Formerly known as Danswer and now backed by over 31,000 GitHub stars with 253 releases, Onyx delivers a production-ready AI platform that turns any LLM into a context-aware enterprise assistant connected to your organization's actual knowledge. The agentic RAG pipeline combines BM-25 keyword search with prefix-aware embedding models in a hybrid index, then deploys AI agents to retrieve, verify, and synthesize answers with source citations from over 40 connected workplace tools including Google Drive, Confluence, Slack, Notion, Jira, SharePoint, GitHub, and Linear. Custom AI assistants with configurable prompts, backing knowledge sets, and document-level access control enable specialized agents for engineering, sales, support, and research workflows. The platform supports every major LLM provider — Anthropic Claude, OpenAI, Google Gemini, plus self-hosted options via Ollama, LiteLLM, and vLLM for fully air-gapped deployments. Beyond chat, Onyx provides web search with Serper, Google PSE, Brave, and SearXNG integration, an in-house web crawler, code execution, file creation, and multi-step deep research with report generation. Enterprise features include SSO via Google OAuth, OIDC, or SAML with SCIM provisioning, role-based access control, usage analytics by team and agent, query history auditing, PII removal through custom code hooks, and full whitelabeling. Deploy via Docker Compose on any infrastructure. Running on a dedicated VPS on RepoCloud with guaranteed CPU, RAM, and SSD, full root SSH access, and a browser serial console. MIT licensed (Community Edition).

Chat with your own documents: AnythingLLM, from Mintplex Labs, wraps retrieval-augmented generation (RAG) in an open-source application anyone can run. You organize content into workspaces, each an isolated namespace with its own documents, vector embeddings, chat history, and settings, so one instance can hold several separate knowledge bases. Upload PDFs, DOCX, TXT, and other formats, or scrape web pages; the built-in collector parses and chunks them into a vector database (LanceDB by default, with Pinecone, Chroma, Qdrant, and others supported). Answers cite their source documents. It works with both cloud LLMs (OpenAI, Anthropic, Gemini) and local ones via Ollama or LM Studio, and the embedding model is separately configurable. Beyond RAG chat, it includes AI agents that can browse the web and run tools, an embeddable chat widget for your website, a developer API, and multi-user mode with admin, manager, and default roles plus per-workspace access control. Context assembly is smarter than naive RAG: pinned documents, attached files, vector search hits, and recent chat history are combined under a token budget so the model's context window is filled efficiently, and each workspace supports multiple independent conversation threads against the same knowledge base. Because the embedding model, vector store, and chat LLM are all independently swappable, you can move between providers without re-ingesting a single document. The stack is Node.js with a React frontend, MIT-licensed.

A self-hosted "second brain": Khoj indexes your own files and answers questions from them, parsing Markdown (whole Obsidian vaults included), org-mode, PDF, Word, plain text, Notion pages, GitHub repositories, and images described by a vision model, then embedding everything with sentence-transformers into a vector index for semantic search and RAG with cited sources. Any LLM backend works: local models like Llama, Qwen, or Mistral via Ollama, or cloud models like GPT, Claude, and Gemini. You can build custom agents, each with its own persona, scoped knowledge base, chat model, and tools such as web search and code execution. Scheduled automations run recurring research and deliver newsletters or notifications to your inbox, and research mode performs multi-hop web searches with inline citations. Access it from a browser, the Obsidian plugin, Emacs, desktop, or WhatsApp - all clients connect to the same self-hosted instance, making Khoj one of the few AI assistants Emacs users can point at decades of org files. Semantic search means recall works without exact keywords: "that paper about forecasting with transformers" surfaces the right PDF even when you cannot remember its title. Switching LLM backends never requires re-indexing your documents, and with a local model via Ollama, even inference stays on hardware you control - journals, research, and private notes are never sent anywhere. Python/FastAPI stack, AGPL-licensed, with PostgreSQL storage.

With over 29,000 GitHub stars and deep integrations into LangChain, LlamaIndex, and CrewAI, Chroma has become the default vector database for developers building retrieval-augmented generation pipelines and AI agent memory systems. Its core API consists of just four functions — create, add, query, and delete — making it the fastest path from zero to semantic search, while the underlying Rust engine handles tokenization, embedding, HNSW indexing, and similarity scoring automatically. Chroma supports dense vector search via HNSW with configurable distance metrics including L2, cosine similarity, and inner product, sparse vector search using SPLADE, full-text BM25 keyword search, and regex matching, all combinable in hybrid queries through a single unified interface. Metadata filtering at query time uses MongoDB-style operators including $eq, $ne, $gt, $lt, $in, and logical combinators $and and $or, enabling precise result scoping without post-processing. The multimodal pipeline powered by OpenCLIP embeds text and images into a shared vector space, allowing cross-modal retrieval where text queries return relevant images and vice versa. Deployment options range from embedded mode via PersistentClient for notebooks and prototypes, to client-server mode with Docker for production, to Chroma Cloud for serverless scalability. Official Python and JavaScript SDKs provide identical APIs, and embedding function integrations support OpenAI, Cohere, Hugging Face, Google, Ollama, and custom models. Running on a dedicated VPS on RepoCloud with guaranteed CPU, RAM, and SSD, full root SSH access, and a browser serial console. Apache 2.0 licensed.

Execute comprehensive multi-angle investigations across scientific journals and web repositories using Local Deep Research, an autonomous intelligence engine designed for researchers, analysts, and knowledge workers. Users can submit complex analytical questions through a self-hosted web console to initiate agentic research cycles powered by LangGraph workflows. The application dynamically queries search providers including SearXNG, arXiv, PubMed, Wikipedia, and Google, iteratively evaluating discovered content to formulate targeted follow-up queries. Discovered academic papers and web sources can be indexed into an encrypted personal library to combine internal documentation with external search intelligence. Reports are synthesized with structured outlines, section headings, and verifiable academic citations that link directly to source references. Individual research sessions, system logs, and user credentials remain isolated in per-user SQLCipher databases protected by AES-256 encryption. Automated subscriptions deliver scheduled news digests and topic monitoring summaries directly to connected notification channels. Running on a dedicated VPS on RepoCloud with guaranteed CPU, RAM, and SSD, full root SSH access, and a browser serial console. MIT licensed.

Perplexity's answer-engine experience, self-hostable and open-source: Morphic searches the web and writes cited answers. Instead of returning a list of links, it searches the web, reads the sources, and generates a complete answer with inline numbered citations. The generative UI streams rich components, source cards with thumbnails, image grids, syntax-highlighted code, and LaTeX math, rather than plain markdown. Quick mode answers fast; Adaptive mode runs deeper multi-step research. Search backends are pluggable: the Docker Compose bundle ships with a private SearXNG instance so no search API key is required, and Tavily, Brave, and Exa are supported alternatives. LLM providers include OpenAI, Anthropic, Google, Ollama, and any OpenAI-compatible endpoint, with per-mode model mapping - fast, cheap models for quick searches, stronger models for adaptive research, tuning the cost-quality trade-off per query type. An inspector panel exposes tool execution during multi-step research, and AI-suggested follow-up questions keep an investigation moving. Chat history persists in PostgreSQL, results are shareable by URL, file uploads feed context into queries, and optional Supabase authentication adds multi-user or guest access. Because the default search path is your private SearXNG instance, research topics never hit a commercial search API - and with local Ollama models the marginal cost of a query approaches zero. Built with Next.js, TypeScript, and the Vercel AI SDK under Apache 2.0.

With over 11,000 GitHub stars and now backed by Datadog while remaining fully Apache 2.0 licensed, Quickwit delivers the search performance Elasticsearch users expect at a fraction of the infrastructure cost by moving the index to object storage instead of expensive local SSDs. The Rust-based engine, built on the Tantivy search library with SIMD-accelerated vectorized processing and zero garbage collection overhead, achieves sub-second search latency directly against Amazon S3, Azure Blob Storage, Google Cloud Storage, or any S3-compatible backend like MinIO and Ceph. The Elasticsearch-compatible REST API covers ingest, search, query DSL, and aggregations, enabling existing log shippers including Vector, Fluent Bit, and Syslog to migrate without rewriting configurations. Native OpenTelemetry Protocol endpoints accept logs and traces via gRPC, while Jaeger integration provides a drop-in distributed tracing backend. Ingestion from Apache Kafka, Amazon Kinesis, and Apache Pulsar supports streaming pipelines with multi-index partitioning, and the schemaless JSON indexing mode eliminates the need for upfront schema definitions. Stateless searchers and indexers scale horizontally on Kubernetes or bare metal, with a control plane that distributes indexing tasks and a janitor that manages retention policies and GDPR-compliant deletions. The built-in web UI displays search results and cluster state, while the official Grafana data source enables log exploration dashboards. Running on a dedicated VPS on RepoCloud with guaranteed CPU, RAM, and SSD, full root SSH access, and a browser serial console. Apache 2.0 licensed.

libSQL bridges the gap between SQLite's legendary simplicity and the server-accessible database model developers expect from PostgreSQL and MySQL, adding network access, replication, and vector search while maintaining full file format and API compatibility. The sqld (SQL daemon) component transforms SQLite into a network-accessible database server accepting queries via HTTP with JSON payloads, WebSocket connections, and a Hrana protocol optimized for low-latency edge access. Client libraries for TypeScript, JavaScript, Rust, Go, and Python connect to sqld identically to any traditional client-server database, while the LD_PRELOAD compatibility layer enables switching existing SQLite applications from local to remote mode without code changes. Embedded replicas synchronize a local SQLite copy inside your application process with the primary server, delivering sub-millisecond read latency while maintaining consistency through streaming replication. Bottomless replication continuously backs up database state to S3-compatible object storage with generation-based snapshots and WAL page shipping, providing point-in-time recovery without manual backup scheduling. Native vector search enables semantic similarity queries directly within SQL using cosine distance functions, eliminating the need for external vector database infrastructure. Read replica support distributes query load across multiple sqld instances, and integration with mvSQLite adds multi-version concurrency for high-availability deployments. Created by Turso with 17,100+ stars and active production deployment. Running on a dedicated VPS on RepoCloud with guaranteed CPU, RAM, and SSD, full root SSH access, and a browser serial console. MIT licensed.

With 26,600 GitHub stars, 50 contributing companies including AWS, Google Cloud, Oracle, and Ericsson, and governance under the Linux Foundation ensuring the BSD 3-Clause license can never be revoked by a single entity, Valkey delivers a truly open-source Redis-compatible key-value datastore that reached 1.19 million requests per second in version 8.0 through redesigned asynchronous I/O threading across CPU cores while maintaining single-threaded data structure operations for predictability. Native data structures include strings, hashes, lists, sets, sorted sets, bitmaps, HyperLogLogs, streams, and geo-spatial indices with JSON support through modules. Valkey 9.0 shipped full-text search and aggregation via Valkey Search, enabling tag queries, numeric filtering, and text matching directly within the datastore without external search engines. Cluster mode provides horizontal scaling with automatic sharding, replication for high availability, and per-slot metrics for granular monitoring. Lua scripting enables complex atomic operations, while the module plugin system extends the server with custom commands and data types including probabilistic Bloom filters. Client libraries for Python, Java, Go, Node.js, and PHP maintain full Redis OSS protocol compatibility — existing Redis applications work without code changes. Deploy as a standalone daemon or in clustered mode with Docker, supporting persistent and ephemeral workloads on any Linux host. Running on a dedicated VPS on RepoCloud with guaranteed CPU, RAM, and SSD, full root SSH access, and a browser serial console. BSD 3-Clause licensed.

OpenSearch is a search and analytics platforms, powering full-text search, log analytics, observability, and AI-powered vector retrieval at petabyte scale. The distributed engine provides BM25 full-text search alongside k-NN vector search using NMSLIB, Faiss, and Lucene libraries, enabling semantic search, hybrid search combining keyword and vector scoring through normalization processors, neural sparse search, and retrieval-augmented generation workflows with built-in ML Commons for model hosting. OpenSearch Dashboards delivers interactive visualization with Discover for log exploration, custom dashboards, alerting, anomaly detection using Random Cut Forest algorithms, and Security Analytics with detection rules mapped to MITRE ATT&CK. Native Prometheus integration with full PromQL support unifies metrics alongside logs and traces in a single observability interface, while Data Prepper handles telemetry ingestion from OpenTelemetry collectors, Fluent Bit, and Logstash-compatible pipelines. SQL and Piped Processing Language queries with a visual PPL builder enable analysts to query data without learning the native DSL. Index State Management automates index lifecycle with rollover, shrink, and delete policies, while cross-cluster replication and searchable snapshots on S3-compatible storage provide disaster recovery. Scoped API keys, field-level security, document-level security, and audit logging deliver enterprise-grade access control. Docker Compose deploys multi-node clusters alongside the Kubernetes operator for orchestrated production environments. Running on a dedicated VPS on RepoCloud with guaranteed CPU, RAM, and SSD, full root SSH access, and a browser serial console. Apache 2.0 licensed.

Perplexity's search experience without Perplexity: Vane deploys Perplexica, an open-source AI answer engine built as the self-hosted alternative. Instead of returning a page of links, it reads your question, searches the live web through the SearxNG metasearch engine, and composes a direct answer with cited sources. Retrieval quality comes from embeddings and similarity search: fetched pages are re-ranked against the query so the model answers from the most relevant passages rather than whatever ranked first. Two query modes cover different needs - Normal mode runs a straightforward web search, while Copilot mode generates multiple reformulated queries and actively pulls content from top matches for harder questions. Focus modes specialize retrieval for academic papers, YouTube, Reddit discussions, Wolfram Alpha calculations, or the general web. The answering model is your choice: OpenAI-compatible APIs or fully local LLMs such as Llama 3 and Mixtral through Ollama, which keeps queries entirely on your infrastructure. Because SearxNG pulls live results, answers reflect current information, and no search history is tracked.

Up to 280 search services - Google, Bing, DuckDuckGo, Brave, Qwant, Startpage - aggregated without tracking or profiling: SearXNG is a privacy-respecting metasearch engine (AGPL-3.0, successor to Searx). Your instance queries the upstream engines on your behalf: your IP address, cookies, and search history never reach them, tracker parameters are stripped from result URLs, and an optional image proxy fetches thumbnails server-side so result pages leak nothing. It can even route outbound queries through Tor for full anonymity. Search is organized into categories - general, images, videos, news, maps, music, IT, science, files - with bang shortcuts for targeting specific engines, and every source can be enabled, disabled, or weighted per category in settings.yml. A plugin system adds calculators, hash tools, tracker removal, and unit conversions inline, and preferences (themes, safe search, languages, engine selection) persist in cookies rather than server-side accounts. The real argument for running your own instance rather than trusting a public one is control: you decide the logging policy (none), the engine mix, rate limiting, and who gets access - making it the default search backend for browsers, families, and teams that want Google-quality results without the profile.

Scira is an AI search engine that breaks complex research questions into sub-tasks, retrieves grounded answers from live sources, and cites every claim with clickable links to the original evidence. Formerly known as MiniPerplx and now backed by nearly 12,000 GitHub stars, the platform implements agentic research planning where language models decompose queries, select appropriate tools, retrieve information in parallel, and synthesize responses with inline citations. Seventeen distinct search modes cover Web, Academic, Extreme, X (Twitter), Reddit, GitHub, Stocks, Crypto, YouTube, and Prediction markets, each activating a curated subset of the 28 built-in tools. The Extreme mode deploys a deep research agent with multi-step planning, parallel source retrieval, code execution in sandboxed Daytona environments, and R2 artifact storage for generated charts. Model routing spans 50+ AI providers through a unified configuration layer supporting OpenAI, Anthropic, Google, xAI Grok, and custom models, with per-mode model assignment based on task requirements. The web search tool supports Exa, Tavily, and Firecrawl as interchangeable providers via a Strategy pattern. Lookouts schedule recurring research agents on cron schedules that monitor topics, track changes, and email summaries using Upstash QStash for reliable execution. PostgreSQL stores chat history, user preferences, and custom instructions while Redis handles caching and rate limiting. Running on a dedicated VPS on RepoCloud with guaranteed CPU, RAM, and SSD, full root SSH access, and a browser serial console. AGPL-3.0 licensed.

Hoarder (now Karakeep) is a bookmark manager that actually fights link rot: every page you save gets archived at capture time using Monolith, so the content survives even when the original URL dies. Beyond archival, an AI layer powered by OpenAI or local Ollama models auto-tags everything by analyzing page content. Prefer full privacy? Ollama keeps all inference on your server with zero external API calls. Full-text search through Meilisearch indexes the actual scraped content of every bookmark, not just titles and tags, so you find articles by what they say rather than labels you half-remember. Save links with automatic metadata extraction, plain text notes, uploaded images, and PDF documents, all organized into shareable lists with collaborative access. Browser extensions for Chrome and Firefox make saving a one-click operation from any page. Migrating is painless with importers for Chrome, Pocket, Linkwarden, Omnivore, and Tab Session Manager. LLM summarization condenses saved pages into brief overviews for quick scanning. The AI layer is entirely optional: Hoarder works perfectly as a manual bookmark manager, with intelligence adding convenience rather than imposing a requirement. SSO integration and responsive dark mode round out the package.

With nearly 12,000 GitHub stars and a lineage tracing back to Sphinx Search, Manticore Search is the C++ search database that delivers the full-text, vector, and hybrid search capabilities of Elasticsearch at a fraction of the resource cost — starting in under a second and consuming just 40MB RAM for an empty instance. The SQL-first interface speaks the MySQL wire protocol, meaning mysql client, MySQL Workbench, and any MySQL-compatible driver connects natively without adapters, while the HTTP JSON API provides RESTful access for modern applications. Over 20 full-text operators handle proximity search, quorum matching, field-start and field-end constraints, MAYBE operators, and regex patterns, backed by stemming, lemmatization, stopwords, synonyms, wordforms, and advanced morphology in 70+ languages. Vector search with HNSW indexing enables semantic similarity queries, and hybrid mode combines keyword relevance with vector distance in a single ranked result set using a cost-based query optimizer. Real-time indexing delivers sub-second document availability after insert, sharded tables distribute data across nodes, and Galera-based synchronous replication ensures high availability. Conversational search via CREATE CHAT MODEL and CALL CHAT integrates LLM-backed responses with KNN retrieval and conversation history directly inside the database. Client libraries ship for PHP, Python, JavaScript, TypeScript, Java, Go, Rust, and Elixir. Columnar storage via the Manticore Columnar Library handles analytical workloads on large datasets. Running on a dedicated VPS on RepoCloud with guaranteed CPU, RAM, and SSD, full root SSH access, and a browser serial console. GPLv3 licensed.

ZincSearch runs full-text search as a single Go binary that consumes a fraction of the memory and CPU that Elasticsearch demands while staying API-compatible, earning 17,800+ GitHub stars as a lightweight alternative. The bluge-powered indexing library processes documents through analyzers, tokenizers, and token filters while maintaining Elasticsearch-compatible ingestion APIs for single-record and bulk operations, letting existing pipelines connect with minimal configuration changes. Schema-less document ingestion accepts JSON payloads without predefined mappings, allowing different documents within the same index to carry different field structures while the engine automatically detects and indexes field types. An embedded Vue.js web console provides a browser-based interface for creating indexes, querying with full-text syntax, browsing results with hit highlighting, managing users, and monitoring system status. A dual API architecture exposes native ZincSearch endpoints under /api alongside Elasticsearch-compatible endpoints under /es, supporting boolean operators, wildcards, phrase matching, fuzzy search, date ranges, and aggregation pipelines including terms, histogram, date histogram, and range aggregations. Multi-tenancy with user-level access control isolates data across teams. Official SDKs for Go, Python, and Node.js provide typed client libraries for programmatic integration. Deploys via Docker or direct binary download with no external dependencies beyond disk storage. Running on a dedicated VPS on RepoCloud with guaranteed CPU, RAM, and SSD, full root SSH access, and a browser serial console. Apache 2.0 licensed.

Processing billions of operations per second across companies like Twitter, GitHub, Snapchat, and Stack Overflow, Redis is the world's fastest and most widely deployed in-memory data store. Redis 8 unifies previously separate modules into a single distribution: RediSearch for full-text indexing with BM25 scoring and vector similarity search via HNSW and FLAT algorithms, RedisJSON for native JSON document storage with JSONPath queries, RedisTimeSeries for timestamped data with configurable downsampling compaction rules, and RedisBloom for probabilistic data structures including Bloom filters, cuckoo filters, count-min sketches, top-k, and t-digest. The core engine provides strings, lists, sets, sorted sets, hashes, streams, HyperLogLog, bitmaps, bitfields, geospatial indexes, and the new array data structure introduced in Redis 8.8. Pub/Sub delivers lightweight real-time messaging between publishers and subscribers, while Streams provide an append-only log with consumer groups for event sourcing and complex consumption patterns. Redis Cluster distributes data across nodes with automatic sharding using 16,384 hash slots, and Sentinel provides high availability with automatic failover monitoring. Lua scripting and Redis Functions enable server-side computation, and ACL-based security provides granular per-command, per-key access control. Official clients exist for Python, Node.js, Java, Go, .NET, Rust, and PHP. Running on a dedicated VPS on RepoCloud with guaranteed CPU, RAM, and SSD, full root SSH access, and a browser serial console. AGPLv3 licensed.

Kotaemon is a document QA platform that combines advanced RAG techniques with a clean Gradio-based web interface for chatting with your documents. Built by Cinnamon, the Python backend supports any LLM provider including OpenAI, Azure OpenAI, Cohere, Groq, and local models via Ollama and llama-cpp-python, with a model management panel for configuring LLM and embedding providers from the UI. The default hybrid RAG pipeline combines full-text keyword retrieval with vector similarity search and applies re-ranking to ensure optimal result quality, while multi-modal document parsing extracts content from tables and figures alongside text. Advanced citations link every answer to specific source passages with relevance scores, viewable directly in the built-in PDF viewer with highlighted text spans. GraphRAG indexing via NanoGraphRAG, LightRAG, or Microsoft GraphRAG builds knowledge graphs from document collections for relationship-aware retrieval. Agent-based reasoning supports question decomposition for multi-hop queries using ReAct and ReWOO strategies. Multi-user authentication organizes documents into private and public collections with sharing and collaboration features. The platform supports Docker deployment in lite, full, and Ollama-bundled variants, runs on port 7860, and stores application data in a persistent volume. MCP tool integration enables external system connections for extended retrieval capabilities. On RepoCloud, deploy Kotaemon on a dedicated VPS with Docker, root SSH access, and complete control over your document AI infrastructure, all under the Apache 2.0 license.