Implement LangGraph multi-agent system with n8n workflow integration

[Copilot]

Build a multi-agent AI system combining LangGraph for stateful agent orchestration with n8n for visual workflow automation. Implements 7 agentic patterns with FastAPI integration and Docker deployment.

LangGraph Core (langgraph/)

• Agents: Base class + 5 specialists (Researcher, Writer, Reviewer, Router, Aggregator)
• Graph Patterns: Sequential, Parallel, Loop, Router, Aggregator, Hierarchical, Network
• State Management: Checkpointing, message history, quality scoring

n8n Workflows (n8n/)

• Main orchestrator, parallel processor, approval workflow, data pipeline, trigger webhook
• Pre-configured for LangGraph API integration

Integration Layer (integration/)

• FastAPI server with endpoints for all patterns
• Webhook handlers for n8n callbacks

Infrastructure

• docker-compose.yml: LangGraph API, n8n, Redis, PostgreSQL
• Full documentation in docs/

Usage

# Docker deployment
docker-compose up -d

# CLI
python -m langgraph.main --pattern sequential --task "Write about AI"

# API
curl -X POST http://localhost:8000/api/v1/run \
  -H "Content-Type: application/json" \
  -d '{"task": "Analyze market trends", "pattern": "router"}'

from langgraph.graphs import create_loop_graph

graph = create_loop_graph(quality_threshold=0.9, max_iterations=5)
result = graph.invoke({"current_task": "Create executive summary", ...})

Original prompt

Overview

Build a fully integrated AI system using multiple agents organized in agentic architecture patterns, leveraging both n8n (visual workflow automation) and LangGraph (stateful agent orchestration).

Requirements

1. LangGraph Multi-Agent System

Create a Python-based multi-agent system implementing these patterns:

Agent Patterns to Implement:

• Sequential Pattern: Agents working in chain order (e.g., Researcher → Writer → Reviewer)
• Parallel Pattern: Multiple agents processing simultaneously
• Loop Pattern: Circular flow for iterative improvement until quality threshold
• Router Pattern: Agent that directs inputs to specialized paths
• Aggregator Pattern: Consolidates multiple agent outputs
• Hierarchical Pattern: Manager-worker structure with delegation
• Network Pattern: Interconnected agents with bidirectional communication

Core Components:

langgraph/
├── agents/
│   ├── __init__.py
│   ├── base_agent.py          # Base agent class
│   ├── researcher_agent.py    # Research & data gathering
│   ├── writer_agent.py        # Content generation
│   ├── reviewer_agent.py      # Quality review & feedback
│   ├── router_agent.py        # Task routing logic
│   └── aggregator_agent.py    # Output consolidation
├── graphs/
│   ├── __init__.py
│   ├── sequential_graph.py    # Sequential workflow
│   ├── parallel_graph.py      # Parallel execution
│   ├── loop_graph.py          # Iterative refinement
│   ├── router_graph.py        # Smart routing pattern
│   ├── aggregator_graph.py    # Output consolidation pattern
│   ├── hierarchical_graph.py  # Manager-worker pattern
│   └── network_graph.py       # Interconnected agents
├── state/
│   ├── __init__.py
│   └── agent_state.py         # State management & checkpointing
├── tools/
│   ├── __init__.py
│   └── shared_tools.py        # Tools accessible by all agents
├── config.py                  # Configuration settings
└── main.py                    # Entry point

State Management:

• Implement persistent state with checkpointing
• Support for human-in-the-loop interrupts
• Message history and context passing between agents

2. n8n Workflow Integration

Create n8n workflow configurations for visual orchestration:

Workflows to Create:

n8n/
├── workflows/
│   ├── main_orchestrator.json      # Main workflow orchestrating agents
│   ├── parallel_processor.json     # Parallel task execution
│   ├── approval_workflow.json      # Human-in-the-loop approval
│   ├── data_pipeline.json          # Data ingestion & processing
│   └── langgraph_trigger.json      # Webhook to trigger LangGraph
├── credentials/
│   └── credentials_template.json   # Template for API credentials
└── README.md                       # n8n setup instructions

Key Features:

• Webhook triggers for event-driven activation
• Switch/Router nodes for conditional routing
• Wait nodes for human approval workflows
• HTTP Request nodes to call LangGraph API
• Merge nodes for aggregating outputs

3. Hybrid Architecture (n8n + LangGraph)

Create an integration layer connecting both systems:

integration/
├── api/
│   ├── __init__.py
│   ├── server.py              # FastAPI server exposing LangGraph
│   └── routes.py              # API endpoints for n8n to call
├── webhooks/
│   ├── __init__.py
│   └── handlers.py            # Webhook handlers
└── docker-compose.yml         # Docker setup for both services

4. Infrastructure & Configuration

Docker Setup:

• docker-compose.yml with services for:
  • n8n (workflow automation)
  • LangGraph API (FastAPI)
  • Redis (for state/caching)
  • PostgreSQL (for n8n persistence)

Configuration Files:

• .env.example with required environment variables
• requirements.txt for Python dependencies
• pyproject.toml for project metadata

5. Documentation

Create comprehensive documentation:

docs/
├── architecture.md            # System architecture overview
├── langgraph_guide.md         # LangGraph usage guide
├── n8n_guide.md               # n8n workflow guide
├── patterns.md                # Agentic patterns explained
└── deployment.md              # Deployment instructions

Update README.md with:

• Project overview
• Architecture diagram (ASCII or mermaid)
• Quick start guide
• Pattern descriptions
• API documentation

6. Example Use Cases

Implement example use cases demonstrating the patterns:

1. Content Pipeline (Sequential): Research → Draft → Review → Publish
2. Multi-Source Analysis (Parallel): Analyze multiple data sources simultaneously
3. Iterative Refinement (Loop): Generate and improve until quality threshold
4. Smart Routing (Router): Route queries to specialized agents
5. Report Generation (Aggregator): Combine insights from multiple agents

Technical Requirements

Dependencies:

• Python 3.11+
• langgraph >= 0.2.0
• langchain >= 0.2.0
• langchain-openai
• fastapi
• uvicorn
• pydantic
• redis...

This pull request was created as a result of the following prompt from Copilot chat.

Overview

Build a fully integrated AI system using multiple agents organized in agentic architecture patterns, leveraging both n8n (visual workflow automation) and LangGraph (stateful agent orchestration).

Requirements

1. LangGraph Multi-Agent System

Create a Python-based multi-agent system implementing these patterns:

Agent Patterns to Implement:

• Sequential Pattern: Agents working in chain order (e.g., Researcher → Writer → Reviewer)
• Parallel Pattern: Multiple agents processing simultaneously
• Loop Pattern: Circular flow for iterative improvement until quality threshold
• Router Pattern: Agent that directs inputs to specialized paths
• Aggregator Pattern: Consolidates multiple agent outputs
• Hierarchical Pattern: Manager-worker structure with delegation
• Network Pattern: Interconnected agents with bidirectional communication

Core Components:

langgraph/
├── agents/
│   ├── __init__.py
│   ├── base_agent.py          # Base agent class
│   ├── researcher_agent.py    # Research & data gathering
│   ├── writer_agent.py        # Content generation
│   ├── reviewer_agent.py      # Quality review & feedback
│   ├── router_agent.py        # Task routing logic
│   └── aggregator_agent.py    # Output consolidation
├── graphs/
│   ├── __init__.py
│   ├── sequential_graph.py    # Sequential workflow
│   ├── parallel_graph.py      # Parallel execution
│   ├── loop_graph.py          # Iterative refinement
│   ├── router_graph.py        # Smart routing pattern
│   ├── aggregator_graph.py    # Output consolidation pattern
│   ├── hierarchical_graph.py  # Manager-worker pattern
│   └── network_graph.py       # Interconnected agents
├── state/
│   ├── __init__.py
│   └── agent_state.py         # State management & checkpointing
├── tools/
│   ├── __init__.py
│   └── shared_tools.py        # Tools accessible by all agents
├── config.py                  # Configuration settings
└── main.py                    # Entry point

State Management:

• Implement persistent state with checkpointing
• Support for human-in-the-loop interrupts
• Message history and context passing between agents

2. n8n Workflow Integration

Create n8n workflow configurations for visual orchestration:

Workflows to Create:

n8n/
├── workflows/
│   ├── main_orchestrator.json      # Main workflow orchestrating agents
│   ├── parallel_processor.json     # Parallel task execution
│   ├── approval_workflow.json      # Human-in-the-loop approval
│   ├── data_pipeline.json          # Data ingestion & processing
│   └── langgraph_trigger.json      # Webhook to trigger LangGraph
├── credentials/
│   └── credentials_template.json   # Template for API credentials
└── README.md                       # n8n setup instructions

Key Features:

• Webhook triggers for event-driven activation
• Switch/Router nodes for conditional routing
• Wait nodes for human approval workflows
• HTTP Request nodes to call LangGraph API
• Merge nodes for aggregating outputs

3. Hybrid Architecture (n8n + LangGraph)

Create an integration layer connecting both systems:

integration/
├── api/
│   ├── __init__.py
│   ├── server.py              # FastAPI server exposing LangGraph
│   └── routes.py              # API endpoints for n8n to call
├── webhooks/
│   ├── __init__.py
│   └── handlers.py            # Webhook handlers
└── docker-compose.yml         # Docker setup for both services

4. Infrastructure & Configuration

Docker Setup:

• docker-compose.yml with services for:
  • n8n (workflow automation)
  • LangGraph API (FastAPI)
  • Redis (for state/caching)
  • PostgreSQL (for n8n persistence)

Configuration Files:

• .env.example with required environment variables
• requirements.txt for Python dependencies
• pyproject.toml for project metadata

5. Documentation

Create comprehensive documentation:

docs/
├── architecture.md            # System architecture overview
├── langgraph_guide.md         # LangGraph usage guide
├── n8n_guide.md               # n8n workflow guide
├── patterns.md                # Agentic patterns explained
└── deployment.md              # Deployment instructions

Update README.md with:

• Project overview
• Architecture diagram (ASCII or mermaid)
• Quick start guide
• Pattern descriptions
• API documentation

6. Example Use Cases

Implement example use cases demonstrating the patterns:

1. Content Pipeline (Sequential): Research → Draft → Review → Publish
2. Multi-Source Analysis (Parallel): Analyze multiple data sources simultaneously
3. Iterative Refinement (Loop): Generate and improve until quality threshold
4. Smart Routing (Router): Route queries to specialized agents
5. Report Generation (Aggregator): Combine insights from multiple agents

Technical Requirements

Dependencies:

• Python 3.11+
• langgraph >= 0.2.0
• langchain >= 0.2.0
• langchain-openai
• fastapi
• uvicorn
• pydantic
• redis
• python-dotenv

n8n Requirements:

• n8n >= 1.0.0
• Docker & Docker Compose

Success Criteria

• All 7 agent patterns implemented and functional
• n8n workflows importable and executable
• API integration between n8n and LangGraph working
• Docker Compose brings up entire system with one command
• Documentation complete and accurate
• Example use cases demonstrable

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[Stacey77]

@copilot open gui interface

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