Open WebUI 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.
Open WebUI
Self-hosted web UI for Ollama and OpenAI-compatible models with users and RAG
LibreChat
Self-hosted multi-provider chat UI with agents, MCP tools and multi-user auth
Verdict
Open WebUI ranks higher, 89 to 83. Of the 4 score parts we measure for both, Open WebUI leads on 2, LibreChat on 1 and 1 is even.
- AdoptionOpen WebUI leads by 20 points, 99 to 79: a wide gap.
- FreshnessEven at 100.
- MaintenanceOpen WebUI leads by 17 points, 98 to 81: a wide gap.
- Agent-readyLibreChat leads by 45 points, 45 to 0: a wide gap.
Pick Open WebUI if you are
Self-hosters and teams running a shared chat UI over local or API models
- Works with Ollama and any OpenAI-compatible endpoint, so local and cloud models mix in one UI
- RBAC, user groups, LDAP, OAuth/SSO and SCIM 2.0 provisioning for multi-user deployments
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 | Open WebUI | LibreChat |
|---|---|---|
| Score parts, out of 100 | ||
| Adoption | 99, widely used | 79, popular |
| Freshness | 100, active | 100, active |
| Maintenance | 98, healthy | 81, healthy |
| Agent-ready | 0, none | 45, minimal |
| Facts from GitHub and the README | ||
| Stars | 157.5k | 46.6k |
| License | custom license (read the license) | MIT (permissive) |
| Last commit | Oct 2026 | Oct 2026 |
| Last release | Oct 2026 | None published |
| Language | Python | TypeScript |
| Docker | Yes | Yes |
| GPU | Optional | Not needed |
| arm64 or Apple Silicon | Mentioned | Not stated |
Open WebUI
Open WebUI is a self-hosted chat front-end for Ollama and any OpenAI-compatible API, such as vLLM, LMStudio, OpenRouter or Groq. It adds role-based access control, chat history, document RAG with 9 vector database options, web search, voice calls, image generation and a plugin system (Filters, Pipes, Tools, MCP). It installs via pip, Docker or Kubernetes and serves on port 8080 by default.
Who it is for: Self-hosters and teams running a shared chat UI over local or API models
Strengths
- Works with Ollama and any OpenAI-compatible endpoint, so local and cloud models mix in one UI
- RBAC, user groups, LDAP, OAuth/SSO and SCIM 2.0 provisioning for multi-user deployments
- RAG with hybrid BM25 plus vector search and 9 vector databases, including ChromaDB, PGVector and Qdrant
- SQLite or PostgreSQL, S3/GCS/Azure storage, Redis and OpenTelemetry for scaled deployments
Weaknesses
- Custom license requires preserving Open WebUI branding; not a standard OSS license
- Per-user isolated terminals are in the Enterprise edition only
- Very broad feature set, so the many settings take time to configure
- Does not run models itself; needs Ollama or an API unless using the :ollama image
- GPU optional
- Docker + Compose
- Needs Ollama or OpenAI-compatible API, Docker (optional), PostgreSQL (optional), Redis (optional)
- Models: Ollama, OpenAI-compatible APIs, vLLM, LMStudio, GroqCloud
- port 8080
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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