Best Self Hosted Alternatives to Swirl

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.

Typesense is an open source search engine designed for low-latency, “search-as-you-type” experiences. It focuses on developer-friendly operations and an easy-to-use API, while supporting both traditional full-text search and modern vector-based retrieval.

Key Features

• Typo-tolerant fuzzy search optimized for instant results
• Search-as-you-type autocomplete and relevance tuning at query time
• Faceting, filtering, grouping/distinct, and dynamic sorting
• Geo search for location-based queries
• Synonyms and pinning/merchandising controls for curated results
• Vector and semantic search, including hybrid retrieval patterns
• Scoped API keys and multi-tenant access patterns
• High-availability options via replication

Use Cases

• Site and in-app search for documentation, content, and product catalogs
• E-commerce discovery with facets, filtering, sorting, and pinned results
• Semantic search and hybrid keyword+vector retrieval for knowledge bases

Typesense is well-suited for teams that want a streamlined search stack with strong defaults, low operational complexity, and an HTTP API that integrates easily into modern applications.

Onyx Community Edition is an open-source, self-hostable AI platform that combines a team chat UI with enterprise search and retrieval-augmented generation (RAG). It is designed to work with a wide range of LLM providers as well as locally hosted models, including deployments in airgapped environments.

Key Features

• AI chat interface designed to work with multiple LLM providers and self-hosted LLMs
• RAG with hybrid retrieval and contextual grounding over ingested and uploaded content
• Connectors to many external knowledge sources with metadata ingestion
• Custom agents with configurable instructions, knowledge, and actions
• Web search integration and deep-research style multi-step querying
• Collaboration features such as chat sharing, feedback collection, and user management
• Enterprise-oriented access controls including RBAC and support for SSO (depending on configuration)

Use Cases

• Company-wide AI assistant grounded in internal documents and connected tools
• Knowledge discovery and enterprise search across large document collections
• Building task-focused AI agents that can retrieve context and trigger actions

Limitations and Considerations

• Some advanced organization-focused capabilities may differ between Community and Enterprise editions
• Retrieval quality and permissions mirroring depend on connector availability and configuration

Onyx CE is a strong fit for teams that want an extensible, transparent AI assistant and search layer over internal knowledge. It emphasizes configurable retrieval, integrations, and deployability across diverse infrastructure setups.

Paperless-AI is an AI-powered extension for Paperless‑ngx that automates document classification, metadata extraction and semantic search. It integrates with OpenAI-compatible APIs and local model backends to provide chat-style Q&A over a Paperless‑ngx archive.

Key Features

• Automated document processing: detects new documents in Paperless‑ngx and extracts title, tags, document type, and correspondent.
• Retrieval-Augmented Generation (RAG) chat: semantic search and contextual Q&A across the full document archive.
• Multi-backend model support: works with OpenAI-compatible APIs, Ollama (local models), DeepSeek-r1, Azure and several other OpenAI-format backends.
• Manual review UI: web interface to manually trigger AI processing, review results, and adjust settings.
• Smart tagging and rule engine: configurable rules to control which documents are processed and what tags are applied.
• Docker-first distribution: official Docker image and docker-compose support for containerized deployment and persistent storage.

Use Cases

• Quickly find facts across scanned bills, contracts and receipts via natural-language Q&A instead of manual search.
• Automatically tag and classify incoming documents to reduce manual filing and speed up archival workflows.
• Create structured metadata from free-text documents for downstream automation or reporting.

Limitations and Considerations

• Quality and consistency of automatic tags and correspondents varies by model and prompt; some users report noisy or incorrect tags that require manual cleanup.
• Resource behavior with local model backends (e.g., Ollama) can be heavy; users have reported long-running sessions or elevated GPU/CPU usage depending on model choice and volume.
• Processing can halt on model/API errors (for example, context-length or API failures); robust retry/monitoring may be required in large archives.
• Requires a running Paperless‑ngx instance and appropriate API credentials and model/back-end configuration to operate.

Paperless-AI provides an accessible way to add AI-driven classification and semantic search to a Paperless‑ngx archive, with flexible backend choices and a modern web UI. It is best suited for users who want automated tagging and conversational access to large document collections but should be configured and monitored to manage resource use and tag quality.

Aleph is an investigative data platform for ingesting and indexing large collections of documents and structured datasets, making them searchable and easier to analyze. It is designed to help researchers find people, companies, and connections across many sources, including watchlists and prior research.

Key Features

• Ingests and indexes documents (such as PDF, Word, and HTML) and structured data (such as CSV and spreadsheets)
• Full-text search and browsing across datasets and uploaded materials
• Entity-centric exploration focused on people, companies, and other known entities
• Cross-referencing and matching entities against watchlists and reference datasets
• Supports operational workflows for managing data imports and collections

Use Cases

• Investigative journalism: search leaks, filings, and datasets for names and relationships
• OSINT research: unify and query diverse sources (documents plus tabular data)
• Compliance or due diligence research: check entities against internal or external lists

Limitations and Considerations

• The open-source version is in a sunsetting phase, with official maintenance planned to end after December 2025

Aleph is well-suited for teams that need to turn large, heterogeneous collections of files and tables into a searchable investigative corpus. Its emphasis on entity discovery and cross-referencing makes it particularly useful for research-driven analysis workflows.

Amurex is an open-source AI copilot designed to live in the background of your existing tools. It unifies knowledge search across connected apps and automates meeting capture and follow-up tasks. It can be self-hosted for data control and privacy.

Key Features

• Unified search across connected apps (Notion, Google Drive, Obsidian, and more)
• Meetings on Autopilot: records, transcribes, summarizes, and tracks action items
• Inbox categorization and email prioritization
• Self-hosted, open-source architecture for data control
• Local-mode/inference options (Ollama) with flexible AI backends (OpenAI, Groq, Mistral)

Use Cases

• Personal knowledge management: search across notes, docs, and sources from multiple tools
• Meeting automation: capture transcripts, generate summaries, and assign follow-ups
• Email triage and workflow orchestration within your existing toolchain

Limitations and Considerations

• Requires self-hosted backend and proper API keys (OpenAI, Groq, etc.) to run online or LOCAL mode for local inference
• Setup can be complex: involves Supabase/PostgreSQL, Redis, and containerized services
• Local inference requires Ollama and related local dependencies
• Open-source licensing (AGPL-3.0) and data residency depend on how you deploy

Conclusion Amurex combines cross-tool search with automated meeting workflows in a privacy-conscious, open-source package and supports flexible self-hosted deployments. It is actively developed with multiple AI backends and deployment options to suit professional workflows.