construct is a Rust framework for defining, validating, and instantiating multi-agent system topologies using a declarative specification. Instead of hardcoding agent relationships in application logic, you describe the topology — nodes (agents), edges (communication channels), and capabilities — as data, and construct materializes it into a running graph.
Modern AI systems rarely consist of a single agent. Orchestrating multiple specialized agents — a planner, a researcher, a critic, a tool-caller — requires explicit topology management. Hardcoding these relationships leads to brittle systems where adding or removing an agent means rewriting routing logic.
Declarative topology solves three problems:
- Separation of concerns — The what (agent capabilities) is decoupled from the how (message routing, lifecycle).
- Runtime reconfiguration — Topologies can be swapped without recompilation, enabling A/B testing of agent graphs.
- Formal verification — A declarative spec can be validated for cycles, deadlocks, and capability coverage before deployment.
A topology is a directed graph G = (V, E) where:
- V is the set of agent nodes, each with a capability vector cᵢ ∈ {0,1}ᵏ over k capability dimensions.
- E ⊆ V × V is the set of communication edges representing message-passing channels.
Given a graph G, the framework validates:
| Property | Method | Complexity |
|---|---|---|
| Acyclicity | DFS-based cycle detection | O(V + E) |
| Connectivity | Union-Find on edge set | O(E · α(V)) ≈ O(E) |
| Capability coverage | Set union over reachable nodes | O(V · k) |
| Deadlock freedom | Detect sinks with no handler | O(V) |
Where α is the inverse Ackermann function (amortized near-constant).
The declarative spec is compiled into an adjacency-list representation:
Topology → Graph { nodes: Vec<AgentNode>, edges: Vec<(NodeId, NodeId)> }
Each AgentNode carries its capability vector and a factory closure for instantiation.
[dependencies]
construct = "0.1"use construct::stub;
fn main() {
println!("{}", stub::hello());
// => "hello from construct"
}pub fn hello() -> &'static str— Returns the framework greeting string. Placeholder for the full topology builder API currently under development.
// Define a topology declaratively
let topology = Topology::builder()
.node("planner", Capability::Planning)
.node("researcher", Capability::Search)
.node("critic", Capability::Verification)
.edge("planner", "researcher")
.edge("researcher", "critic")
.edge("critic", "planner") // feedback loop
.build()?;
// Validate before deployment
topology.validate()?; // checks acyclicity (if required), coverage, etc.
// Instantiate
let graph = topology.instantiate(&runtime);construct is part of the SuperInstance ecosystem, where the γ + η = C principle applies:
- γ (gamma): The declarative topology specification — the static description of what the agent graph should be.
- η (eta): The runtime instantiation — the dynamic, running agent graph with live message passing.
- C (Configuration): The emergent system behavior that arises when the topology specification (γ) meets the runtime environment (η).
The construct crate provides γ (the specification language and validator). The runtime engine (η) consumes validated topologies and manages their lifecycle. Together they yield C — a correctly-configured multi-agent system whose behavior is predictable, auditable, and modifiable at the topology level.
This separation mirrors the classical policy vs. mechanism split in operating systems: the topology declares policy (who talks to whom, who does what), while the runtime provides mechanism (message queues, lifecycle hooks, failure handling).
- Bond, A. H., & Gasser, L. (1988). Readings in Distributed Artificial Intelligence. Morgan Kaufmann. — Foundational treatment of multi-agent coordination topologies.
- Durfee, E. H., Lesser, V. R., & Corkill, D. D. (1987). "Coherent Cooperation Among Communicating Problem Solvers." IEEE Transactions on Computers, 36(11), 1275–1291. — Partial global planning and agent network topology.
- Sycara, K. P. (1998). "Multiagent Systems." AI Magazine, 19(2), 79–92. — Survey of multi-agent architectures and communication patterns.
- Stone, P., & Veloso, M. (2000). "Multiagent Systems: A Survey from a Machine Learning Perspective." Autonomous Robots, 8(3), 345–383. — Taxonomy of agent topologies and learning in multi-agent settings.
- Werbos, P. J. (2009). "Intelligence in the Brain: A Theory of How it Works and How to Build it." Neural Networks, 22(3), 200–212. — Recurrent cognitive topologies relevant to agent graph design.
- Tarjan, R. E. (1972). "Depth-First Search and Linear Graph Algorithms." SIAM Journal on Computing, 1(2), 146–160. — O(V + E) cycle detection and strongly connected components.
- Cormen, T. H., et al. (2022). Introduction to Algorithms, 4th ed., Ch. 21 (Disjoint Sets) and Ch. 22 (Graph Algorithms). MIT Press. — Union-Find and graph traversal foundations.
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