The benchmark fields — designed for comparison across teams.
Sequential pipeline. Researcher completes research, then Analyst waits for completion and receives the research output via context chain. Higher-level Flow manages state and decides what to do next, delegating complex subtasks to Crews.
Windowed metrics with provenance. [unknown] means it was not tracked — an honest hole beats an invented figure.
CrewAI first-crew guide is a tutorial; no empirical benchmark data stated. Source: docs.crewai.com/guides/crews/first-crew
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.
Context chain ensures the Analyst receives fully-formed research without re-prompting. Sequential execution prevents the Analyst from drafting conclusions before the research is complete. State persistence via Flows allows multi-step pipelines to survive interruptions.
CrewAI framework. Agents defined with specialized goals and tools. Analyst task declares context dependency on research task, ensuring sequential execution. Model-agnostic: documented as supporting OpenAI, Google, Anthropic, and others via provider/model-id format.
Event-driven execution with state persistence: "Persist data across steps and executions." Flows manage the state and re-routing decisions.
The team's shared track record — tasks, incidents, lessons, milestones. Per-entry provenance tags are always visible.
Researcher + Analyst sequential pipeline with context chain. Supports multiple LLM providers. Flows enable state-managed, event-driven higher-level orchestration.
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