hero.mp4
blocks.mp4
visualization.mp4
evals.mp4
optimization.mp4
- Easy-to-hack, eg., one can add new workflow nodes by simply creating a single Python file.
- JSON configs of workflow graphs, enabling easy sharing and version control.
- Lightweight via minimal dependencies, avoiding bloated LLM frameworks.
You can get PySpur up and running in three quick steps.
-
Clone the repository:
git clone https://github.com/PySpur-com/PySpur.git cd pyspur -
Start the docker services:
sudo docker compose up --build -dThis will start a local instance of PySpur that will store spurs and their runs in a local SQLite file.
-
Access the portal:
Go to
http://localhost:6080/in your browser.Enter
pyspur/canaryhattanas username/password. -
Add your LLM provider keys:
Go to the settings menu on the top right corner of the portal
Select API keys tab
Enter your provider's key and click save (save button will appear after you add/modify a key)
Set up is completed. Click on "New Spur" to create a workflow, or start with one of the stock templates.
PySpur can work with local models served using Ollama.
Steps to configure PySpur to work with Ollama running on the same host.
To ensure Ollama API is reachable from PySpur, we need to start the Ollama service with environment variable OLLAMA_HOST=0.0.0.0 . This allows requests coming from PySpur docker's bridge network to get through to Ollama.
An easy way to do this is to launch the ollama service with the following command:
OLLAMA_HOST="0.0.0.0" ollama serve
Next up we need to update the OLLAMA_BASE_URL environment value in the .env file.
If your Ollama port is 11434 (the default port), then the entry in .env file should look like this:
OLLAMA_BASE_URL=http://host.docker.internal:11434
(Please make sure that there is no trailing slash in the end!)
In PySpur's set up, host.docker.internal refers to the host machine where both PySpur and Ollama are running.
Follow the usual steps to launch the PySpur app, starting with the command:
docker compose up --build -d
You will be able to select Ollama models [ollama/llama3.2, ollama/llama3, ...] from the sidebar for LLM nodes.
Please make sure the model you select is explicitly downloaded in ollama. That is, you will need to manually manage these models via ollama. To download a model you can simply run ollama pull <model-name>.
PySpur only works with models that support structured-output and json mode. Most newer models should be good, but it would still be good to confirm this from Ollama documentation for the model you wish to use.
- Canvas
- Async/Batch Execution
- Evals
- Spur API
- Support Ollama
- New Nodes
- LLM Nodes
- If-Else
- Merge Branches
- Tools
- Loops
- Pipeline optimization via DSPy and related methods
- Templates
- Compile Spurs to Code
- Multimodal support
- Containerization of Code Verifiers
- Leaderboard
- Generate Spurs via AI
Your feedback will be massively appreciated. Please tell us which features on that list you like to see next or request entirely new ones.