3dae8db7ae
* Add the ability to edit existing SQL connections * Enhance SQL connection management by adding connections prop to DBConnection and SQLConnectionModal components for improved duplicate detection and handling. * format * fix: prevent input defocus in SQL connection edit modal Fixed an issue where typing in input fields would cause the field to lose focus during editing. The useEffect dependency array was using the entire existingConnection object, which could change reference on parent re-renders, triggering unnecessary re-fetches and unmounting form inputs. Changed the dependency to use the primitive database_id value instead of the object reference, ensuring the effect only runs when the actual connection being edited changes. * fix: prevent duplicate SQL connections from being created Fixed an issue where saving SQL connections multiple times would create duplicate entries with auto-generated hash suffixes (e.g., my-db-abc7). This occurred because the frontend maintained stale action properties on connections after saves, causing the backend to treat already-saved connections as new additions. Backend changes (server/models/systemSettings.js): - Modified mergeConnections to skip action:add items that already exist - Reject duplicate updates instead of auto-renaming with UUID suffixes - Check if original connection exists before applying updates Frontend changes: - Added hasChanges prop to SQL connector component - Automatically refresh connections from backend after successful save - Ensures local state has clean data without stale action properties This prevents the creation of confusing duplicate entries and ensures only the connections the user explicitly created are stored. * Refactor to use existing system settings endpoint for getting agent SQL connections | Add better documentation * Simplify handleUpdateConnection handler * refactor mergeConnections to use map * remove console log * fix bug where edit SQL connection modal values werent recomputed after re-opening * Add loading state for fetching agent SQL connections * tooltip * remove unused import * Put skip conditions in switch statement * throw error if default switch case is triggered --------- Co-authored-by: shatfield4 <seanhatfield5@gmail.com> Co-authored-by: Timothy Carambat <rambat1010@gmail.com>
AnythingLLM: The all-in-one AI app you were looking for.
Chat with your docs, use AI Agents, hyper-configurable, multi-user, & no frustrating setup required.
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👉 AnythingLLM for desktop (Mac, Windows, & Linux)! Download Now
A full-stack application that enables you to turn any document, resource, or piece of content into context that any LLM can use as a reference during chatting. This application allows you to pick and choose which LLM or Vector Database you want to use as well as supporting multi-user management and permissions.
Watch the demo!
Product Overview
AnythingLLM is a full-stack application where you can use commercial off-the-shelf LLMs or popular open source LLMs and vectorDB solutions to build a private ChatGPT with no compromises that you can run locally as well as host remotely and be able to chat intelligently with any documents you provide it.
AnythingLLM divides your documents into objects called workspaces. A Workspace functions a lot like a thread, but with the addition of containerization of your documents. Workspaces can share documents, but they do not talk to each other so you can keep your context for each workspace clean.
Cool features of AnythingLLM
- 🆕 Full MCP-compatibility
- 🆕 No-code AI Agent builder
- 🖼️ Multi-modal support (both closed and open-source LLMs!)
- Custom AI Agents
- 👤 Multi-user instance support and permissioning Docker version only
- 🦾 Agents inside your workspace (browse the web, etc)
- 💬 Custom Embeddable Chat widget for your website Docker version only
- 📖 Multiple document type support (PDF, TXT, DOCX, etc)
- Simple chat UI with Drag-n-Drop functionality and clear citations.
- 100% Cloud deployment ready.
- Works with all popular closed and open-source LLM providers.
- Built-in cost & time-saving measures for managing very large documents compared to any other chat UI.
- Full Developer API for custom integrations!
- Much more...install and find out!
Supported LLMs, Embedder Models, Speech models, and Vector Databases
Large Language Models (LLMs):
- Any open-source llama.cpp compatible model
- OpenAI
- OpenAI (Generic)
- Azure OpenAI
- AWS Bedrock
- Anthropic
- NVIDIA NIM (chat models)
- Google Gemini Pro
- Hugging Face (chat models)
- Ollama (chat models)
- LM Studio (all models)
- LocalAI (all models)
- Together AI (chat models)
- Fireworks AI (chat models)
- Perplexity (chat models)
- OpenRouter (chat models)
- DeepSeek (chat models)
- Mistral
- Groq
- Cohere
- KoboldCPP
- LiteLLM
- Text Generation Web UI
- Apipie
- xAI
- Z.AI (chat models)
- Novita AI (chat models)
- PPIO
- Gitee AI
- Moonshot AI
- Microsoft Foundry Local
- CometAPI (chat models)
- Docker Model Runner
Embedder models:
- AnythingLLM Native Embedder (default)
- OpenAI
- Azure OpenAI
- LocalAI (all)
- Ollama (all)
- LM Studio (all)
- Cohere
Audio Transcription models:
- AnythingLLM Built-in (default)
- OpenAI
TTS (text-to-speech) support:
- Native Browser Built-in (default)
- PiperTTSLocal - runs in browser
- OpenAI TTS
- ElevenLabs
- Any OpenAI Compatible TTS service.
STT (speech-to-text) support:
- Native Browser Built-in (default)
Vector Databases:
Technical Overview
This monorepo consists of six main sections:
frontend: A viteJS + React frontend that you can run to easily create and manage all your content the LLM can use.server: A NodeJS express server to handle all the interactions and do all the vectorDB management and LLM interactions.collector: NodeJS express server that processes and parses documents from the UI.docker: Docker instructions and build process + information for building from source.embed: Submodule for generation & creation of the web embed widget.browser-extension: Submodule for the chrome browser extension.
🛳 Self-Hosting
Mintplex Labs & the community maintain a number of deployment methods, scripts, and templates that you can use to run AnythingLLM locally. Refer to the table below to read how to deploy on your preferred environment or to automatically deploy.
| Docker | AWS | GCP | Digital Ocean | Render.com |
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| Railway | RepoCloud | Elestio | Northflank |
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or set up a production AnythingLLM instance without Docker →
How to setup for development
yarn setupTo fill in the required.envfiles you'll need in each of the application sections (from root of repo).- Go fill those out before proceeding. Ensure
server/.env.developmentis filled or else things won't work right.
- Go fill those out before proceeding. Ensure
yarn dev:serverTo boot the server locally (from root of repo).yarn dev:frontendTo boot the frontend locally (from root of repo).yarn dev:collectorTo then run the document collector (from root of repo).
External Apps & Integrations
These are apps that are not maintained by Mintplex Labs, but are compatible with AnythingLLM. A listing here is not an endorsement.
- Midori AI Subsystem Manager - A streamlined and efficient way to deploy AI systems using Docker container technology.
- Coolify - Deploy AnythingLLM with a single click.
- GPTLocalhost for Microsoft Word - A local Word Add-in for you to use AnythingLLM in Microsoft Word.
Telemetry & Privacy
AnythingLLM by Mintplex Labs Inc contains a telemetry feature that collects anonymous usage information.
More about Telemetry & Privacy for AnythingLLM
Why?
We use this information to help us understand how AnythingLLM is used, to help us prioritize work on new features and bug fixes, and to help us improve AnythingLLM's performance and stability.
Opting out
Set DISABLE_TELEMETRY in your server or docker .env settings to "true" to opt out of telemetry. You can also do this in-app by going to the sidebar > Privacy and disabling telemetry.
What do you explicitly track?
We will only track usage details that help us make product and roadmap decisions, specifically:
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Type of your installation (Docker or Desktop)
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When a document is added or removed. No information about the document. Just that the event occurred. This gives us an idea of use.
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Type of vector database in use. This helps us prioritize changes when updates arrive for that provider.
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Type of LLM provider & model tag in use. This helps us prioritize changes when updates arrive for that provider or model, or combination thereof. eg: reasoning vs regular, multi-modal models, etc.
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When a chat is sent. This is the most regular "event" and gives us an idea of the daily-activity of this project across all installations. Again, only the event is sent - we have no information on the nature or content of the chat itself.
You can verify these claims by finding all locations Telemetry.sendTelemetry is called. Additionally these events are written to the output log so you can also see the specific data which was sent - if enabled. No IP or other identifying information is collected. The Telemetry provider is PostHog - an open-source telemetry collection service.
We take privacy very seriously, and we hope you understand that we want to learn how our tool is used, without using annoying popup surveys, so we can build something worth using. The anonymous data is never shared with third parties, ever.
👋 Contributing
- Contributing to AnythingLLM - How to contribute to AnythingLLM.
💖 Sponsors
Premium Sponsors
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🌟 Contributors
🔗 More Products
- VectorAdmin: An all-in-one GUI & tool-suite for managing vector databases.
- OpenAI Assistant Swarm: Turn your entire library of OpenAI assistants into one single army commanded from a single agent.
Copyright © 2025 Mintplex Labs.
This project is MIT licensed.