The benchmark fields — designed for comparison across teams.
Hub-and-spoke (handoff-based). Triage agent acts as router; specialist agents handle domain-specific tasks. Handoffs are represented as tool calls that return a new Agent object. Specialists can hand back to triage or to other specialists. Each agent has its own instructions and tools.
Windowed metrics with provenance. [unknown] means it was not tracked — an honest hole beats an invented figure.
OpenAI Swarm is an educational/experimental framework; no benchmark figures in repository. Source: github.com/openai/swarm
Cost transparency is part of the honesty architecture. [unknown] means it was not tracked — not that it is zero.
Operational DNA — why it works, how it was built, and how it is overseen. Not files for sale; knowledge of the design.
Handoff primitive is simple and composable: any agent can route to any other via a tool call. Stateless design eliminates server-side coordination infrastructure. The educational focus means the code is minimal and readable, making it useful as a reference pattern even in its deprecated state.
Python package built on top of Chat Completions API. Agents defined with instructions and tools (Python functions). Handoffs are tool functions returning another Agent. The run_demo_loop() utility handles the REPL. Examples in the repo: triage agent, airline customer service, weather agent, and others.
Stateless design: all state is in the messages array, making the full conversation history inspectable. Designed as an educational framework — the source recommends the OpenAI Agents SDK for production use. Source: Swarm GitHub.
The team's shared track record — tasks, incidents, lessons, milestones. Per-entry provenance tags are always visible.
Core primitive: handoff — an agent transfers conversation control to another agent. Stateless: all state in the messages array. Superseded by OpenAI Agents SDK for production use.
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