![The ninth planet around the sun](https://raw.githubusercontent.com/CharlesT100/ix/master/ix_350.png)
Amidst the swirling sands of the cosmos, Ix stands as an enigmatic jewel,
where the brilliance of human ingenuity dances on the edge of forbidden
knowledge, casting a shadow of intrigue over the galaxy.
- Atreides Scribe, The Chronicles of Ixian Innovation
The backend is designed to support multiple agents running in parallel and communicating with each other. Each agent
may be customized and may utilize parallel processes to complete tasks.
Ix uses GPT-4 by default, but agents may be configured to use any model supported by LangChain.
You chat with an agent that uses that direction to investigate, plan, and complete tasks. The agents are capable of searching the web, writing code, creating images, interacting with other APIs and services. If it can be coded, it's within the realm of possibility that an agent can be built to assist you.
-
Setup the server and visit
http://localhost:8000
, a new chat will be created automatically -
Enter a request and the Ix moderator will delegate the task to the agent best suited for the response. Or @mention an agent to request a specific agent to complete the task.
-
Customized agents may be added or removed from the chat as needed to process your tasks
Ix provides the moderator agent Ix, a coder agent, and a few example agents. Additional agents may be built using the chain editor or the python API.
- Chains no-code editor
- Chains python API docs
Agents and chains are built from a graph of LangChain components. Each node in the graph is either a property config node or a runnable Chain or Agent node. The graph configures the properties and the flow of the agent.
Ix doesn't support all LangChain components yet, but it's easy to add new components. More will be added in subsequent releases.
- Scalable model for running a fleet of GPT agents.
- Responsive user interface for interacting with agents.
- Graphical "no-code" editor for creating agents and chains.
- Persistent storage of interactions, processes, and metrics.
- Message queue for agent jobs and inter-agent communication.
- Deployment using Docker.
- OpenAI
- Google PaLM (Experimental)
- Anthropic (Experimental)
- Python 3.11
- Django 4.2
- PostgreSQL 15.3 + pg_vector
- GraphQL / Graphene / Relay
- React 18
- LangChain
- Integrated with OpenAI GPT models
Before getting started, ensure you have the following software installed on your system:
- Windows Linux Subsystem (windows only)
- Open powershell
- run
wsl --install
to install and/or activate WSL
- git
- make
- Docker:
git clone https://github.com/kreneskyp/ix.git
cd ix
Setup config in .env
cp .env.template .env
OPENAI_API_KEY=YOUR_KEY_HERE
make dev_setup
make server
Start a worker
make worker
Visit http://localhost:8000
to access the user interface and start creating tasks for the autonomous GPT-4 agents.
The platform will automatically spawn agent processes to research and complete tasks as needed.
Run as many worker processes as you want with make worker
.
Here are some helpful commands for developers to set up and manage the development environment:
make server
: Start the application in development mode on0.0.0.0:8000
.make worker
: Start an Agent worker.
make image
: Build the Docker image.make frontend
: Rebuild the front end (GraphQL, relay, webpack).make webpack
: Rebuild JavaScript only.make webpack-watch
: Rebuild JavaScript on file changes.make dev_setup
: Builds frontend and generates database.
make migrate
: Run Django database migrations.make migrations
: Generate new Django database migration files.
make bash
: Open a bash shell in the Docker container.make shell
: Open a Django shell_plus session.