Best Self Hosted Alternatives to Poe by Quora

Open WebUI is a web-based, extensible AI interface that provides a unified GUI for interacting with local and cloud LLMs. It supports multiple LLM runners and OpenAI-compatible APIs, built-in RAG, artifact storage, and collaboration features.

Key Features

• Multi-runner support (Ollama and OpenAI-compatible endpoints) and built-in inference integrations for flexible model selection
• Local Retrieval-Augmented Generation (RAG) with support for multiple vector databases and content extractors
• Image generation and editing integrations with local and remote engines; prompt-based editing workflows
• Granular role-based access control (RBAC), user groups, and enterprise provisioning (SCIM, LDAP/AD, SSO integrations)
• Persistent artifact/key-value storage for journals, leaderboards, and shared session data
• Progressive Web App (PWA) experience, responsive UI, and multi-device support
• Native Python function-calling tools (BYOF) and a web-based code editor for tool/workspace development
• Docker/Kubernetes deployment options, prebuilt image tags for CPU/GPU and Ollama bundles
• Production observability with OpenTelemetry traces, metrics and Redis-backed session management

Use Cases

• Teams wanting a central, auditable chat interface to query multiple LLMs and manage permissions
• Knowledge workers and developers using local RAG pipelines to query private document collections securely
• Experimentation and model comparison workflows combining multiple models, image tools, and custom functions

Limitations and Considerations

• Advanced features (model inference, heavy image generation) require external runners or GPU resources; performance depends on the chosen backend
• Some enterprise integrations and optional storage backends require additional configuration and credentials
• Desktop app is experimental; recommended production deployment paths are Docker, Docker Compose or Kubernetes

Open WebUI is positioned as a flexible interface layer for LLM workflows, emphasizing provider-agnostic integration, RAG, and enterprise features. It is suited for teams that need a full-featured, customizable web UI for local and cloud model workflows.

AnythingLLM is a full-stack AI application for building a private ChatGPT-like experience around your own documents and content. It supports local and hosted LLMs, integrates with multiple vector database backends, and organizes content into isolated workspaces for cleaner context management.

Key Features

• Retrieval-augmented generation (RAG) to chat with PDFs, DOCX, TXT, CSV, codebases, and more
• Workspace-based organization with separated context and optional document sharing
• AI agents, including a no-code agent builder and MCP compatibility
• Supports local and commercial LLM providers (including Ollama and llama.cpp-compatible models)
• Multiple vector database options (default local-first setup, with external backends available)
• Multi-user deployment with permissions (Docker deployment)
• Embeddable website chat widget (Docker deployment)
• Developer API for integrations and automation

Use Cases

• Internal knowledge base chat for teams (policies, runbooks, product docs)
• Private document Q&A for sensitive datasets and client files
• Building agent-assisted workflows that reference curated business content

AnythingLLM is a strong choice when you want a configurable, privacy-conscious AI application that can run locally or on a server, while staying flexible about which LLM and vector database you use.

LibreChat is an open-source, self-hostable AI chat application that provides a ChatGPT-style interface while supporting many AI providers and OpenAI-compatible endpoints. It focuses on multi-user deployments, flexible model switching, and extensible agent/tool workflows.

Key Features

• Multi-provider model selection (including OpenAI-compatible APIs) with per-chat switching and presets
• Agents and tool integrations, including MCP support for connecting external tools
• Code Interpreter capabilities for sandboxed code execution and file handling
• Multimodal interactions: chat with files and analyze images (provider-dependent)
• Generative “artifacts” for creating code outputs (such as React/HTML) and Mermaid diagrams in chat
• Conversation and message search, plus import/export of conversations
• Multi-user authentication options (OAuth2, LDAP, and email login) and basic moderation/spend controls

Use Cases

• A unified internal AI chat portal for teams using multiple LLM vendors and endpoints
• Building no-code or low-code AI assistants that can call tools, search, and execute code
• Secure, self-hosted chat workflows for analyzing documents and iterating on code artifacts

Limitations and Considerations

• Some capabilities (multimodal, image generation, web search, specific tools) depend on configured providers and credentials
• Running code execution and tool integrations increases operational and security requirements and should be carefully sandboxed and access-controlled

LibreChat fits organizations and individuals who want a single, customizable chat UI for many models, with advanced features like agents, tool connectivity, and searchable conversation history. It is best suited for deployments that need multi-user access and flexible endpoint configuration.

Khoj is an open-source personal AI platform that combines chat, semantic document search, custom agents and scheduled automations. It can run locally or as a cloud-hosted service and integrates with local or remote LLMs to answer questions, generate content and automate research.

Key Features

• Multi-client access: web, desktop, Obsidian, Emacs, mobile (PWA) and chat integrations (e.g., WhatsApp).
• Model-agnostic LLM support: connect local GGUF models or remote OpenAI-compatible, Anthropic and Google-compatible endpoints; supports on-device and cloud models.
• Semantic search and embeddings: document ingestion (PDF, Markdown, Word, org-mode, Notion, images) with vector storage and retrieval for fast, contextual search.
• Custom agents and automations: build agents with distinct personas, tools and knowledge bases; schedule research tasks and email newsletters.
• Document processing and code tools: built-in extractors, simple code execution sandbox support (local Terrarium or remote sandboxes) and image generation features.
• Enterprise & self-hosting options: deploy via Docker or pip, use Postgres with pgvector for embeddings, and configure authentication and domains.

Use Cases

• Personal knowledge management: query a private document corpus and get grounded answers across notes, PDFs and files.
• Research automation: schedule recurring research queries and receive summarized results by email.
• Team/private deployments: host a private assistant for a team with custom agents, model selection and on-premise data control.

Limitations and Considerations

• Some optional integrations require extra setup or external services (e.g., code sandboxes, email providers); self-hosting needs correct environment configuration.
• A few plugins/integrations may be unmaintained or platform-specific; users should check the chosen connectors and follow the docs for compatibility and maintenance status.

Khoj is designed to be extensible and model-agnostic, emphasizing private data control and flexible deployment. It is suited for individuals and teams who need a searchable, automatable assistant that can run with local or cloud language models.

Recommendarr is a web application that generates personalized movie and TV show recommendations using data from your existing media library and watch history. It integrates with popular media managers and can use cloud or local LLM providers to tailor suggestions to your preferences.

Key Features

• AI-powered recommendations based on Radarr and Sonarr libraries
• Watch history analysis via Plex and Jellyfin, with optional Tautulli and Trakt integration
• Supports multiple AI backends, including OpenAI-compatible APIs and local LLMs
• Web UI with configurable recommendation settings (count and model parameters)
• Light/dark theme support and poster display with fallbacks
• Built-in authentication with optional OAuth login support

Use Cases

• Discover new movies and series that match your existing collection
• Generate recommendations based on what household members actually watch
• Run a local-LLM recommendation workflow for a privacy-focused media setup

Limitations and Considerations

• Recommendation quality depends heavily on the completeness of your library metadata and watch history
• External access should be deployed behind a properly configured reverse proxy and authentication

Recommendarr is a practical companion for Sonarr/Radarr-centric media stacks, combining library context with LLMs to produce tailored suggestions. It fits well in Plex or Jellyfin environments where you want recommendations driven by your own viewing habits.