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:
- LangChain: github.com/langchain-ai/langchain - The foundation for LLM applications
- LangGraph: github.com/langchain-ai/langgraph - Our underlying orchestration engine
- CrewAI: github.com/joaomdmoura/crewAI - Inspiration for our agent patterns
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
Project details
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