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‎.gitmodules‎

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path = assets
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url = https://github.com/PosetMage/jekyll_assets.git
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branch = main
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[submodule "_layouts/page"]
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path = _layouts/page
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url = https://github.com/PosetMage/jekyll_layouts.git
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branch = main

‎About/index.md‎

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![LangGraph-GUI](/cover.webp)
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## Docker Compose
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To run LangGraph-GUI using Docker Compose, refer to the detailed instructions available in the [LangGraph-GUI Docker Compose Setup](https://github.com/LangGraph-GUI/LangGraph-GUI?tab=readme-ov-file#getting-started).
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## Components
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LangGraph-GUI comprises the following main components:
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- [**Frontend**](https://github.com/LangGraph-GUI/LangGraph-GUI-frontend): The default frontend is built with ReactFlow, providing an intuitive interface for graph editing.
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- **Frontend**: The default frontend is built with ReactFlow, providing an intuitive interface for graph editing.
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- for more info, please view [Frontend](/Frontend)
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- [**Backend**](https://github.com/LangGraph-GUI/LangGraph-GUI-backend): The backend is implemented using Flask, offering a robust and scalable server-side solution.
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- **Backend**: The backend is implemented using Flask, offering a robust and scalable server-side solution.
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- for more info, please view [Backend](/Backend)
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- AI-end
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- For running local language models with Ollama AI service, we recommend using NVIDIA Docker.
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## Docker COmpose
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To run LangGraph-GUI using Docker Compose, refer to the detailed instructions available in the [LangGraph-GUI repository](https://github.com/LangGraph-GUI/LangGraph-GUI).
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## Electron wrapper
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For those who prefer a desktop application, we also provide an [Electron wrapper](https://github.com/LangGraph-GUI/LangGraph-GUI?tab=readme-ov-file#electron-wrapper).

‎Frontend/Graph.md‎

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---
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layout: frontend
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---
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# GUI Graph
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## Explanation
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#### Start Node
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A graph(json) only have one start node, this will mapping to LangGraph START
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![](images/start.webp)
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#### Step Node
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Step Node will mapping to LangGraph add_node, you can drag edge from left node(right port) to right node(left port)
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![](images/step.webp)
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if you drag two node toward to each other, can create cycle.
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#### Step Node with tool
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Step node fill tool will call function, the tool definition need a tool node
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![](images/use_tool.webp)
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#### Tool Node
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Tool node need write real python function code, and need `@tool` decorator like LangChain Custom Tools
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![](images/tool.webp)
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#### Contition Node
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CONDITION node will mapping to LangGraph conditional_edge
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* green edge is true case path
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* red edge is false case path
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![](images/condition.webp)
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## Other Resource
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*If you want to learn more about LangGraph, we have LangGraph for dummy : [LangGraph-learn](https://github.com/LangGraph-GUI/LangGraph-learn)*
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‎Frontend/Run.md‎

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---
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layout: frontend
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---
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# Run Graph
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This will Run graph.json on server.
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That is, you need upload graph.json first.
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![](./images/run.webp)
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## Run
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Execute graph.json into LangGraph with Ollama.
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After Run, recommand download files.
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## Stop
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To terminal remote running server. (Currently, many bugs. If stuck, plese restart docker compose)
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## Cancel
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Leave run menu
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## Text Space
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Show infomation when server running

‎Frontend/UI.md‎

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---
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layout: frontend
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---
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# UI
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There are several buttoms
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![](./images/button.webp)
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## New
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New will clean the canvas.
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## Save
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Save the graph into json to your computer.
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## Load
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Read the graph json from your computer.
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## Run
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Run graph.json on server, for more info, see [Run](./Run)
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## Upload
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a file to server env, for Run.
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* upload graph.json for Run on server
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* upload other file for workflow data
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## Download
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download all files from server such generated files and log file

‎Frontend/images/button.webp‎

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‎Frontend/images/run.webp‎

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‎Frontend/index.md‎

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# LangGraph-GUI Frontend
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---
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layout: frontend
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---
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![](https://raw.githubusercontent.com/LangGraph-GUI/LangGraph-GUI-frontend/main/cover.webp)
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LangGraph-GUI is a user-friendly interface for managing and visualizing Node-Edge workflows with LangGraph. It supports creating, editing, and running workflows locally using language models by Ollama.
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# About Frontend
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This is node-edge based gui will export to json as saved graph. To execute json by LangGraph, you need to run [LangGraph-GUI-backend](https://github.com/LangGraph-GUI/LangGraph-GUI-backend)
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This is node-edge based gui will export to json as saved graph.
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*This is LangGraph-GUI ReactFlow frontend, If you want to run in docker compose, see [LangGraph-GUI](https://github.com/LangGraph-GUI/LangGraph-GUI)*
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*If you want to learn more about LangGraph, we have LangGraph for dummy : [LangGraph-learn](https://github.com/LangGraph-GUI/LangGraph-learn)*
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## Explanation
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#### Start Node
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A graph(json) only have one start node, this will mapping to LangGraph START
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![](images/start.webp)
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#### Step Node
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Step Node will mapping to LangGraph add_node, you can drag edge from left node(right port) to right node(left port)
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![](/cover.webp)
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![](images/step.webp)
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if you drag two node toward to each other, can create cycle.
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#### Step Node with tool
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Step node fill tool will call function, the tool definition need a tool node
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![](images/use_tool.webp)
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#### Tool Node
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Tool node need write real python function code, and need `@tool` decorator like LangChain Custom Tools
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![](images/tool.webp)
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#### Contition Node
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CONDITION node will mapping to LangGraph conditional_edge
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* green edge is true case path
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* red edge is false case path
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*This is LangGraph-GUI ReactFlow frontend, If you want to run in docker compose, see [LangGraph-GUI](https://github.com/LangGraph-GUI/LangGraph-GUI)*
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![](images/condition.webp)

‎Frontend/outline.html‎

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<h2>Frontend</h2>
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<a href="/Frontend/">About</a></br>
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<a href="/Frontend/UI">UI</a></br>
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<a href="/Frontend/Graph">Graph</a></br>
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<a href="/Frontend/Run">Run</a></br>

‎Others/index.md‎

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If you want to learn more about LangGraph, we also have [learning LangGraph for dummy](https://github.com/LangGraph-GUI/LangGraph-learn)
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## Side project
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## deprecated projects
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We have Qt version: [LangGraph-GUI-Qt](https://github.com/LangGraph-GUI/LangGraph-GUI-Qt)
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We also have Qt version of [CrewAI-GUI](https://github.com/LangGraph-GUI/CrewAI-GUI)

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