LocalAGI Logo

Your AI. Your Hardware. Your Rules.

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We empower you building AI Agents that you can run locally, without coding. **LocalAGI** is a powerful, self-hostable AI Agent platform designed for maximum privacy and flexibility. A complete drop-in replacement for OpenAI's Responses APIs with advanced agentic capabilities. No clouds. No data leaks. Just pure local AI that works on consumer-grade hardware (CPU and GPU). ## πŸ›‘οΈ Take Back Your Privacy Are you tired of AI wrappers calling out to cloud APIs, risking your privacy? So were we. LocalAGI ensures your data stays exactly where you want itβ€”on your hardware. No API keys, no cloud subscriptions, no compromise. ## 🌟 Key Features - πŸŽ› **No-Code Agents**: Easy-to-configure multiple agents via Web UI. - πŸ–₯ **Web-Based Interface**: Simple and intuitive agent management. - πŸ€– **Advanced Agent Teaming**: Instantly create cooperative agent teams from a single prompt. - πŸ“‘ **Connectors Galore**: Built-in integrations with Discord, Slack, Telegram, GitHub Issues, and IRC. - πŸ›  **Comprehensive REST API**: Seamless integration into your workflows. Every agent created will support OpenAI Responses API out of the box. - πŸ“š **Short & Long-Term Memory**: Powered by [LocalRecall](https://github.com/mudler/LocalRecall). - 🧠 **Planning & Reasoning**: Agents intelligently plan, reason, and adapt. - πŸ”„ **Periodic Tasks**: Schedule tasks with cron-like syntax. - πŸ’Ύ **Memory Management**: Control memory usage with options for long-term and summary memory. - πŸ–Ό **Multimodal Support**: Ready for vision, text, and more. - πŸ”§ **Extensible Custom Actions**: Easily script dynamic agent behaviors in Go (interpreted, no compilation!). - πŸ›  **Fully Customizable Models**: Use your own models or integrate seamlessly with [LocalAI](https://github.com/mudler/LocalAI). ## πŸ› οΈ Quickstart ```bash # Clone the repository git clone https://github.com/mudler/LocalAGI cd LocalAGI # CPU setup (default) docker compose up # NVIDIA GPU setup docker compose -f docker-compose.nvidia.yaml up # Intel GPU setup (for Intel Arc and integrated GPUs) docker compose -f docker-compose.intel.yaml up # Start with a specific model (see available models in models.localai.io, or localai.io to use any model in huggingface) MODEL_NAME=gemma-3-12b-it docker compose up # NVIDIA GPU setup with custom multimodal and image models MODEL_NAME=gemma-3-12b-it \ MULTIMODAL_MODEL=minicpm-v-2_6 \ IMAGE_MODEL=flux.1-dev \ docker compose -f docker-compose.nvidia.yaml up ``` Now you can access and manage your agents at [http://localhost:8080](http://localhost:8080) ## πŸ“šπŸ†• Local Stack Family πŸ†• LocalAI is now part of a comprehensive suite of AI tools designed to work together:
LocalAI Logo

LocalAI

LocalAI is the free, Open Source OpenAI alternative. LocalAI act as a drop-in replacement REST API that's compatible with OpenAI (Elevenlabs, Anthropic... ) API specifications for local AI inferencing. It allows you to run LLMs, generate images, audio (and not only) locally or on-prem with consumer grade hardware, supporting multiple model families. Does not require GPU.
LocalRecall Logo

LocalRecall

A REST-ful API and knowledge base management system that provides persistent memory and storage capabilities for AI agents.
## πŸ–₯️ Hardware Configurations LocalAGI supports multiple hardware configurations through Docker Compose profiles: ### CPU (Default) - No special configuration needed - Runs on any system with Docker - Best for testing and development - Supports text models only ### NVIDIA GPU - Requires NVIDIA GPU and drivers - Uses CUDA for acceleration - Best for high-performance inference - Supports text, multimodal, and image generation models - Run with: `docker compose --profile nvidia up` - Default models: - Text: `arcee-agent` - Multimodal: `minicpm-v-2_6` - Image: `flux.1-dev` - Environment variables: - `MODEL_NAME`: Text model to use - `MULTIMODAL_MODEL`: Multimodal model to use - `IMAGE_MODEL`: Image generation model to use - `LOCALAI_SINGLE_ACTIVE_BACKEND`: Set to `true` to enable single active backend mode ### Intel GPU - Supports Intel Arc and integrated GPUs - Uses SYCL for acceleration - Best for Intel-based systems - Supports text, multimodal, and image generation models - Run with: `docker compose --profile intel up` - Default models: - Text: `arcee-agent` - Multimodal: `minicpm-v-2_6` - Image: `sd-1.5-ggml` - Environment variables: - `MODEL_NAME`: Text model to use - `MULTIMODAL_MODEL`: Multimodal model to use - `IMAGE_MODEL`: Image generation model to use - `LOCALAI_SINGLE_ACTIVE_BACKEND`: Set to `true` to enable single active backend mode ## Customize models You can customize the models used by LocalAGI by setting environment variables when running docker-compose. For example: ```bash # CPU with custom model MODEL_NAME=gemma-3-12b-it docker compose up # NVIDIA GPU with custom models MODEL_NAME=gemma-3-12b-it \ MULTIMODAL_MODEL=minicpm-v-2_6 \ IMAGE_MODEL=flux.1-dev \ docker compose -f docker-compose.nvidia.yaml up # Intel GPU with custom models MODEL_NAME=gemma-3-12b-it \ MULTIMODAL_MODEL=minicpm-v-2_6 \ IMAGE_MODEL=sd-1.5-ggml \ docker compose -f docker-compose.intel.yaml up ``` If no models are specified, it will use the defaults: - Text model: `arcee-agent` - Multimodal model: `minicpm-v-2_6` - Image model: `flux.1-dev` (NVIDIA) or `sd-1.5-ggml` (Intel) Good (relatively small) models that have been tested are: - `qwen_qwq-32b` (best in co-ordinating agents) - `gemma-3-12b-it` - `gemma-3-27b-it` ## πŸ† Why Choose LocalAGI? - **βœ“ Ultimate Privacy**: No data ever leaves your hardware. - **βœ“ Flexible Model Integration**: Supports GGUF, GGML, and more thanks to [LocalAI](https://github.com/mudler/LocalAI). - **βœ“ Developer-Friendly**: Rich APIs and intuitive interfaces. - **βœ“ Effortless Setup**: Simple Docker compose setups and pre-built binaries. - **βœ“ Feature-Rich**: From planning to multimodal capabilities, connectors for Slack, MCP support, LocalAGI has it all. ## 🌐 The Local Ecosystem LocalAGI is part of the powerful Local family of privacy-focused AI tools: - [**LocalAI**](https://github.com/mudler/LocalAI): Run Large Language Models locally. - [**LocalRecall**](https://github.com/mudler/LocalRecall): Retrieval-Augmented Generation with local storage. - [**LocalAGI**](https://github.com/mudler/LocalAGI): Deploy intelligent AI agents securely and privately. ## 🌟 Screenshots ### Powerful Web UI ![Web UI Dashboard](https://github.com/user-attachments/assets/a40194f9-af3a-461f-8b39-5f4612fbf221) ![Web UI Agent Settings](https://github.com/user-attachments/assets/fb3c3e2a-cd53-4ca8-97aa-c5da51ff1f83) ![Web UI Create Group](https://github.com/user-attachments/assets/102189a2-0fba-4a1e-b0cb-f99268ef8062) ### Connectors Ready-to-Go

Telegram Discord Slack IRC GitHub

## πŸ“– Full Documentation Explore detailed documentation including: - [Installation Options](#installation-options) - [REST API Documentation](#rest-api) - [Connector Configuration](#connectors) - [Agent Configuration](#agent-configuration-reference) ### Environment Configuration LocalAGI supports environment configurations. Note that these environment variables needs to be specified in the localagi container in the docker-compose file to have effect. | Variable | What It Does | |----------|--------------| | `LOCALAGI_MODEL` | Your go-to model | | `LOCALAGI_MULTIMODAL_MODEL` | Optional model for multimodal capabilities | | `LOCALAGI_LLM_API_URL` | OpenAI-compatible API server URL | | `LOCALAGI_LLM_API_KEY` | API authentication | | `LOCALAGI_TIMEOUT` | Request timeout settings | | `LOCALAGI_STATE_DIR` | Where state gets stored | | `LOCALAGI_LOCALRAG_URL` | LocalRecall connection | | `LOCALAGI_ENABLE_CONVERSATIONS_LOGGING` | Toggle conversation logs | | `LOCALAGI_API_KEYS` | A comma separated list of api keys used for authentication | ## Installation Options ### Pre-Built Binaries Download ready-to-run binaries from the [Releases](https://github.com/mudler/LocalAGI/releases) page. ### Source Build Requirements: - Go 1.20+ - Git - Bun 1.2+ ```bash # Clone repo git clone https://github.com/mudler/LocalAGI.git cd LocalAGI # Build it cd webui/react-ui && bun i && bun run build cd ../.. go build -o localagi # Run it ./localagi ``` ### Development The development workflow is similar to the source build, but with additional steps for hot reloading of the frontend: ```bash # Clone repo git clone https://github.com/mudler/LocalAGI.git cd LocalAGI # Install dependencies and start frontend development server cd webui/react-ui && bun i && bun run dev ``` Then in seperate terminal: ```bash # Start development server cd ../.. && go run main.go ``` > Note: see webui/react-ui/.vite.config.js for env vars that can be used to configure the backend URL ## CONNECTORS Link your agents to the services you already use. Configuration examples below. ### GitHub Issues ```json { "token": "YOUR_PAT_TOKEN", "repository": "repo-to-monitor", "owner": "repo-owner", "botUserName": "bot-username" } ``` ### Discord After [creating your Discord bot](https://discordpy.readthedocs.io/en/stable/discord.html): ```json { "token": "Bot YOUR_DISCORD_TOKEN", "defaultChannel": "OPTIONAL_CHANNEL_ID" } ``` > Don't forget to enable "Message Content Intent" in Bot(tab) settings! > Enable " Message Content Intent " in the Bot tab! ### Slack Use the included `slack.yaml` manifest to create your app, then configure: ```json { "botToken": "xoxb-your-bot-token", "appToken": "xapp-your-app-token" } ``` - Create Oauth token bot token from "OAuth & Permissions" -> "OAuth Tokens for Your Workspace" - Create App level token (from "Basic Information" -> "App-Level Tokens" ( scope connections:writeRoute authorizations:read )) ### Telegram Get a token from @botfather, then: ```json { "token": "your-bot-father-token" } ``` ### IRC Connect to IRC networks: ```json { "server": "irc.example.com", "port": "6667", "nickname": "LocalAGIBot", "channel": "#yourchannel", "alwaysReply": "false" } ``` ## REST API ### Agent Management | Endpoint | Method | Description | Example | |----------|--------|-------------|---------| | `/api/agents` | GET | List all available agents | [Example](#get-all-agents) | | `/api/agent/:name/status` | GET | View agent status history | [Example](#get-agent-status) | | `/api/agent/create` | POST | Create a new agent | [Example](#create-agent) | | `/api/agent/:name` | DELETE | Remove an agent | [Example](#delete-agent) | | `/api/agent/:name/pause` | PUT | Pause agent activities | [Example](#pause-agent) | | `/api/agent/:name/start` | PUT | Resume a paused agent | [Example](#start-agent) | | `/api/agent/:name/config` | GET | Get agent configuration | | | `/api/agent/:name/config` | PUT | Update agent configuration | | | `/api/meta/agent/config` | GET | Get agent configuration metadata | | | `/settings/export/:name` | GET | Export agent config | [Example](#export-agent) | | `/settings/import` | POST | Import agent config | [Example](#import-agent) | ### Actions and Groups | Endpoint | Method | Description | Example | |----------|--------|-------------|---------| | `/api/actions` | GET | List available actions | | | `/api/action/:name/run` | POST | Execute an action | | | `/api/agent/group/generateProfiles` | POST | Generate group profiles | | | `/api/agent/group/create` | POST | Create a new agent group | | ### Chat Interactions | Endpoint | Method | Description | Example | |----------|--------|-------------|---------| | `/api/chat/:name` | POST | Send message & get response | [Example](#send-message) | | `/api/notify/:name` | POST | Send notification to agent | [Example](#notify-agent) | | `/api/sse/:name` | GET | Real-time agent event stream | [Example](#agent-sse-stream) | | `/v1/responses` | POST | Send message & get response | [OpenAI's Responses](https://platform.openai.com/docs/api-reference/responses/create) |
Curl Examples #### Get All Agents ```bash curl -X GET "http://localhost:3000/api/agents" ``` #### Get Agent Status ```bash curl -X GET "http://localhost:3000/api/agent/my-agent/status" ``` #### Create Agent ```bash curl -X POST "http://localhost:3000/api/agent/create" \ -H "Content-Type: application/json" \ -d '{ "name": "my-agent", "model": "gpt-4", "system_prompt": "You are an AI assistant.", "enable_kb": true, "enable_reasoning": true }' ``` #### Delete Agent ```bash curl -X DELETE "http://localhost:3000/api/agent/my-agent" ``` #### Pause Agent ```bash curl -X PUT "http://localhost:3000/api/agent/my-agent/pause" ``` #### Start Agent ```bash curl -X PUT "http://localhost:3000/api/agent/my-agent/start" ``` #### Get Agent Configuration ```bash curl -X GET "http://localhost:3000/api/agent/my-agent/config" ``` #### Update Agent Configuration ```bash curl -X PUT "http://localhost:3000/api/agent/my-agent/config" \ -H "Content-Type: application/json" \ -d '{ "model": "gpt-4", "system_prompt": "You are an AI assistant." }' ``` #### Export Agent ```bash curl -X GET "http://localhost:3000/settings/export/my-agent" --output my-agent.json ``` #### Import Agent ```bash curl -X POST "http://localhost:3000/settings/import" \ -F "file=@/path/to/my-agent.json" ``` #### Send Message ```bash curl -X POST "http://localhost:3000/api/chat/my-agent" \ -H "Content-Type: application/json" \ -d '{"message": "Hello, how are you today?"}' ``` #### Notify Agent ```bash curl -X POST "http://localhost:3000/api/notify/my-agent" \ -H "Content-Type: application/json" \ -d '{"message": "Important notification"}' ``` #### Agent SSE Stream ```bash curl -N -X GET "http://localhost:3000/api/sse/my-agent" ``` Note: For proper SSE handling, you should use a client that supports SSE natively.
### Agent Configuration Reference The agent configuration defines how an agent behaves and what capabilities it has. You can view the available configuration options and their descriptions by using the metadata endpoint: ```bash curl -X GET "http://localhost:3000/api/meta/agent/config" ``` This will return a JSON object containing all available configuration fields, their types, and descriptions. Here's an example of the agent configuration structure: ```json { "name": "my-agent", "model": "gpt-4", "multimodal_model": "gpt-4-vision", "hud": true, "standalone_job": false, "random_identity": false, "initiate_conversations": true, "enable_planning": true, "identity_guidance": "You are a helpful assistant.", "periodic_runs": "0 * * * *", "permanent_goal": "Help users with their questions.", "enable_kb": true, "enable_reasoning": true, "kb_results": 5, "can_stop_itself": false, "system_prompt": "You are an AI assistant.", "long_term_memory": true, "summary_long_term_memory": false } ``` ## LICENSE MIT License β€” See the [LICENSE](LICENSE) file for details. ---

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