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Published directory entries matching your search.
Open RAG Eval is an open-source Python evaluation toolkit for Retrieval-Augmented Generation pipelines. It provides TREC-RAG evaluation metrics and a modular design for custom metrics and connectors, with core metrics that do not require golden chunks or golden answers.
A curated list of tools and resources for building production RAG systems.
This repository collects notebooks that explore Retrieval-Augmented Generation (RAG) for various applications, from introductory to advanced implementations. It includes a full_basic_rag.ipynb starter and notebooks on multi-querying, routing, query construction, indexing, and advanced retrieval. The materials cover environment setup, data loading, embedding generation, and vector store configuration with ChromaDB or Pinecone.
Chunk Tuner is a tool that benchmarks chunking strategies for a RAG corpus and recommends a configuration. It benchmarks strategies such as fixed-token windows, recursive character splitting, semantic splitting and PDF structural chunking, scoring each against retrieval metrics. It is available as a Python library, a CLI and an MCP server.
Retrieval Augmented Generation (RAG) framework and context engine powered by Pinecone.
This GitHub repository hosts the ragapp project, a solution described as the easiest way to use Agentic RAG in any enterprise environment. The repository contains code and documentation for the tool, which is intended for enterprise deployment of agentic retrieval-augmented generation workflows.
CLI tool for debugging and benchmarking RAG retrieval. EXPLAIN ANALYZE for your retrieval layer.
Retrieval Augmented Generation (RAG) chatbot powered by Weaviate.
A GitHub repository offering 168 free AI developer resources, described as samples from a larger AI Engineering Stack. It contains prompts, frameworks and configuration files organized into categories such as agent frameworks, code review, security and vibe coding, intended for use with Claude, ChatGPT, Gemini, Cursor and other language models.
An open-source project that provides tools to debug, evaluate, and monitor large language model (LLM) applications, retrieval-augmented generation (RAG) systems, and agentic workflows, with features including comprehensive tracing, automated evaluations, and production-ready dashboards.
Dify is an open-source LLM app development platform with a visual canvas for AI workflows and RAG pipelines, plus model management and observability. It can be deployed on cloud, VPC, or self-hosted, and integrates with proprietary and open-source LLMs.
Haystack is an open-source AI orchestration framework for building production-ready LLM applications in Python. It provides modular pipelines and agent workflows with explicit control over retrieval, routing, memory and generation, and is intended for RAG systems, multimodal applications, semantic search, question answering and autonomous agents. Installation is available via pip.
Kaito is the Kubernetes AI Toolchain Operator, an operator suite that automates LLM inference, fine-tuning and RAG engine deployment in a Kubernetes cluster. It simplifies the CRD API and provides optimized preset configurations for inference engine scheduling parameters. Workspaces are the basic CRD for managing inference and tuning workloads, and all vLLM-supported models can be run.
LLM App is an open-source project providing ready-to-run cloud templates for retrieval-augmented generation (RAG), AI pipelines and enterprise search with live data. It is Docker-friendly and designed to stay synchronized with data sources including Sharepoint, Google Drive, S3, Kafka, PostgreSQL and real-time data APIs, hosted by pathwaycom on GitHub.
LocalGPT is an on-premise document intelligence platform that lets users chat with their documents on their local device using GPT models. It supports PDF, DOCX, HTML, Markdown, and TXT files and combines dense vector search with LanceDB full-text search and a cross-encoder reranker. It runs with a plain Python RAG core and offers generation models via Ollama.
PraisonAI is an open-source project hosted on GitHub under the MervinPraison/PraisonAI repository. It enables the deployment of autonomous, self-improving AI agents capable of researching, planning, coding, and executing tasks. The tool includes built-in memory and retrieval-augmented generation (RAG) capabilities, supports over 100 large language models, and can be deployed with 5 lines of code.
Neurolink is described as the pipe layer of an AI nervous system, providing one interface connecting provider neurons to an application across three inference types: generate, stream, and a calibrated decide via TypeSafe Jev. It lists MCP-native, voice, RAG, memory and file processor features and powers several Juspay projects.
Onyx is an open-source AI platform that connects to applications to index and surface knowledge, with self-hosted deployment options. It provides agentic RAG search, deep research reports, custom agents, web search, a secure sandbox for code execution, artifacts, and voice mode. It works with major LLMs.
PrivateGPT is an open-source API layer for building private AI applications on local models, following the Claude API model. It provides a messages API, file and artifact ingestion, retrieval with citations and agentic RAG, built-in and custom tools, MCP connectors, structured database and CSV access, and embeddings. It connects to any OpenAI-compatible inference server and does not run models itself.
Promptfoo is a tool for testing AI prompts, agents and RAG systems. It supports red teaming, penetration testing and vulnerability scanning for AI, enables performance comparison across models including GPT, Claude, Gemini and DeepSeek, uses simple declarative configuration, and integrates with command line and CI/CD workflows.
Supermemory is a memory and context engine for AI that learns from conversations, extracts facts, builds user profiles, and delivers context. It includes hybrid search combining RAG and memory, connectors, and multi-modal extractors. It can be run fully locally.
Vectara-agentic is a Python library hosted on GitHub for creating AI assistants with Vectara using Agentic RAG. The repository provides an overview, key features, and example AI assistants to help developers integrate the technology.
VeritasGraph is an open-source knowledge graph and GraphRAG framework. It supports multi-hop reasoning, ontology-aware retrieval, and verifiable attribution over user data, using nodes, edges, RDF, and linked data. The project runs locally or in the cloud.
