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Pipeline Teams Guide

CrewForm has three team modes. Choose the right one for your workflow:

Mode	How it works	Best for
Pipeline ← you are here	Fixed sequential steps — each agent’s output feeds the next	Structured multi-step workflows
Orchestration	A brain agent dynamically delegates to workers	Autonomous, adaptive task execution
Collaboration	Agents take turns in a shared discussion	Brainstorming, reviews, consensus-building

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How Pipelines Work

Input → Agent A → Agent B → Agent C → Final Output
         (step 1)   (step 2)   (step 3)

• The original task input
• The previous step’s output (if not the first step)
• Its own step instructions and expected output format

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Creating a Pipeline Team

1. Navigate to Teams → New Team
2. Give it a name and description
3. Select Pipeline as the team mode
4. Add steps in order — each step maps to an agent

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Step Configuration

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Example: Content Pipeline

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Step 1: Research Agent

• Agent: Research Specialist (Claude Sonnet)
• Instructions: “Research the given topic. Find 5 key facts, statistics, and expert quotes.”
• Expected Output: “Bullet-point list of findings with sources”
• On Failure: retry (max 2)

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Step 2: Writer Agent

• Agent: Content Writer (GPT-4o)
• Instructions: “Using the research provided, write a 1000-word blog post. Use an engaging, professional tone.”
• Expected Output: “Markdown-formatted blog post with headers”
• On Failure: retry (max 1)

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Step 3: Editor Agent

• Agent: Copy Editor (Claude Haiku)
• Instructions: “Review and polish the blog post. Fix grammar, improve flow, ensure factual accuracy against the research.”
• Expected Output: “Final polished blog post in Markdown”
• On Failure: stop

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Running a Pipeline

1. Go to the team detail page
2. Click Run Pipeline
3. Enter the task input (e.g., “Write a blog post about AI in healthcare”)
4. Watch each step execute in real-time

• Overall pipeline status
• Per-step status and output
• Token usage per step
• Total execution time

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Pipeline Context

## Task
[Original input from the user]

## Previous Step Output
[Output from the previous step]

## Your Instructions
[Step-specific instructions]

## Expected Output Format
[What format to respond in]

## Pipeline Context
This is step 3 in a multi-step pipeline. 2 previous steps have completed.

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Failure Handling

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Fan-Out (Parallel Branching)

Input → Agent A → [Fan-Out] → Agent B  ─┐
                             → Agent C  ─┤→ Merge Agent D → Agent E → Output
                             → Agent D  ─┘
                  (parallel branches)    (merge)

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Creating a Fan-Out Step

1. In the pipeline step list, click + Add Fan-Out Step
2. Select Parallel Agents — the agents that will run concurrently
3. Select a Merge Agent — the agent that receives all branch results and synthesizes them
4. Configure Merge Instructions — how the merge agent should combine the outputs
5. Choose a Failure Mode — what happens if a branch fails

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Fan-Out Step Configuration

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Failure Modes

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Canvas Visualization

• A fan-out node splits into parallel branch edges
• Each branch agent appears as a separate node
• Branches converge at a merge node
• During execution, each branch shows its individual status (running/completed/failed)

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Example: Multi-Perspective Analysis

• Financial Analyst → evaluates ROI and risk
• Technical Reviewer → assesses feasibility
• Market Researcher → checks competitive landscape

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Pipeline Context for Merge Agents

## Fan-Out Branch Results

### Branch 1: Financial Analyst
[Financial analysis output]

### Branch 2: Technical Reviewer
[Technical assessment output]

### Branch 3: Market Researcher
[Market research output]

## Merge Instructions
Synthesize all branch results into a single strategic recommendation.

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Best Practices

1. Start simple — Begin with 2-3 steps and add complexity gradually
2. Specialized agents — Each agent should do one thing well
3. Clear handoffs — Define expected output format so the next step knows what to expect
4. Use retry for API steps — LLM APIs can have transient failures
5. Use stop for critical steps — If step 1 fails, there’s no point running step 2
6. Monitor costs — Each step uses tokens; longer pipelines cost more

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Visual Workflow Builder (Canvas)

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Canvas Features

• Drag agents from the sidebar onto the canvas to add them as steps
• Connect nodes by dragging edges to define execution order
• Delete nodes via right-click context menu or keyboard shortcut
• Drag to rearrange — reposition nodes freely; positions are saved automatically
• Glassmorphism styling — frosted glass nodes with hover lift effects
• Searchable sidebar — filter agents by name or model when you have many agents

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Live Execution Visualization

• Node states — Idle (default), Running (blue pulse), Completed (green ✓), Failed (red ✕)
• Camera auto-follow — Canvas pans smoothly to the currently executing agent
• Execution timeline — Horizontal progress rail showing step-by-step status
• Animated edges — Flowing dashed edges indicate data flow direction

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Observability Panels

• Transcript (T) — Real-time message feed with filter buttons and tool call expansion
• Tool Heatmap — Aggregated tool usage with success rates and latency stats

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Keyboard Shortcuts

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Auto-Layout

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Position Persistence

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Output Routes

1. Open Teams → [Team Name] → Settings
2. Scroll to Output Routes
3. Select one or more specific channels — or leave blank to send to all

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Monitoring

• Total tasks completed per team
• Average execution time
• Token usage breakdown by step
• Cost per pipeline run

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Related

• Orchestration Teams — Brain agent dynamically delegates to workers
• Collaboration Teams — Agents discuss and reach consensus