# AdalFlow — Auto-Optimize LLM Applications

> A PyTorch-inspired library for building and auto-optimizing LLM task pipelines, treating prompts, tools, and retrievers as trainable parameters that can be tuned with gradient-like optimization.

## Install

Save as a script file and run:

# AdalFlow — Auto-Optimize LLM Applications

## Quick Use
```bash
pip install adalflow
# Basic usage in Python
python -c "
from adalflow.core import Generator
gen = Generator(model_client='openai', model_kwargs={'model': 'gpt-4o-mini'})
print(gen({'input_str': 'What is the capital of France?'}))
"
```

## Introduction
AdalFlow is a Python library from SylphAI that brings a PyTorch-like paradigm to LLM application development. It treats prompts, few-shot examples, tools, and retrieval components as optimizable parameters, enabling automatic prompt tuning and pipeline optimization through a training loop similar to neural network training.

## What AdalFlow Does
- Provides a composable component system (Generator, Retriever, Agent) for building LLM pipelines
- Treats prompts and few-shot demonstrations as trainable parameters that can be auto-optimized
- Implements text-gradient-based optimization and few-shot bootstrap methods for prompt tuning
- Supports evaluation and benchmarking of pipeline variants against custom datasets
- Offers a unified interface for multiple LLM providers and embedding models

## Architecture Overview
AdalFlow mirrors PyTorch's design philosophy. Components like Generator, Retriever, and Embedder are analogous to neural network layers. A pipeline assembles components into a computational graph. The Trainer runs an optimization loop: it evaluates the pipeline on training examples, computes text-based gradients (feedback on what went wrong), and uses an optimizer (an LLM) to propose improved prompts and examples. This loop continues until performance converges. The library uses a DataClass system for structured input/output handling throughout the pipeline.

## Self-Hosting & Configuration
- Install via pip with Python 3.9+; no GPU required for the optimization framework itself
- Configure LLM providers through environment variables (supports OpenAI, Anthropic, Google, Groq, Ollama)
- Define optimization targets and evaluation metrics in Python using the Trainer API
- Training datasets are simple lists of input-output examples in Python dictionaries
- Integrate with existing LLM applications by wrapping components in AdalFlow's parameter system

## Key Features
- PyTorch-like API: build, train, and optimize LLM pipelines with familiar patterns
- Auto-prompt optimization eliminates manual prompt engineering through automated feedback loops
- Few-shot bootstrap automatically selects the best demonstration examples for in-context learning
- Unified provider interface wraps OpenAI, Anthropic, Google, and local models behind consistent APIs
- Composable components can be assembled into arbitrarily complex pipelines with automatic tracing

## Comparison with Similar Tools
- **DSPy** — also does programmatic prompt optimization; AdalFlow uses a more PyTorch-familiar API and text-gradient approach
- **LangChain** — chain-building framework without optimization; AdalFlow adds trainable parameters and auto-tuning
- **Instructor** — structured output extraction; AdalFlow covers the full pipeline from retrieval to generation with optimization
- **Guidance** — constrained generation with grammars; AdalFlow focuses on pipeline-level optimization rather than output formatting
- **PromptFlow** — visual prompt pipeline builder; AdalFlow is code-first with automatic optimization capabilities

## FAQ
**Q: What does "text-gradient" optimization mean?**
A: Instead of numerical gradients, AdalFlow uses an LLM to generate textual feedback explaining why a prompt failed on certain examples. This feedback guides prompt improvements in the next iteration.

**Q: Do I need labeled training data?**
A: Yes, a small set of input-output examples (as few as 10-20) is needed for the optimization loop to evaluate and improve the pipeline.

**Q: Can I optimize retrieval components, not just prompts?**
A: Yes. Retrievers and their parameters (query templates, top-k settings) can be included in the optimization scope.

**Q: How does AdalFlow compare to fine-tuning?**
A: AdalFlow optimizes prompts and pipeline configuration without modifying model weights. It is faster and cheaper than fine-tuning while achieving significant performance gains on task-specific benchmarks.

## Sources
- https://github.com/SylphAI-Inc/AdalFlow
- https://adalflow.sylph.ai

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Source: https://tokrepo.com/en/workflows/asset-b804a92c
Author: Script Depot