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Free all-in-one desktop AI app to chat with your documents and run RAG and AI agents fully local and private.
High-performance open-source vector database for production AI retrieval and RAG, for teams needing scale, hybrid search, or self-hosting.
AI coding platform and IDE that orchestrates multiple agent sessions and lets teams plug in their own AI subscriptions.
Well-known chat-with-PDF tool for summarizing and extracting; strong traffic.
Fast, developer-friendly open-source search engine and AI retrieval platform offering full-text, semantic, and hybrid search.
No public pricing
No public pricing
No public pricing
Free trial available
- ✦Chat with your documents (RAG)
- ✦Runs locally and offline for privacy
- ✦Supports any LLM (local or cloud)
- ✦Built-in AI agents
- ✦Handles PDFs, Word, CSV, codebases
- ✦No-code setup
- ✦Hybrid dense and sparse vector search (BM25, SPLADE, miniCOIL)
- ✦Advanced metadata filtering applied during search traversal
- ✦Multivector support for multimodal retrieval
- ✦Reranking with score boosting and late-interaction models (ColBERT, MMR)
- ✦Flexible deployment: cloud, hybrid, private, or edge
- ✦Rust-based engine optimized for low-latency, high-scale search
- ✦AI coding IDE with agent orchestration
- ✦Run and manage multiple agent sessions
- ✦Task, artifact and collaboration tools
- ✦Bring-your-own AI subscription or API keys
- ✦Cloud-scale agent execution
- ✦Chat with PDF documents
- ✦Summarize PDF content
- ✦Extract information from PDFs
- ✦Source citation for answers
- ✦OCR support
- ✦AI Agents for document analysis
- ✦Capture & Ask feature
- ✦Chatbot widget (add-on)
- ✦Sub-50ms search-as-you-type
- ✦Full-text, semantic, and hybrid search
- ✦Vector storage for RAG and similarity queries
- ✦Multimodal and federated search
- ✦Filtering, faceting, sorting, and geosearch
- ✦Managed cloud or self-hosted open source
- →Privately querying your own documents
- →Running local AI without the cloud
- →Building AI agents over your data
- →Using multiple LLM providers in one app
- →Building retrieval-augmented generation (RAG) pipelines
- →Powering AI recommendation and semantic search systems
- →Enterprises needing on-prem or hybrid deployment for compliance
- →AI agent platforms needing fast contextual retrieval at scale
- →Shipping code faster with AI agents
- →Coordinating agent work across a team
- →Managing tasks and artifacts in one place
- →Running many parallel agent sessions
- →Analyzing legal documents
- →Summarizing financial reports
- →Extracting key information from books
- →Reviewing scientific papers
- →Understanding user manuals
- →Creating employee training materials
- →Adding fast site or app search
- →Building AI/RAG retrieval pipelines
- →E-commerce and media search experiences
- →Semantic search over private data