We stopped drawing graphs: an event‑driven runtime for agents

说明与用途

Reactifact 用状态驱动执行取代图形定义，让代理自动响应新工件。工件被版本化并关联，支持可重现性和透明审计。

问题

Agent frameworks force users to pre‑define a graph of tasks, which is inflexible for knowledge questions that depend on dynamic data and lack a fixed execution path, and they provide little auditability of how answers are derived.

结果

Reactifact enables dynamic, state‑driven agent execution without explicit graphs, providing deterministic, auditable answers with versioned artifacts and provenance links. A single‑command demo (examples/fintech_audit) shows reproducible variance calculations and audit reports.

工作流

• 1. Define artifact types (Question, Evidence, Claim, Calculation, Answer).
• 2. Annotate agents with @produce and Consume(Type) to declare inputs and outputs.
• 3. Create input artifacts (e.g., Question, CSV data).
• 4. Reactifact runtime detects new artifacts and triggers relevant agents automatically.
• 5. Agents perform deterministic Python computations or LLM reasoning, producing new artifacts.
• 6. Each produced artifact records provenance edges to its inputs.
• 7. Run reactifact replay to verify determinism and reproduce results.
• 8. Use reactifact.audit to generate a report with SHA‑256 hashes and provenance.
• 9. Iterate by creating new artifacts or modifying existing ones; only dependent artifacts recompute.

证据

Reactifact is a Celery‑shaped model, not its distributed runtime.

来源

The audit is a report. reactifact.audit renders the answer with a sha256 per artifact, its producing author, and the provenance edges.

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Run it twice: the hash is the same. Change a fact, and only what actually consumed it recomputes.

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