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vixues/leagent

18PythonAudience · vibe coderLicenseSetup · easy

TLDR

A self-hosted desktop app where you chat with AI models and build drag-and-drop automation workflows, connects to DeepSeek, OpenAI, Ollama, and more, with over 100 built-in tools and no data sent to third parties.

Mindmap

mindmap
  root((LeAgent))
    Chat
      Multi-turn conversations
      Streaming responses
      Memory system
    AI providers
      DeepSeek
      Qwen
      OpenAI-compatible
      Ollama local models
    Workflow editor
      Visual node canvas
      100+ tool nodes
      YAML export
    Install options
      One-command script
      Docker
      Desktop installer
      Windows macOS Linux
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Things people build with this

USE CASE 1

Chat with local AI models like Ollama on your own machine without sending any data to a third-party service.

USE CASE 2

Build a visual automation workflow that browses the web, queries a database, and runs code, all connected on a drag-and-drop canvas.

USE CASE 3

Package a reusable bundle of instructions and tools as a skill file and share it with others via a link or archive.

USE CASE 4

Switch between DeepSeek, OpenAI, and local models mid-project without reconfiguring the rest of your setup.

Tech stack

PythonDockerOllamaYAML

Getting it running

Difficulty · easy Time to first run · 30min

Desktop installers bundle their own Python runtime, no separate Python install needed, Docker option available for server deployment.

In plain English

LeAgent is an open-source desktop application that lets you chat with AI models and build automated workflows on your own computer or server. It runs as a self-hosted stack, meaning you install and run it yourself rather than sending your data to a third-party service. The chat side supports multi-turn conversations with streaming responses. It connects to several AI model providers including DeepSeek, Qwen, OpenAI-compatible APIs, and locally-run models via Ollama. You can swap between providers without reconfiguring anything else. The application also includes a memory system that stores conversational history, semantic facts, and learned procedures so the agent can refer back to earlier context. On top of chat, LeAgent has a visual workflow editor where you can build automated sequences by connecting nodes on a canvas. Each node represents a tool or action. The built-in toolset covers over 100 operations across categories like web browsing, document processing, database queries, code execution, and generating interface elements. Workflows can be exported as YAML files. The application supports a skill system where bundles of instructions and tools can be packaged into files, installed from links or archives, and loaded on demand. There is also support for MCP, a protocol for connecting external tool servers. For installation there are several options. A one-command script handles the full setup for people comfortable with the terminal. A Docker container works for anyone who already runs Docker. Downloadable desktop installers are available for Windows, macOS on both Apple Silicon and Intel, and Linux. The desktop build bundles its own Python runtime so no separate installation is required. The project also includes an optional sidebar character, described as a desk pet, that displays a customizable animated avatar synchronized with the chat session state. LeAgent is released under the Apache 2.0 license.

Copy-paste prompts

Prompt 1
Walk me through installing LeAgent on my Mac with Apple Silicon and connecting it to a local Ollama model.
Prompt 2
How do I build a workflow in LeAgent that scrapes a website, extracts key facts, and saves them to a database?
Prompt 3
How do I package a set of LeAgent instructions and tools into a skill file so a teammate can install it from a link?
Prompt 4
Show me how to export a LeAgent workflow as YAML and then import it on a different machine.
Prompt 5
Help me set up LeAgent with Docker so my whole team can use a shared self-hosted AI chat and automation server.
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