diff --git a/.gitignore b/.gitignore index 14fcd52f..fd333349 100644 --- a/.gitignore +++ b/.gitignore @@ -32,6 +32,7 @@ examples/openclaw_runner/package-lock.json playbook.json runs/ pilot_runs/ +wandb/ # CodeBoarding generated analysis (regenerable) .codeboarding/*.json diff --git a/clawloop/core/archive_recorder.py b/clawloop/core/archive_recorder.py new file mode 100644 index 00000000..9e660748 --- /dev/null +++ b/clawloop/core/archive_recorder.py @@ -0,0 +1,241 @@ +"""Archive-store recorder extracted from ``learning_loop``. + +The recorder encapsulates all writes to an ``ArchiveStore`` during a run +and owns the run-level counters (``run_id``, ``best_reward``, +``initial_reward``, ``total_cost``, ``prev_variant_hash``) that would +otherwise pollute ``learning_loop``'s local scope. + +All writes are wrapped in ``try / log.warning(..., exc_info=True)`` — +archive failures are non-fatal by design so that training continues +even if the backing store is misbehaving. +""" + +from __future__ import annotations + +import logging +import time +from typing import TYPE_CHECKING, Any + +from clawloop.archive.null_store import NullArchiveStore +from clawloop.archive.schema import ( + AgentVariant, + EpisodeRecord, + IterationRecord, + RunRecord, +) +from clawloop.archive.store import ArchiveStore +from clawloop.core.episode import Episode +from clawloop.core.state import StateID +from clawloop.core.types import FBResult +from clawloop.learning_layers.harness import Harness +from clawloop.utils.content_hash import canonical_hash + +if TYPE_CHECKING: + from clawloop.core.loop import AgentState + +log = logging.getLogger(__name__) + + +def _build_agent_config(agent_state: "AgentState") -> dict[str, Any]: + """Serializable snapshot of the agent's config for archive identity.""" + config: dict[str, Any] = {} + if isinstance(agent_state.harness, Harness): + config["system_prompts"] = dict(agent_state.harness.system_prompts) + config["playbook"] = agent_state.harness.playbook.to_dict() + config["router"] = agent_state.router.to_dict() + return config + + +def _variant_from_state( + agent_state: "AgentState", + variant_hash: str, + run_id: str, + created_at: float, +) -> AgentVariant: + harness = agent_state.harness + is_harness = isinstance(harness, Harness) + return AgentVariant( + variant_hash=variant_hash, + system_prompt=(next(iter(harness.system_prompts.values()), "") if is_harness else ""), + playbook_snapshot=harness.playbook.to_dict() if is_harness else {}, + model="", + tools=[], + first_seen_run_id=run_id, + created_at=created_at, + ) + + +class ArchiveRecorder: + """Owns all ``ArchiveStore`` writes for one ``learning_loop`` run. + + Construction writes the initial ``RunRecord`` + ``AgentVariant``. Each + iteration calls :meth:`record_episodes` and :meth:`record_iteration`. + :meth:`record_complete` writes the terminal ``log_run_complete`` row + with accumulated best-reward / improvement-delta / total-cost metrics. + """ + + def __init__( + self, + archive: ArchiveStore | None, + agent_state: "AgentState", + bench: str, + domain_tags: list[str] | None, + n_iterations: int, + ) -> None: + self._store: ArchiveStore = archive if archive is not None else NullArchiveStore() + self._run_id = RunRecord.new_id() + self._best_reward: float | None = None + self._initial_reward: float | None = None + self._total_cost = 0 + + initial_config = _build_agent_config(agent_state) + initial_hash = canonical_hash(initial_config) + self._prev_variant_hash = initial_hash + + now = time.time() + try: + self._store.log_run_start( + RunRecord( + run_id=self._run_id, + bench=bench, + domain_tags=list(domain_tags or []), + agent_config=initial_config, + config_hash=initial_hash, + n_iterations=n_iterations, + best_reward=0.0, + improvement_delta=0.0, + total_cost_tokens=0, + parent_run_id=None, + created_at=now, + completed_at=None, + ) + ) + except Exception: + log.warning("Archive: failed to log run start", exc_info=True) + + try: + self._store.log_variant( + _variant_from_state(agent_state, initial_hash, self._run_id, now) + ) + except Exception: + log.warning("Archive: failed to log initial variant", exc_info=True) + + @property + def run_id(self) -> str: + """Stable run-id used for all records emitted by this recorder.""" + return self._run_id + + def record_episodes(self, iteration: int, episodes: list[Episode]) -> None: + """Write one ``EpisodeRecord`` per episode. No-op when ``episodes`` is empty.""" + if not episodes: + return + records: list[EpisodeRecord] = [] + for ep in episodes: + tool_call_count = sum(len(m.tool_calls or []) for m in ep.messages) + token_usage: dict[str, Any] + if ep.summary.token_usage is not None: + token_usage = { + "prompt_tokens": ep.summary.token_usage.prompt_tokens, + "completion_tokens": ep.summary.token_usage.completion_tokens, + "total_tokens": ep.summary.token_usage.total_tokens, + } + else: + token_usage = {} + records.append( + EpisodeRecord( + run_id=self._run_id, + iteration_num=iteration, + episode_id=ep.id, + task_id=ep.task_id, + bench=ep.bench, + model=ep.model or "", + reward=ep.summary.normalized_reward(), + signals=( + { + k: {"value": s.value, "confidence": s.confidence} + for k, s in ep.summary.signals.items() + } + if ep.summary.signals + else {} + ), + n_steps=ep.n_steps(), + n_tool_calls=tool_call_count, + token_usage=token_usage, + latency_ms=int(ep.summary.timing.total_ms if ep.summary.timing else 0), + messages_ref=f"traces/{ep.id}.json", + created_at=ep.created_at if ep.created_at is not None else time.time(), + ) + ) + try: + self._store.log_episodes(records) + except Exception: + log.warning("Archive: failed to log episodes", exc_info=True) + + def record_iteration( + self, + iteration: int, + agent_state: "AgentState", + state_id: StateID, + fb_results: dict[str, FBResult], + episodes: list[Episode], + avg_reward: float, + prev_avg_reward: float, + ) -> None: + """Write the per-iteration ``IterationRecord`` and, if the agent's + config hash changed, a new ``AgentVariant``. Also updates + ``best_reward`` / ``initial_reward`` / ``total_cost`` counters used + by :meth:`record_complete`. + """ + iter_cost = sum( + r.metrics.get("tokens_used", 0) for r in fb_results.values() if r.status == "ok" + ) + self._total_cost += iter_cost + + try: + cur_config = _build_agent_config(agent_state) + cur_variant_hash = canonical_hash(cur_config) + evolver_action: dict[str, Any] = {} + for name, result in fb_results.items(): + if result.status == "ok": + evolver_action[name] = result.metrics + self._store.log_iteration( + IterationRecord( + run_id=self._run_id, + iteration_num=iteration, + harness_snapshot_hash=state_id.harness_hash, + mean_reward=avg_reward, + reward_trajectory=[ep.summary.normalized_reward() for ep in episodes], + evolver_action=evolver_action, + cost_tokens=iter_cost, + parent_variant_hash=self._prev_variant_hash, + child_variant_hash=cur_variant_hash, + reward_delta=avg_reward - prev_avg_reward, + created_at=time.time(), + ) + ) + if cur_variant_hash != self._prev_variant_hash: + self._store.log_variant( + _variant_from_state(agent_state, cur_variant_hash, self._run_id, time.time()) + ) + self._prev_variant_hash = cur_variant_hash + except Exception: + log.warning("Archive: failed to log iteration %d", iteration, exc_info=True) + + if self._best_reward is None or avg_reward > self._best_reward: + self._best_reward = avg_reward + if self._initial_reward is None: + self._initial_reward = avg_reward + + def record_complete(self) -> None: + """Write the terminal ``log_run_complete`` with accumulated metrics.""" + try: + best = self._best_reward if self._best_reward is not None else 0.0 + initial = self._initial_reward if self._initial_reward is not None else 0.0 + self._store.log_run_complete( + self._run_id, + best, + best - initial, + total_cost_tokens=self._total_cost, + ) + except Exception: + log.warning("Archive: failed to log run complete", exc_info=True) diff --git a/clawloop/core/loop.py b/clawloop/core/loop.py index 52ff7e1d..3e654e25 100644 --- a/clawloop/core/loop.py +++ b/clawloop/core/loop.py @@ -8,32 +8,25 @@ from __future__ import annotations -import copy import json import logging -import random import time from dataclasses import dataclass, field from pathlib import Path from typing import Any, Callable, Protocol -from clawloop.archive.null_store import NullArchiveStore -from clawloop.archive.schema import ( - AgentVariant, - EpisodeRecord, - IterationRecord, - RunRecord, -) from clawloop.archive.store import ArchiveStore +from clawloop.core.archive_recorder import ArchiveRecorder from clawloop.core.episode import Episode from clawloop.core.evolution_log import EvolutionEntry, EvolutionLog from clawloop.core.intensity import AdaptiveIntensity +from clawloop.core.runner import EpisodeCollectorRunner from clawloop.core.state import StateID -from clawloop.core.types import Datum, FBResult +from clawloop.core.transaction import LayerTransaction +from clawloop.core.types import FBResult from clawloop.learning_layers.harness import Harness from clawloop.learning_layers.router import Router from clawloop.learning_layers.weights import Weights -from clawloop.utils.content_hash import canonical_hash log = logging.getLogger(__name__) @@ -244,14 +237,158 @@ class AdapterLike(Protocol): def run_episode(self, task: Any, agent_state: AgentState) -> Episode: ... -def _build_agent_config(agent_state: AgentState) -> dict[str, Any]: - """Extract serializable agent config snapshot for archive identity.""" - config: dict[str, Any] = {} - if isinstance(agent_state.harness, Harness): - config["system_prompts"] = dict(agent_state.harness.system_prompts) - config["playbook"] = agent_state.harness.playbook.to_dict() - config["router"] = agent_state.router.to_dict() - return config +def _avg_reward(episodes: list[Episode]) -> float: + """Mean ``total_reward`` across episodes; 0.0 when empty.""" + if not episodes: + return 0.0 + return sum(ep.summary.total_reward for ep in episodes) / len(episodes) + + +def _refresh_tinker_fields( + agent_state: AgentState, + active_layers: list[str] | None, +) -> tuple[AgentState, list[tuple[str, Any]]]: + """Refresh Tinker-driven fields on ``AgentState`` from current backend. + + Gated on ``hasattr`` so non-Tinker backends (e.g. SkyRL) are untouched. + """ + backend = getattr(agent_state.weights, "_backend", None) + if backend is None or not hasattr(backend, "current_sampling_client"): + return agent_state, agent_state.get_layers(active_layers) + + from dataclasses import replace as _replace + + agent_state = _replace( + agent_state, + sampling_client=backend.current_sampling_client(), + renderer=getattr(backend, "renderer", None), + tokenizer=getattr(backend, "tokenizer", None), + ) + return agent_state, agent_state.get_layers(active_layers) + + +def _set_evolver_context( + agent_state: AgentState, + intensity: AdaptiveIntensity | None, + iteration: int, +) -> None: + """Push reward history + stagnation signal onto the harness's evolver.""" + if not isinstance(agent_state.harness, Harness) or agent_state.harness.evolver is None: + return + from clawloop.core.evolver import EvolverContext + + agent_state.harness.set_evolver_context( + EvolverContext( + reward_history=list(intensity._rewards) if intensity else [], + is_stagnating=intensity.is_stagnating() if intensity else False, + iteration=iteration, + tried_paradigms=list(agent_state.tried_paradigms), + ) + ) + + +def _maybe_save_backend_state( + agent_state: AgentState, + weights_fb: FBResult | None, + iteration: int, +) -> None: + """Tinker save_state hook — persist intermediate weights between iters. + + Skips when the weights step produced zero datums (GRPO filtered every + group for zero variance) to avoid polluting the durable-path timeline. + """ + backend = getattr(agent_state.weights, "_backend", None) + if backend is None or not hasattr(backend, "save_state"): + return + n_datums = weights_fb.metrics.get("n_datums", 0) if weights_fb and weights_fb.metrics else 0 + if n_datums > 0: + try: + backend.save_state(f"iter_{iteration}").result() + except Exception: + log.exception("backend.save_state failed for iter_%d", iteration) + else: + log.info( + " [save_state] skipped iter_%d — no datums produced (GRPO filtered all groups)", + iteration, + ) + + +def _flush_generation_if_advanced(agent_state: AgentState, optim_failed: bool) -> None: + """Drop stale weights-buffer episodes when the playbook generation advances. + + Prevents RL from learning against pre-adaptation behavior. No-op when + optim failed (we already rolled back) or harness isn't a ``Harness``. + """ + if optim_failed or not isinstance(agent_state.harness, Harness): + return + current_gen = agent_state.harness.playbook_generation + prev_gen = agent_state._prev_playbook_generation + if current_gen > prev_gen: + stale = agent_state.weights.pending_advantage_count() + agent_state.weights.clear_pending_state() + log.info( + " Generation %d->%d: flushed %d stale episodes from weights buffer", + prev_gen, + current_gen, + stale, + ) + agent_state._prev_playbook_generation = current_gen + + +def _log_evolution_entry( + evo_log: EvolutionLog, + agent_state: AgentState, + fb_results: dict[str, FBResult], + prev_hash: str, + new_hash: str, + prev_avg_reward: float, + avg_reward: float, + iteration: int, +) -> None: + """Append one ``EvolutionEntry`` iff any fb result produced a named action.""" + actions: list[str] = [] + for result in fb_results.values(): + if result.status != "ok": + continue + if result.metrics.get("insights_generated"): + actions.append("reflect") + if result.metrics.get("candidates_generated"): + actions.append("mutate") + if result.metrics.get("paradigm_shifted"): + actions.append("paradigm_shift") + if not actions: + return + backend = ( + agent_state.harness.evolver.name() + if isinstance(agent_state.harness, Harness) and agent_state.harness.evolver + else "none" + ) + evo_log.append( + EvolutionEntry( + iteration=iteration, + state_hash_before=prev_hash, + state_hash_after=new_hash, + actions=actions, + reward_before=prev_avg_reward, + reward_after=avg_reward, + backend=backend, + ) + ) + + +def _run_after_iteration( + cb: "Callable[[int, AgentState, list[Episode]], None] | None", + iteration: int, + agent_state: AgentState, + episodes: list[Episode], +) -> None: + """Invoke the optional after-iteration callback, logging any exception.""" + if cb is None: + return + try: + cb(iteration, agent_state, episodes) + except Exception: + log.exception("after_iteration callback failed") def learning_loop( @@ -307,436 +444,71 @@ def learning_loop( wandb_name=wandb_name, ) evo_log = EvolutionLog(output_dir) - _archive: ArchiveStore = archive if archive is not None else NullArchiveStore() - _run_id = RunRecord.new_id() - _initial_config = _build_agent_config(agent_state) - _initial_variant_hash = canonical_hash(_initial_config) - _now = time.time() - try: - _archive.log_run_start( - RunRecord( - run_id=_run_id, - bench=bench, - domain_tags=list(domain_tags or []), - agent_config=_initial_config, - config_hash=_initial_variant_hash, - n_iterations=n_iterations, - best_reward=0.0, - improvement_delta=0.0, - total_cost_tokens=0, - parent_run_id=None, - created_at=_now, - completed_at=None, - ) - ) - except Exception: - log.warning("Archive: failed to log run start", exc_info=True) - try: - _archive.log_variant( - AgentVariant( - variant_hash=_initial_variant_hash, - system_prompt=( - next(iter(agent_state.harness.system_prompts.values()), "") - if isinstance(agent_state.harness, Harness) - else "" - ), - playbook_snapshot=( - agent_state.harness.playbook.to_dict() - if isinstance(agent_state.harness, Harness) - else {} - ), - model="", - tools=[], - first_seen_run_id=_run_id, - created_at=_now, - ) - ) - except Exception: - log.warning("Archive: failed to log initial variant", exc_info=True) - _prev_variant_hash = _initial_variant_hash - _best_reward: float | None = None # None until first iteration — handles negative rewards - _initial_reward: float | None = None - _total_cost = 0 + runner = EpisodeCollectorRunner(adapter) + recorder = ArchiveRecorder( + archive, + agent_state, + bench=bench, + domain_tags=domain_tags, + n_iterations=n_iterations, + ) prev_avg_reward = 0.0 log.info("Starting learning loop — initial state: %s", state_id.combined_hash[:12]) for iteration in range(n_iterations): log.info("Iteration %d/%d", iteration + 1, n_iterations) - # 0. Refresh Tinker-driven fields on AgentState from current backend. - # Gated on hasattr so non-Tinker backends (e.g. SkyRL) are untouched. - backend = getattr(agent_state.weights, "_backend", None) - if backend is not None and hasattr(backend, "current_sampling_client"): - from dataclasses import replace as _replace - - agent_state = _replace( - agent_state, - sampling_client=backend.current_sampling_client(), - renderer=getattr(backend, "renderer", None), - tokenizer=getattr(backend, "tokenizer", None), - ) - # Refresh the layers view against the (possibly) new agent_state. - layers = agent_state.get_layers(active_layers) - - # 1. Collect episodes - if not tasks or n_episodes <= 0: - episodes: list[Episode] = [] - else: - if n_episodes <= len(tasks): - selected_tasks = random.sample(tasks, n_episodes) - else: - selected_tasks = random.choices(tasks, k=n_episodes) - - if hasattr(adapter, "run_batch") and callable(getattr(adapter, "run_batch", None)): - episodes = adapter.run_batch(agent_state, selected_tasks) - elif hasattr(adapter, "run_episodes_batch") and callable( - getattr(adapter, "run_episodes_batch", None) - ): - # Concurrent rollout path (OpenSpielGameAdapter): all - # episodes for this iter fan out under one event loop, - # Tinker queues them as parallel ConcurrentFutures. - episodes = adapter.run_episodes_batch(selected_tasks, agent_state) - else: - episodes = [] - for task in selected_tasks: - ep = adapter.run_episode(task, agent_state) - episodes.append(ep) - - avg_reward = ( - sum(ep.summary.total_reward for ep in episodes) / len(episodes) if episodes else 0.0 - ) + agent_state, layers = _refresh_tinker_fields(agent_state, active_layers) + episodes: list[Episode] = runner.collect(agent_state, tasks, n_episodes) + avg_reward = _avg_reward(episodes) log.info(" Collected %d episodes, avg reward: %.4f", len(episodes), avg_reward) - - if episodes: - _ep_records: list[EpisodeRecord] = [] - for ep in episodes: - tool_call_count = sum(len(m.tool_calls or []) for m in ep.messages) - _ep_records.append( - EpisodeRecord( - run_id=_run_id, - iteration_num=iteration, - episode_id=ep.id, - task_id=ep.task_id, - bench=ep.bench, - model=ep.model or "", - reward=ep.summary.normalized_reward(), - signals={ - k: {"value": s.value, "confidence": s.confidence} - for k, s in ep.summary.signals.items() - } - if ep.summary.signals - else {}, - n_steps=ep.n_steps(), - n_tool_calls=tool_call_count, - token_usage=( - { - "prompt_tokens": ep.summary.token_usage.prompt_tokens, - "completion_tokens": ep.summary.token_usage.completion_tokens, - "total_tokens": ep.summary.token_usage.total_tokens, - } - if ep.summary.token_usage - else {} - ), - latency_ms=int(ep.summary.timing.total_ms if ep.summary.timing else 0), - messages_ref=f"traces/{ep.id}.json", - created_at=ep.created_at if ep.created_at is not None else time.time(), - ) - ) - try: - _archive.log_episodes(_ep_records) - except Exception: - log.warning("Archive: failed to log episodes", exc_info=True) - - # Record reward for adaptive intensity + recorder.record_episodes(iteration, episodes) if intensity is not None: intensity.record_reward(avg_reward) - # 2. Build per-layer datums - # NOTE: Support-query split (failures→harness, - # successes→weights) is disabled. GRPO needs all episodes for - # advantage variance, and the on-policy vs off-policy boundary - # after harness updates needs more work. See roadmap Task 2.1. - layer_datums: dict[str, Datum] = { - "harness": Datum(episodes=episodes), - "weights": Datum(episodes=episodes), - "router": Datum(episodes=episodes), - } + _set_evolver_context(agent_state, intensity, iteration) - # 2b. Set evolver context on harness (for Evolver-based optimization) - if isinstance(agent_state.harness, Harness) and agent_state.harness.evolver is not None: - from clawloop.core.evolver import EvolverContext - - ctx = EvolverContext( - reward_history=list(intensity._rewards) if intensity else [], - is_stagnating=intensity.is_stagnating() if intensity else False, - iteration=iteration, - tried_paradigms=list(agent_state.tried_paradigms), - ) - agent_state.harness.set_evolver_context(ctx) - - # 3. Phase 1: forward_backward (all active layers) - fb_results: dict[str, FBResult] = {} - for name, layer in layers: - # Skip harness reflection when intensity says not to - if ( - name == "harness" - and intensity is not None - and not intensity.should_reflect(iteration) - ): - log.info(" skipping harness fb (adaptive intensity)") - fb_results[name] = FBResult(status="skipped") - continue - if name in layer_datums: - datum = layer_datums[name] - else: - log.warning(" unknown layer %s — using all episodes as fallback", name) - datum = Datum(episodes=episodes) - should_clear = False - try: - fut = layer.forward_backward(datum) - fb_result = fut.result() - fb_results[name] = fb_result - if fb_result.status in ("error", "skipped"): - should_clear = True - except Exception: - log.exception("forward_backward failed for %s", name) - fb_results[name] = FBResult(status="error") - should_clear = True - - if should_clear: - try: - layer.clear_pending_state() - except Exception: - log.exception("Failed to clear pending for %s", name) - - for name, result in fb_results.items(): - log.info(" fb %s: %s %s", name, result.status, result.metrics) - - # Track paradigm shifts before optim drains _pending - harness_fb = fb_results.get("harness") - if ( - harness_fb is not None - and harness_fb.metrics.get("paradigm_shifted") - and isinstance(agent_state.harness, Harness) - ): - for insight in agent_state.harness._pending.insights: - if "paradigm" in (insight.tags or []): - agent_state.tried_paradigms.append(insight.content) - - # 4. Phase 2: optim_step with cross-layer rollback - layers_to_optim = [ - (name, layer) - for name, layer in layers - if fb_results.get(name, FBResult(status="error")).status not in ("error", "skipped") - ] - - # Snapshot all layers before optim (for cross-layer rollback) - snapshots: dict[str, dict[str, Any]] = {} - try: - for name, layer in layers_to_optim: - snapshots[name] = copy.deepcopy(layer.to_dict()) - except Exception: - log.exception("Snapshot failed — skipping optim this iteration") - for name, layer in layers_to_optim: - try: - layer.clear_pending_state() - except Exception: - log.exception("Failed to clear pending for %s", name) - layers_to_optim = [] - - optim_failed = False - for name, layer in layers_to_optim: - try: - result = layer.optim_step().result() - log.info( - " optim %s: %s, %d updates", - name, - result.status, - result.updates_applied, - ) - if result.status == "error": - optim_failed = True - log.error( - " optim %s returned error — triggering rollback", - name, - ) - break - except Exception: - log.exception( - "optim_step failed for %s — triggering rollback", - name, - ) - optim_failed = True - break - - if optim_failed: - log.warning(" rolling back all layers to pre-optim state") - for name, layer in layers_to_optim: - if name in snapshots: - try: - lr = layer.load_state(snapshots[name]).result() - if lr.status != "ok": - log.error( - " rollback returned %s for %s", - lr.status, - name, - ) - except Exception: - log.exception(" rollback failed for %s", name) - - # 4b. Tinker save_state hook — persist intermediate weights and swap - # the backend's internal SamplingClient so the next iter picks up the - # freshly-trained adapter on its top-of-iter refresh. Gated on - # hasattr so SkyRL-style backends are unaffected. - # - # Skip the save entirely when the weights step produced zero datums - # (GRPO filtered every group for zero variance) — checkpointing an - # unchanged adapter wastes Tinker quota and pollutes the durable-path - # timeline. - weights_fb = fb_results.get("weights") - n_datums = ( - weights_fb.metrics.get("n_datums", 0) - if weights_fb is not None and weights_fb.metrics - else 0 - ) - if backend is not None and hasattr(backend, "save_state") and n_datums > 0: - try: - backend.save_state(f"iter_{iteration}").result() - except Exception: - log.exception("backend.save_state failed for iter_%d", iteration) - elif backend is not None and hasattr(backend, "save_state"): - log.info( - " [save_state] skipped iter_%d — no datums produced " - "(GRPO filtered all groups)", - iteration, - ) - - # Generation flush: when playbook_generation advances, clear stale - # episodes from weights buffer to prevent RL learning pre-adaptation behavior - if isinstance(agent_state.harness, Harness) and not optim_failed: - current_gen = agent_state.harness.playbook_generation - prev_gen = agent_state._prev_playbook_generation - if current_gen > prev_gen: - stale = agent_state.weights.pending_advantage_count() - agent_state.weights.clear_pending_state() - log.info( - " Generation %d->%d: flushed %d stale episodes from weights buffer", - prev_gen, - current_gen, - stale, - ) - agent_state._prev_playbook_generation = current_gen + tx_result = LayerTransaction( + layers, + intensity=intensity, + episodes=episodes, + agent_state=agent_state, + ).run(iteration) + fb_results = tx_result.fb_results + + _maybe_save_backend_state(agent_state, fb_results.get("weights"), iteration) + _flush_generation_if_advanced(agent_state, tx_result.optim_failed) - # 5. Log iteration results harness_ref = agent_state.harness if isinstance(agent_state.harness, Harness) else None + backend = getattr(agent_state.weights, "_backend", None) exp_log.log_iteration(iteration, episodes, fb_results, harness_ref, backend=backend) - # 6. Recompute state identity and log evolution entry prev_hash = state_id.combined_hash state_id = agent_state.state_id() + _log_evolution_entry( + evo_log, + agent_state, + fb_results, + prev_hash, + state_id.combined_hash, + prev_avg_reward, + avg_reward, + iteration, + ) - # Build actions list from fb results for evolution log - actions: list[str] = [] - for name, result in fb_results.items(): - if result.status == "ok": - if result.metrics.get("insights_generated"): - actions.append("reflect") - if result.metrics.get("candidates_generated"): - actions.append("mutate") - if result.metrics.get("paradigm_shifted"): - actions.append("paradigm_shift") - if actions: - evo_log.append( - EvolutionEntry( - iteration=iteration, - state_hash_before=prev_hash, - state_hash_after=state_id.combined_hash, - actions=actions, - reward_before=prev_avg_reward, - reward_after=avg_reward, - backend=( - agent_state.harness.evolver.name() - if isinstance(agent_state.harness, Harness) and agent_state.harness.evolver - else "none" - ), - ) - ) - - try: - _cur_config = _build_agent_config(agent_state) - _cur_variant_hash = canonical_hash(_cur_config) - _evolver_action: dict[str, Any] = {} - for name, result in fb_results.items(): - if result.status == "ok": - _evolver_action[name] = result.metrics - _iter_cost = sum( - r.metrics.get("tokens_used", 0) for r in fb_results.values() if r.status == "ok" - ) - _total_cost += _iter_cost - _archive.log_iteration( - IterationRecord( - run_id=_run_id, - iteration_num=iteration, - harness_snapshot_hash=state_id.harness_hash, - mean_reward=avg_reward, - reward_trajectory=[ep.summary.normalized_reward() for ep in episodes], - evolver_action=_evolver_action, - cost_tokens=_iter_cost, - parent_variant_hash=_prev_variant_hash, - child_variant_hash=_cur_variant_hash, - reward_delta=avg_reward - prev_avg_reward, - created_at=time.time(), - ) - ) - if _cur_variant_hash != _prev_variant_hash: - _archive.log_variant( - AgentVariant( - variant_hash=_cur_variant_hash, - system_prompt=( - next(iter(agent_state.harness.system_prompts.values()), "") - if isinstance(agent_state.harness, Harness) - else "" - ), - playbook_snapshot=( - agent_state.harness.playbook.to_dict() - if isinstance(agent_state.harness, Harness) - else {} - ), - model="", - tools=[], - first_seen_run_id=_run_id, - created_at=time.time(), - ) - ) - _prev_variant_hash = _cur_variant_hash - except Exception: - log.warning("Archive: failed to log iteration %d", iteration, exc_info=True) - - if _best_reward is None or avg_reward > _best_reward: - _best_reward = avg_reward - if _initial_reward is None: - _initial_reward = avg_reward - + recorder.record_iteration( + iteration=iteration, + agent_state=agent_state, + state_id=state_id, + fb_results=fb_results, + episodes=episodes, + avg_reward=avg_reward, + prev_avg_reward=prev_avg_reward, + ) prev_avg_reward = avg_reward - # 7. Optional after-iteration callback (e.g. eval scoring) - if after_iteration is not None: - try: - after_iteration(iteration, agent_state, episodes) - except Exception: - log.exception("after_iteration callback failed") - - try: - final_best = _best_reward if _best_reward is not None else 0.0 - final_initial = _initial_reward if _initial_reward is not None else 0.0 - _archive.log_run_complete( - _run_id, - final_best, - final_best - final_initial, - total_cost_tokens=_total_cost, - ) - except Exception: - log.warning("Archive: failed to log run complete", exc_info=True) + _run_after_iteration(after_iteration, iteration, agent_state, episodes) + recorder.record_complete() log.info("Loop complete — final state: %s", state_id.combined_hash[:12]) return agent_state, state_id diff --git a/clawloop/core/runner.py b/clawloop/core/runner.py new file mode 100644 index 00000000..69dcc965 --- /dev/null +++ b/clawloop/core/runner.py @@ -0,0 +1,64 @@ +"""Episode-collection helper extracted from ``learning_loop``. + +The runner owns the per-iteration task-sampling + adapter-dispatch protocol. +It is deliberately a thin wrapper: the loop still owns iteration control flow, +and the runner just encapsulates the "get me ``n_episodes`` rollouts" step so +``learning_loop`` stays readable end-to-end. +""" + +from __future__ import annotations + +import random +from typing import Any + +from clawloop.core.episode import Episode + + +class EpisodeCollectorRunner: + """Samples tasks and dispatches to the adapter to collect a batch of episodes. + + Parameters + ---------- + adapter: + Environment adapter. Must expose ``run_episode(task, agent_state)`` and + may optionally expose ``run_batch(agent_state, tasks)`` for a faster + path. Typed as ``Any`` to avoid a circular import with the + ``AdapterLike`` Protocol, which lives in ``loop.py``. + """ + + def __init__(self, adapter: Any) -> None: + self._adapter = adapter + + def collect( + self, + agent_state: Any, + tasks: list[Any], + n_episodes: int, + ) -> list[Episode]: + """Return ``n_episodes`` rollouts against ``tasks``. + + Behavior mirrors the block previously inlined in ``learning_loop``: + + * Empty ``tasks`` or ``n_episodes <= 0`` returns ``[]`` without + touching the adapter. + * Samples without replacement when ``n_episodes <= len(tasks)``, + with replacement otherwise. + * Prefers ``adapter.run_batch(agent_state, selected_tasks)`` when + available; otherwise falls back to a per-task ``run_episode`` loop. + """ + if not tasks or n_episodes <= 0: + return [] + + if n_episodes <= len(tasks): + selected_tasks = random.sample(tasks, n_episodes) + else: + selected_tasks = random.choices(tasks, k=n_episodes) + + run_batch = getattr(self._adapter, "run_batch", None) + if callable(run_batch): + return run_batch(agent_state, selected_tasks) + + episodes: list[Episode] = [] + for task in selected_tasks: + episodes.append(self._adapter.run_episode(task, agent_state)) + return episodes diff --git a/clawloop/core/transaction.py b/clawloop/core/transaction.py new file mode 100644 index 00000000..10e5236b --- /dev/null +++ b/clawloop/core/transaction.py @@ -0,0 +1,180 @@ +"""Two-phase fb→optim→rollback layer transaction. + +Extracted from ``learning_loop`` — this module owns the subtle +invariants of the per-iteration training protocol: + +1. Build one ``Datum`` per layer name. +2. Run ``forward_backward`` on every active layer, clearing pending + state on error/skipped results. The harness gets an intensity-gate + short-circuit that records ``status="skipped"`` without calling fb. +3. Track paradigm shifts onto ``agent_state.tried_paradigms`` *before* + optim drains ``_pending``. +4. Snapshot every layer that had ok fb. If snapshotting fails, + ``clear_pending_state`` fires on every snapshotted layer and optim is + skipped entirely. +5. Run ``optim_step`` on every snapshotted layer. On the first error or + exception, roll back *every* snapshotted layer (even those whose + optim hadn't run yet) to its pre-optim state. +""" + +from __future__ import annotations + +import copy +import logging +from dataclasses import dataclass +from typing import TYPE_CHECKING, Any + +from clawloop.core.episode import Episode +from clawloop.core.intensity import AdaptiveIntensity +from clawloop.core.types import Datum, FBResult +from clawloop.learning_layers.harness import Harness + +if TYPE_CHECKING: + from clawloop.core.loop import AgentState + +log = logging.getLogger(__name__) + + +@dataclass +class TransactionResult: + """Outcome of one ``LayerTransaction.run()`` call.""" + + fb_results: dict[str, FBResult] + optim_failed: bool + + +class LayerTransaction: + """One iteration's fb→optim→rollback protocol across active layers.""" + + def __init__( + self, + layers: list[tuple[str, Any]], + intensity: AdaptiveIntensity | None, + episodes: list[Episode], + agent_state: "AgentState", + ) -> None: + self._layers = layers + self._intensity = intensity + self._episodes = episodes + self._agent_state = agent_state + + def run(self, iteration: int) -> TransactionResult: + """Execute the transaction and return fb_results + optim_failed flag.""" + layer_datums: dict[str, Datum] = { + "harness": Datum(episodes=self._episodes), + "weights": Datum(episodes=self._episodes), + "router": Datum(episodes=self._episodes), + } + fb_results = self._forward_backward(iteration, layer_datums) + self._track_paradigm_shifts(fb_results) + optim_failed = self._optim_with_rollback(fb_results) + return TransactionResult(fb_results=fb_results, optim_failed=optim_failed) + + def _forward_backward( + self, + iteration: int, + layer_datums: dict[str, Datum], + ) -> dict[str, FBResult]: + fb_results: dict[str, FBResult] = {} + for name, layer in self._layers: + if ( + name == "harness" + and self._intensity is not None + and not self._intensity.should_reflect(iteration) + ): + log.info(" skipping harness fb (adaptive intensity)") + fb_results[name] = FBResult(status="skipped") + continue + if name in layer_datums: + datum = layer_datums[name] + else: + log.warning(" unknown layer %s — using all episodes as fallback", name) + datum = Datum(episodes=self._episodes) + + should_clear = False + try: + fb_result = layer.forward_backward(datum).result() + fb_results[name] = fb_result + if fb_result.status in ("error", "skipped"): + should_clear = True + except Exception: + log.exception("forward_backward failed for %s", name) + fb_results[name] = FBResult(status="error") + should_clear = True + + if should_clear: + try: + layer.clear_pending_state() + except Exception: + log.exception("Failed to clear pending for %s", name) + + for name, result in fb_results.items(): + log.info(" fb %s: %s %s", name, result.status, result.metrics) + return fb_results + + def _track_paradigm_shifts(self, fb_results: dict[str, FBResult]) -> None: + """Append paradigm-tagged pending insights to ``tried_paradigms``. + + Must run before optim drains ``_pending`` — otherwise the insights + we need to remember have already been consumed. + """ + harness_fb = fb_results.get("harness") + if ( + harness_fb is not None + and harness_fb.metrics.get("paradigm_shifted") + and isinstance(self._agent_state.harness, Harness) + ): + for insight in self._agent_state.harness.pending_paradigm_insights(): + self._agent_state.tried_paradigms.append(insight.content) + + def _optim_with_rollback(self, fb_results: dict[str, FBResult]) -> bool: + layers_to_optim = [ + (name, layer) + for name, layer in self._layers + if fb_results.get(name, FBResult(status="error")).status not in ("error", "skipped") + ] + + snapshots: dict[str, dict[str, Any]] = {} + try: + for name, layer in layers_to_optim: + snapshots[name] = copy.deepcopy(layer.to_dict()) + except Exception: + log.exception("Snapshot failed — skipping optim this iteration") + for name, layer in layers_to_optim: + try: + layer.clear_pending_state() + except Exception: + log.exception("Failed to clear pending for %s", name) + return False + + optim_failed = False + for name, layer in layers_to_optim: + try: + result = layer.optim_step().result() + log.info( + " optim %s: %s, %d updates", + name, + result.status, + result.updates_applied, + ) + if result.status == "error": + optim_failed = True + log.error(" optim %s returned error — triggering rollback", name) + break + except Exception: + log.exception("optim_step failed for %s — triggering rollback", name) + optim_failed = True + break + + if optim_failed: + log.warning(" rolling back all layers to pre-optim state") + for name, layer in layers_to_optim: + if name in snapshots: + try: + lr = layer.load_state(snapshots[name]).result() + if lr.status != "ok": + log.error(" rollback returned %s for %s", lr.status, name) + except Exception: + log.exception(" rollback failed for %s", name) + + return optim_failed diff --git a/clawloop/environments/_car_purple.py b/clawloop/environments/_car_purple.py index 22a8c073..7060f46f 100644 --- a/clawloop/environments/_car_purple.py +++ b/clawloop/environments/_car_purple.py @@ -3,27 +3,36 @@ from __future__ import annotations -import asyncio -import json import logging -import threading -from typing import Any +from typing import Any, ClassVar from uuid import uuid4 -import litellm -import uvicorn -from starlette.applications import Starlette -from starlette.requests import Request -from starlette.responses import JSONResponse -from starlette.routing import Route - +from clawloop.environments._purple_base import ( + _PurpleAgentBase, + create_app, + start_purple_server, +) from clawloop.learning_layers.harness import Harness log = logging.getLogger(__name__) +__all__ = ["CarPurpleAgent", "create_app", "start_purple_server"] + + +class CarPurpleAgent(_PurpleAgentBase): + """Purple agent for CAR-bench: parses System/User prompt, wraps replies in ``message``.""" -class CarPurpleAgent: - """A2A-compliant purple agent that injects clawloop harness state into LLM calls.""" + default_bench: ClassVar[str] = "car" + agent_card_name: ClassVar[str] = "clawloop-purple-agent" + agent_card_description: ClassVar[str] = "ClawLoop harness-optimized agent under test" + agent_card_skills: ClassVar[list[dict]] = [ + { + "id": "car_assistant", + "name": "In-Car Voice Assistant", + "description": "Agent under test for CAR-bench evaluation", + "tags": ["benchmark", "car-bench"], + } + ] def __init__( self, @@ -33,28 +42,16 @@ def __init__( api_base: str | None = None, api_key: str | None = None, ): - self.model = model - self.harness = harness - self.bench = bench - self.api_base = api_base - self.api_key = api_key - self._sessions: dict[str, list[dict]] = {} - self._tool_cache: dict[str, list[dict]] = {} + super().__init__(model, harness, bench, api_base, api_key) self._captured: dict[str, list[dict]] = {} - def update_harness(self, harness: Harness) -> None: - self.harness = harness - def clear_all_sessions(self) -> None: - self._sessions.clear() - self._tool_cache.clear() + super().clear_all_sessions() self._captured.clear() - # -- Message parsing -- - @staticmethod def _parse_first_message(raw_text: str) -> tuple[str, str]: - """Parse 'System: ...\\n\\nUser: ...' format from green agent.""" + """Parse ``System: ...\\n\\nUser: ...`` format from green agent.""" if "System:" in raw_text and "\n\nUser:" in raw_text: parts = raw_text.split("\n\nUser:", 1) system = parts[0].replace("System:", "", 1).strip() @@ -62,281 +59,27 @@ def _parse_first_message(raw_text: str) -> tuple[str, str]: return system, user return "", raw_text - @staticmethod - def _convert_tools_to_openai(car_tools: list[dict]) -> list[dict]: - """Normalize tool schemas to OpenAI function-calling format. + def _build_initial_messages(self, text_parts: list[str]) -> list[dict]: + raw_text = text_parts[0] if text_parts else "" + system_prompt, user_text = self._parse_first_message(raw_text) - Green agent may send tools already wrapped ({type: function, function: ...}) - or flat ({name, description, parameters}). Handle both. - """ - result = [] - for t in car_tools: - if t.get("type") == "function" and "function" in t: - # Already in OpenAI format - result.append(t) - else: - result.append( - { - "type": "function", - "function": { - "name": t["name"], - "description": t.get("description", ""), - "parameters": t.get("parameters", {}), - }, - } - ) - return result + harness_prompt = self.harness.system_prompt(self.bench) + if harness_prompt: + system_prompt = f"{harness_prompt}\n\n{system_prompt}" - @staticmethod - def _normalize_assistant_msg(litellm_msg: Any) -> dict: - """Normalize litellm response to stable internal format.""" - normalized: dict[str, Any] = { - "role": "assistant", - "content": litellm_msg.content or "", - } - if litellm_msg.tool_calls: - normalized["tool_calls"] = [ - { - "id": tc.id, - "type": "function", - "function": { - "name": tc.function.name, - "arguments": tc.function.arguments, - }, - } - for tc in litellm_msg.tool_calls - ] - return normalized + out: list[dict] = [{"role": "system", "content": system_prompt}] + if user_text: + out.append({"role": "user", "content": user_text}) + return out def _format_a2a_response(self, assistant_msg: Any) -> dict: - """Format LLM response as A2A result body.""" - parts: list[dict] = [{"kind": "text", "text": assistant_msg.content or ""}] - - if assistant_msg.tool_calls: - tool_calls = [] - for tc in assistant_msg.tool_calls: - args = tc.function.arguments - if args is None: - args = {} - elif isinstance(args, str): - try: - args = json.loads(args) - except json.JSONDecodeError: - log.warning("Malformed tool args for %s", tc.function.name) - args = {"raw": args} - tool_calls.append({"tool_name": tc.function.name, "arguments": args}) - parts.append({"kind": "data", "data": {"tool_calls": tool_calls}}) - return { "message": { "messageId": uuid4().hex, "role": "agent", - "parts": parts, + "parts": self._build_message_parts(assistant_msg), } } - # -- Core message handling -- - - def handle_message_sync(self, jsonrpc_request: dict) -> dict: - """Handle one message/send request (sync — litellm.completion is sync).""" - params = jsonrpc_request["params"] - msg = params["message"] - context_id = params.get("contextId", "default") - - text_parts = [p["text"] for p in msg["parts"] if p.get("kind") == "text"] - data_parts = [p["data"] for p in msg["parts"] if p.get("kind") == "data"] - - # Initialize session - if context_id not in self._sessions: - self._sessions[context_id] = [] - self._captured[context_id] = [] - - messages = self._sessions[context_id] - - if not messages: - # First message: extract system prompt + tools - raw_text = text_parts[0] if text_parts else "" - system_prompt, user_text = self._parse_first_message(raw_text) - - # HARNESS INJECTION - harness_prompt = self.harness.system_prompt(self.bench) - if harness_prompt: - system_prompt = f"{harness_prompt}\n\n{system_prompt}" - - messages.append({"role": "system", "content": system_prompt}) - if user_text: - messages.append({"role": "user", "content": user_text}) - - # Cache tools - for d in data_parts: - if "tools" in d: - self._tool_cache[context_id] = self._convert_tools_to_openai(d["tools"]) - else: - # Subsequent: tool results and/or user text - for d in data_parts: - if "tool_results" in d: - for tr in d["tool_results"]: - # Reconcile tool_call_ids: rewrite last assistant msg's - # tool_call ids to match green's generated ids - green_id = tr["tool_call_id"] - tool_name = tr.get("tool_name", "") - self._reconcile_tool_call_id(messages, tool_name, green_id) - - messages.append( - { - "role": "tool", - "tool_call_id": green_id, - "content": tr["content"], - } - ) - for text in text_parts: - if text.strip(): - messages.append({"role": "user", "content": text}) - - # Call LLM - tools = self._tool_cache.get(context_id) - completion_kwargs: dict[str, Any] = { - "model": self.model, - "messages": messages, - "temperature": 0.0, - } - if tools: - completion_kwargs["tools"] = tools - if self.api_base: - completion_kwargs["api_base"] = self.api_base - if self.api_key: - completion_kwargs["api_key"] = self.api_key - - response = litellm.completion(**completion_kwargs) - assistant_msg = response.choices[0].message - - # Normalize and store - normalized = self._normalize_assistant_msg(assistant_msg) - messages.append(normalized) - self._captured[context_id].append(normalized) - - return self._format_a2a_response(assistant_msg) - - @staticmethod - def _reconcile_tool_call_id(messages: list[dict], tool_name: str, green_id: str) -> None: - """Rewrite last assistant message's tool_call id to match green's id. - - Green generates its own tool_call_ids. The LLM needs matching ids between - assistant tool_calls and tool-role messages. We rewrite the assistant msg's - id to match what green sent back. - - Handles duplicate tool names by only rewriting tool calls that haven't - already been reconciled (i.e., still have their original LLM-generated id). - """ - # Collect green IDs already used in existing tool-role messages - used_green_ids = { - m["tool_call_id"] for m in messages if m.get("role") == "tool" and "tool_call_id" in m - } - # Walk backwards to find the last assistant message with tool_calls - for msg in reversed(messages): - if msg.get("role") != "assistant" or "tool_calls" not in msg: - continue - for tc in msg["tool_calls"]: - # Match by name, skip if already reconciled (id is a known green id) - if tc["function"]["name"] == tool_name and tc["id"] not in used_green_ids: - tc["id"] = green_id - return - return # found assistant msg but no matching tool name - - -def create_app(agent: CarPurpleAgent, port: int = 0) -> Starlette: - """Create the A2A Starlette app.""" - - async def agent_card(request: Request) -> JSONResponse: - return JSONResponse( - { - "name": "clawloop-purple-agent", - "description": "ClawLoop harness-optimized agent under test", - "url": f"http://127.0.0.1:{port}/", - "version": "0.1.0", - "protocol_version": "0.3.0", - "preferred_transport": "JSONRPC", - "default_input_modes": ["text/plain"], - "default_output_modes": ["text/plain"], - "capabilities": {"streaming": False, "push_notifications": False}, - "skills": [ - { - "id": "car_assistant", - "name": "In-Car Voice Assistant", - "description": "Agent under test for CAR-bench evaluation", - "tags": ["benchmark", "car-bench"], - } - ], - } - ) - - async def handle_jsonrpc(request: Request) -> JSONResponse: - body = await request.json() - if body.get("jsonrpc") != "2.0" or "id" not in body: - return JSONResponse( - { - "jsonrpc": "2.0", - "id": None, - "error": {"code": -32600, "message": "Invalid Request"}, - } - ) - - method = body.get("method") - if method != "message/send": - return JSONResponse( - { - "jsonrpc": "2.0", - "id": body["id"], - "error": {"code": -32601, "message": f"Method not found: {method}"}, - } - ) - - # Run sync litellm call in thread to avoid blocking event loop - loop = asyncio.get_running_loop() - result = await loop.run_in_executor(None, agent.handle_message_sync, body) - - return JSONResponse({"jsonrpc": "2.0", "id": body["id"], "result": result}) - - return Starlette( - routes=[ - Route("/.well-known/agent.json", agent_card, methods=["GET"]), - Route("/.well-known/agent-card.json", agent_card, methods=["GET"]), - Route("/", handle_jsonrpc, methods=["POST"]), - ] - ) - - -def start_purple_server( - agent: CarPurpleAgent, host: str = "127.0.0.1", port: int = 0 -) -> tuple[threading.Thread, int]: - """Start the purple agent server in a background thread. Returns (thread, actual_port).""" - import socket - import time - - # Bind socket to get a free port; pass it directly to uvicorn to avoid race. - sock = socket.socket(socket.AF_INET, socket.SOCK_STREAM) - sock.setsockopt(socket.SOL_SOCKET, socket.SO_REUSEADDR, 1) - sock.bind((host, port)) - actual_port = sock.getsockname()[1] - - app = create_app(agent, actual_port) - config = uvicorn.Config(app, host=host, port=actual_port, log_level="warning") - server = uvicorn.Server(config) - - thread = threading.Thread(target=server.run, kwargs={"sockets": [sock]}, daemon=True) - thread.start() - - # Poll for readiness - import httpx - - for _ in range(50): - try: - r = httpx.get(f"http://{host}:{actual_port}/.well-known/agent-card.json", timeout=0.5) - if r.status_code == 200: - break - except httpx.ConnectError: - time.sleep(0.1) - else: - log.warning("Purple server did not become ready within 5s") - - return thread, actual_port + def _capture_assistant(self, context_id: str, normalized: dict) -> None: + self._captured.setdefault(context_id, []).append(normalized) diff --git a/clawloop/environments/_entropic_purple.py b/clawloop/environments/_entropic_purple.py index e7e4977f..5b5ed824 100644 --- a/clawloop/environments/_entropic_purple.py +++ b/clawloop/environments/_entropic_purple.py @@ -1,134 +1,38 @@ # clawloop/environments/_entropic_purple.py -"""A2A purple agent server for Entropic CRMArenaPro with clawloop harness injection. - -The entropic green agent sends CRM task prompts as plain text via A2A -``message/send``. This purple agent: - 1. Injects the clawloop harness system prompt. - 2. Forwards the task to the configured LLM. - 3. Returns the answer as an A2A text response. - -Multi-turn is supported: the green agent may send follow-up messages when the -agent requests clarification or when tool results are returned. -""" +"""A2A purple agent server for Entropic CRMArenaPro with clawloop harness injection.""" from __future__ import annotations -import asyncio import json import logging -import threading -from typing import Any +from typing import Any, ClassVar from uuid import uuid4 -import litellm -import uvicorn -from starlette.applications import Starlette -from starlette.requests import Request -from starlette.responses import JSONResponse -from starlette.routing import Route - -from clawloop.learning_layers.harness import Harness +from clawloop.environments._purple_base import ( + _PurpleAgentBase, + create_app, + start_purple_server, +) log = logging.getLogger(__name__) +__all__ = ["EntropicPurpleAgent", "create_app", "start_purple_server"] -class EntropicPurpleAgent: - """A2A-compliant purple agent for CRM tasks with clawloop harness injection.""" - - def __init__( - self, - model: str, - harness: Harness, - bench: str = "entropic", - api_base: str | None = None, - api_key: str | None = None, - ): - self.model = model - self.harness = harness - self.bench = bench - self.api_base = api_base - self.api_key = api_key - self._sessions: dict[str, list[dict]] = {} - self._tool_cache: dict[str, list[dict]] = {} - def update_harness(self, harness: Harness) -> None: - self.harness = harness - - def clear_all_sessions(self) -> None: - self._sessions.clear() - self._tool_cache.clear() - - # -- Tool schema helpers -- - - @staticmethod - def _convert_tools_to_openai(raw_tools: list[dict]) -> list[dict]: - """Normalize tool schemas to OpenAI function-calling format.""" - result = [] - for t in raw_tools: - if t.get("type") == "function" and "function" in t: - result.append(t) - else: - result.append( - { - "type": "function", - "function": { - "name": t["name"], - "description": t.get("description", ""), - "parameters": t.get("parameters", {}), - }, - } - ) - return result +class EntropicPurpleAgent(_PurpleAgentBase): + """Purple agent for Entropic CRMArenaPro: formats CRM task, flat message envelope.""" - @staticmethod - def _normalize_assistant_msg(litellm_msg: Any) -> dict: - """Normalize litellm response to stable internal dict.""" - normalized: dict[str, Any] = { - "role": "assistant", - "content": litellm_msg.content or "", - } - if litellm_msg.tool_calls: - normalized["tool_calls"] = [ - { - "id": tc.id, - "type": "function", - "function": { - "name": tc.function.name, - "arguments": tc.function.arguments, - }, - } - for tc in litellm_msg.tool_calls - ] - return normalized - - def _format_a2a_response(self, assistant_msg: Any) -> dict: - """Format LLM response as A2A result body.""" - parts: list[dict] = [{"kind": "text", "text": assistant_msg.content or ""}] - - if assistant_msg.tool_calls: - tool_calls = [] - for tc in assistant_msg.tool_calls: - args = tc.function.arguments - if args is None: - args = {} - elif isinstance(args, str): - try: - args = json.loads(args) - except json.JSONDecodeError: - log.warning("Malformed tool args for %s", tc.function.name) - args = {"raw": args} - tool_calls.append({"tool_name": tc.function.name, "arguments": args}) - parts.append({"kind": "data", "data": {"tool_calls": tool_calls}}) - - # Return Message directly (not wrapped) — a2a-sdk expects result=Message - return { - "kind": "message", - "messageId": uuid4().hex, - "role": "agent", - "parts": parts, + default_bench: ClassVar[str] = "entropic" + agent_card_name: ClassVar[str] = "clawloop-entropic-purple-agent" + agent_card_description: ClassVar[str] = "ClawLoop harness-optimized CRM agent under test" + agent_card_skills: ClassVar[list[dict]] = [ + { + "id": "crm_assistant", + "name": "CRM Assistant", + "description": "Agent under test for Entropic CRMArenaPro evaluation", + "tags": ["benchmark", "entropic", "crmarena"], } - - # -- CRM task formatting -- + ] @staticmethod def _format_crm_task(raw_text: str) -> str: @@ -136,12 +40,10 @@ def _format_crm_task(raw_text: str) -> str: The green agent sends ``json.dumps(task_context)`` as an A2A TextPart. We extract the structured fields and present them clearly to the LLM. - If the text isn't valid JSON, return it unchanged. + If the text isn't valid JSON (or lacks ``prompt``), return it unchanged. """ - import json as _json - try: - ctx = _json.loads(raw_text) + ctx = json.loads(raw_text) except (ValueError, TypeError): return raw_text @@ -178,10 +80,8 @@ def _format_crm_task(raw_text: str) -> str: @staticmethod def _extract_task_tags(raw_text: str) -> set[str] | None: """Extract task category from CRM task JSON for selective playbook retrieval.""" - import json as _json - try: - ctx = _json.loads(raw_text) + ctx = json.loads(raw_text) except (ValueError, TypeError): return None if not isinstance(ctx, dict): @@ -192,188 +92,23 @@ def _extract_task_tags(raw_text: str) -> set[str] | None: tags.add(cat) return tags or None - # -- Core message handling -- - - def handle_message_sync(self, jsonrpc_request: dict) -> dict: - """Handle one message/send request (sync).""" - params = jsonrpc_request["params"] - msg = params["message"] - context_id = params.get("contextId", "default") - - text_parts = [p["text"] for p in msg["parts"] if p.get("kind") == "text"] - data_parts = [p["data"] for p in msg["parts"] if p.get("kind") == "data"] - - # Initialize session - if context_id not in self._sessions: - self._sessions[context_id] = [] - - messages = self._sessions[context_id] - - if not messages: - # First message — inject harness as system prompt. - # Extract task tags for selective playbook retrieval (ACE/DC-RS). - raw_text = "\n".join(text_parts) - task_tags = self._extract_task_tags(raw_text) - harness_prompt = self.harness.system_prompt(self.bench, task_tags=task_tags) - system_content = harness_prompt or "You are a helpful CRM assistant." - messages.append({"role": "system", "content": system_content}) - - user_text = raw_text - user_text = self._format_crm_task(user_text) - if user_text.strip(): - messages.append({"role": "user", "content": user_text}) - - # Cache tools if provided - for d in data_parts: - if "tools" in d: - self._tool_cache[context_id] = self._convert_tools_to_openai(d["tools"]) - else: - # Subsequent turns — tool results and/or user text - for d in data_parts: - if "tool_results" in d: - for tr in d["tool_results"]: - green_id = tr["tool_call_id"] - tool_name = tr.get("tool_name", "") - self._reconcile_tool_call_id(messages, tool_name, green_id) - messages.append( - { - "role": "tool", - "tool_call_id": green_id, - "content": tr["content"], - } - ) - for text in text_parts: - if text.strip(): - messages.append({"role": "user", "content": text}) - - # Call LLM - tools = self._tool_cache.get(context_id) - completion_kwargs: dict[str, Any] = { - "model": self.model, - "messages": messages, - "temperature": 0.0, - } - if tools: - completion_kwargs["tools"] = tools - if self.api_base: - completion_kwargs["api_base"] = self.api_base - if self.api_key: - completion_kwargs["api_key"] = self.api_key - - response = litellm.completion(**completion_kwargs) - assistant_msg = response.choices[0].message + def _build_initial_messages(self, text_parts: list[str]) -> list[dict]: + raw_text = "\n".join(text_parts) + task_tags = self._extract_task_tags(raw_text) + harness_prompt = self.harness.system_prompt(self.bench, task_tags=task_tags) + system_content = harness_prompt or "You are a helpful CRM assistant." - normalized = self._normalize_assistant_msg(assistant_msg) - messages.append(normalized) + out: list[dict] = [{"role": "system", "content": system_content}] + user_text = self._format_crm_task(raw_text) + if user_text.strip(): + out.append({"role": "user", "content": user_text}) + return out - return self._format_a2a_response(assistant_msg) - - @staticmethod - def _reconcile_tool_call_id(messages: list[dict], tool_name: str, green_id: str) -> None: - """Rewrite last assistant tool_call id to match green's id.""" - used_green_ids = { - m["tool_call_id"] for m in messages if m.get("role") == "tool" and "tool_call_id" in m + def _format_a2a_response(self, assistant_msg: Any) -> dict: + # Return Message directly (not wrapped) — a2a-sdk expects result=Message + return { + "kind": "message", + "messageId": uuid4().hex, + "role": "agent", + "parts": self._build_message_parts(assistant_msg), } - for msg in reversed(messages): - if msg.get("role") != "assistant" or "tool_calls" not in msg: - continue - for tc in msg["tool_calls"]: - if tc["function"]["name"] == tool_name and tc["id"] not in used_green_ids: - tc["id"] = green_id - return - return - - -def create_app(agent: EntropicPurpleAgent, port: int = 0) -> Starlette: - """Create the A2A Starlette app for the entropic purple agent.""" - - async def agent_card(request: Request) -> JSONResponse: - return JSONResponse( - { - "name": "clawloop-entropic-purple-agent", - "description": "ClawLoop harness-optimized CRM agent under test", - "url": f"http://127.0.0.1:{port}/", - "version": "0.1.0", - "protocol_version": "0.3.0", - "preferred_transport": "JSONRPC", - "default_input_modes": ["text/plain"], - "default_output_modes": ["text/plain"], - "capabilities": {"streaming": False, "push_notifications": False}, - "skills": [ - { - "id": "crm_assistant", - "name": "CRM Assistant", - "description": "Agent under test for Entropic CRMArenaPro evaluation", - "tags": ["benchmark", "entropic", "crmarena"], - } - ], - } - ) - - async def handle_jsonrpc(request: Request) -> JSONResponse: - body = await request.json() - if body.get("jsonrpc") != "2.0" or "id" not in body: - return JSONResponse( - { - "jsonrpc": "2.0", - "id": None, - "error": {"code": -32600, "message": "Invalid Request"}, - } - ) - - method = body.get("method") - if method != "message/send": - return JSONResponse( - { - "jsonrpc": "2.0", - "id": body["id"], - "error": {"code": -32601, "message": f"Method not found: {method}"}, - } - ) - - loop = asyncio.get_running_loop() - result = await loop.run_in_executor(None, agent.handle_message_sync, body) - - return JSONResponse({"jsonrpc": "2.0", "id": body["id"], "result": result}) - - return Starlette( - routes=[ - Route("/.well-known/agent.json", agent_card, methods=["GET"]), - Route("/.well-known/agent-card.json", agent_card, methods=["GET"]), - Route("/", handle_jsonrpc, methods=["POST"]), - ] - ) - - -def start_purple_server( - agent: EntropicPurpleAgent, host: str = "127.0.0.1", port: int = 0 -) -> tuple[threading.Thread, int]: - """Start the purple agent server in a background thread. Returns (thread, actual_port).""" - import socket - import time - - sock = socket.socket(socket.AF_INET, socket.SOCK_STREAM) - sock.setsockopt(socket.SOL_SOCKET, socket.SO_REUSEADDR, 1) - sock.bind((host, port)) - actual_port = sock.getsockname()[1] - - app = create_app(agent, actual_port) - config = uvicorn.Config(app, host=host, port=actual_port, log_level="warning") - server = uvicorn.Server(config) - - thread = threading.Thread(target=server.run, kwargs={"sockets": [sock]}, daemon=True) - thread.start() - - import httpx - - for _ in range(50): - try: - r = httpx.get(f"http://{host}:{actual_port}/.well-known/agent-card.json", timeout=0.5) - if r.status_code == 200: - break - except httpx.ConnectError: - time.sleep(0.1) - else: - log.warning("Entropic purple server did not become ready within 5s") - - return thread, actual_port diff --git a/clawloop/environments/_purple_base.py b/clawloop/environments/_purple_base.py new file mode 100644 index 00000000..7e7aeee7 --- /dev/null +++ b/clawloop/environments/_purple_base.py @@ -0,0 +1,303 @@ +# clawloop/environments/_purple_base.py +"""Shared scaffolding for A2A purple-agent servers (CAR, Entropic, ...). + +Subclasses override two bench-specific seams: +``_build_initial_messages`` (parse first-turn payload) and +``_format_a2a_response`` (wire envelope the green agent expects). +""" + +from __future__ import annotations + +import asyncio +import json +import logging +import socket +import threading +import time +from typing import Any, ClassVar + +import httpx +import litellm +import uvicorn +from starlette.applications import Starlette +from starlette.requests import Request +from starlette.responses import JSONResponse +from starlette.routing import Route + +from clawloop.learning_layers.harness import Harness + +log = logging.getLogger(__name__) + + +class _PurpleAgentBase: + """Base class for A2A-compliant purple agents with harness injection.""" + + # Subclasses override: + default_bench: ClassVar[str] = "" + agent_card_name: ClassVar[str] = "clawloop-purple-agent" + agent_card_description: ClassVar[str] = "ClawLoop purple agent" + agent_card_skills: ClassVar[list[dict]] = [] + + def __init__( + self, + model: str, + harness: Harness, + bench: str | None = None, + api_base: str | None = None, + api_key: str | None = None, + ): + self.model = model + self.harness = harness + self.bench = bench if bench is not None else self.default_bench + self.api_base = api_base + self.api_key = api_key + self._sessions: dict[str, list[dict]] = {} + self._tool_cache: dict[str, list[dict]] = {} + + def update_harness(self, harness: Harness) -> None: + self.harness = harness + + def clear_all_sessions(self) -> None: + self._sessions.clear() + self._tool_cache.clear() + + @staticmethod + def _convert_tools_to_openai(raw_tools: list[dict]) -> list[dict]: + """Normalize tool schemas to OpenAI function-calling format. + + Green may send tools already OpenAI-wrapped or flat; handle both. + """ + result = [] + for t in raw_tools: + if t.get("type") == "function" and "function" in t: + result.append(t) + else: + result.append( + { + "type": "function", + "function": { + "name": t["name"], + "description": t.get("description", ""), + "parameters": t.get("parameters", {}), + }, + } + ) + return result + + @staticmethod + def _normalize_assistant_msg(litellm_msg: Any) -> dict: + """Normalize litellm response to stable internal dict.""" + normalized: dict[str, Any] = { + "role": "assistant", + "content": litellm_msg.content or "", + } + if litellm_msg.tool_calls: + normalized["tool_calls"] = [ + { + "id": tc.id, + "type": "function", + "function": { + "name": tc.function.name, + "arguments": tc.function.arguments, + }, + } + for tc in litellm_msg.tool_calls + ] + return normalized + + @staticmethod + def _reconcile_tool_call_id(messages: list[dict], tool_name: str, green_id: str) -> None: + """Rewrite last assistant tool_call id to match green's id. + + The LLM needs matching ids between assistant tool_calls and tool-role + messages. Handles duplicate tool names by only rewriting calls that + still hold their LLM-generated id (not a green id already in use). + + Only the most recent assistant message is searched: tool results + always follow their triggering assistant call in this protocol, so + reconciling against older assistant messages would be incorrect. + """ + used_green_ids = { + m["tool_call_id"] for m in messages if m.get("role") == "tool" and "tool_call_id" in m + } + for msg in reversed(messages): + if msg.get("role") != "assistant" or "tool_calls" not in msg: + continue + for tc in msg["tool_calls"]: + if tc["function"]["name"] == tool_name and tc["id"] not in used_green_ids: + tc["id"] = green_id + return + return # most recent assistant msg had no matching tool_call — stop + + def _build_message_parts(self, assistant_msg: Any) -> list[dict]: + """Build the A2A ``parts`` list from an assistant message.""" + parts: list[dict] = [{"kind": "text", "text": assistant_msg.content or ""}] + if assistant_msg.tool_calls: + tool_calls = [] + for tc in assistant_msg.tool_calls: + args = tc.function.arguments + if args is None: + args = {} + elif isinstance(args, str): + try: + args = json.loads(args) + except json.JSONDecodeError: + log.warning("Malformed tool args for %s", tc.function.name) + args = {"raw": args} + tool_calls.append({"tool_name": tc.function.name, "arguments": args}) + parts.append({"kind": "data", "data": {"tool_calls": tool_calls}}) + return parts + + def _build_initial_messages(self, text_parts: list[str]) -> list[dict]: + raise NotImplementedError + + def _format_a2a_response(self, assistant_msg: Any) -> dict: + raise NotImplementedError + + def _capture_assistant(self, context_id: str, normalized: dict) -> None: + pass + + def handle_message_sync(self, jsonrpc_request: dict) -> dict: + """Handle one ``message/send`` request (sync — litellm is sync).""" + params = jsonrpc_request["params"] + msg = params["message"] + context_id = params.get("contextId", "default") + + text_parts = [p["text"] for p in msg["parts"] if p.get("kind") == "text"] + data_parts = [p["data"] for p in msg["parts"] if p.get("kind") == "data"] + + if context_id not in self._sessions: + self._sessions[context_id] = [] + messages = self._sessions[context_id] + + if not messages: + messages.extend(self._build_initial_messages(text_parts)) + for d in data_parts: + if "tools" in d: + self._tool_cache[context_id] = self._convert_tools_to_openai(d["tools"]) + else: + for d in data_parts: + if "tool_results" in d: + for tr in d["tool_results"]: + green_id = tr["tool_call_id"] + tool_name = tr.get("tool_name", "") + self._reconcile_tool_call_id(messages, tool_name, green_id) + messages.append( + { + "role": "tool", + "tool_call_id": green_id, + "content": tr["content"], + } + ) + for text in text_parts: + if text.strip(): + messages.append({"role": "user", "content": text}) + + tools = self._tool_cache.get(context_id) + completion_kwargs: dict[str, Any] = { + "model": self.model, + "messages": messages, + "temperature": 0.0, + } + if tools: + completion_kwargs["tools"] = tools + if self.api_base: + completion_kwargs["api_base"] = self.api_base + if self.api_key: + completion_kwargs["api_key"] = self.api_key + + response = litellm.completion(**completion_kwargs) + assistant_msg = response.choices[0].message + + normalized = self._normalize_assistant_msg(assistant_msg) + messages.append(normalized) + self._capture_assistant(context_id, normalized) + + return self._format_a2a_response(assistant_msg) + + +def create_app(agent: _PurpleAgentBase, port: int = 0) -> Starlette: + """Build the A2A Starlette app for a purple agent.""" + + async def agent_card(request: Request) -> JSONResponse: + return JSONResponse( + { + "name": agent.agent_card_name, + "description": agent.agent_card_description, + "url": f"http://127.0.0.1:{port}/", + "version": "0.1.0", + "protocol_version": "0.3.0", + "preferred_transport": "JSONRPC", + "default_input_modes": ["text/plain"], + "default_output_modes": ["text/plain"], + "capabilities": {"streaming": False, "push_notifications": False}, + "skills": agent.agent_card_skills, + } + ) + + async def handle_jsonrpc(request: Request) -> JSONResponse: + body = await request.json() + if body.get("jsonrpc") != "2.0" or "id" not in body: + return JSONResponse( + { + "jsonrpc": "2.0", + "id": None, + "error": {"code": -32600, "message": "Invalid Request"}, + } + ) + + method = body.get("method") + if method != "message/send": + return JSONResponse( + { + "jsonrpc": "2.0", + "id": body["id"], + "error": {"code": -32601, "message": f"Method not found: {method}"}, + } + ) + + # Run sync litellm call in thread to avoid blocking event loop + loop = asyncio.get_running_loop() + result = await loop.run_in_executor(None, agent.handle_message_sync, body) + + return JSONResponse({"jsonrpc": "2.0", "id": body["id"], "result": result}) + + return Starlette( + routes=[ + Route("/.well-known/agent.json", agent_card, methods=["GET"]), + Route("/.well-known/agent-card.json", agent_card, methods=["GET"]), + Route("/", handle_jsonrpc, methods=["POST"]), + ] + ) + + +def start_purple_server( + agent: _PurpleAgentBase, host: str = "127.0.0.1", port: int = 0 +) -> tuple[threading.Thread, int]: + """Start the purple agent server in a background thread. Returns (thread, actual_port).""" + sock = socket.socket(socket.AF_INET, socket.SOCK_STREAM) + sock.setsockopt(socket.SOL_SOCKET, socket.SO_REUSEADDR, 1) + sock.bind((host, port)) + actual_port = sock.getsockname()[1] + + app = create_app(agent, actual_port) + config = uvicorn.Config(app, host=host, port=actual_port, log_level="warning") + server = uvicorn.Server(config) + + thread = threading.Thread(target=server.run, kwargs={"sockets": [sock]}, daemon=True) + thread.start() + + for _ in range(50): + try: + r = httpx.get( + f"http://{host}:{actual_port}/.well-known/agent-card.json", + timeout=0.5, + ) + if r.status_code == 200: + break + except httpx.ConnectError: + time.sleep(0.1) + else: + log.warning("Purple server did not become ready within 5s") + + return thread, actual_port diff --git a/clawloop/integrations/__init__.py b/clawloop/integrations/__init__.py new file mode 100644 index 00000000..ccb67f65 --- /dev/null +++ b/clawloop/integrations/__init__.py @@ -0,0 +1 @@ +"""First-class observability sinks for ClawLoop.""" diff --git a/clawloop/integrations/wandb.py b/clawloop/integrations/wandb.py new file mode 100644 index 00000000..f9a809c4 --- /dev/null +++ b/clawloop/integrations/wandb.py @@ -0,0 +1,280 @@ +"""Weights & Biases sink — log reward curves, playbook growth, and layer state hashes. + +Usage:: + + from clawloop.integrations.wandb import WandbSink + + sink = WandbSink(run_id="my-run", project="clawloop-experiments") + # As an after_iteration callback in the learning loop: + learning_loop(..., after_iteration=sink.after_iteration) + # Or log a batch of episodes directly: + sink.log_episodes(episodes, iteration=0) + sink.finish() + +Requires the ``wandb`` optional extra: ``uv sync --extra wandb``. +""" + +from __future__ import annotations + +import logging +from typing import TYPE_CHECKING, Any + +if TYPE_CHECKING: + from clawloop.core.episode import Episode + from clawloop.core.state import StateID + from clawloop.core.types import FBResult + from clawloop.learning_layers.harness import Harness + +_log = logging.getLogger(__name__) + + +class WandbSink: + """Log ClawLoop metrics to Weights & Biases. + + Parameters + ---------- + run_id: + W&B run ID. When resuming, pass the same ID to append. + project: + W&B project name. + entity: + W&B entity (team or user). ``None`` uses the default entity. + config: + Dict merged into ``wandb.config`` at init time. + log_episodes: + If ``True`` (default), log a ``wandb.Table`` of per-episode detail + each iteration. Set to ``False`` to reduce upload volume. + """ + + def __init__( + self, + *, + run_id: str | None = None, + project: str | None = None, + entity: str | None = None, + config: dict[str, Any] | None = None, + log_episodes: bool = True, + ) -> None: + try: + import wandb # noqa: F811 + except ImportError as exc: + raise ImportError( + "wandb is required for WandbSink. " "Install with: pip install 'clawloop[wandb]'" + ) from exc + + self._wandb = wandb + self._log_episodes = log_episodes + self._step = 0 + + init_kwargs: dict[str, Any] = {} + if run_id is not None: + init_kwargs["id"] = run_id + init_kwargs["resume"] = "allow" + if project is not None: + init_kwargs["project"] = project + if entity is not None: + init_kwargs["entity"] = entity + if config is not None: + init_kwargs["config"] = config + + self._run = wandb.init(**init_kwargs) + + # ------------------------------------------------------------------ + # Public API + # ------------------------------------------------------------------ + + def log_episodes( + self, + episodes: list[Episode], + *, + iteration: int | None = None, + ) -> None: + """Log per-episode reward curves and summary metrics for one batch. + + Parameters + ---------- + episodes: + Batch of completed episodes. + iteration: + Explicit iteration counter. When ``None``, an internal + auto-incrementing counter is used. + """ + if not episodes: + return + + step = iteration if iteration is not None else self._step + self._step = step + 1 + + try: + self._log_reward_scalars(episodes, step) + if self._log_episodes: + self._log_episode_table(episodes, step) + except Exception: + _log.warning("WandbSink.log_episodes failed", exc_info=True) + + def log_iteration( + self, + iteration: int, + episodes: list[Episode], + fb_results: dict[str, FBResult] | None = None, + *, + harness: Harness | None = None, + state_id: StateID | None = None, + ) -> None: + """Log a full iteration: rewards + playbook growth + layer hashes. + + Designed to be called from the learning loop alongside + :class:`~clawloop.core.loop.ExperimentLog`. + """ + self._step = iteration + 1 + + try: + self._log_reward_scalars(episodes, iteration) + + if harness is not None: + self._log_playbook(harness, iteration) + + if state_id is not None: + self._log_state_hashes(state_id, iteration) + + if fb_results is not None: + self._log_fb_results(fb_results, iteration) + + if self._log_episodes and episodes: + self._log_episode_table(episodes, iteration) + except Exception: + _log.warning("WandbSink.log_iteration failed", exc_info=True) + + def after_iteration( + self, + iteration: int, + agent_state: Any, + episodes: list[Episode], + ) -> None: + """Drop-in ``after_iteration`` callback for :func:`learning_loop`. + + Extracts harness and state_id from *agent_state* automatically. + """ + from clawloop.learning_layers.harness import Harness + + harness = agent_state.harness if isinstance(agent_state.harness, Harness) else None + state_id = agent_state.state_id() + + self.log_iteration( + iteration, + episodes, + harness=harness, + state_id=state_id, + ) + + def finish(self) -> None: + """Finalize the W&B run.""" + try: + self._run.finish() + except Exception: + _log.warning("WandbSink.finish failed", exc_info=True) + + # ------------------------------------------------------------------ + # Internal helpers + # ------------------------------------------------------------------ + + def _log_reward_scalars(self, episodes: list[Episode], step: int) -> None: + """Log aggregate reward metrics as scalars.""" + if not episodes: + return + + rewards = [ep.summary.effective_reward() for ep in episodes] + normalized = [ep.summary.normalized_reward() for ep in episodes] + + metrics: dict[str, Any] = { + "reward/mean": sum(rewards) / len(rewards), + "reward/min": min(rewards), + "reward/max": max(rewards), + "reward/mean_normalized": sum(normalized) / len(normalized), + "episodes/count": len(episodes), + } + + # Per-signal averages across the batch + signal_sums: dict[str, list[float]] = {} + for ep in episodes: + for name, sig in ep.summary.signals.items(): + signal_sums.setdefault(name, []).append(sig.value) + for name, values in signal_sums.items(): + metrics[f"reward/{name}_mean"] = sum(values) / len(values) + + self._run.log(metrics, step=step) + + def _log_playbook(self, harness: Harness, step: int) -> None: + """Log playbook growth metrics.""" + entries = harness.playbook.entries + metrics: dict[str, Any] = { + "playbook/size": len(entries), + } + + if entries: + scores = [e.effective_score() for e in entries] + metrics["playbook/mean_score"] = sum(scores) / len(scores) + metrics["playbook/max_score"] = max(scores) + metrics["playbook/helpful_total"] = sum(e.helpful for e in entries) + metrics["playbook/harmful_total"] = sum(e.harmful for e in entries) + + self._run.log(metrics, step=step) + + def _log_state_hashes(self, state_id: StateID, step: int) -> None: + """Log layer state hashes as string summaries.""" + self._run.log( + { + "state/combined": state_id.combined_hash[:12], + "state/harness": state_id.harness_hash[:12], + "state/router": state_id.router_hash[:12], + "state/weights": state_id.weights_hash[:12], + }, + step=step, + ) + + def _log_fb_results(self, fb_results: dict[str, FBResult], step: int) -> None: + """Log per-layer forward_backward result metrics.""" + metrics: dict[str, Any] = {} + for name, result in fb_results.items(): + metrics[f"fb/{name}/status"] = result.status + for mk, mv in (result.metrics or {}).items(): + if isinstance(mv, (int, float, bool)): + metrics[f"fb/{name}/{mk}"] = mv + if metrics: + self._run.log(metrics, step=step) + + def _log_episode_table(self, episodes: list[Episode], step: int) -> None: + """Log a W&B Table with per-episode detail.""" + wandb = self._wandb + + columns = [ + "episode_id", + "task_id", + "bench", + "state_id", + "model", + "effective_reward", + "normalized_reward", + "n_steps", + "n_messages", + "filtered", + ] + data = [] + for ep in episodes: + data.append( + [ + ep.id, + ep.task_id, + ep.bench, + ep.state_id[:12], + ep.model or "", + ep.summary.effective_reward(), + ep.summary.normalized_reward(), + ep.n_steps(), + len(ep.messages), + ep.summary.filtered, + ] + ) + + table = wandb.Table(columns=columns, data=data) + self._run.log({"episodes/table": table}, step=step) diff --git a/clawloop/learning_layers/harness.py b/clawloop/learning_layers/harness.py index e42a5f33..800b703a 100644 --- a/clawloop/learning_layers/harness.py +++ b/clawloop/learning_layers/harness.py @@ -823,6 +823,10 @@ def set_evolver_context(self, ctx: Any) -> None: """Set the EvolverContext for the next forward_backward call.""" self._evolver_context = ctx + def pending_paradigm_insights(self) -> list[Insight]: + """Return pending insights tagged ``paradigm`` (not yet drained by optim).""" + return [i for i in self._pending.insights if "paradigm" in (i.tags or [])] + def _build_snapshot(self) -> Any: """Build a HarnessSnapshot from current state for the Evolver.""" from clawloop.core.evolver import HarnessSnapshot diff --git a/examples/observability/wandb_sink_demo.py b/examples/observability/wandb_sink_demo.py new file mode 100644 index 00000000..b0dc6994 --- /dev/null +++ b/examples/observability/wandb_sink_demo.py @@ -0,0 +1,69 @@ +"""Minimal example: WandbSink logging ClawLoop episode metrics to W&B. + +Run with:: + + uv sync --extra wandb + WANDB_API_KEY=... python examples/observability/wandb_sink_demo.py + +Or use the sink as a learning-loop callback:: + + from clawloop.integrations.wandb import WandbSink + + sink = WandbSink(project="my-project") + learning_loop(..., after_iteration=sink.after_iteration) + sink.finish() +""" + +from clawloop.core.episode import ( + Episode, + EpisodeSummary, + Message, + StepMeta, + Timing, + TokenUsage, +) +from clawloop.core.reward import RewardSignal +from clawloop.integrations.wandb import WandbSink + + +def _make_demo_episode(iteration: int) -> Episode: + """Build a synthetic episode whose reward improves over iterations.""" + reward_value = min(0.5 + iteration * 0.1, 1.0) + mapped = reward_value * 2.0 - 1.0 + + return Episode( + id=f"ep-{iteration:04d}", + state_id=f"state-{iteration}", + task_id="demo-task", + bench="demo", + messages=[ + Message(role="user", content=f"Iteration {iteration} input"), + Message(role="assistant", content=f"Iteration {iteration} response"), + ], + step_boundaries=[0], + steps=[StepMeta(t=0, reward=reward_value, done=True, timing_ms=150.0)], + summary=EpisodeSummary( + signals={ + "outcome": RewardSignal(name="outcome", value=mapped, confidence=1.0), + }, + token_usage=TokenUsage(prompt_tokens=30, completion_tokens=20, total_tokens=50), + timing=Timing(total_ms=150.0, per_step_ms=[150.0]), + ), + model="gpt-4o-mini", + ) + + +def main() -> None: + sink = WandbSink(project="clawloop-demo") + + for i in range(10): + episodes = [_make_demo_episode(i) for _ in range(4)] + sink.log_episodes(episodes, iteration=i) + print(f"Logged iteration {i}") + + sink.finish() + print("Done — check your W&B dashboard.") + + +if __name__ == "__main__": + main() diff --git a/pyproject.toml b/pyproject.toml index 707e99d1..601a1714 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -41,6 +41,9 @@ car = [ "uvicorn>=0.20", "httpx>=0.24", ] +wandb = [ + "wandb>=0.26", +] otel = [ "opentelemetry-api>=1.20", "opentelemetry-sdk>=1.20", diff --git a/tests/test_wandb_sink.py b/tests/test_wandb_sink.py new file mode 100644 index 00000000..a08d5a25 --- /dev/null +++ b/tests/test_wandb_sink.py @@ -0,0 +1,425 @@ +"""Tests for clawloop.integrations.wandb — WandbSink.""" + +from __future__ import annotations + +import sys +from dataclasses import dataclass, field +from typing import Any +from unittest.mock import MagicMock + +import pytest + +from clawloop.core.episode import ( + Episode, + EpisodeSummary, + Message, + StepMeta, + Timing, + TokenUsage, +) +from clawloop.core.reward import RewardSignal +from clawloop.core.state import StateID + +# --------------------------------------------------------------------------- +# Fake wandb module — injected via sys.modules before importing WandbSink +# --------------------------------------------------------------------------- + +_LOGGED: list[tuple[dict, int | None]] = [] +_FINISHED: list[bool] = [] + + +class _FakeTable: + def __init__(self, columns: list[str], data: list[list]) -> None: + self.columns = columns + self.data = data + + +class _FakeRun: + def __init__(self, **kwargs: Any) -> None: + self.init_kwargs = kwargs + self.config = kwargs.get("config", {}) + + def log(self, metrics: dict, step: int | None = None) -> None: + _LOGGED.append((dict(metrics), step)) + + def finish(self) -> None: + _FINISHED.append(True) + + +class _FakeWandb: + Table = _FakeTable + _last_run: _FakeRun | None = None + + @staticmethod + def init(**kwargs: Any) -> _FakeRun: + run = _FakeRun(**kwargs) + _FakeWandb._last_run = run + return run + + +@pytest.fixture(autouse=True) +def _reset_logged(): + _LOGGED.clear() + _FINISHED.clear() + _FakeWandb._last_run = None + + +@pytest.fixture(autouse=True) +def _inject_fake_wandb(): + """Ensure the fake wandb is importable before WandbSink is loaded.""" + original = sys.modules.get("wandb") + sys.modules["wandb"] = _FakeWandb # type: ignore[assignment] + yield + if original is not None: + sys.modules["wandb"] = original + else: + sys.modules.pop("wandb", None) + + +# Import after fake is in place +from clawloop.integrations.wandb import WandbSink # noqa: E402 + +# --------------------------------------------------------------------------- +# Helpers +# --------------------------------------------------------------------------- + +_BASE_TS = 1_700_000_000.0 + + +def _make_episode( + *, + reward: float = 0.8, + episode_id: str = "ep-001", + task_id: str = "task-1", + bench: str = "test", + n_steps: int = 2, +) -> Episode: + mapped = reward * 2.0 - 1.0 + signals = { + "outcome": RewardSignal(name="outcome", value=mapped, confidence=1.0), + "execution": RewardSignal(name="execution", value=0.5, confidence=0.9), + } + messages = [ + Message(role="user", content="hi"), + Message(role="assistant", content="hello"), + ] + steps = [ + StepMeta( + t=i, + reward=(reward if i == n_steps - 1 else 0.0), + done=(i == n_steps - 1), + timing_ms=100.0, + ) + for i in range(n_steps) + ] + return Episode( + id=episode_id, + state_id="state-abc123", + task_id=task_id, + bench=bench, + messages=messages, + step_boundaries=[0], + steps=steps, + summary=EpisodeSummary( + signals=signals, + token_usage=TokenUsage(prompt_tokens=50, completion_tokens=20, total_tokens=70), + timing=Timing(total_ms=200.0, per_step_ms=[100.0, 100.0]), + ), + model="gpt-4o", + created_at=_BASE_TS, + ) + + +def _find_logged(key: str) -> list[tuple[Any, int | None]]: + """Return all (value, step) pairs where *key* was logged.""" + results = [] + for metrics, step in _LOGGED: + if key in metrics: + results.append((metrics[key], step)) + return results + + +# --------------------------------------------------------------------------- +# TestInit +# --------------------------------------------------------------------------- + + +class TestInit: + def test_basic_init(self) -> None: + sink = WandbSink() + assert sink._run is not None + + def test_run_id_passed(self) -> None: + WandbSink(run_id="my-run") + assert _FakeWandb._last_run is not None + assert _FakeWandb._last_run.init_kwargs["id"] == "my-run" + assert _FakeWandb._last_run.init_kwargs["resume"] == "allow" + + def test_project_passed(self) -> None: + WandbSink(project="test-proj") + assert _FakeWandb._last_run.init_kwargs["project"] == "test-proj" + + def test_entity_passed(self) -> None: + WandbSink(entity="my-team") + assert _FakeWandb._last_run.init_kwargs["entity"] == "my-team" + + def test_config_passed(self) -> None: + WandbSink(config={"lr": 0.01}) + assert _FakeWandb._last_run is not None + assert _FakeWandb._last_run.init_kwargs["config"] == {"lr": 0.01} + + def test_import_error_without_wandb(self) -> None: + original = sys.modules.get("wandb") + sys.modules["wandb"] = None # type: ignore[assignment] + try: + with pytest.raises(ImportError, match="wandb"): + WandbSink() + finally: + if original is not None: + sys.modules["wandb"] = original + + +# --------------------------------------------------------------------------- +# TestLogEpisodes +# --------------------------------------------------------------------------- + + +class TestLogEpisodes: + def test_reward_scalars_logged(self) -> None: + sink = WandbSink() + eps = [_make_episode(reward=0.8)] + sink.log_episodes(eps, iteration=0) + + means = _find_logged("reward/mean") + assert len(means) == 1 + assert abs(means[0][0] - 0.6) < 1e-6 # effective_reward for 0.8 mapped + assert means[0][1] == 0 + + def test_min_max_reward(self) -> None: + sink = WandbSink() + eps = [_make_episode(reward=0.3), _make_episode(reward=0.9, episode_id="ep-002")] + sink.log_episodes(eps, iteration=5) + + mins = _find_logged("reward/min") + maxs = _find_logged("reward/max") + assert len(mins) == 1 + assert len(maxs) == 1 + # 0.3 → mapped -0.4, 0.9 → mapped 0.8 + assert abs(mins[0][0] - (-0.4)) < 1e-6 + assert abs(maxs[0][0] - 0.8) < 1e-6 + + def test_episode_count(self) -> None: + sink = WandbSink() + eps = [_make_episode(), _make_episode(episode_id="ep-002")] + sink.log_episodes(eps, iteration=0) + + counts = _find_logged("episodes/count") + assert counts[0][0] == 2 + + def test_per_signal_mean(self) -> None: + sink = WandbSink() + eps = [_make_episode(reward=0.8)] + sink.log_episodes(eps, iteration=0) + + outcome = _find_logged("reward/outcome_mean") + assert len(outcome) == 1 + assert abs(outcome[0][0] - 0.6) < 1e-6 + + def test_empty_episodes_noop(self) -> None: + sink = WandbSink() + sink.log_episodes([], iteration=0) + assert len(_LOGGED) == 0 + + def test_auto_step_increments(self) -> None: + sink = WandbSink() + sink.log_episodes([_make_episode()]) + sink.log_episodes([_make_episode()]) + steps = [step for _, step in _LOGGED] + assert 0 in steps + assert 1 in steps + + def test_log_iteration_syncs_auto_step(self) -> None: + """log_iteration should advance _step so a subsequent log_episodes picks up.""" + sink = WandbSink() + sink.log_iteration(5, [_make_episode()]) + sink.log_episodes([_make_episode()]) # should use step=6, not step=0 + steps = [step for _, step in _LOGGED] + assert 6 in steps + + def test_episode_table_logged(self) -> None: + sink = WandbSink() + sink.log_episodes([_make_episode()], iteration=0) + + tables = _find_logged("episodes/table") + assert len(tables) == 1 + table = tables[0][0] + assert isinstance(table, _FakeTable) + assert "episode_id" in table.columns + assert len(table.data) == 1 + assert table.data[0][0] == "ep-001" + + def test_table_disabled(self) -> None: + sink = WandbSink(log_episodes=False) + sink.log_episodes([_make_episode()], iteration=0) + + tables = _find_logged("episodes/table") + assert len(tables) == 0 + + +# --------------------------------------------------------------------------- +# TestLogIteration +# --------------------------------------------------------------------------- + + +class TestLogIteration: + def test_rewards_logged(self) -> None: + sink = WandbSink() + sink.log_iteration(3, [_make_episode(reward=0.7)]) + + means = _find_logged("reward/mean") + assert means[0][1] == 3 + + def test_playbook_metrics(self) -> None: + sink = WandbSink() + + harness = MagicMock() + entry = MagicMock() + entry.effective_score.return_value = 2.0 + entry.helpful = 5 + entry.harmful = 1 + harness.playbook.entries = [entry] + + sink.log_iteration(0, [_make_episode()], harness=harness) + + sizes = _find_logged("playbook/size") + assert sizes[0][0] == 1 + + scores = _find_logged("playbook/mean_score") + assert scores[0][0] == 2.0 + + def test_playbook_empty(self) -> None: + sink = WandbSink() + harness = MagicMock() + harness.playbook.entries = [] + sink.log_iteration(0, [_make_episode()], harness=harness) + + sizes = _find_logged("playbook/size") + assert sizes[0][0] == 0 + # No mean_score when empty + assert len(_find_logged("playbook/mean_score")) == 0 + + def test_state_hashes(self) -> None: + sink = WandbSink() + sid = StateID( + harness_hash="aabbccddee11" * 6, + router_hash="112233445566" * 6, + weights_hash="ffeeddccbbaa" * 6, + combined_hash="abcdef012345" * 6, + created_at=_BASE_TS, + ) + sink.log_iteration(0, [_make_episode()], state_id=sid) + + combined = _find_logged("state/combined") + assert combined[0][0] == "abcdef012345" + + harness = _find_logged("state/harness") + assert harness[0][0] == "aabbccddee11" + + def test_fb_results(self) -> None: + sink = WandbSink() + + @dataclass + class FBR: + status: str = "ok" + metrics: dict = field( + default_factory=lambda: {"insights_generated": 3, "tokens_used": 100} + ) + + fb = {"harness": FBR()} + sink.log_iteration(0, [_make_episode()], fb_results=fb) + + insights = _find_logged("fb/harness/insights_generated") + assert insights[0][0] == 3 + + def test_fb_results_skip_non_numeric(self) -> None: + sink = WandbSink() + + @dataclass + class FBR: + status: str = "ok" + metrics: dict = field(default_factory=lambda: {"detail": "some string", "count": 5}) + + fb = {"harness": FBR()} + sink.log_iteration(0, [_make_episode()], fb_results=fb) + + # String metrics should not be logged + assert len(_find_logged("fb/harness/detail")) == 0 + assert len(_find_logged("fb/harness/count")) == 1 + + +# --------------------------------------------------------------------------- +# TestAfterIteration +# --------------------------------------------------------------------------- + + +class TestAfterIteration: + def test_callback_shape(self) -> None: + """after_iteration should work as a learning_loop callback.""" + sink = WandbSink() + + agent_state = MagicMock() + from clawloop.learning_layers.harness import Harness + + harness = Harness() + agent_state.harness = harness + agent_state.state_id.return_value = StateID( + harness_hash="a" * 64, + router_hash="b" * 64, + weights_hash="c" * 64, + combined_hash="d" * 64, + created_at=_BASE_TS, + ) + + eps = [_make_episode()] + sink.after_iteration(0, agent_state, eps) + + # Should have logged rewards + playbook + state hashes + assert len(_find_logged("reward/mean")) == 1 + assert len(_find_logged("playbook/size")) == 1 + assert len(_find_logged("state/combined")) == 1 + + +# --------------------------------------------------------------------------- +# TestFinish +# --------------------------------------------------------------------------- + + +class TestFinish: + def test_finish_calls_run_finish(self) -> None: + sink = WandbSink() + sink.finish() + assert len(_FINISHED) == 1 + + +# --------------------------------------------------------------------------- +# TestFailSoft +# --------------------------------------------------------------------------- + + +class TestFailSoft: + def test_log_episodes_swallows_error(self) -> None: + sink = WandbSink() + # Make the run.log raise + sink._run.log = MagicMock(side_effect=RuntimeError("boom")) + # Should not raise + sink.log_episodes([_make_episode()], iteration=0) + + def test_log_iteration_swallows_error(self) -> None: + sink = WandbSink() + sink._run.log = MagicMock(side_effect=RuntimeError("boom")) + sink.log_iteration(0, [_make_episode()]) + + def test_finish_swallows_error(self) -> None: + sink = WandbSink() + sink._run.finish = MagicMock(side_effect=RuntimeError("boom")) + sink.finish() diff --git a/tests/unit/core/test_archive_recorder.py b/tests/unit/core/test_archive_recorder.py new file mode 100644 index 00000000..d59966aa --- /dev/null +++ b/tests/unit/core/test_archive_recorder.py @@ -0,0 +1,323 @@ +"""Unit tests for ``clawloop.core.archive_recorder.ArchiveRecorder``. + +The recorder is a thin wrapper around an ``ArchiveStore`` that owns the +run-level counters (run_id, best_reward, initial_reward, total_cost, +prev_variant_hash) so ``learning_loop`` doesn't have to. These tests pin +its contract: what it writes, when, and that archive exceptions are +caught and logged rather than propagated. +""" + +from __future__ import annotations + +import logging +from typing import Any +from unittest.mock import MagicMock + +import pytest + +from clawloop.archive.schema import ( + AgentVariant, + EpisodeRecord, + IterationRecord, + RunRecord, +) +from clawloop.core.archive_recorder import ArchiveRecorder +from clawloop.core.loop import AgentState +from clawloop.core.state import StateID +from clawloop.core.types import FBResult + + +class _RecordingStore: + """Minimal ArchiveStore that remembers every write for inspection.""" + + def __init__(self) -> None: + self.runs: list[RunRecord] = [] + self.iterations: list[IterationRecord] = [] + self.episode_batches: list[list[EpisodeRecord]] = [] + self.variants: list[AgentVariant] = [] + self.completions: list[tuple[str, float, float, int]] = [] + + def log_run_start(self, run: RunRecord) -> None: + self.runs.append(run) + + def log_iteration(self, iteration: IterationRecord) -> None: + self.iterations.append(iteration) + + def log_episodes(self, episodes: list[EpisodeRecord]) -> None: + self.episode_batches.append(list(episodes)) + + def log_variant(self, variant: AgentVariant) -> None: + self.variants.append(variant) + + def log_run_complete( + self, + run_id: str, + best_reward: float, + improvement_delta: float, + total_cost_tokens: int = 0, + ) -> None: + self.completions.append((run_id, best_reward, improvement_delta, total_cost_tokens)) + + +def _mk_episode(episode_id: str = "ep1", task_id: str = "t1", reward: float = 0.5) -> Any: + """Minimal duck-typed Episode stub — recorder only touches documented fields.""" + ep = MagicMock() + ep.id = episode_id + ep.task_id = task_id + ep.bench = "unit" + ep.model = "test-model" + ep.messages = [] + ep.created_at = 1234567.0 + ep.summary.total_reward = reward + ep.summary.normalized_reward.return_value = reward + ep.summary.signals = {} + ep.summary.token_usage = None + ep.summary.timing = None + ep.n_steps.return_value = 3 + return ep + + +def test_init_writes_run_start_and_initial_variant() -> None: + store = _RecordingStore() + agent_state = AgentState() + recorder = ArchiveRecorder( + store, agent_state, bench="unit-bench", domain_tags=["a", "b"], n_iterations=5 + ) + + assert len(store.runs) == 1 + run = store.runs[0] + assert run.bench == "unit-bench" + assert run.domain_tags == ["a", "b"] + assert run.n_iterations == 5 + assert run.best_reward == 0.0 + assert run.improvement_delta == 0.0 + assert run.total_cost_tokens == 0 + assert run.parent_run_id is None + assert run.completed_at is None + assert run.run_id # non-empty + # run_id is stable and exposed so the loop doesn't regenerate + assert recorder.run_id == run.run_id + + assert len(store.variants) == 1 + variant = store.variants[0] + assert variant.variant_hash == run.config_hash + assert variant.first_seen_run_id == run.run_id + + +def test_archive_none_uses_null_store_silently() -> None: + # archive=None should fall back to NullArchiveStore — no errors, no writes + recorder = ArchiveRecorder(None, AgentState(), bench="b", domain_tags=None, n_iterations=1) + recorder.record_episodes(0, [_mk_episode()]) + recorder.record_complete() + # Nothing to assert except the absence of exceptions. + assert recorder.run_id # still generated + + +def test_record_episodes_writes_one_batch_with_correct_fields() -> None: + store = _RecordingStore() + recorder = ArchiveRecorder(store, AgentState(), bench="b", domain_tags=None, n_iterations=1) + + ep_a = _mk_episode("ep-a", "task-a", reward=0.7) + ep_b = _mk_episode("ep-b", "task-b", reward=0.3) + recorder.record_episodes(iteration=2, episodes=[ep_a, ep_b]) + + assert len(store.episode_batches) == 1 + batch = store.episode_batches[0] + assert len(batch) == 2 + assert [r.episode_id for r in batch] == ["ep-a", "ep-b"] + assert all(r.iteration_num == 2 for r in batch) + assert all(r.run_id == recorder.run_id for r in batch) + assert batch[0].messages_ref == "traces/ep-a.json" + + +def test_record_episodes_noop_on_empty_list() -> None: + store = _RecordingStore() + recorder = ArchiveRecorder(store, AgentState(), bench="b", domain_tags=None, n_iterations=1) + + recorder.record_episodes(iteration=0, episodes=[]) + + # record_episodes with no episodes must not emit an empty batch + assert store.episode_batches == [] + + +def test_record_iteration_writes_iteration_record_and_no_variant_when_unchanged() -> None: + store = _RecordingStore() + agent_state = AgentState() + recorder = ArchiveRecorder(store, agent_state, bench="b", domain_tags=None, n_iterations=3) + + initial_variant_count = len(store.variants) + state_id = agent_state.state_id() + fb_results = {"harness": FBResult(status="ok", metrics={"tokens_used": 100})} + ep = _mk_episode() + + recorder.record_iteration( + iteration=0, + agent_state=agent_state, + state_id=state_id, + fb_results=fb_results, + episodes=[ep], + avg_reward=0.5, + prev_avg_reward=0.0, + ) + + assert len(store.iterations) == 1 + it = store.iterations[0] + assert it.iteration_num == 0 + assert it.run_id == recorder.run_id + assert it.mean_reward == 0.5 + assert it.reward_delta == 0.5 + assert it.cost_tokens == 100 + assert it.evolver_action == {"harness": {"tokens_used": 100}} + # Variant hasn't changed — no new variant row + assert len(store.variants) == initial_variant_count + + +def test_record_iteration_writes_new_variant_when_config_changes() -> None: + store = _RecordingStore() + agent_state = AgentState() + recorder = ArchiveRecorder(store, agent_state, bench="b", domain_tags=None, n_iterations=1) + initial_variant_count = len(store.variants) + + agent_state.harness.system_prompts["main"] = "you are a changed agent" + + recorder.record_iteration( + iteration=0, + agent_state=agent_state, + state_id=agent_state.state_id(), + fb_results={}, + episodes=[_mk_episode()], + avg_reward=0.1, + prev_avg_reward=0.0, + ) + + assert len(store.variants) == initial_variant_count + 1 + new_variant = store.variants[-1] + assert new_variant.first_seen_run_id == recorder.run_id + + +def test_record_complete_writes_completion_with_accumulated_metrics() -> None: + store = _RecordingStore() + recorder = ArchiveRecorder(store, AgentState(), bench="b", domain_tags=None, n_iterations=2) + + recorder.record_iteration( + iteration=0, + agent_state=AgentState(), + state_id=AgentState().state_id(), + fb_results={"w": FBResult(status="ok", metrics={"tokens_used": 50})}, + episodes=[_mk_episode(reward=0.2)], + avg_reward=0.2, + prev_avg_reward=0.0, + ) + recorder.record_iteration( + iteration=1, + agent_state=AgentState(), + state_id=AgentState().state_id(), + fb_results={"w": FBResult(status="ok", metrics={"tokens_used": 75})}, + episodes=[_mk_episode(reward=0.8)], + avg_reward=0.8, + prev_avg_reward=0.2, + ) + + recorder.record_complete() + + assert len(store.completions) == 1 + run_id, best_reward, improvement_delta, total_cost = store.completions[0] + assert run_id == recorder.run_id + assert best_reward == 0.8 + assert improvement_delta == pytest.approx(0.8 - 0.2) # best - initial + assert total_cost == 125 + + +def test_record_complete_handles_zero_iterations() -> None: + # best_reward and initial_reward stay None if no iterations ran — must not crash + store = _RecordingStore() + recorder = ArchiveRecorder(store, AgentState(), bench="b", domain_tags=None, n_iterations=0) + + recorder.record_complete() + + assert len(store.completions) == 1 + run_id, best, delta, cost = store.completions[0] + assert best == 0.0 + assert delta == 0.0 + assert cost == 0 + + +def test_archive_exceptions_are_swallowed_with_warning(caplog: pytest.LogCaptureFixture) -> None: + class _BrokenStore(_RecordingStore): + def log_episodes(self, episodes: list[EpisodeRecord]) -> None: + raise RuntimeError("archive disk full") + + store = _BrokenStore() + recorder = ArchiveRecorder(store, AgentState(), bench="b", domain_tags=None, n_iterations=1) + + with caplog.at_level(logging.WARNING, logger="clawloop.core.archive_recorder"): + recorder.record_episodes(iteration=0, episodes=[_mk_episode()]) + + # Exception must not propagate; a warning must be emitted. + assert any("failed to log episodes" in rec.message.lower() for rec in caplog.records) + + +def test_run_start_exception_does_not_block_construction(caplog: pytest.LogCaptureFixture) -> None: + class _RunStartFails(_RecordingStore): + def log_run_start(self, run: RunRecord) -> None: + raise RuntimeError("db down") + + store = _RunStartFails() + with caplog.at_level(logging.WARNING, logger="clawloop.core.archive_recorder"): + recorder = ArchiveRecorder( + store, AgentState(), bench="b", domain_tags=None, n_iterations=1 + ) + + assert recorder.run_id # still usable + assert any("run start" in rec.message.lower() for rec in caplog.records) + + +def test_state_id_parameter_is_used_for_harness_snapshot_hash() -> None: + store = _RecordingStore() + agent_state = AgentState() + recorder = ArchiveRecorder(store, agent_state, bench="b", domain_tags=None, n_iterations=1) + + sid = StateID( + harness_hash="HHH", + router_hash="R", + weights_hash="W", + combined_hash="C", + created_at=0.0, + ) + recorder.record_iteration( + iteration=0, + agent_state=agent_state, + state_id=sid, + fb_results={}, + episodes=[], + avg_reward=0.0, + prev_avg_reward=0.0, + ) + + assert store.iterations[0].harness_snapshot_hash == "HHH" + + +def test_cost_tracking_survives_archive_exception() -> None: + # Regression: if log_iteration raises, tokens_used must still be accumulated + # onto total_cost_tokens. Cost tracking is independent of archive availability. + class _BrokenIter(_RecordingStore): + def log_iteration(self, iteration: IterationRecord) -> None: + raise RuntimeError("db down") + + store = _BrokenIter() + recorder = ArchiveRecorder(store, AgentState(), bench="b", domain_tags=None, n_iterations=1) + + recorder.record_iteration( + iteration=0, + agent_state=AgentState(), + state_id=AgentState().state_id(), + fb_results={"h": FBResult(status="ok", metrics={"tokens_used": 42})}, + episodes=[_mk_episode(reward=0.1)], + avg_reward=0.1, + prev_avg_reward=0.0, + ) + recorder.record_complete() + + assert len(store.completions) == 1 + _, _, _, total_cost = store.completions[0] + assert total_cost == 42 # accumulated despite log_iteration raising diff --git a/tests/unit/core/test_runner.py b/tests/unit/core/test_runner.py new file mode 100644 index 00000000..619155aa --- /dev/null +++ b/tests/unit/core/test_runner.py @@ -0,0 +1,153 @@ +"""Unit tests for ``clawloop.core.runner.EpisodeCollectorRunner``.""" + +from __future__ import annotations + +import random +from typing import Any + +import pytest + +from clawloop.core.runner import EpisodeCollectorRunner + + +def _mk_episode(task_id: str) -> Any: + """Sentinel stand-in for Episode. Runner never inspects episodes — it + only passes them through from adapter to caller — so a plain object + tagged with ``task_id`` is sufficient and keeps the test decoupled + from Episode's evolving required fields. + """ + return {"task_id": task_id} + + +class _PerTaskAdapter: + """Adapter exposing only ``run_episode`` (fallback path).""" + + def __init__(self) -> None: + self.calls: list[tuple[Any, Any]] = [] + + def run_episode(self, task: Any, agent_state: Any) -> Any: + self.calls.append((task, agent_state)) + return _mk_episode(str(task)) + + +class _BatchAdapter: + """Adapter exposing ``run_batch`` (fast path).""" + + def __init__(self) -> None: + self.calls: list[tuple[Any, list[Any]]] = [] + + def run_batch(self, agent_state: Any, tasks: list[Any]) -> list[Any]: + self.calls.append((agent_state, list(tasks))) + return [_mk_episode(str(t)) for t in tasks] + + +def test_empty_tasks_returns_empty_without_touching_adapter() -> None: + adapter = _PerTaskAdapter() + runner = EpisodeCollectorRunner(adapter) + assert runner.collect(agent_state=object(), tasks=[], n_episodes=3) == [] + assert adapter.calls == [] + + +def test_zero_n_episodes_returns_empty_without_touching_adapter() -> None: + adapter = _PerTaskAdapter() + runner = EpisodeCollectorRunner(adapter) + assert runner.collect(agent_state=object(), tasks=["a", "b"], n_episodes=0) == [] + assert adapter.calls == [] + + +def test_negative_n_episodes_returns_empty() -> None: + adapter = _PerTaskAdapter() + runner = EpisodeCollectorRunner(adapter) + assert runner.collect(agent_state=object(), tasks=["a"], n_episodes=-1) == [] + assert adapter.calls == [] + + +def test_sample_branch_uses_sample_without_replacement(monkeypatch: pytest.MonkeyPatch) -> None: + calls: dict[str, Any] = {} + + def fake_sample(pool: list[Any], k: int) -> list[Any]: + calls["sample"] = (list(pool), k) + return pool[:k] + + def fake_choices(pool: list[Any], k: int) -> list[Any]: # pragma: no cover — must not run + raise AssertionError("random.choices should not be called when n <= len(tasks)") + + monkeypatch.setattr(random, "sample", fake_sample) + monkeypatch.setattr(random, "choices", fake_choices) + + adapter = _PerTaskAdapter() + runner = EpisodeCollectorRunner(adapter) + episodes = runner.collect(agent_state="S", tasks=["a", "b", "c"], n_episodes=2) + + assert [ep["task_id"] for ep in episodes] == ["a", "b"] + assert calls["sample"] == (["a", "b", "c"], 2) + + +def test_choices_branch_used_when_n_exceeds_pool(monkeypatch: pytest.MonkeyPatch) -> None: + calls: dict[str, Any] = {} + + def fake_sample(pool: list[Any], k: int) -> list[Any]: # pragma: no cover + raise AssertionError("random.sample should not be called when n > len(tasks)") + + def fake_choices(pool: list[Any], *, k: int) -> list[Any]: + calls["choices"] = (list(pool), k) + return [pool[0]] * k + + monkeypatch.setattr(random, "sample", fake_sample) + monkeypatch.setattr(random, "choices", fake_choices) + + adapter = _PerTaskAdapter() + runner = EpisodeCollectorRunner(adapter) + episodes = runner.collect(agent_state="S", tasks=["x"], n_episodes=3) + + assert len(episodes) == 3 + assert calls["choices"] == (["x"], 3) + + +def test_prefers_run_batch_when_available(monkeypatch: pytest.MonkeyPatch) -> None: + monkeypatch.setattr(random, "sample", lambda pool, k: list(pool[:k])) + + adapter = _BatchAdapter() + runner = EpisodeCollectorRunner(adapter) + agent = object() + episodes = runner.collect(agent_state=agent, tasks=["a", "b", "c"], n_episodes=2) + + assert len(episodes) == 2 + assert len(adapter.calls) == 1 + called_agent, called_tasks = adapter.calls[0] + assert called_agent is agent + assert called_tasks == ["a", "b"] + + +def test_per_task_fallback_when_no_run_batch(monkeypatch: pytest.MonkeyPatch) -> None: + monkeypatch.setattr(random, "sample", lambda pool, k: list(pool[:k])) + + adapter = _PerTaskAdapter() + runner = EpisodeCollectorRunner(adapter) + agent = object() + episodes = runner.collect(agent_state=agent, tasks=["a", "b"], n_episodes=2) + + assert [ep["task_id"] for ep in episodes] == ["a", "b"] + assert [agent_state for _, agent_state in adapter.calls] == [agent, agent] + + +def test_non_callable_run_batch_attribute_falls_back(monkeypatch: pytest.MonkeyPatch) -> None: + """A non-callable ``run_batch`` attribute must not short-circuit the fallback.""" + monkeypatch.setattr(random, "sample", lambda pool, k: list(pool[:k])) + + class _WeirdAdapter: + run_batch = "not-a-callable" + + def __init__(self) -> None: + self.calls: list[tuple[Any, Any]] = [] + + def run_episode(self, task: Any, agent_state: Any) -> Any: + self.calls.append((task, agent_state)) + return _mk_episode(str(task)) + + adapter = _WeirdAdapter() + runner = EpisodeCollectorRunner(adapter) + episodes = runner.collect(agent_state="S", tasks=["a"], n_episodes=1) + + assert [ep["task_id"] for ep in episodes] == ["a"] + assert adapter.calls == [("a", "S")] diff --git a/tests/unit/core/test_transaction.py b/tests/unit/core/test_transaction.py new file mode 100644 index 00000000..caa17b9c --- /dev/null +++ b/tests/unit/core/test_transaction.py @@ -0,0 +1,233 @@ +"""Unit tests for ``clawloop.core.transaction.LayerTransaction``. + +The transaction owns the two-phase ``forward_backward`` → ``optim_step`` +protocol with cross-layer rollback. These tests pin the invariants that +the ``learning_loop`` depended on: which layers run, when +``clear_pending_state`` fires, and under what conditions every layer is +rolled back to its pre-optim snapshot. +""" + +from __future__ import annotations + +from typing import Any + +from clawloop.core.loop import AgentState +from clawloop.core.transaction import LayerTransaction +from clawloop.core.types import Datum, FBResult, Future, LoadResult, OptimResult + + +class _StubLayer: + """Hand-rolled Layer stub — records call order and returns canned results. + + Using a stub (not MagicMock) so assertions read like a state machine: + who was called, with what, in what order. That is the exact contract + the rollback invariant depends on. + """ + + def __init__( + self, + fb_result: FBResult | None = None, + fb_raises: bool = False, + optim_result: OptimResult | None = None, + optim_raises: bool = False, + load_result: LoadResult | None = None, + load_raises: bool = False, + state: dict[str, Any] | None = None, + ) -> None: + self._fb_result = fb_result or FBResult(status="ok", metrics={}) + self._fb_raises = fb_raises + self._optim_result = optim_result or OptimResult(status="ok", updates_applied=1) + self._optim_raises = optim_raises + self._load_result = load_result or LoadResult(status="ok") + self._load_raises = load_raises + self._state = state or {"version": 1} + + self.fb_calls: list[Datum] = [] + self.optim_calls = 0 + self.load_calls: list[dict[str, Any]] = [] + self.clear_calls = 0 + + def forward_backward(self, datum: Datum) -> Future[FBResult]: + self.fb_calls.append(datum) + if self._fb_raises: + raise RuntimeError("fb boom") + return Future.immediate(self._fb_result) + + def optim_step(self) -> Future[OptimResult]: + self.optim_calls += 1 + if self._optim_raises: + raise RuntimeError("optim boom") + return Future.immediate(self._optim_result) + + def load_state(self, state: dict[str, Any]) -> Future[LoadResult]: + self.load_calls.append(state) + if self._load_raises: + raise RuntimeError("load boom") + return Future.immediate(self._load_result) + + def clear_pending_state(self) -> None: + self.clear_calls += 1 + + def to_dict(self) -> dict[str, Any]: + return dict(self._state) + + +def test_happy_path_runs_all_layers_and_no_rollback() -> None: + h, r, w = _StubLayer(), _StubLayer(), _StubLayer() + layers = [("harness", h), ("router", r), ("weights", w)] + tx = LayerTransaction(layers, intensity=None, episodes=[], agent_state=AgentState()) + + result = tx.run(iteration=0) + + assert result.optim_failed is False + assert set(result.fb_results.keys()) == {"harness", "router", "weights"} + assert all(r.status == "ok" for r in result.fb_results.values()) + # Every layer saw both fb and optim; nothing was rolled back or cleared + assert all(layer.optim_calls == 1 for layer in (h, r, w)) + assert all(layer.load_calls == [] for layer in (h, r, w)) + assert all(layer.clear_calls == 0 for layer in (h, r, w)) + + +def test_fb_exception_clears_pending_and_skips_that_layer_from_optim() -> None: + h = _StubLayer(fb_raises=True) + w = _StubLayer() + layers = [("harness", h), ("weights", w)] + tx = LayerTransaction(layers, intensity=None, episodes=[], agent_state=AgentState()) + + result = tx.run(iteration=0) + + # Harness fb raised → status=error, clear_pending_state called, no optim + assert result.fb_results["harness"].status == "error" + assert h.clear_calls == 1 + assert h.optim_calls == 0 + # Weights is independent and proceeds through optim normally + assert result.fb_results["weights"].status == "ok" + assert w.optim_calls == 1 + # The failing layer wasn't rolled back (it never reached optim) + assert h.load_calls == [] + assert result.optim_failed is False + + +def test_fb_skipped_status_also_clears_pending() -> None: + # fb_result.status == "skipped" must trigger clear_pending_state + # (matches the original in-loop behavior where skipped fb also clears) + h = _StubLayer(fb_result=FBResult(status="skipped")) + layers = [("harness", h)] + tx = LayerTransaction(layers, intensity=None, episodes=[], agent_state=AgentState()) + + tx.run(iteration=0) + + assert h.clear_calls == 1 + assert h.optim_calls == 0 + + +def test_optim_error_rolls_back_all_snapshotted_layers() -> None: + # When any optim returns status="error", EVERY layer that had a + # successful fb gets load_state called with its pre-optim snapshot. + h = _StubLayer(state={"v": "h-before"}) + r = _StubLayer( + state={"v": "r-before"}, + optim_result=OptimResult(status="error"), # trigger rollback + ) + w = _StubLayer(state={"v": "w-before"}) + layers = [("harness", h), ("router", r), ("weights", w)] + tx = LayerTransaction(layers, intensity=None, episodes=[], agent_state=AgentState()) + + result = tx.run(iteration=0) + + assert result.optim_failed is True + # Rollback fires for every layer that was snapshotted — even weights, + # whose optim never ran because the loop broke on router's error + assert h.load_calls == [{"v": "h-before"}] + assert r.load_calls == [{"v": "r-before"}] + assert w.load_calls == [{"v": "w-before"}] + + +def test_optim_exception_rolls_back_all_snapshotted_layers() -> None: + # Same rollback contract when optim_step raises (not just returns error) + h = _StubLayer(state={"v": "h"}) + r = _StubLayer(state={"v": "r"}, optim_raises=True) + layers = [("harness", h), ("router", r)] + tx = LayerTransaction(layers, intensity=None, episodes=[], agent_state=AgentState()) + + result = tx.run(iteration=0) + + assert result.optim_failed is True + assert h.load_calls == [{"v": "h"}] + assert r.load_calls == [{"v": "r"}] + + +def test_snapshot_failure_skips_optim_and_clears_all() -> None: + # If deepcopy(layer.to_dict()) raises, optim must not run for anyone, + # and every layer that had ok fb gets clear_pending_state called. + class _BadToDict(_StubLayer): + def to_dict(self) -> dict[str, Any]: + raise RuntimeError("cannot serialize") + + h = _BadToDict() + w = _StubLayer() + layers = [("harness", h), ("weights", w)] + tx = LayerTransaction(layers, intensity=None, episodes=[], agent_state=AgentState()) + + result = tx.run(iteration=0) + + assert h.optim_calls == 0 + assert w.optim_calls == 0 + assert h.clear_calls == 1 + assert w.clear_calls == 1 + # No rollback happened because no optim ran + assert h.load_calls == [] + assert w.load_calls == [] + # optim_failed stays False — this is a snapshot failure, not an optim failure + assert result.optim_failed is False + + +def test_intensity_skips_harness_reflection_but_other_layers_proceed() -> None: + class _NoReflect: + def should_reflect(self, iteration: int) -> bool: + return False + + h = _StubLayer() + w = _StubLayer() + layers = [("harness", h), ("weights", w)] + tx = LayerTransaction(layers, intensity=_NoReflect(), episodes=[], agent_state=AgentState()) + + result = tx.run(iteration=3) + + # Harness fb was skipped entirely — no call, no clear (clear fires on + # error/skipped result; "intensity gate" short-circuits before fb runs + # and records skipped status) + assert h.fb_calls == [] + assert result.fb_results["harness"].status == "skipped" + assert h.optim_calls == 0 + # Weights proceeds normally + assert w.fb_calls != [] + assert w.optim_calls == 1 + + +def test_paradigm_shift_on_harness_fb_mutates_tried_paradigms() -> None: + # When harness fb returns metrics["paradigm_shifted"]=True and the + # harness has _pending insights tagged "paradigm", those contents + # must be appended to agent_state.tried_paradigms BEFORE optim runs + # (optim drains _pending, so ordering matters). + agent_state = AgentState() + # Seed _pending with a paradigm-tagged insight via the public helper + from clawloop.learning_layers.harness import Insight, _HarnessPending + + agent_state.harness._pending = _HarnessPending( + insights=[Insight(content="shift-to-x", tags=["paradigm"])], + ) + assert [i.content for i in agent_state.harness.pending_paradigm_insights()] == ["shift-to-x"] + + class _HarnessShiftLayer(_StubLayer): + def __init__(self) -> None: + super().__init__(fb_result=FBResult(status="ok", metrics={"paradigm_shifted": True})) + + # The transaction must resolve paradigm tracking against + # agent_state.harness, not the stub layer passed as ("harness", ...). + layers = [("harness", _HarnessShiftLayer())] + tx = LayerTransaction(layers, intensity=None, episodes=[], agent_state=agent_state) + + tx.run(iteration=0) + + assert "shift-to-x" in agent_state.tried_paradigms diff --git a/uv.lock b/uv.lock index 00065a24..6ade58e8 100644 --- a/uv.lock +++ b/uv.lock @@ -1,5 +1,5 @@ version = 1 -revision = 3 +revision = 1 requires-python 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