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https://github.com/eschnou/opengpa

Free and open General Purpose Agent
https://github.com/eschnou/opengpa

agent agent-sdk agentic ai llm mcp ollama openai rag self-hosted

Last synced: 3 months ago
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Free and open General Purpose Agent

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README 

          
# OpenGPA - Self-hosted General Purpose Agent



[![Twitter Follow](https://img.shields.io/twitter/follow/opengpa?style=social)](https://twitter.com/opengpa)  

[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)



**OpenGPA** is an Open-source General Purpose Agent. A self-hosted solution supporting smart AI agent developments

with chain of thought, tool use and memory access through RAG.



- Agentic system with multi-step-reasoning, chain-of-thought, ReAct, tool use etc

- Support for [all major LLMs](https://docs.spring.io/spring-ai/reference/api/chatmodel.html) such as LLama, Mistral, Anthropic, OpenAI, etc.

- Off-line first. You can run on your own box with a local LLM. Data won't leave the server. All the power of GPT without the data privacy nightmare.

- Extensible framework allows to plug your own actions, such as calling into internal services and APIs

- Simple UI exposing insight on the GPA internal reasoning and actions

- Built-in RAG to search internal document stores

- Free and open source, deploy anywhere, customize to your needs





  




## :pencil2: Design principles



OpenGPA is much more than a UI on top of a LLM. It implements an agentic workflow, where a LLM is used as the 

*brain* of an agent to reason through multiple steps of planning, reasoning, and tool use.  In particular, OpenGpa 

is using the [ReAct](https://arxiv.org/abs/2210.03629) approach to verbally reason on the next step, decide on the action to execute, 

and observe the outcome.



```

{

  "reasoning": "The user wants to know the current weather in Liege, Belgium. 

                The best action to get this information is to perform a web search with 

                the query 'current weather in Liege, Belgium'. The result of this action 

                will then be used to respond to the user's request. 

                This is not the final action as we have to get the results from the web 

                search first."

  "action": {

    "name": "webSearch",

    "arguments": {

      "query": "current weather in Liege, Belgium"

    }

  },

  "is_final": false

}

```



Action selection is based on a catalog of action that can easily be extended through code. You could add an action

to tap into an internal service to fetch some data, or a workflow engine to trigger a next step, etc.



## 🛠️️ Key Features



The current version already packs a few interesting pieces:

- Works with all LLM supported by [spring-ai](https://spring.io/projects/spring-ai), including running **LLama** locally

- Multi-step task processing with **chain-of-thought** approach

- Action model with easy to extend **actions** for use by the agent

- Upload of **artifacts** to process by the agent

- Download of **artifacts** generated by the agent

- Import documents in knowledge base to support **Retrieval Augmented Generation** search

- Web browsing and web search

- Model Context Protocol [MCP](https://modelcontextprotocol.io/introduction) tap into existing enterprise APIs



## 🚧 Roadmap



Improve the Agentic capabilities:

- **Memory** enabling the agent to remember key facts and use them later

- **code generation** and **execution** within the agent (using Groovy scripts?)

- **scheduled** jobs for automating workflow

- **triggers** to create complete end-to-end workflows



Make OpenGPA enterprise ready:

- Persistence of tasks/steps

- Auditing of task processing costs (token usage)

- Instrumentation and observability



# 🚀 Getting started



In case of trouble, please reach out on [Discord](https://discord.gg/3XPsmCRNE2) for help!



## Quickstart (no need to clone this repo)



The `docker-compose.quickstart.yml` makes it easy to launch the latest stable version with 

a postgresql database (with pg_vector extension), the opengpa backend and the frontend, on the same

host.



```

curl -O https://raw.githubusercontent.com/eschnou/OpenGPA/main/docker-compose.quickstart.yml

echo "OPENAI_API_KEY=your-key-here" > .env

docker compose -f docker-compose.quickstart.yml up -d

```



## Build and run with Docker



The simplest way to build and launch OpenGPA is using the provided Docker compose file:



```bash

export OPENAI_API_KEY=sk-*** 

docker compose up --build

```



This will build the opengpa image from source with all required dependencies (in particular the Playwright

dependencies for web browsing) and launch the service on port 3000. Opening http://localhost:3000 will lead you

to the OpenAPI documentation.



## Build and run the server



If you are on a Mac, the following should be enough to get you started and running this

locally.  



> [!WARNING]

> Building requires __Java 21__. If you are on a Mac, you can easily install it

> with `brew install openjdk@21`



By default, opengpa is using OpenAI gpt-4o as its LLM. Check the `application.properties` file 

for configuration options and the spring-ai documentation to configure support for other LLMs.



```bash

mvn clean package -Pproduction

docker compose up -d db

OPENAI_API_KEY=sk-*** java -jar opengpa-server/target/opengpa-server-0.2.0.jar

```



The `docker compose up -d db` lauches the database part from the docker compose as opengpa requires

a postgres database with pg_vector for the RAG feature. 



# 🖥️ User Interface



The User Interface is available in the [OpenGPA Frontend repository](https://github.com/eschnou/opengpa-frontend) and

can be launched with docker.



```

git clone https://github.com/eschnou/opengpa-frontend

cd opengpa-frontend

docker build -t opengpa-frontend:latest .

docker run -p 8000:8000 opengpa-frontend

```



# 🐛 Debugging



For debugging purposes you can log all interactions and prompts using the following config:

```

opengpa.server.log-prompt=true

opengpa.server.log-folder=/tmp/opengpa/logs

```



# 🤖 REST API



You can interact with your agent programatically through the [REST API](documentation/api.md). The up-to-date API Documentation

is available on the backend server at [http://localhost:3000/swagger-ui.html](http://localhost:3000/swagger-ui.html)

with Swagger. You can also download the [yml](http://localhost:3000/api-docs.yml) or [json](http://localhost:3000/api-docs)

version of the API Documentation.



# Using MCP with OpenGPA



OpenGPA can leverage [MCP](https://modelcontextprotocol.io/introduction) to connect to third party services and use

their tools. You can look at the maintained list of servers on the [official repository](https://github.com/modelcontextprotocol/servers) or

on marketplaces like [mcp.so](https://mcp.so/).



MCP cannot be used with the starter Docker image. You need to rebuild your own docker image or run the java server

directly from CLI. In order to use MCP, you should:



1. Configure the path to your MCP server configuration in application.properties

```

spring.ai.mcp.client.stdio.servers-configuration=/path/to/mcp-servers.json

```



2. Configure your mcp-servers.json

```

{

  "mcpServers": {

      "time": {

        "command": "docker",

        "args": ["run", "-i", "--rm", "mcp/time"]

      }

  }

}

```



# 📚 Using OpenGPA Libraries



OpenGPA is designed as a modular system with reusable libraries. You can integrate these libraries into your own Java applications.



## Maven Repository



Our libraries are hosted at `https://dist.opengpa.org/packages`. To use them, add the repository to your pom.xml:



```xml



    

        opengpa-repository

        OpenGPA Repository

        https://dist.opengpa.org/packages

    



```



## Available Modules



### Core Module



The core module provides the foundational components for building agentic applications:



```xml



    org.opengpa

    opengpa-core

    0.4.0



```



### Actions Module



For integrating with the extended action catalog:



```xml



    org.opengpa

    opengpa-actions

    0.4.0



```



### RAG Module



For retrieval-augmented generation capabilities:



```xml



    org.opengpa

    opengpa-rag

    0.4.0



```



### MCP Module



For Model Context Protocol integration:



```xml



    org.opengpa

    opengpa-mcp

    0.4.0



```



# Documentation

- [Using local open-source LLM](documentation/offline.md)

- [Creating a custom Action](documentation/actions.md)



# Support

- Join us on [Discord](https://discord.gg/3XPsmCRNE2)

- Reach out on [X](https://x.com/opengpa)

- File an [issue](https://github.com/eschnou/OpenGPA/issues) 



# License



MIT License



Copyright (c) 2024-2025 Laurent Eschenauer



Permission is hereby granted, free of charge, to any person obtaining a copy

of this software and associated documentation files (the "Software"), to deal

in the Software without restriction, including without limitation the rights

to use, copy, modify, merge, publish, distribute, sublicense, and/or sell

copies of the Software, and to permit persons to whom the Software is

furnished to do so, subject to the following conditions:



The above copyright notice and this permission notice shall be included in all

copies or substantial portions of the Software.



THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR

IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,

FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE

AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER

LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,

OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE

SOFTWARE.