Multi-Agent System Topology Harness
Visually compose agent graphs: add agent nodes, wire sequential, parallel and conditional edges, drag to rearrange, then export a formatted topology and a Python skeleton.
Professional Insights & Guide
The graph-theory and orchestration logic behind multi-agent topologies, with worked wiring patterns and failure points.
Core Use Case scenario
A multi-agent system is a directed graph: each agent is a node carrying a name, role, model, and temperature, and each edge is a hand-off. A sequential edge means B starts only after A returns. A parallel edge means several downstream agents run concurrently on the same context, trading latency for fan-out. A conditional edge routes to one of two targets based on a flag or score, which is how you build decision trees such as retry-on-failure or route-by-language.
Troubleshooting & Edge-Case Failure Points
- Self-referencing edges create infinite loops — a source must never equal its own target.
- Orphan agents (no incoming edge) never receive context, so their output never reaches the pipeline.
- Parallel fan-out without a join step produces multiple streams that downstream sequential steps cannot consume atomically.
- Dead-end branches (no path to a terminal node) silently drop work and inflate token spend.
Detailed Step-by-Step Instructions
- Fill the Agent Nodes form with a name, role, model, and temperature, then press Add Node or click directly on the canvas to place it.
- Select a node on the canvas to load its values back into the form for editing, or press Delete Selected to remove it.
- Pick a source, target, and edge type, then press Add Edge to wire a hand-off between agents.
- Drag nodes to lay out the graph so edges stay readable; the canvas re-clamps nodes inside the viewport automatically.
- Review the formatted topology text and Python skeleton, then use the copy buttons to paste them into your orchestration codebase.
Topology Rules Used
edge(A, B, sequential) -> B runs only after A completes
edge(A, {B, C}, parallel) -> B and C run concurrently on A's context
edge(A, B|C, conditional) -> branch on a flag/score from A's output
in-degree 0 -> orphan agent (never receives context)
out-degree 0 -> sink node (terminal output)
cycles -> reject self-loops; guard long chains
Informative Guides & Helper Articles
Ultimate Guide to Multi-Agent System Topology
How topology shapes agent systems: node roles, sequential vs. parallel vs. conditional edges, and when each wiring pattern wins.
Read Article →Common Errors in Multi-Agent System Topology
Self-referencing edges, orphan agents, parallel fan-out mistakes, and dead-end branches that stall pipelines.
Read Article →Top Optimization Tips for Multi-Agent Topology
Cut redundant hops, parallelize independent tasks, and size context hand-offs to trim cost and latency.
Read Article →Future Trends in Multi-Agent System Topology
Graph-native orchestration, dynamic routing, self-healing topologies, and visual agent blueprints.
Read Article →How to Use the Multi-Agent System Topology Harness
Designs multi-agent systems visually - roles, models, edges - and exports topology text plus a Python skeleton.
- Add agent nodes: name, role, model, temperature.
- Connect sequential, parallel, or conditional edges.
- Export topology + skeleton; prototype routing before writing tool code.
Sequential vs Parallel vs Conditional Edges
The three edge types encode the design decision: sequential (pipeline - simplest, easiest debug), parallel (fan-out/join for independent subtasks - costs coordination), conditional (router decides - most powerful, most failure-prone). Rules from shipping teams: start sequential, parallelize only measured bottlenecks; keep router destinations to 3-5; one verb per agent role ("critique", "extract", "draft"). Fan-in needs an explicit join policy (wait-all, first-wins, merge) or you debug race conditions that look like model errors.
Multi-Agent System Topology Harness FAQ
When should I use parallel agents?
Only for genuinely independent subtasks. Parallelism adds coordination cost and race conditions that masquerade as model failures.
What is a conditional edge?
A router edge choosing the next agent at runtime. Keep destinations to 3-5 and log every routing decision.
One agent with many tools or several agents?
Split when verbs differ (drafting vs verifying); keep together when steps share context - re-passing context costs tokens and fidelity.