![agentok](/statics/github-mark.png)
agentok
AG2 Visualized - Build Agentic Apps with Drag-and-Drop Simplicity.
Stars: 242
![screenshot](/screenshots_githubs/dustland-agentok.jpg)
Agentok Studio is a tool built upon AG2, a powerful agent framework from Microsoft, offering intuitive visual tools to streamline the creation and management of complex agent-based workflows. It simplifies the process for creators and developers by generating native Python code with minimal dependencies, enabling users to create self-contained code that can be executed anywhere. The tool is currently under development and not recommended for production use, but contributions are welcome from the community to enhance its capabilities and functionalities.
README:
AG2 Visualized - Build Agentic Apps with Drag-and-Drop Simplicity.
Agentok Studio is a tool built upon AG2 (Previously AutoGen), a powerful agent framework from Microsoft and a vibrant community of contributors.
We consider AG2 to be at the forefront of next-generation Multi-Agent Applications technology. Agentok Studio takes this concept to the next level by offering intuitive visual tools that streamline the creation and management of complex agent-based workflows.
The relationship between two agents is essential. To incorporate tool calls in a conversation, the LLM must determine which tools to invoke, while informing the user proxy about which nodes to execute. Configuring tools on the edge between these nodes is crucial for optimal operation.
You can switch between light and dark themes using the toggle in the top right corner.
For more information related to Conversation Patterns, please refer to Conversation Patterns.
We provide a tool editor to help you create and manage tools.
The tool can contain variables, which users can configure in the tool management page.
We strive to create a user-friendly tool that generates native Python code with minimal dependencies. Simply put, Agentok Studio is a diagram-based code generator for AG2. The generated code is self-contained and can be executed anywhere as a normal Python program, relying solely on the official ag2
library.
As shown above, we provide full visibility into the underlying data representation of the flow for diagnosis and debugging.
We also attached the original logs(stdout and stderr) from AG2 execution, so you can fully understand the underlying execution process:
[!Note] RAG feature has been removed from this project, as we believe it should be a separate service.
Visit https://studio.agentok.ai to quickly explore Agentok Studio's features. While we offer an online deployment, please note that it is not intended for production use. The service level agreement is not guaranteed, and stored data may be wiped due to breaking changes.
After logging in as a Guest or with your OAuth2 account, click Create New Project to start a new project. Each new project comes with a sample workflow. Switch to the Chat tab to begin the conversation.
For a more in-depth look at the project, please refer to Getting Started.
The project consists of a Frontend (built with Next.js) and Backend service (built with FastAPI in Python), both fully dockerized.
Before running the project, create .env
files in both the frontend
and api
directories:
cp frontend/.env.sample frontend/.env
cp api/.env.sample api/.env
cp api/OAI_CONFIG_LIST.sample api/OAI_CONFIG_LIST
Note: Supabase provides both anon and service_role keys for each project. Use the anon key for NEXT_PUBLIC_SUPABASE_ANON_KEY
(frontend) and service role key for SUPABASE_SERVICE_KEY
(backend).
The easiest way to run locally is using docker-compose:
docker-compose up -d
You can also build and run the UI and service separately with Docker:
docker build -t agentok-api ./api
docker run -d -p 5004:5004 agentok-api
docker build -t agentok-frontend ./frontend
docker run -d -p 2855:2855 agentok-frontend
(Port 2855 represents our first office address.)
For development or running from source:
- Navigate to the frontend directory:
cd frontend
- Rename
.env.sample
to.env.local
and configure variables - Install dependencies:
pnpm install
oryarn
- Start the development server:
pnpm dev
oryarn dev
Note: If you encounter frequent Server Errors related to 'useContext', try removing
--turbo
from the dev command in package.json.
- Navigate to the api directory:
cd api
- Rename
.env.sample
to.env
andOAI_CONFIG_LIST.sample
toOAI_CONFIG_LIST
- Install Poetry
- Start the service:
poetry run uvicorn agentok_api.main:app --reload --port 5004
REPLICATE_API_TOKEN
is required for LLaVa agent functionality.
IMPORTANT: AG2's latest version requires Docker for code execution by default. Either:
- Install Docker locally, OR
- Set
AUTOGEN_USE_DOCKER=False
inapi/.env
Note: Docker requirement is disabled by default in our dockerized deployment.
This project uses Supabase for authentication and data storage. Follow ./db/README.md to prepare the database and set environment variables (SUPABASE_* in .env.sample).
Once services are running, access:
- API: http://localhost:5004 (OpenAPI docs: http://localhost:5004/docs)
- Frontend: http://localhost:2855
We welcome all contributions! This includes code, documentation, and other project aspects. Open a GitHub Issue or join our Discord Server.
Please read our Contributing Guide before getting started.
New to GitHub? Check out their guide on collaborating with issues and pull requests.
Consider contributing to AG2 as well, since Agentok Studio builds upon its capabilities.
This project uses π¦πsemantic-release for versioning and releases. Releases are triggered manually via GitHub Actions to avoid excessive automation.
We enforce Commitlint conventions for commit messages.
This project is licensed under the Apache 2.0 License with additional terms and conditions.
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