| name | openserv-multi-agent-workflows |
| description | Multi-agent workflow examples to work together on the OpenServ Platform. Covers agent discovery, multi-agent workspaces, task dependencies, and workflow orchestration using the Platform Client. Read reference.md for the full API reference. Read openserv-agent-sdk and openserv-client for building and running agents. |
Multi-Agent Workflows on OpenServ
Build workflows where multiple AI agents collaborate to complete complex tasks.
Reference files:
reference.md - Workflow patterns, declarative sync, triggers, monitoringtroubleshooting.md - Common issues and solutionsexamples/ - Complete pipeline examples (blog, youtube-to-blog, etc.)
Quick Start
See examples/ for complete runnable examples:
blog-pipeline.md - Simple 2-agent workflow (research → write)content-creation-pipeline.md - 3-agent workflow (research → write → image)life-coaching-pipeline.md - Complex 6-agent workflow with comprehensive input schema
Recommended pattern using workflows.sync():
- Authenticate with
client.authenticate()
- Find agents with
client.agents.listMarketplace()
- Create workflow with
client.workflows.create() including:
- Triggers
- Tasks
- Edges (⚠️ CRITICAL - connects triggers and tasks together)
⚠️ CRITICAL: Always define edges when creating workflows. Setting task dependencies is NOT enough - you must create workflow edges to actually connect triggers to tasks and tasks to each other.
Workflow Name & Goal
When creating workflows (via workflows.create() or provision()), two properties are critical:
name (string) - This becomes the agent name in ERC-8004. Make it polished, punchy, and memorable — this is the public-facing brand name users see. Think product launch, not variable name. Examples: 'Instant Blog Machine', 'AI Video Studio', 'Polymarket Intelligence'.goal (string, required) - A detailed description of what the workflow accomplishes. Must be descriptive and thorough — short or vague goals will cause API calls to fail. Write at least a full sentence explaining the end-to-end purpose of the workflow.
Core Concepts
Workflows
A workflow (workspace) is a container that holds multiple agents and their tasks.
Task Dependencies
- Each task is assigned to a specific agent
- Tasks can depend on other tasks:
dependencies: [taskId1, taskId2]
- A task only starts when all dependencies are
done
- Output from dependencies is passed to dependent tasks
Workflow Graph
- Nodes: Triggers and tasks
- Edges: Connections between nodes
- When Task A completes, its output flows to dependent tasks via edges
Agent Discovery
const result = await client.agents.listMarketplace({ search: 'research' })
const agents = result.items
const agent = await client.agents.get({ id: 123 })
console.log(agent.capabilities_description)
Common agent types: Research (Grok, Perplexity), Content writers, Data analysis, Social media (Nano Banana Pro), Video/audio creators.
Edge Design Best Practices
CRITICAL: Carefully design your workflow edges to avoid creating tangled "spaghetti" graphs.
A well-designed workflow has clear, intentional data flow. Common mistakes lead to unmaintainable workflows.
Bad Pattern - Everything Connected to Everything
┌──────────────────────────────────┐
│ ┌─────────┐ │
│ ┌─────┤ Agent A ├─────┐ │
│ │ └────┬────┘ │ │
│ │ │ │ │
Trigger ─┼─────┼──────────┼──────────┼──────┤
│ │ │ │ │
│ │ ┌────┴────┐ │ │
│ └─────┤ Agent B ├─────┘ │
│ └─────────┘ │
└──────────────────────────────────┘
(Spaghetti - avoid this!)
This creates:
- Unclear execution order
- Difficult debugging
- Agents receiving redundant/conflicting inputs
- Hard to understand what depends on what
Good Patterns
Sequential Pipeline:
Trigger → Research → Content → Enhancement → Output
Staged Fan-Out:
┌─ Task A ─┐
Trigger → Research ─┼─ Task B ─┼─→ Combiner → Output
└─ Task C ─┘
Conditional Branching (v1.1.3+):
┌─[approved]─→ Process
Trigger → Review ──┤
└─[rejected]─→ Reject Handler
Use outputOptions on tasks and sourcePort on edges for branching.
Guidelines for Clean Workflows
- Linear is usually best: Start with a simple chain, only add complexity when truly needed
- Each task should have a clear purpose: If you can't explain why Task A connects to Task B, remove the edge
- Minimize cross-connections: Avoid connecting every agent to every other agent
- Use fan-out only for parallel work: Multiple tasks from one source is fine; connecting everything to everything is not
- One combiner at the end: If you need to merge outputs, have ONE final task that depends on all parallel branches
Before Adding an Edge, Ask:
- Does Task B actually need the output of Task A?
- Would Task B work without this connection?
- Am I adding this edge "just in case"? (Don't!)
Updating This Skill
This skill may be outdated. The OpenServ team ships updates regularly—new features, API changes, and bug fixes won't be reflected here until you update. If something isn't working as expected, or you want to make sure you have the latest information, update the skill before proceeding.
npx skills check
npx skills update
Or reinstall the OpenServ skills directly:
npx skills add openserv-labs/skills
Related Skills
- openserv-agent-sdk - Building individual agent capabilities
- openserv-client - Full Platform Client API reference
- openserv-launch - Launch tokens on Base blockchain
- openserv-ideaboard-api - Find ideas and ship agent services on the Ideaboard
