AnythingLLM vs LibreChat
Two of the top projects in Chat UIs, side by side: who leads on each score part and who each one suits, then setup, license, activity and what each review found.
AnythingLLM
Document chat app with agents, workspaces and many LLM and vector DB backends
LibreChat
Self-hosted multi-provider chat UI with agents, MCP tools and multi-user auth
Verdict
Both score 83; AnythingLLM ranks higher on stars, 67,814 to 46,602. Of the 4 score parts we measure for both, AnythingLLM leads on 2, LibreChat on 1 and 1 is even.
- AdoptionAnythingLLM leads by 5 points, 84 to 79.
- FreshnessEven at 100.
- MaintenanceAnythingLLM leads by 13 points, 94 to 81.
- Agent-readyLibreChat leads by 45 points, 45 to 0: a wide gap.
Pick AnythingLLM if you are
Self-hosters and teams wanting private document chat with agents over any LLM
- Supports roughly 40 LLM providers and 9 vector databases, with LanceDB as the zero-setup default
- Built-in embedder and audio transcription, so a local setup needs no external services
Pick LibreChat if you are
Teams and self-hosters wanting one shared chat UI across many model providers
- Many providers in one UI, plus custom OpenAI-compatible endpoints without a proxy
- Multi-user auth via OAuth2, LDAP and email, with admin panel and token spend tools
| What we compare | AnythingLLM | LibreChat |
|---|---|---|
| Score parts, out of 100 | ||
| Adoption | 84, widely used | 79, popular |
| Freshness | 100, active | 100, active |
| Maintenance | 94, healthy | 81, healthy |
| Agent-ready | 0, none | 45, minimal |
| Facts from GitHub and the README | ||
| Stars | 67.8k | 46.6k |
| License | MIT (permissive) | MIT (permissive) |
| Last commit | Oct 2026 | Oct 2026 |
| Last release | Oct 2026 | None published |
| Language | JavaScript | TypeScript |
| Docker | Yes | Yes |
| GPU | Optional | Not needed |
| arm64 or Apple Silicon | Mentioned | Not stated |
AnythingLLM
AnythingLLM ingests PDFs, DOCX and TXT files into workspaces and answers questions over them with source citations. It connects to local or hosted LLMs (llama.cpp-compatible models, Ollama, LM Studio, OpenAI, Anthropic, Bedrock and many more) and stores vectors in LanceDB by default or PGVector, Qdrant, Weaviate, Milvus and others. It also includes a no-code agent builder, MCP support, scheduled tasks, and a desktop app plus a Docker server version.
Who it is for: Self-hosters and teams wanting private document chat with agents over any LLM
Strengths
- Supports roughly 40 LLM providers and 9 vector databases, with LanceDB as the zero-setup default
- Built-in embedder and audio transcription, so a local setup needs no external services
- Includes no-code agent builder, MCP compatibility and cron-scheduled agent tasks
- Available as a desktop app for Mac, Windows and Linux, and as a Docker server
Weaknesses
- Multi-user permissions and the embeddable chat widget are Docker version only
- Telemetry is on by default; opt out via DISABLE_TELEMETRY or in-app
- Still makes outbound calls to cdn.anythingllm.com and GitHub with telemetry disabled
- Browser-native STT is the only speech-to-text option listed
- GPU optional
- Docker + Compose
- Needs LLM provider (local or API), Docker (for server version)
- Models: llama.cpp-compatible models, Ollama, LM Studio, OpenAI, Anthropic
LibreChat
LibreChat is a ChatGPT-style web interface that connects to Anthropic, OpenAI, Azure, Google, AWS Bedrock, Ollama and any OpenAI-compatible endpoint. It adds multi-user login (OAuth2, LDAP, email), conversation search and branching, no-code agents with MCP tools, a sandboxed code interpreter, artifacts and web search. An admin panel manages users, roles and settings.
Who it is for: Teams and self-hosters wanting one shared chat UI across many model providers
Strengths
- Many providers in one UI, plus custom OpenAI-compatible endpoints without a proxy
- Multi-user auth via OAuth2, LDAP and email, with admin panel and token spend tools
- Agents with MCP servers, Skills, subagents, file search and code execution
- Resumable streams and Redis support for horizontally scaled deployments
Weaknesses
- Large feature surface; the README lists many components to configure and operate
- Some features, such as attached code workspaces, are marked highly experimental
- Code interpreter relies on a separate ClickHouse/code-interpreter service
- README gives no RAM or hardware requirements and no default port
- no GPU
- Docker + Compose
- Needs Docker Compose (optional), Redis (for scaled deployments), ClickHouse/code-interpreter (for code execution), LibreChat RAG API (for file chat)
- Models: Anthropic, OpenAI, Azure OpenAI, Google, Vertex AI
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