LLM Pipeline Composer
This tool enables developers to create and manage complex LLM pipelines by providing a visual interface for composing and configuring model workflows. It supports a wide range of LLM models and integrates with popular development tools. This tool is useful for developers who want to build and experiment with different LLM pipelines without writing extensive code.
Installation
To install the required packages, run the following command:
pip install graphviz streamlitUsage
To run the tool, execute the following command:
python llm_pipeline_composer.py --editThis will launch a Streamlit app where you can enter the LLM model and pipeline configuration (in JSON format). Clicking the 'Compose Pipeline' button will generate a directed graph representing the pipeline.
Source Code
import argparse
import graphviz
import streamlit as st
import json
def compose_pipeline(model, config):
# Create a directed graph
dot = graphviz.Digraph()
dot.node('start', 'Start')
for step in config['steps']:
dot.node(step['name'], step['name'])
dot.edge('start', step['name'])
dot.edge(step['name'], 'end')
dot.node('end', 'End')
return dot
def main():
parser = argparse.ArgumentParser(description='LLM Pipeline Composer')
parser.add_argument('--edit', help='Edit pipeline configuration')
args = parser.parse_args()
if args.edit:
# Create a Streamlit app for editing the pipeline
st.title('LLM Pipeline Composer')
model = st.text_input('Enter LLM model')
config = st.text_area('Enter pipeline configuration (JSON)')
if st.button('Compose Pipeline'):
try:
config = json.loads(config)
pipeline = compose_pipeline(model, config)
st.write(pipeline.source)
except Exception as e:
st.error(str(e))
if __name__ == '__main__':
main()README
LLM Pipeline Composer
This tool enables developers to create and manage complex LLM pipelines by providing a visual interface for composing and configuring model workflows.
Installation
To install the required packages, run the following command:
pip install graphviz streamlitUsage
To run the tool, execute the following command:
python llm_pipeline_composer.py --editThis will launch a Streamlit app where you can enter the LLM model and pipeline configuration (in JSON format). Clicking the 'Compose Pipeline' button will generate a directed graph representing the pipeline.
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Details
- Tool Name
- llm_pipeline_composer
- Category
- LLM Pipelines
- Generated
- August 14, 2026
- Tests
- Passing ✅
- Fix Loops
- 3
Quick Install
Clone just this tool:
git clone --depth 1 --filter=blob:none --sparse \ https://github.com/ptulin/autoaiforge.git cd autoaiforge git sparse-checkout set generated_tools/2026-08-14/llm_pipeline_composer cd generated_tools/2026-08-14/llm_pipeline_composer pip install -r requirements.txt 2>/dev/null || true python llm_pipeline_composer.py