AnythingLLM vs Onyx
Two of the top chat uis, side by side: score, setup, license, activity and what each review found.
AnythingLLM
Document chat and agent app with built-in RAG, MCP and multi-user support
Onyx
Team knowledge chat that indexes 50+ apps for RAG and agents
| What we compare | AnythingLLM | Onyx |
|---|---|---|
| Score parts, out of 100 | ||
| Adoption | 81, widely used | 58, popular |
| Freshness | 100, active | 100, active |
| Maintenance | 93, healthy | 83, healthy |
| Easy to run | 83, very easy | 50, easy |
| Agent-ready | 0, none | 85, ready |
| Facts from GitHub and the README | ||
| Stars | 67k | 32.4k |
| License | MIT (permissive) | custom license (read the license) |
| Last commit | Oct 2026 | Oct 2026 |
| Last release | Oct 2026 | Oct 2026 |
| Language | Not stated | Not stated |
| Docker | Yes | Yes |
| GPU | Not needed | Not needed |
| arm64 or Apple Silicon | Mentioned | Mentioned |
AnythingLLM
AnythingLLM is a Node.js app that ingests PDF, TXT, DOCX and other files into a workspace and chats over them with any of 40+ LLM providers, from llama.cpp, Ollama and LM Studio to OpenAI, Anthropic, Bedrock and Gemini. It ships a native embedder, LanceDB by default plus 8 other vector stores, a no-code agent builder, MCP support, scheduled tasks, model routing and a developer API.
Who it is for: Small teams and individuals wanting private document chat without extra services
Strengths
- LanceDB embedded by default; PGVector, Qdrant, Milvus, Chroma, Weaviate, Pinecone optional
- Native embedder and audio transcription run locally with no extra service
- Multi-user instance with per-user permissions in the Docker build
- Embeddable website chat widget and a full developer API
Weaknesses
- Anonymous telemetry to PostHog is on by default; opt out with DISABLE_TELEMETRY=true
- Multi-user support and the embed widget are Docker-only, not in the desktop app
- Speech-to-text is limited to the browser built-in engine
- No root Dockerfile; container build lives under docker/
- no GPU
- Docker + Compose
- Models: llama.cpp-compatible models, OpenAI, Azure OpenAI, AWS Bedrock, Anthropic
Onyx
Onyx indexes content and permissions from 50+ apps into a hybrid vector and keyword index, then answers through agentic RAG, deep research and custom agents with web search, MCP actions and a code sandbox. It works with Ollama, LiteLLM, vLLM, Anthropic, OpenAI or Gemini, deploys via Docker, Kubernetes or Helm, and is reachable from the web app, Slack and Discord bots, an MCP server or a Chrome extension.
Who it is for: Teams wanting an internal search and chat layer over company apps
Strengths
- Lite mode runs the chat UI and agents in under 1 GB of memory
- Air-gappable: index, database and processing all run inside your environment
- MCP server gives Claude Code, Codex or any MCP client company context with user permissions
- Community Edition is MIT; install script sets up Docker in one command
Weaknesses
- SSO (OIDC, SAML), SCIM, RBAC, analytics and whitelabeling are Enterprise Edition only
- Standard mode adds index, worker, inference, Redis and MinIO containers
- Lite mode cannot index documents
- README gives no port or hardware figures for the Standard deployment
- RAM ≥ 1 GB
- no GPU
- Compose
- Needs Redis (standard mode), MinIO (standard mode)
- Models: Ollama, LiteLLM, vLLM, Anthropic, OpenAI