Pipelines

Format

name: my-pipeline
description: "What this pipeline does"
version: "1.0"
inputs:
 - name: topic
 description: "The topic to research and write about"
chains:
 - id: research
 chain: deep-researcher # Name of an existing chain
 label: "Research phase"
 inputs:
 topic: "{input.topic}" # Map pipeline input → chain input
 depth: "deep" # Literal value
 - id: write
 chain: content-engine
 label: "Writing phase"
 depends_on: [research] # Wait for research to complete
 inputs:
 topic: "{input.topic}"
 tone: "professional"
 condition: '{research} != ""' # Skip if research returned empty
output: write # Which chain's output is the pipeline result

Variable References

Dependency Graph

chains:
 # Phase 1 (parallel)
 - id: market
 chain: market-research
 inputs: { industry: "{input.industry}" }
 - id: competitors
 chain: competitive-intel
 inputs: { company: "{input.company}" }
 # Phase 2 (waits for both)
 - id: strategy
 chain: strategy-builder
 depends_on: [market, competitors]
 inputs:
 market_data: "{market}"
 competitor_data: "{competitors}"
output: strategy

Conditional Execution

- id: optional_step
 chain: advanced-analysis
 depends_on: [basic_analysis]
 condition: '{basic_analysis} contains "needs deeper investigation"'
 inputs:
 data: "{basic_analysis}"

Executing Pipelines

REST API

curl -X POST http://localhost:4242/pipelines/my-pipeline/execute \
 -H "Content-Type: application/json" \
 -d '{"input": {"topic": "AI agents"}}'
# Response: {"executionId": "..."}
# Check status
curl http://localhost:4242/pipeline-executions/{id}

MCP

Use run_pipeline with name "my-pipeline" and inputs: topic = "AI agents"

Included Demo Pipelines

Best Practices

See Also