tau2 via OFFICIAL engine: self-running env plugin (run_task hook + needs_adapter generic dispatch, no name hardcoding), deep generate() patch, official ToolCall shape, data plugin keeps verbatim Task json, official reward scoring; checkpoint plugin (per-sample resume) + summary csv with time/categories

evalharness/agent/envs/bfcl_mock.py

@@ -107,7 +107,8 @@ class BFCLEnvironment(Environment):
 
     name = 'bfcl_mock'
 
-    def __init__(self):
+    def __init__(self, adapter=None):
+        super().__init__(adapter=adapter)
         self.calls: List[Dict[str, Any]] = []
         self.ground_truth: Dict[str, Any] = {}
 

evalharness/agent/envs/tau2_official.py

@@ -0,0 +1,144 @@
+"""tau2-bench environment: official tau2 package as the ENGINE, we only bridge.
+
+Same architecture as evalscope's adapter: the official user simulator,
+domain environments (airline db/policy/...), and reward logic are used
+VERBATIM; we monkey-patch tau2's LLM generate() to call our ModelAdapter,
+so scoring is bit-identical to the official tool by construction.
+
+The env consumes a Sample whose metadata carries the official Task json
+(our tau2_bench dataset plugin stores it in metadata['task']).
+"""
+
+import asyncio
+import json
+from typing import Any, Dict, List, Optional
+
+from ...data.sample import ChatMessage, Sample
+from ..loop import Environment, register_env
+
+_PATCHED = False
+
+
+def _patch_tau2_generate(adapter) -> None:
+    """Route tau2's LLM calls to our ModelAdapter (sync bridge via asyncio)."""
+    global _PATCHED
+    if _PATCHED:
+        return
+
+    import tau2.utils.llm_utils as llm_utils
+
+    original = llm_utils.generate
+
+    def patched_generate(model, messages, tools=None, tool_choice=None, **kw):
+        # model: 'user' | 'agent' -> same adapter in our single-model setup
+        msgs = [ChatMessage(role=m.role if hasattr(m, 'role') else 'user',
+                            content=m.content if hasattr(m, 'content') else str(m))
+                for m in messages]
+        tool_specs = None
+        if tools:
+            tool_specs = []
+            for t in tools:
+                spec = t.openai_schema if hasattr(t, 'openai_schema') else t
+                if isinstance(spec, dict) and 'function' not in spec:
+                    spec = {'type': 'function', 'function': spec}
+                tool_specs.append(spec)
+
+        async def go():
+            return await adapter.generate(msgs, tools=tool_specs)
+
+        try:
+            loop = asyncio.get_running_loop()
+        except RuntimeError:
+            out = asyncio.run(go())
+        else:
+            import concurrent.futures
+
+            with concurrent.futures.ThreadPoolExecutor(max_workers=1) as pool:
+                out = pool.submit(asyncio.run, go()).result()
+
+        # tau2 AssistantMessage shape
+        from tau2.data_model.message import AssistantMessage, ToolCall as TauToolCall
+
+        import json as _json
+
+        calls = []
+        for c in out.tool_calls:
+            try:
+                args = _json.loads(c.arguments or '{}')
+            except (ValueError, TypeError):
+                args = {'raw': c.arguments}
+            calls.append(TauToolCall(id=c.id or f'call_{c.name}', name=c.name,
+                                     arguments=args or {}))
+        return AssistantMessage(role='assistant', content=out.text or '',
+                                tool_calls=calls or None, cost=None,
+                                usage=None, raw_data=None)
+
+    # patch module attr AND every already-imported reference (official
+    # modules do `from tau2.utils.llm_utils import generate`)
+    import importlib
+    import sys
+
+    llm_utils.generate = patched_generate
+    for mod_name, mod in list(sys.modules.items()):
+        if not mod_name.startswith('tau2'):
+            continue
+        try:
+            if getattr(mod, 'generate', None) is original:
+                mod.generate = patched_generate
+        except Exception:
+            pass
+    _PATCHED = True
+
+
+@register_env('tau2_official')
+class Tau2Environment(Environment):
+    """Per-sample wrapper around official run_task; reward from env final state."""
+
+    name = 'tau2_official'
+    needs_adapter = True
+
+    def __init__(self, adapter=None):
+        super().__init__(adapter=adapter)
+        self.reward_info: Dict[str, Any] = {}
+
+    def reset(self, sample: Sample) -> List[ChatMessage]:
+        self.reward_info = {}
+        return []
+
+    async def step(self, tool_calls, text: str, sample: Sample) -> List[ChatMessage]:
+        return []  # the official engine runs the whole loop itself
+
+    def final_state(self) -> Dict[str, Any]:
+        return self.reward_info
+
+    async def run_task(self, adapter, sample, max_turns: int = 40, **kw):
+        from tau2.data_model.tasks import Task
+        from tau2.run import run_task
+
+        _patch_tau2_generate(adapter)
+        task_json = (sample.metadata or {}).get('task')
+        if task_json is None:
+            raise ValueError("tau2 sample missing metadata['task'] "
+                             '(dataset plugin must keep the official Task json)')
+        task = Task.model_validate(task_json if not isinstance(task_json, str)
+                                   else json.loads(task_json))
+        domain = (sample.metadata or {}).get('domain') or 'airline'
+        res = run_task(domain=domain, task=task, agent='llm_agent',
+                       user='user_simulator', max_steps=max_turns)
+        rewards = {}
+        try:
+            info = res.reward_info
+            rewards = info.model_dump() if hasattr(info, 'model_dump') else dict(info)
+            env_r = rewards.get('environment_reward')
+            comm_r = rewards.get('communication_reward')
+            vals = [r for r in (env_r, comm_r) if isinstance(r, (int, float))]
+            self.reward_info = {'tau2_rewards': rewards,
+                                'reward': float(sum(vals) / len(vals)) if vals else 0.0}
+        except Exception:
+            self.reward_info = {'tau2_rewards': rewards, 'reward': 0.0}
+        traj = [{'role': str(getattr(m, 'role', 'user')),
+                 'content': str(getattr(m, 'content', '')), 'turn': 0}
+                for m in (res.messages or [])]
+        return {'raw': json.dumps(self.reward_info, default=str),
+                'trajectory': traj, 'env_state': self.reward_info,
+                'usage': {}, 'group_key': domain}

evalharness/agent/loop.py

@@ -16,24 +16,39 @@ from ..model.output import ModelOutput, Usage
 
 
 class Environment:
-    """Minimal env contract. One instance per sample."""
+    """Minimal env contract. One instance per sample.
+
+    Two execution models, auto-selected by the runner:
+    - MESSAGE PUMP (default): implement reset/step/final_state; the runner
+      drives the model turn-by-turn (bfcl_mock style).
+    - SELF-RUNNING (official engines): implement run_task(adapter, sample);
+      the whole simulation happens inside (official tau2/swe bundles), and
+      a prediction dict is returned directly. Falls back to the pump when
+      run_task is not overridden.
+    """
 
     name = 'base'
+    needs_adapter = False   # self-running engines set True; the runner passes
+                            # the adapter to the constructor (no name hardcoding)
+
+    def __init__(self, adapter=None):
+        self.adapter = adapter
 
     def reset(self, sample: Sample) -> List[ChatMessage]:
-        """Prepare per-sample state; return any extra opening messages
-        (e.g. tool/user-simulator turns). Default: nothing."""
         return []
 
     async def step(self, tool_calls: List[Any], text: str, sample: Sample
                    ) -> List[ChatMessage]:
-        """Execute the model's calls; return observation messages."""
         raise NotImplementedError
 
     def final_state(self) -> Dict[str, Any]:
-        """Terminal state handed to env_reward scorers."""
         return {}
 
+    async def run_task(self, adapter, sample: Sample, **kw) -> Optional[Dict[str, Any]]:
+        """Self-running hook. Return a prediction dict (raw/trajectory/
+        env_state/usage/group_key) or None to fall back to the pump."""
+        return None
+
 
 # ---------------- environment registry (万物皆可插件: envs too) ----------------
 
@@ -53,9 +68,9 @@ def register_env(name: str):
     return decorator
 
 
-def get_env(name: str):
+def get_env(name: str, adapter=None):
     cls = ENV_REGISTRY.get(name)
-    return cls()
+    return cls(adapter=adapter)
 
 
 class Trajectory:

evalharness/cli.py

@@ -149,6 +149,7 @@ def _cmd_eval_run(args) -> int:
                 report = asyncio.run(run_eval(
                     ds, args.model, concurrency=args.concurrency, limit=args.limit,
                     limit_per_task=args.limit_per_task,
+                    checkpoint=args.resume,
                     judge_spec=args.judge, env=args.env))
             else:
                 from evalharness.eval import evaluate
@@ -170,10 +171,17 @@ def _cmd_eval_run(args) -> int:
                 print(render(report, style=args.style))
                 print(render(report, style=args.style))
             primary = next(iter(report.metrics), '')
+            secs_total = sum(float((s.usage or {}).get('latency_s', 0) or 0)
+                             for s in report.samples)
+            groups = {k: v for k, v in report.metric_groups.items()
+                      if isinstance(v, dict) and k not in ('run_info',)
+                      and not k.startswith('agg_error')}
             rows.append({'name': name, 'metric': primary, 'value': report.metrics.get(primary),
                          'n': report.num_samples,
                          'extract_fail': report.num_failed_extractions,
-                         'secs': round(_time.time() - t0, 1), 'ok': True})
+                         'secs': round(secs_total, 1),
+                         'hours': round(secs_total / 3600, 2),
+                         'groups': groups, 'ok': True})
         except Exception as e:
             rows.append({'name': name, 'metric': '-', 'value': None,
                          'secs': round(_time.time() - t0, 1), 'ok': False,
@@ -181,11 +189,13 @@ def _cmd_eval_run(args) -> int:
             print(f'{name}: FAILED {type(e).__name__}: {str(e)[:160]}', file=sys.stderr)
 
     if len(rows) > 1:
-        print(f'\n{"dataset":<20} {"metric":<14} {"value":<8} {"secs":>5}')
+        print(f'\n{"benchmark":<20} {"metric":<14} {"score":>8} {"n":>5} {"time":>9}')
-        print('-' * 52)
+        print('-' * 62)
         for r in rows:
-            val = 'ERR' if not r['ok'] else round(r['value'], 4)
+            val = 'ERR' if not r['ok'] else _f3(r['value'])
-            print(f"{r['name']:<20} {r['metric']:<14} {val!s:<8} {r['secs']:>5}"
+            h = r.get('hours') or 0
+            tdisp = f'{h:.2f}h' if h >= 0.995 else f"{r.get('secs', 0):.0f}s"
+            print(f"{r['name']:<20} {r['metric']:<14} {val!s:>8} {r.get('n', '')!s:>5} {tdisp:>9}"
                   + (f"  {r.get('err', '')}" if not r['ok'] else ''))
         ok = sum(1 for r in rows if r['ok'])
         print(f'\n{ok}/{len(rows)} ok' + (f' -> artifacts in {out_dir}/' if out_dir else ''))
@@ -194,11 +204,16 @@ def _cmd_eval_run(args) -> int:
 
             with open(f'{out_dir}/viz/summary.csv', 'w', newline='', encoding='utf-8') as f:
                 w = _csv.writer(f)
-                w.writerow(['dataset', 'model', 'n', 'metric', 'value', 'extract_fail', 'secs'])
+                w.writerow(['benchmark', 'score', 'metric', 'num_samples',
+                            'time_h', 'time_s', 'extract_fail', 'categories'])
                 for r in rows:
-                    w.writerow([r['name'], args.model, r.get('n', ''),
+                    cats = '; '.join(f'{g}={_f3(v)}'
-                                r['metric'], r.get('value', ''), r.get('extract_fail', ''),
+                                     for gname, gv in (r.get('groups') or {}).items()
-                                r['secs']])
+                                     for g, v in (gv or {}).items()
+                                     if isinstance(v, (int, float)))[:2000]
+                    w.writerow([r['name'], _f3(r.get('value')), r['metric'], r.get('n', ''),
+                                r.get('hours', ''), r.get('secs', ''),
+                                r.get('extract_fail', 0), cats])
             with open(f'{out_dir}/viz/summary.md', 'w', encoding='utf-8') as f:
                 f.write(f'# eval run summary\n\n| dataset | metric | value | secs |\n|---|---|---|---|\n')
                 for r in rows:
@@ -207,6 +222,13 @@ def _cmd_eval_run(args) -> int:
     return 0 if all(r['ok'] for r in rows) else 1
 
 
+def _f3(v):
+    try:
+        return round(float(v), 4)
+    except (TypeError, ValueError):
+        return v
+
+
 def _cmd_viz_show(args) -> int:
     from evalharness.viz import render
 
@@ -264,6 +286,9 @@ def build_parser() -> argparse.ArgumentParser:
     p.add_argument('--env', default='', help="agent environment (e.g. 'bfcl_mock') -> message pump")
     p.add_argument('--concurrency', type=int, default=32, help='parallel model calls (default 32)')
     p.add_argument('--limit', type=int, help='evaluate only the first N samples total')
+    p.add_argument('--resume', nargs='?', const=True, default=False,
+                   help='resume from per-sample checkpoint (default path auto-derived; '
+                        'pass a path to override)')
     p.add_argument('--limit-per-task', type=int,
                    help='first N samples PER subset/category (evalscope --limit semantics); '
                         'composable with --limit (intersection)')

evalharness/data/datasets/tau2_bench.py

@@ -34,6 +34,10 @@ def tau2_bench():
             target='',
             metadata={
                 'id': record.get('id'),
+                'domain': record.get('domain'),
+                'task': record,          # OFFICIAL Task json, verbatim -- the
+                                         # tau2_official env runs the official
+                                         # engine on this; nothing is lost
                 'notes': desc.get('notes'),
                 'task_instructions': instructions,
                 'user_scenario': scenario,

evalharness/eval/checkpoint.py

@@ -0,0 +1,85 @@
+"""Resumable evaluation: checkpoint plugin.
+
+Run-level checkpointing: every completed sample is appended to a jsonl
+checkpoint file; on restart (crash/OOM/service down), completed samples are
+restored and ONLY the missing ones are generated. The report is then
+assembled from restored + fresh predictions.
+
+    from evalharness.eval.checkpoint import CheckpointStore
+    store = CheckpointStore(path='run.jsonl', model=model_spec, dataset=name)
+    done = store.load()                    # {sample_key: prediction-dict}
+    ... generate only missing ...
+    store.append(sample_key, pred)         # after EACH sample (crash-safe)
+"""
+
+import json
+import os
+import time
+from pathlib import Path
+from typing import Any, Dict, Optional
+
+
+class CheckpointStore:
+    """Append-only jsonl checkpoint; keyed by stable sample identity."""
+
+    def __init__(self, path: str, model: str = '', dataset: str = ''):
+        self.path = Path(path)
+        self.path.parent.mkdir(parents=True, exist_ok=True)
+        self.model = model
+        self.dataset = dataset
+        self._entries: Dict[str, Dict[str, Any]] = {}
+        self._fh = None
+
+    @staticmethod
+    def key_for(sample, idx: int) -> str:
+        """Stable per-sample key: prefer dataset-native ids, fall back to
+        a hash of the input text (survives re-orderings)."""
+        native = (sample.metadata or {}).get('task_id') \
+            or (sample.metadata or {}).get('id') \
+            or (sample.metadata or {}).get('instance_id')
+        if native:
+            return str(native)
+        import hashlib
+
+        h = hashlib.md5((sample.input_text or '').encode('utf-8')).hexdigest()[:16]
+        return f'{idx}:{h}'
+
+    def load(self) -> Dict[str, Dict[str, Any]]:
+        """Read all checkpointed predictions (idempotent)."""
+        self._entries = {}
+        if not self.path.exists():
+            return self._entries
+        with open(self.path, encoding='utf-8') as f:
+            for line in f:
+                line = line.strip()
+                if not line:
+                    continue
+                try:
+                    rec = json.loads(line)
+                    self._entries[rec['key']] = rec.get('pred', {})
+                except (ValueError, KeyError):
+                    continue  # torn tail line from a crash -- safe to skip
+        return self._entries
+
+    def append(self, key: str, pred: Dict[str, Any]) -> None:
+        """Persist one prediction immediately (fsync-free append is fine:
+        worst case loses the last in-flight sample on crash)."""
+        rec = {'key': key, 'ts': time.time(), 'pred': pred}
+        with open(self.path, 'a', encoding='utf-8') as f:
+            f.write(json.dumps(rec, ensure_ascii=False) + '\n')
+        self._entries[key] = pred
+
+    def __len__(self) -> int:
+        return len(self._entries)
+
+    def summary(self) -> Dict[str, Any]:
+        return {'path': str(self.path), 'restored': len(self._entries),
+                'size_kb': self.path.stat().st_size // 1024 if self.path.exists() else 0}
+
+
+def checkpoint_path(root: str, dataset: str, model: str, tag: str = '') -> str:
+    """Deterministic path per (dataset, model[, tag]) so re-runs resume."""
+    import hashlib
+
+    h = hashlib.md5(f'{dataset}|{model}|{tag}'.encode()).hexdigest()[:10]
+    return os.path.join(root, 'ckpt', f'{dataset}-{h}.jsonl')

evalharness/eval/recipes/agent.py

@@ -185,13 +185,28 @@ def swe_bench_verified():
     )
 
 
+def _tau2_reward(pred, target, sample, ctx):
+    """Score from the official engine's reward_info (env_state)."""
+    env_state = ctx.params.get('env_state') or {}
+    rewards = env_state.get('tau2_rewards') or {}
+    env_r = rewards.get('environment_reward')
+    comm_r = rewards.get('communication_reward')
+    vals = [r for r in (env_r, comm_r) if isinstance(r, (int, float))]
+    score = float(sum(vals) / len(vals)) if vals else 0.0
+    return ({'acc': score}, {'acc': {'mode': 'official_tau2',
+                                     'env_reward': env_r,
+                                     'comm_reward': comm_r}})
+
+
 @register_eval('tau2_bench')
 def tau2_bench():
     return EvalRecipe(
         name='tau2_bench',
         extract='identity',
-        scorers={'acc': 'env_reward'},
+        scorers={'acc': _tau2_reward},
-        description='tau2-bench; user-simulated dialog, environment reward.',
+        aggregators={'acc': 'grouped_avg'},
+        description='tau2-bench via OFFICIAL engine (user simulator + env + reward); '
+                    "run with env='tau2_official'",
     )
 
 

evalharness/model/adapter.py

@@ -117,6 +117,9 @@ class OpenAICompatible(ModelAdapter):
     name = 'openai'
 
     async def generate(self, messages, tools=None, **kw) -> ModelOutput:
+        import time as _time
+
+        t0 = _time.time()
         payload = self._payload(messages, tools, kw)
         headers = {'Content-Type': 'application/json'}
         if self.api_key:
@@ -126,7 +129,9 @@ class OpenAICompatible(ModelAdapter):
         for attempt in range(retries + 1):
             try:
                 data = await self._post(f'{self.api_base}/chat/completions', payload, headers)
-                return self._parse(data)
+                out = self._parse(data)
+                out.usage.latency_s = round(_time.time() - t0, 3)
+                return out
             except Exception as e:  # 5xx/429/timeouts: worth retrying
                 last_exc = e
                 retryable = 'Server error' in str(e) or '504' in str(e) or '502' in str(e) \

evalharness/model/runner.py

@@ -11,6 +11,7 @@ raw strings to the sync evaluate().
 """
 
 import asyncio
+import os
 import time
 from typing import Any, Dict, List, Optional, Union
 
@@ -36,6 +37,7 @@ async def generate_predictions(
     max_input_chars: int = 0,
     attach_context_keys: tuple = ('passage', 'context'),
     limit_per_task: Optional[int] = None,
+    checkpoint: Union[bool, str] = False,
     few_shot_num: int = 0,
     few_shot_samples: Optional[List[Sample]] = None,
     few_shot_text: Optional[str] = None,
@@ -104,15 +106,34 @@ async def generate_predictions(
         nonlocal done_count, total_usage
         if env_factory is not None:
             from ..agent import drive, trajectory_to_prediction
+            from ..agent.loop import Environment, Usage as _U  # noqa: F401
 
             async with sem:
-                traj = await drive(adapter, sample, env=env_factory(),
+                env = env_factory()
+                if type(env).run_task is not Environment.run_task:
+                    # self-running env (official engine bundles: tau2/swe)
+                    pred = await env.run_task(adapter, sample,
+                                              max_turns=max_turns, system=system)
+                    if pred is None:
+                        traj = await drive(adapter, sample, env=env,
                                            max_turns=max_turns, system=system)
-            total_usage = total_usage + traj.usage
                         pred = trajectory_to_prediction(traj)
-            pred['group_key'] = str(sample.metadata.get('test_category')
+                else:
+                    traj = await drive(adapter, sample, env=env,
+                                       max_turns=max_turns, system=system)
+                    pred = trajectory_to_prediction(traj)
+            if not pred.get('usage'):
+                pred['usage'] = traj.usage.model_dump() if 'traj' in dir() else {}
+            pred.setdefault('group_key', str(sample.metadata.get('test_category')
                                              or sample.metadata.get('category')
-                                    or sample.metadata.get('id') or sample.id or '')
+                                             or sample.metadata.get('domain')
+                                             or sample.metadata.get('id') or sample.id or ''))
+            u = pred.get('usage') or {}
+            total_usage = total_usage + Usage(
+                input_tokens=int(u.get('input_tokens', 0) or 0),
+                output_tokens=int(u.get('output_tokens', 0) or 0),
+                total_tokens=int(u.get('total_tokens', 0) or 0),
+                latency_s=float(u.get('latency_s', 0) or 0))
             done_count += 1
             _progress(progress, done_count, len(samples), t0, total_usage)
             return pred
@@ -143,7 +164,48 @@ async def generate_predictions(
         return {'raw': text, 'usage': out.usage.model_dump()}
 
     work = _apply_limits(samples, limit, limit_per_task)
-    preds = list(await asyncio.gather(*(one(s) for s in work)))
+    # checkpointing: restore completed samples, generate only the rest
+    ckpt_store = None
+    if checkpoint:
+        from ..eval.checkpoint import CheckpointStore, checkpoint_path
+
+        ckpt = checkpoint if isinstance(checkpoint, str) else None
+        if ckpt is None:
+            from ..eval.checkpoint import checkpoint_path as _cp
+
+            ckpt = _cp('/data/evalharness' if False else os.path.expanduser('~/.cache/evalharness'),
+                       getattr(spec, 'name', 'adhoc') if spec is not None else 'adhoc',
+                       model_spec)
+        ckpt_store = CheckpointStore(ckpt, model=adapter.model or str(adapter))
+        restored = ckpt_store.load()
+    else:
+        restored = {}
+
+    keys = []
+    pending = []
+    preds_by_key: Dict[str, Dict[str, Any]] = {}
+    for i, s in enumerate(work):
+        k = CheckpointStore.key_for(s, i) if ckpt_store else str(i)
+        keys.append(k)
+        if k in restored:
+            preds_by_key[k] = restored[k]
+        else:
+            pending.append((i, s))
+    if ckpt_store is not None and restored:
+        print(f'checkpoint: restored {len(restored)} predictions '
+              f'({len(pending)} to generate) -> {ckpt_store.path}', flush=True)
+
+    async def run_one(i_s):
+        i, s = i_s
+        pred = await one(s)
+        if ckpt_store is not None:
+            ckpt_store.append(keys[i], pred)
+        return i, pred
+
+    fresh = await asyncio.gather(*(run_one((i, s)) for i, s in pending))
+    for i, pred in fresh:
+        preds_by_key[keys[i]] = pred
+    preds = [preds_by_key[k] for k in keys]
     usages = [p.get('usage', {}) for p in preds]
     return preds, usages, total_usage
 
@@ -191,6 +253,7 @@ async def run_eval(
     max_turns: int = 8,
     max_input_chars: int = 0,
     limit_per_task: Optional[int] = None,
+    checkpoint: Union[bool, str] = False,
     few_shot_num: int = -1,
     prompt_style: str = 'strict_letter',
 ) -> EvalReport:
@@ -229,10 +292,14 @@ async def run_eval(
 
     env_factory = None
     if env:
-        from ..agent.loop import ENV_REGISTRY, get_env
+        from ..agent import ENV_REGISTRY, get_env
 
         if env not in ENV_REGISTRY:
             raise KeyError(f'unknown env {env!r}; available: {", ".join(ENV_REGISTRY.names())}')
+        probe = get_env(env)
+        if getattr(probe, 'needs_adapter', False):
+            env_factory = lambda: get_env(env, adapter=adapter)  # noqa: E731
+        else:
             env_factory = lambda: get_env(env)  # noqa: E731
 
     # paper-faithful few-shot exemplars: official hand-written hooks first
@@ -268,6 +335,7 @@ async def run_eval(
             gen_kwargs=gen_kwargs, env_factory=env_factory,
             system=system, max_turns=max_turns, max_input_chars=max_input_chars,
             limit_per_task=limit_per_task,
+            checkpoint=checkpoint,
             few_shot_num=few_shot_num,
             few_shot_samples=few_shot_samples, few_shot_text=few_shot_text,
             prompt_style=prompt_style)

evalharness/viz/renderers/text.py

@@ -25,6 +25,18 @@ def text_table(target: Union[EvalReport, List[EvalReport]], opts: Dict) -> str:
         out.append('=' * max(len(head), 40))
         out.append(head)
         out.append('=' * max(len(head), 40))
+        # run stats: duration, tokens, cost (from run_info + usage aggregates)
+        info = rep.metric_groups.get('run_info', {}) or {}
+        total_tokens = info.get('gen_total_tokens', 0)
+        tok_s = ''
+        if total_tokens:
+            tok_s = f'  tokens={total_tokens}'
+        secs = 0.0
+        for s in rep.samples:
+            secs += float((s.usage or {}).get('latency_s', 0) or 0)
+        dur = f'  time={secs / 3600:.2f}h' if secs >= 3600 else (f'  time={secs:.0f}s' if secs else '')
+        if dur or tok_s:
+            out.append(f'n_samples={rep.num_samples}{dur}{tok_s}')
         if rep.num_failed_extractions:
             warn = (f'!! {rep.num_failed_extractions}/{rep.num_samples} extractions failed '
                     f'({_pct(rep.metrics.get("extraction_failure_rate", 0))}) -- check recipe/model fit')