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Developer framework and LlamaParse service for parsing documents and building AI agents and RAG workflows over them.
Serverless platform for running and fine-tuning image, video, audio and 3D generative models via one fast API.
Developer API that gives AI agents persistent memory, retrieval, and connectors, usable both as infrastructure and a personal app.
High-performance open-source vector database for production AI retrieval and RAG, for teams needing scale, hybrid search, or self-hosting.
Well-known chat-with-PDF tool for summarizing and extracting; strong traffic.
No public pricing
No public pricing
- ✦LlamaParse document parsing and extraction
- ✦Open-source framework for AI agents and workflows
- ✦Document indexing for retrieval/RAG
- ✦Prebuilt solutions by industry and use case
- ✦Free starter credits for LlamaParse
- ✦1,000+ generative model APIs
- ✦Serverless GPU inference engine
- ✦On-demand and dedicated GPU clusters
- ✦Model fine-tuning and custom deployments
- ✦Bring-your-own-weights and private endpoints
- ✦SOC 2 compliance and enterprise features
- ✦Persistent, structured memory built as a knowledge graph
- ✦Sub-300ms hybrid retrieval (RAG) with reranking
- ✦Native filesystem mount for agent memory access
- ✦Connectors to Slack, Notion, Drive, Gmail, GitHub, S3
- ✦Automatic extraction from PDFs, images, and audio
- ✦User profile and behavior tracking across sessions
- ✦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
- ✦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)
- →Parse complex documents for AI apps
- →Build RAG and agent workflows
- →Automate invoice and claims processing
- →Search across technical documents
- →Adding image/video generation to an app
- →Running fast diffusion-model inference at scale
- →Training or fine-tuning custom generative models
- →Developers adding long-term memory to AI agents
- →Teams building agents that need to sync with existing tools
- →Individuals wanting one memory layer shared across multiple AI assistants
- →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
- →Analyzing legal documents
- →Summarizing financial reports
- →Extracting key information from books
- →Reviewing scientific papers
- →Understanding user manuals
- →Creating employee training materials