automation-orchestration

name	automation-orchestration
description	Workflow automation and multi-agent orchestration tools. Use when the user mentions n8n, Langflow, claude-squad, container-use, Dagger, workflow automation, pipeline orchestration, multi-agent coordination, or wants to set up automated workflows. Also trigger for 'automate this process', 'create a workflow', 'run agents in parallel', or integration of multiple tools into a pipeline.

Automation & Orchestration

Tools and patterns for workflow automation, LLM pipelines, and multi-agent coordination.

n8n - Visual Workflow Automation

Self-hosted workflow automation with 400+ integrations. Connects APIs, databases, and services with a visual editor.

Setup

npx n8n                              # Quick start
# Or with Docker (production)
docker run -it --rm -p 5678:5678 n8nio/n8n

Core Concepts

• Nodes: Individual operations (HTTP request, transform, filter)
• Triggers: Start workflows (webhook, cron, file change, email)
• Connections: Data flow between nodes
• Credentials: Stored authentication for services
• Code Nodes: Custom JavaScript or Python for complex logic

Common Patterns

Webhook Trigger → Validate Input → Process Data → Send Result
Cron Schedule → Fetch Data → Transform → Store in DB → Notify
File Upload → Extract Text → AI Analysis → Email Report
GitHub PR → Run Tests → Post Results → Update Status

When to Use

• Connecting multiple SaaS services (Slack, GitHub, Gmail, Notion)
• Scheduled data processing and reporting
• Event-driven automation (webhook triggers)
• Non-developer-friendly workflow creation

Langflow - Visual LLM Pipeline Builder

Drag-and-drop builder for RAG pipelines, agent chains, and LLM applications.

Setup

pip install langflow
langflow run                          # Opens at localhost:7860

Core Concepts

• Components: Modular building blocks (LLMs, embeddings, vector stores, tools)
• Flows: Connected component graphs forming a pipeline
• Custom Components: Python classes extending base components
• API Export: Deploy flows as REST APIs

Common Patterns

Document Loader → Text Splitter → Embeddings → Vector Store → RAG Chain
User Input → Prompt Template → LLM → Output Parser → Structured Response
Agent → Tool Selection → Execution → Memory → Response

When to Use

• Building RAG applications visually
• Prototyping LLM chains before coding them
• Non-code-first LLM pipeline development
• Quick comparison of different LLM configurations

claude-squad - Multi-Agent Terminal Orchestration

Run multiple Claude Code instances in parallel, each with its own git worktree.

Setup

# Install (requires Go)
go install github.com/smtg-ai/claude-squad@latest

# Or download binary from GitHub releases

Core Concepts

• Sessions: Independent Claude Code instances
• Worktrees: Each agent gets its own git worktree (isolated branches)
• TUI Dashboard: Monitor all agents from a single terminal
• Auto-merge: Combine agent outputs back to main branch

Usage

claude-squad                          # Launch TUI dashboard
# Create new session: press 'n'
# View session: press 'Enter'
# Toggle diff: press 'd'

When to Use

• Large refactoring tasks that touch many independent files
• Parallel feature development across modules
• Research tasks where multiple agents investigate different angles
• Any task that decomposes into independent, parallelizable subtasks

container-use - Containerized Agent Execution (Dagger)

Run AI agents in isolated Docker containers for safe code execution.

Setup

// MCP configuration
{
  "container-use": {
    "command": "npx",
    "args": ["-y", "@anthropic/container-use-mcp"],
    "env": {
      "DOCKER_HOST": "unix:///var/run/docker.sock"
    }
  }
}

Core Concepts

• Sandboxed execution: Agent code runs in containers, not on host
• Reproducible environments: Dockerfile defines exact dependencies
• MCP integration: Available as MCP server for Claude Code
• Resource limits: CPU, memory, network constraints per container

When to Use

• Running untrusted or experimental code safely
• Reproducible build and test environments
• Isolating agent actions from the host system
• CI/CD-like workflows triggered by AI agents

Orchestration Patterns

Fan-Out / Fan-In

Distribute work to multiple agents, aggregate results:

Coordinator
├── Agent A → Result A ─┐
├── Agent B → Result B ──┼→ Aggregate → Final Output
└── Agent C → Result C ─┘

Use when: Tasks are independent and can run in parallel.

Pipeline (Sequential Stages)

Each stage processes and passes to the next:

Research → Plan → Implement → Test → Review → Deploy

Use when: Each stage depends on the previous stage's output.

Event-Driven

Agents react to events rather than being explicitly scheduled:

File Change → Lint Agent
PR Created → Review Agent
Test Failure → Debug Agent
Deploy Success → Monitor Agent

Use when: Responses should be triggered by external events.

Supervisor / Worker

One coordinator manages many workers:

Supervisor (assigns tasks, monitors progress)
├── Worker 1 (executes assigned task)
├── Worker 2 (executes assigned task)
└── Worker 3 (executes assigned task)

Use when: Tasks need dynamic assignment and coordination.

Map-Reduce

Split input, process in parallel, combine results:

Input Data → Split into chunks
├── Process chunk 1 → Partial result
├── Process chunk 2 → Partial result
└── Process chunk 3 → Partial result
Combine partial results → Final output

Use when: Processing large datasets or documents.

Tool Comparison

Tool	Best For	Setup	Code Required
n8n	SaaS integrations	Docker/npx	Low (visual)
Langflow	LLM pipelines	pip install	Low (visual)
claude-squad	Parallel Claude agents	Go binary	None
container-use	Safe code execution	Docker + MCP	Low

Choosing the Right Tool

• "Connect Slack to GitHub to Notion" → n8n
• "Build a RAG pipeline with embeddings" → Langflow
• "Refactor 10 files in parallel" → claude-squad
• "Run untrusted code safely" → container-use
• "Complex multi-step agent workflow" → Combine tools as needed