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A LangChain-based crew management system

Project description

LangCrew

High-level multi-agent framework built on LangGraph - combines CrewAI's simplicity with enterprise-grade capabilities

LangCrew bridges the gap between LangGraph's flexibility and CrewAI's simplicity, offering pre-built agent orchestration, memory management, and production-ready features that eliminate the complexity of multi-agent development.

Quick Start

Install LangCrew:

pip install langcrew

Create your first multi-agent workflow:

import os
# Note: You'll need to install: pip install langchain-openai
from langchain_openai import ChatOpenAI
from langcrew import Agent, Task, Crew

# Create agents
researcher = Agent(
    role="Research Analyst",
    goal="Find and analyze information about any topic",
    backstory="You excel at finding key information and insights",
    llm=ChatOpenAI(model="gpt-4.1", api_key=os.getenv("OPENAI_API_KEY"))
)

writer = Agent(
    role="Content Writer", 
    goal="Create engaging content based on research",
    backstory="You're skilled at turning complex information into clear, compelling content",
    llm=ChatOpenAI(model="gpt-4.1", api_key=os.getenv("OPENAI_API_KEY"))
)

# Define tasks
research_task = Task(
    description="Research the latest trends in {topic}",
    agent=researcher,
    expected_output="A comprehensive analysis of current trends"
)

write_task = Task(
    description="Write a blog post about the research findings",
    agent=writer,
    expected_output="A well-structured blog post"
)

# Create and run crew
crew = Crew(
    agents=[researcher, writer],
    tasks=[research_task, write_task]
)

result = crew.kickoff(inputs={"topic": "AI agents"})
print(result)

That's it! Your agents will collaborate to research and write about any topic you choose.

Core Capabilities

Intelligent Orchestration - LangGraph-Powered Workflow Engine

State-driven agent coordination with optimized graph compilation, supporting sequential pipelines, conditional routing, and parallel execution patterns. Built-in context propagation ensures seamless information flow between agents.

Multi-Layer Memory Architecture - Persistent Knowledge Systems

Hierarchical memory with short-term, long-term, and entity storage. Native PostgreSQL/Redis backends with vector similarity search. Thread-based conversation continuity and cross-session knowledge retention.

Human-in-the-Loop Workflows - Enterprise HITL Integration

Configurable interruption points with approval mechanisms. Supports pre/post task interventions, critical decision checkpoints, and async human feedback loops. Full bilingual UI components for seamless interaction.

Real-time Event Streaming - Async Execution & Monitoring

Token-level streaming with granular event dispatch. Monitor agent thoughts, tool calls, and intermediate results in real-time. Supports WebSocket/SSE for live UI updates with v2 event protocol.

Flexible Task Execution - Advanced Orchestration Patterns

Dynamic task dependencies with context inheritance. Conditional execution based on outputs, retry mechanisms with exponential backoff, and parallel task processing with result aggregation.

Documentation

Core Concepts

  • Agents: Learn about intelligent agent creation and configuration
  • Tasks: Understand task definition and orchestration
  • Crews: Master multi-agent coordination and workflows

Related Projects

LangCrew builds on the shoulders of giants:

Contributing

You are welcome to open issues or submit PRs to improve this app, however, please note that we may not review all suggestions.

License

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

Next Steps

Learn More: Complete Documentation | Examples | API Reference

Ready-to-Use: Recruitment System | Marketing Strategy | Travel Planning

Get Help: GitHub Issues | Discussions

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