What is AgentFlow?¶

The 30-Second Explanation¶

AgentFlow is a Python framework for building AI agents and orchestrating multi-agent workflows.

An AI agent is a program that:

Listens — receives input (user message, event, API call)
Thinks — uses an LLM (Gemini, GPT-4, Claude) to reason
Acts — calls tools, generates output, or triggers other agents
Loops — repeats until the task is complete

AgentFlow gives you the graph-based runtime to wire all of that together, so you focus on your logic — not on orchestration plumbing.

Why Does This Matter?¶

Without a framework, building a production agent means manually handling:

Conversation state across multiple turns
Tool discovery, calling, and result injection
Routing decisions (which step runs next?)
Error handling and retries
Memory and checkpointing
Streaming responses to clients
Multi-agent coordination

That's months of infrastructure work. AgentFlow provides it all out of the box.

Real-World Use Cases¶

Use Case	What the Agent Does
Customer Support Bot	Reads queries → searches knowledge base → drafts reply
Code Review Agent	Receives PR diff → analyzes code → suggests improvements
Research Assistant	Gets a topic → searches web → reads articles → summarizes
Data Pipeline Agent	Gets a task → queries DB → transforms data → writes report
Multi-Agent Team	Orchestrator delegates tasks to specialized sub-agents

How AgentFlow Works¶

AgentFlow is built around a StateGraph — a directed graph where:

Nodes are processing steps (your agent, your tools, your logic)
Edges define what runs next (fixed or conditional)
State flows through every node, carrying messages and context

User Message
     ↓
  [MAIN node]    ← Agent (LLM) thinks about what to do
     ↓
  [TOOL node]    ← Tool executes (e.g., searches database)
     ↓
  [MAIN node]    ← Agent sees tool result, generates final answer
     ↓
  END → Response

Every time you call app.invoke(...), the graph runs — routing through nodes, executing tools, and stopping when complete.

What Makes AgentFlow Different?¶

Provider-Agnostic¶

Use the official SDK for your LLM provider. AgentFlow doesn't force you through a wrapper:

# Google Gemini
Agent(model="google/gemini-2.5-flash", ...)

# OpenAI GPT-4
Agent(model="openai/gpt-4o", ...)

# Anthropic Claude
Agent(model="anthropic/claude-3-5-sonnet-20241022", ...)

All work with the same graph code. Switching providers is one line.

Production-Ready Out of the Box¶

Feature	Description
Checkpointing	InMemory (dev) or PostgreSQL + Redis (prod)
Streaming	Real-time token streaming to clients
Human-in-the-loop	Pause execution, await human input, resume
Async-first	Native async/await, parallel tool execution
Observability	Built-in event publishers (Console, Redis, Kafka)
Multi-agent	Agent handoff and collaborative pipelines

Minimal Boilerplate¶

# This is a complete, working tool-calling agent:
from agentflow.graph import Agent, StateGraph, ToolNode
from agentflow.state import Message

def search(query: str) -> str:
    return f"Results for: {query}"

graph = StateGraph()
graph.add_node("MAIN", Agent(model="google/gemini-2.5-flash", tool_node_name="TOOL"))
graph.add_node("TOOL", ToolNode([search]))
graph.set_entry_point("MAIN")

app = graph.compile()
result = app.invoke({"messages": [Message.text_message("Search Python tutorials")]})

What You Need to Know¶

Prerequisites¶

Python basics — functions, classes, async/await
Command line — running pip install and python script.py
An API key — from Google, OpenAI, or Anthropic

You Do NOT Need¶

Prior experience with LangChain, LlamaIndex, or other frameworks
Graph theory or advanced architecture knowledge
Databases or infrastructure (use in-memory mode to start)

Comparison¶

	AgentFlow	LangChain	AutoGen
Learning curve	Low	High	Medium
Provider flexibility	Any SDK	Via LangChain adapters	Via model wrappers
Production checkpointing	Built-in	Built-in	Limited
Multi-agent	Built-in	Built-in	Core feature
TypeScript client	Built-in	Separate package	None
First agent in	5 min	20–30 min	15 min

Your Learning Path¶

What is AgentFlow? ← YOU ARE HERE
         ↓
   Installation (pick your LLM provider)
         ↓
   Hello World (your first working agent with tools)
         ↓
   Core Concepts (5 building blocks explained)
         ↓
   Tutorials (memory, RAG, multi-agent, streaming...)

Ready? Let's install AgentFlow →