LangGraph AgentFlow

AgentFlow is a Python library that automates the orchestration of multi-step agent workflows by integrating intelligent planning, routing, and execution of specialized operations.

Features

• Single-Step Agents: Create hierarchical agent systems with a router that delegates to specialized agents
• Multi-Step Workflows: Dynamically decompose complex tasks into sequences of steps executed by specialized agents
• Tool Integration: Seamlessly integrate external tools with agents
• Planning & Synthesis: Automatically plan task execution and synthesize results
• Built on LangGraph: Leverages LangGraph for efficient agent orchestration

Installation

pip install langgraph-agentflow

Quick Start

Single-Step Agent

Create a hierarchical agent system with a router that delegates to specialized agents:

from langchain_core.tools import Tool
from langchain_ollama import ChatOllama
from langgraph_agentflow import create_hierarchical_agent

# Initialize LLM
llm = ChatOllama(model="llama3")

# Define agent configurations
agent_config = [
    {
        "name": "news",
        "tools": news_tools,
        "description": NEWS_TOOL_DESCRIPTION,
    },
    {
        "name": "sector",
        "tools": sector_tools,
        "description": SECTOR_TOOL_DESCRIPTION,
    },
    {
        "name": "ticker",
        "tools": ticker_tools,
        "description": TICKER_TOOL_DESCRIPTION,
    },
    {
        "name": "general",
        "description": "Handles general information and queries not specific to other domains",
    },
]

# Create the agent
graph, config, stream_fn, interactive_loop = create_hierarchical_agent(llm, agent_config)

# Use the agent
stream_fn("What's the latest news about Tesla?")

Multi-Step Agent

Create a multi-step agent that breaks complex tasks into simpler subtasks:

from langchain_core.tools import Tool
from langchain_ollama import ChatOllama
from langgraph_agentflow import create_multi_step_agent, invoke_multi_step_agent

# Initialize LLM
llm = ChatOllama(model="llama3.3")

# Create the multi-step agent
agent = create_multi_step_agent(
    llm=llm,
    agent_tools=[
        {
            "name": "news",
            "tools": news_tools,
            "description": NEWS_TOOL_DESCRIPTION,
        },
        {
            "name": "sector",
            "tools": sector_tools,
            "description": SECTOR_TOOL_DESCRIPTION,
        },
        {
            "name": "ticker",
            "tools": ticker_tools,
            "description": TICKER_TOOL_DESCRIPTION,
        },
        {
            "name": "general",
            "description": "Handles general information and queries not specific to other domains",
        },
    ]
)

# Use the agent
response = invoke_multi_step_agent(
    agent, 
    "Compare the recent performance of Tesla and the overall EV market based on news"
)

Architecture

AgentFlow is built on two main architectural patterns:

1. Single-Step Agents: Router-based delegation to specialized agents

   • Router analyzes user requests and delegates to the most appropriate specialized agent
   • Each agent can access tools relevant to its domain
   • Useful for clear-cut, domain-specific tasks

2. Multi-Step Workflows: Sequential execution of agent-based subtasks

   • Planner breaks complex tasks into subtasks
   • Each subtask is routed to the appropriate specialized agent
   • Results are synthesized into a comprehensive response
   • Useful for complex tasks requiring multiple capabilities

Examples

See the examples directory for full working examples.

License

This project is licensed under the MIT License - see the LICENSE file for details.