GitHub - mudler/LocalAGI: 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).

Your AI. Your Hardware. Your Rules.

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).

🛡️ 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 it—on your hardware. No API keys, no cloud subscriptions, no compromise.

🌟 Key Features

🎛 No-Code Agents: Easy-to-configure multiple agents via Web UI.
🖥 Web-Based Interface: Simple and intuitive agent management.
🤖 Advanced Agent Teaming: Instantly create cooperative agent teams from a single prompt.
📡 Connectors Galore: Built-in integrations with Discord, Slack, Telegram, GitHub Issues, and IRC.
🛠 Comprehensive REST API: Seamless integration into your workflows. Every agent created will support OpenAI Responses API out of the box.
📚 Short & Long-Term Memory: Powered by LocalRecall.
🧠 Planning & Reasoning: Agents intelligently plan, reason, and adapt.
🔄 Periodic Tasks: Schedule tasks with cron-like syntax.
💾 Memory Management: Control memory usage with options for long-term and summary memory.
🖼 Multimodal Support: Ready for vision, text, and more.
🔧 Extensible Custom Actions: Easily script dynamic agent behaviors in Go (interpreted, no compilation!).
🛠 Fully Customizable Models: Use your own models or integrate seamlessly with LocalAI.

🛠️ Quickstart

# 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

📚🆕 Local Stack Family

🆕 LocalAI is now part of a comprehensive suite of AI tools designed to work together:

	LocalAI LocalAI is the free, Open Source OpenAI alternative. LocalAI act as a drop-in replacement REST API that's compatible with OpenAI API specifications for local AI inferencing. Does not require GPU.
	LocalRecall A REST-ful API and knowledge base management system that provides persistent memory and storage capabilities for AI agents.

🖥️ 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 -f docker-compose.nvidia.yaml 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 -f docker-compose.intel.yaml 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:

# 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

🏆 Why Choose LocalAGI?

✓ Ultimate Privacy: No data ever leaves your hardware.
✓ Flexible Model Integration: Supports GGUF, GGML, and more thanks to LocalAI.
✓ Developer-Friendly: Rich APIs and intuitive interfaces.
✓ Effortless Setup: Simple Docker compose setups and pre-built binaries.
✓ 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: Run Large Language Models locally.
LocalRecall: Retrieval-Augmented Generation with local storage.
LocalAGI: Deploy intelligent AI agents securely and privately.

🌟 Screenshots

Powerful Web UI

Connectors Ready-to-Go

📖 Full Documentation

Explore detailed documentation including:

Installation Options
REST API Documentation
Connector Configuration
Agent Configuration

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 page.

Source Build

Requirements:

Go 1.20+
Git
Bun 1.2+

# 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:

# 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:

# 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

{
  "token": "YOUR_PAT_TOKEN",
  "repository": "repo-to-monitor",
  "owner": "repo-owner",
  "botUserName": "bot-username"
}

Discord

After creating your Discord bot:

{
  "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:

{
  "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:

{ 
  "token": "your-bot-father-token" 
}

IRC

Connect to IRC networks:

{
  "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
`/api/agent/:name/status`	GET	View agent status history	Example
`/api/agent/create`	POST	Create a new agent	Example
`/api/agent/:name`	DELETE	Remove an agent	Example
`/api/agent/:name/pause`	PUT	Pause agent activities	Example
`/api/agent/:name/start`	PUT	Resume a paused agent	Example
`/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
`/settings/import`	POST	Import agent config	Example

Actions and Groups

Endpoint	Method	Description
`/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
`/api/notify/:name`	POST	Send notification to agent	Example
`/api/sse/:name`	GET	Real-time agent event stream	Example
`/v1/responses`	POST	Send message & get response	OpenAI's Responses

Curl Examples

Get All Agents

curl -X GET "http://localhost:3000/api/agents"

Get Agent Status

curl -X GET "http://localhost:3000/api/agent/my-agent/status"

Create Agent

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

curl -X DELETE "http://localhost:3000/api/agent/my-agent"

Pause Agent

curl -X PUT "http://localhost:3000/api/agent/my-agent/pause"

Start Agent

curl -X PUT "http://localhost:3000/api/agent/my-agent/start"

Get Agent Configuration

curl -X GET "http://localhost:3000/api/agent/my-agent/config"

Update Agent Configuration

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

curl -X GET "http://localhost:3000/settings/export/my-agent" --output my-agent.json

Import Agent

curl -X POST "http://localhost:3000/settings/import" \
  -F "file=@/path/to/my-agent.json"

Send Message

curl -X POST "http://localhost:3000/api/chat/my-agent" \
  -H "Content-Type: application/json" \
  -d '{"message": "Hello, how are you today?"}'

Notify Agent

curl -X POST "http://localhost:3000/api/notify/my-agent" \
  -H "Content-Type: application/json" \
  -d '{"message": "Important notification"}'

Agent SSE Stream

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:

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:

{
  "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 — See the LICENSE file for details.

LOCAL PROCESSING. GLOBAL THINKING.
Made with ❤️ by mudler

Name		Name	Last commit message	Last commit date
Latest commit History 506 Commits
.github/workflows		.github/workflows
core		core
example/realtimesst		example/realtimesst
pkg		pkg
services		services
tests/e2e		tests/e2e
webui		webui
.dockerignore		.dockerignore
.gitignore		.gitignore
.goreleaser.yml		.goreleaser.yml
Dockerfile.realtimesst		Dockerfile.realtimesst
Dockerfile.webui		Dockerfile.webui
LICENSE		LICENSE
Makefile		Makefile
README.md		README.md
docker-compose.intel.yaml		docker-compose.intel.yaml
docker-compose.nvidia.yaml		docker-compose.nvidia.yaml
docker-compose.yaml		docker-compose.yaml
go.mod		go.mod
go.sum		go.sum
jsconfig.json		jsconfig.json
main.go		main.go
slack.yaml		slack.yaml
start_realtimesst.sh		start_realtimesst.sh

License

mudler/LocalAGI

Folders and files

Latest commit

History

Repository files navigation