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Provides vector search, LLM orchestration and language-model workflows with an embeddings database, RAG pipelines, multimodal indexing and agents. Runs locally or in containers and supports multiple models and language bindings (JS/Java/Rust/Go).
Turns documents, web pages and audio into a private, searchable knowledge base and builder for AI assistants and agents — supports wide-format ingestion, multi-model (cloud or local) execution, retrieval-augmented responses, and enterprise deployment features like RBAC and SSO.
Provides a self-hosted inference engine that serves all models an agent needs—embeddings, retrieval/reranking, document-to-markdown OCR, structured extraction, content-safety scoring, and LLM generation—through an OpenAI-compatible API. Bundles a 100+ model catalog, SDKs, and production deployment tooling.
Generates Wikipedia-style articles from web search using LLMs: it researches topics, produces multi-perspective outlines via perspective-guided question asking and simulated conversations, then drafts full articles with citations. Supports human-AI collaboration (Co-STORM) and grounding with multiple retrievers.
Unifies enterprise knowledge into a permission-aware context layer that delivers citation-backed, explainable search and no-code or SDK-driven agentic workflow automation. Supports 50+ connectors, knowledge-graph retrieval, an MCP server, and bring-your-own-model self-hosting.
Provides MS MARCO queries, passages and answers translated into 14 Indic languages while keeping the original English content and per-example translation metadata. Includes train/validation splits, passage selection flags, and translation model parameters for multilingual IR, QA and RAG research.
Open-source companion to a technical book that teaches how to design, evaluate and ship LLM-based AI agents — includes the full Chinese manuscript, community translations, chapter-aligned runnable example projects, and reproducible evaluation harnesses.
Provides a systematic, project-driven tutorial and runnable codebase for building AI agents, RAG pipelines, and multi-agent systems—focused on Python, LangChain/LangGraph, tooling, deployment, and an interview question bank for engineers aiming to ship production agent applications.
Provides a local MCP server that returns precise, symbol-level code (functions, classes, imports) via tree-sitter parsing so AI agents send only the bytes they need—commonly cutting code-reading token usage 95%+ and enabling compact packed responses for further savings.
Provides a local-first desktop workspace that runs and coordinates AI agents across your files, browser, and third-party tools with a shared, editable memory. Offers built-in models or bring-your-own-keys, one-click OAuth to 100+ integrations, and browser-driven agent automation.
Compiles raw documents into a persistent, interlinked Markdown wiki that LLMs can query; uses PageIndex for vectorless, reasoning-based retrieval of long documents, supports native multi-modality, bundled web Workbench, and skill distillation.
Turns source documents into a linked, source-cited Obsidian wiki for grounded Q&A and research. Local-first vault workflows with content-addressed capture, provenance ledgers, BM25 retrieval, and Claude Code / Agent Skills integration.