable.

PDF extraction that audits its own output — and certifies any other extractor's, catching pages they silently dropped. Verify signed manifests offline: free, MIT, no account. 0.903 on opendataloader-bench, #2 of 8 engines. 7-tool MCP server.

🐕 Self-hosted security-first AI agent · 28 providers · 11 chat channels · WebUI · 3-level memory · task-schedule · automation · skills · MCP

A High-Efficiency System of Large Language Model Based Search Agents

Django BM25 full-text search using PostgreSQL - a lightweight Elasticsearch alternative

Scientific paper evidence search for ChatGPT, MCP, REST, Python, and AI agents.

The complete toolkit for building AI agents in JavaScript. Lightweight core, React & Terminal UIs, autonomous runtime, tools, skills, memory, RAG and observability. Plug-and-play from chat UI to full agents.

analystOS - AI research platform for stocks and crypto with Web UI + Notion automation. Upload docs, scrape URLs, chat with research via RAG. Powered by OpenRouter (50+ models).

The E2E AI testing tool | No ML Overhead

Open-source, self-hosted knowledge backend for AI agents — hybrid search (vector + keyword), MCP server, 5 connectors, Docker-ready

One memory, every terminal — a shared memory layer for any MCP client.

Self-hosted AI OS: RAG · multi-LLM failover · visual Workflow Studio · Skill Market · multi-tenant workspaces · sandboxed tools · multi-channel (Slack/Discord/Telegram/Feishu) · full observability — one docker compose up