AdalFlow

Field	Value
Developer	Sylph.AI
Language	Python
License	MIT
GitHub	SylphAI-Inc/AdalFlow
Stars	2.5K+
Used By	AdaL CLI

Overview

AdalFlow is the open-source library that powers AdaL CLI. It provides auto-differentiable LLM pipelines — meaning you can define an agent workflow and the framework will automatically optimize prompts, tool selection, and reasoning chains.

Think of it as PyTorch for LLM applications: modular, composable, and optimizable.

Key Features

• Auto-optimization — Built-in prompt tuning and pipeline optimization
• Component architecture — Generator, Retriever, Agent components that compose naturally
• Multi-model support — OpenAI, Anthropic, Google, local models
• MCP integration — Native Model Context Protocol support
• Memory management — Conversation history, compaction, session persistence
• Skills system — Reusable agent capabilities (SKILL.md format)

Architecture

AdalFlow Stack
├── Components (Generator, Retriever, Agent)
├── Optimization (Auto-diff, prompt tuning)
├── Model Clients (OpenAI, Anthropic, Google, Groq)
├── Tools (MCP, file ops, web search)
└── Memory (conversation, vector store, compaction)

When to Use AdalFlow

✅ Building a self-evolving coding agent ✅ Need automatic prompt optimization ✅ Want MCP-native tool integration ✅ Building production agents with multi-model support

Quick Example

from adalflow.core import Generator
from adalflow.components.model_client import OpenAIClient

generator = Generator(
    model_client=OpenAIClient(),
    model_kwargs={"model": "gpt-4o"},
    template="Answer the coding question: {{question}}"
)

response = generator(prompt_kwargs={"question": "How do I reverse a linked list?"})

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