+Live numbers (this container, via HTTPS proxy, XAI_API_KEY): T1 direct grok-3-mini connectivity smoke 2958 ms ("OK"). T2/T3: the W3c oracle-node shape — a graph_flow::Task whose run() consults a rig agent (grok-4-0709, max_tokens 64, FailureTicket pronoun-disambiguation prompt) — executed through GraphBuilder + FlowRunner + InMemorySessionStorage: e2e 8660/8406 ms, API 8658/8405 ms, **framework overhead 2/1 ms**. Both runs Completed; the oracle answered the disambiguation correctly ("it" = the board, animacy reasoning). Verdict: the orchestration layer costs SIX orders of magnitude less than the oracle call it wraps — W3c's budget is purely the LLM round trip (seconds), reinforcing oracle-frequency-only (never per-transition) and the two-speed split. Ergonomics: the whole oracle node is ~30 lines; three frictions: rig-core's lib name is rig_core (not rig), .agent() needs the rig_core::client::CompletionClient trait in scope, errors map via graph_flow::GraphError::TaskExecutionFailed. Operator references folded: (a) rs-graph-llm IS the LangGraph-inspired Rig-integrated stateful/interruptible handler by design (author's framing; the queue-vs-state debate in that thread is answered in V3 by the kanban board being BOTH — WAL intent log + state); (b) GraphRAG-rs (automataIA/graphrag-rs) noted as RAG-side prior art with native LanceDB/Arrow support, Leiden, LightRAG, cAST tree-sitter chunking — candidate reference for the retrieval stages, not a dependency decision; (c) test-bench delta: planner serve.rs (Axum, OpenAI-compatible /v1/chat/completions, JITSON behind it) can serve as a LOCAL zero-cost oracle endpoint via rig's openai-compatible provider + graceful retry — the self-hosted langgraph test bench for CI (no external key, no spend).
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