ChatGPT
AI conversational assistant for answering questions, writing, and coding help
AnythingLLM
All-in-one AI app for chatting with your documents
Turn any document into context for LLMs. Chat with your docs using AI agents, support multi-user workspaces, and deploy anywhere with hyper-configurable LLM and vector database options.
Overview
What is AnythingLLM?
AnythingLLM is a full-stack application that transforms documents, resources, and content into context for large language models. Whether you need a private ChatGPT alternative or a multi-user knowledge base, AnythingLLM lets you choose your preferred LLM provider and vector database without vendor lock-in.
Who Uses AnythingLLM?
Development teams building custom AI solutions, enterprises requiring document-based chat with permissioning, and individuals seeking local AI deployments all benefit from AnythingLLM's flexibility. The workspace model containerizes documents into isolated threads, keeping context clean across different projects while allowing document sharing when needed.
Key Capabilities
AnythingLLM supports 30+ LLM providers including OpenAI, Anthropic, Ollama, and llama.cpp-compatible models. It integrates with 9 vector databases, offers multi-modal chat, and includes a no-code AI agent builder with MCP compatibility. Deploy via Docker for multi-user instances with embeddable chat widgets, or run the desktop app on Mac, Windows, and Linux. A full developer API enables custom integrations, while built-in cost controls optimize large document processing.
Highlights
MCP-compatible no-code AI agent builder with web browsing capabilities
Workspace isolation with shared documents across 30+ LLM providers
Multi-user permissioning and embeddable chat widgets (Docker deployments)
Native support for PDF, DOCX, TXT with cost-optimized document processing
Pros
- Vendor-agnostic architecture supports both commercial and open-source LLMs
- Desktop and cloud deployment options with one-click hosting templates
- Full developer API for custom integrations and automation
- Workspace containerization prevents context bleed between projects
Considerations
- Multi-user features and embeddable widgets require Docker deployment
- Initial setup demands configuration of LLM providers and vector databases
- Monorepo architecture requires running multiple services for development
- Advanced features like custom agents may require technical expertise
Managed products teams compare with
When teams consider AnythingLLM, these hosted platforms usually appear on the same shortlist.
Looking for a hosted option? These are the services engineering teams benchmark against before choosing open source.
Fit guide
Great for
- Teams needing private document chat without sending data to third parties
- Organizations requiring granular user permissions and workspace isolation
- Developers building custom AI applications via the full API
- Users wanting flexibility to switch between LLM providers and vector databases
Not ideal when
- Users seeking a fully managed SaaS solution without self-hosting
- Projects requiring real-time collaboration on the same conversation thread
- Teams without technical resources to configure LLM and database integrations
- Use cases needing cross-workspace context sharing and unified knowledge graphs
How teams use it
Enterprise Knowledge Base
Deploy multi-user workspaces with role-based permissions, allowing departments to chat with internal documentation while maintaining data isolation and compliance.
Customer Support Automation
Embed chat widgets on websites that answer questions using product documentation, reducing support ticket volume with accurate, cited responses.
Research Document Analysis
Upload academic papers, reports, and datasets into isolated workspaces, then query across documents using AI agents that browse supplementary web sources.
Local AI Development
Run entirely offline using Ollama and LanceDB on desktop, enabling privacy-focused prototyping with llama.cpp-compatible models and custom embeddings.
Tech snapshot
JavaScript98%
CSS2%
Dockerfile1%
HTML1%
Shell1%
Smarty1%
Frequently asked questions
What's the difference between Docker and desktop deployments?
Docker deployments support multi-user instances with permissions and embeddable chat widgets. Desktop versions (Mac, Windows, Linux) are single-user applications ideal for local, private use.
Can I use my own LLM models?
Yes. AnythingLLM supports any llama.cpp-compatible model, plus 30+ providers including OpenAI, Anthropic, Ollama, LM Studio, and LocalAI for both commercial and self-hosted models.
How do workspaces prevent context mixing?
Workspaces function as isolated threads with containerized documents. While workspaces can share documents, conversations and context remain separate, preventing information bleed between projects.
What document formats are supported?
AnythingLLM processes PDF, TXT, DOCX, and other common formats. Built-in cost-saving measures optimize processing for very large documents compared to typical chat interfaces.
Is there an API for custom integrations?
Yes. AnythingLLM provides a full developer API for building custom integrations, automating workflows, and embedding chat functionality into external applications.
Project at a glance
Active- Stars
- 53,573
- Watchers
- 53,573
- Forks
- 5,752
LicenseMIT
Repo age2 years old
Last commit2 days ago
Primary languageJavaScript
Last synced yesterday