Open Arsenal

Swarm logic, graph-structured.

ThreadGraph is a modular, graph-structured agent swarm runtime designed to turn complex tasks into executable systems rather than linear prompt chains. Instead of relying on a single model or sequential agent workflows, it decomposes problems into structured execution graphs where each node represents an agent, tool, memory operation, or routing decision. This allows intelligence to be expressed as a system of connected, parallelizable components that can be observed, modified, and replayed.

At its core, ThreadGraph introduces a swarm execution model where multiple specialized agents operate simultaneously under a shared orchestration layer. Tasks are dynamically broken down into subgraphs, enabling parallel reasoning, multi-path exploration, and structured synthesis of results. A built-in coordination layer handles conflict resolution, confidence scoring, and final aggregation, ensuring outputs are not just generated, but reconciled and validated across multiple perspectives.

The system is built around strict modularity. Agents, tools, schedulers, and memory systems are all independent plugins that can be swapped, extended, or versioned without modifying the core runtime. This makes ThreadGraph highly adaptable, allowing developers to build custom agent behaviors, integrate external tools, or experiment with different orchestration strategies while maintaining a consistent execution model.

ThreadGraph also emphasizes observability and reproducibility. Every execution is represented as a graph that can be inspected, traced, and replayed step by step. Developers can visualize how tasks branch, how agents interact, and how results are produced, making debugging and optimization significantly more transparent than traditional agent frameworks. Combined with its plugin ecosystem and AGPL-3.0+ licensing, ThreadGraph is designed to evolve into an open, extensible foundation for graph-based agent intelligence.