5 Commits

Include renames

Author	SHA1	Message	Date
Omar SobhandClaude Opus 4.8	c595cebab0	feat(topology): runnable multi-topology benchmark example ... `cargo run -p cm-orchestrator --example topology_bench --features provider` runs one task across hierarchical/pipeline/swarm topologies and prints a leaderboard + quality/cost Pareto front. Offline-deterministic via the scripted provider; set ANTHROPIC_API_KEY to run against a real model. Demonstrates milestone B end-to-end and seeds the reproducible paper harness. Co-Authored-By: Claude Opus 4.8 <[email protected]>	2026-06-15 20:45:26 -07:00
Omar SobhandClaude Opus 4.8	3725c96e4f	feat(topology): LLM-judge scorer + async Scorer trait ... Make Scorer async and add JudgeScorer (behind the `provider` feature): asks a cm-llm model to rate a run's output 0-100 vs the task and normalizes to [0,1], giving the comparison harness real quality numbers. Robust integer parsing (handles "Score: 92/100", clamps >100); provider errors score 0.0. 11 tests with --features provider (judge incl. parse + scripted-provider score); core stays 7. Clippy clean. Co-Authored-By: Claude Opus 4.8 <[email protected]>	2026-06-15 20:44:08 -07:00
Omar SobhandClaude Opus 4.8	7370adb78e	feat(topology): multi-topology comparison harness (Phase 4 core) ... compare(graphs, task, executor, scorer) runs the same task across a set of topologies on the same executor, scores each, and returns a Comparison: - per-topology results (quality, tokens, turns, blocked approvals, output), - a leaderboard (quality desc), - a quality/cost Pareto front (on_pareto flags), - best_quality and best_value (quality-per-token) picks. This is the "which patterns yield better results" engine and the structured output the paper's benchmark tables consume. Generic over TurnExecutor + Scorer (pluggable LLM-judge later); pure core, no new deps. 9 tests, clippy clean. Co-Authored-By: Claude Opus 4.8 <[email protected]>	2026-06-15 20:38:49 -07:00
Omar SobhandClaude Opus 4.8	60d6493751	feat(topology): cm-llm-backed ProviderExecutor (Phase 2b) ... Add a real TurnExecutor (behind the `provider` feature) that runs each topology turn as a single tool-free LLM call via any cm-llm provider (Anthropic / OpenAI-compat / scripted). Topologies now execute real model calls and produce real metrics (tokens), feeding the comparison harness. - ProviderExecutor builds a per-role system prompt + threads upstream context into the user message; collects TextDelta → output, Usage → tokens. - Tool-free reasoning turns take no sandbox-leaving actions (gated = []); §15 remains satisfied. Tool-using turns will route through a cm-runtime adapter. - Core crate stays dependency-light; cm-llm/futures are optional (feature). - Tests: pipeline + hierarchical run over the deterministic scripted provider (7 tests with --features provider). Clippy clean. Co-Authored-By: Claude Opus 4.8 <[email protected]>	2026-06-15 20:36:52 -07:00
Omar SobhandClaude Opus 4.8	e93fb24b79	feat(topology): cm-orchestrator topology runtime engine (Phase 2) ... A pure async control-flow engine that executes a task across a TopologyGraph by sequencing safe agent turns. Safety by construction: the engine can only invoke turns via a generic TurnExecutor — it performs no side effects itself, so §15 gating (inside each turn) is inherited and switching topology cannot escalate authority. - TurnExecutor trait + TurnRequest/TurnOutcome (real impl will wrap cm-runtime::Runtime; tests use a scripted Echo executor). - Pure planners (plan.rs): hierarchical (delegate down / synthesize up), pipeline (topo-ordered threading), swarm (parallel attempts + aggregate). - RunRecord journal (per-step + RunMetrics: tokens, gated actions, approvals granted/blocked, turns) — feeds the Phase 4 comparison harness/paper. - Unsupported kinds return an error (no panic). 5 tests, clippy clean. Next (Phase 2b): a real TurnExecutor adapter over cm-runtime::send_message. Co-Authored-By: Claude Opus 4.8 <[email protected]>	2026-06-15 20:14:12 -07:00