Blame for main.py

from contextlib import asynccontextmanager
from dotenv import load_dotenv
import json
import logging
import traceback

load_dotenv()

logging.basicConfig(level=logging.INFO, format="%(asctime)s %(name)s %(levelname)s %(message)s")
import yaml
import asyncio

from fastapi import Depends, FastAPI, Request
from fastapi.responses import HTMLResponse, JSONResponse, StreamingResponse
from fastapi.staticfiles import StaticFiles
from fastapi.templating import Jinja2Templates
from sqlalchemy.orm import Session
from sqlalchemy import func
from sqlalchemy.exc import IntegrityError
from pydantic import BaseModel

from database import create_all, get_db, SessionLocal
from models import Task, Agent, Run, RunStatus
from core.runner import Runner
from core.metrics import compute_pass_at_k
from core.statistics import wilson_interval, task_bootstrap_ci, paired_bootstrap_delta

logger = logging.getLogger(__name__)

runner = Runner()


def _log_task_exception(task: asyncio.Task):
    """Callback for background asyncio tasks — logs exceptions instead of silently swallowing them."""
    if task.cancelled():
        return
    exc = task.exception()
    if exc:
        logger.error(
            "Background run task failed: %s\n%s",
            exc,
            "".join(traceback.format_exception(exc)),
        )


@asynccontextmanager
async def lifespan(app: FastAPI):
    create_all()
    # Sweep zombie runs from previous server instance
    db = SessionLocal()
    try:
        zombies = db.query(Run).filter(Run.status.in_([RunStatus.pending, RunStatus.running])).all()
        for run in zombies:
            run.status = RunStatus.error
            run.judge_notes = "Marked as error: server restarted while run was in progress"
        if zombies:
            db.commit()
    finally:
        db.close()
    yield


app = FastAPI(title="AgentArena", lifespan=lifespan)
app.mount("/static", StaticFiles(directory="static"), name="static")
templates = Jinja2Templates(directory="templates")


# ── Helpers ──────────────────────────────────────────────────────────────────

def _agent_stats(db: Session):
    """Return list of dicts with per-agent aggregate statistics."""
    agents = db.query(Agent).all()
    result = []
    for agent in agents:
        runs = agent.runs
        if not runs:
            result.append({
                "agent": agent,
                "tasks_solved": 0,
                "pass_at_1": None,
                "pass_at_3": None,
                "avg_cost": None,
                "avg_duration": None,
                "avg_composite": None,
                "total_runs": 0,
            })
            continue

        finished = [r for r in runs if r.status in (RunStatus.success, RunStatus.failure, RunStatus.error, RunStatus.timeout)]
        successful = [r for r in runs if (r.functional_success or 0) >= 1]

        # Tasks where at least one run succeeded
        tasks_solved = len(set(r.task_id for r in successful))

        # Group finished runs per task_id for unbiased pass@k estimation
        runs_per_task: dict = {}
        for r in finished:
            runs_per_task.setdefault(r.task_id, []).append(r)

        # pass@1 / pass@3: unbiased estimators aggregated across all tasks
        pass_at_1_values = []
        pass_at_3_values = []
        for task_runs in runs_per_task.values():
            n = len(task_runs)
            c = sum(1 for r in task_runs if (r.functional_success or 0) >= 1)
            pass_at_1_values.append(compute_pass_at_k(n, c, 1))
            if n >= 3:
                pass_at_3_values.append(compute_pass_at_k(n, c, 3))

        pass_at_1 = (sum(pass_at_1_values) / len(pass_at_1_values)) if pass_at_1_values else None
        pass_at_3 = (sum(pass_at_3_values) / len(pass_at_3_values)) if pass_at_3_values else None

        avg_cost = (sum(r.cost_usd for r in finished) / len(finished)) if finished else None
        avg_duration = (sum((r.agent_duration_s or r.duration_s or 0.0) for r in finished) / len(finished)) if finished else None
        functional_successes = sum(1 for r in finished if (r.functional_success or 0) >= 1)
        functional_ci = wilson_interval(functional_successes, len(finished)) if finished else (0.0, 0.0)

        task_alignment_scores = []
        for task_runs in runs_per_task.values():
            per_task = [r.alignment_score_llm for r in task_runs if r.alignment_score_llm is not None]
            if per_task:
                task_alignment_scores.append(sum(per_task) / len(per_task))
        avg_alignment = (
            sum(task_alignment_scores) / len(task_alignment_scores)
            if task_alignment_scores else None
        )
        alignment_ci = (
            task_bootstrap_ci(task_alignment_scores)
            if task_alignment_scores else (0.0, 0.0)
        )

        composite_scores = []
        for r in finished:
            try:
                m = json.loads(r.metrics_json or "{}")
                cs = m.get("composite_score")
                if cs is not None:
                    composite_scores.append(float(cs))
            except (json.JSONDecodeError, TypeError, ValueError):
                pass
        avg_composite = (sum(composite_scores) / len(composite_scores)) if composite_scores else None

        result.append({
            "agent": agent,
            "tasks_solved": tasks_solved,
            "pass_at_1": pass_at_1,
            "pass_at_3": pass_at_3,
            "avg_cost": avg_cost,
            "avg_duration": avg_duration,
            "functional_success_rate": (functional_successes / len(finished)) if finished else None,
            "functional_ci_low": functional_ci[0],
            "functional_ci_high": functional_ci[1],
            "avg_alignment": avg_alignment,
            "alignment_ci_low": alignment_ci[0],
            "alignment_ci_high": alignment_ci[1],
            "avg_composite": avg_composite,
            "total_runs": len(finished),
        })

    result.sort(
        key=lambda x: (
            -(x["tasks_solved"]),
            -(x["functional_success_rate"] or 0),
            -(x["avg_alignment"] or 0),
        )
    )
    return result


# ── Routes ────────────────────────────────────────────────────────────────────

@app.get("/", response_class=HTMLResponse)
def index(request: Request, db: Session = Depends(get_db)):
    stats = _agent_stats(db)
    tasks = db.query(Task).order_by(Task.created_at.desc()).all()
    return templates.TemplateResponse("index.html", {
        "request": request,
        "agent_stats": stats,
        "tasks": tasks,
    })


@app.get("/tasks/{slug}", response_class=HTMLResponse)
def task_detail(slug: str, request: Request, db: Session = Depends(get_db)):
    task = db.query(Task).filter(Task.slug == slug).first()
    if not task:
        return HTMLResponse("Task not found", status_code=404)

    runs = (
        db.query(Run)
        .filter(Run.task_id == task.id)
        .order_by(Run.created_at.desc())
        .all()
    )

    # Group by agent — best result per agent (success > others, then by score)
    best_by_agent: dict[int, Run] = {}
    for run in runs:
        existing = best_by_agent.get(run.agent_id)
        if existing is None:
            best_by_agent[run.agent_id] = run
        else:
            # Prefer success
            if (run.functional_success or 0) >= 1 and (existing.functional_success or 0) < 1:
                best_by_agent[run.agent_id] = run
            elif (run.functional_success or 0) == (existing.functional_success or 0):
                # Same status — prefer higher judge_score
                run_score = run.judge_score or 0
                existing_score = existing.judge_score or 0
                if run_score > existing_score:
                    best_by_agent[run.agent_id] = run

    agents = {a.id: a for a in db.query(Agent).all()}

    return templates.TemplateResponse("task_detail.html", {
        "request": request,
        "task": task,
        "runs": runs,
        "best_by_agent": list(best_by_agent.values()),
        "agents": agents,
    })


@app.get("/runs/{run_id}", response_class=HTMLResponse)
def run_detail(run_id: int, request: Request, db: Session = Depends(get_db)):
    run = db.query(Run).filter(Run.id == run_id).first()
    if not run:
        return HTMLResponse("Run not found", status_code=404)

    try:
        trajectory = json.loads(run.trajectory or "[]")
    except (json.JSONDecodeError, TypeError):
        trajectory = []

    diff_lines = []
    if run.diff_text:
        for line in run.diff_text.splitlines():
            if line.startswith("+") and not line.startswith("+++"):
                diff_lines.append(("added", line))
            elif line.startswith("-") and not line.startswith("---"):
                diff_lines.append(("removed", line))
            else:
                diff_lines.append(("context", line))

    try:
        metrics = json.loads(run.metrics_json or "{}")
    except (json.JSONDecodeError, TypeError):
        metrics = {}

    return templates.TemplateResponse("run_detail.html", {
        "request": request,
        "run": run,
        "trajectory": trajectory,
        "diff_lines": diff_lines,
        "metrics": metrics,
    })


# ── API ───────────────────────────────────────────────────────────────────────

class RunRequest(BaseModel):
    task_slug: str
    agent_name: str
    adapter_type: str = "claude_code"
    model: str = ""
    k: int = 1


class AgentCreate(BaseModel):
    name: str
    adapter_type: str
    model: str = ""
    config_json: str = "{}"


@app.post("/api/agents")
def create_agent(req: AgentCreate, db: Session = Depends(get_db)):
    existing = db.query(Agent).filter(Agent.name == req.name).first()
    if existing:
        return JSONResponse({"error": "Agent already exists"}, status_code=409)
    # Merge model into config_json if provided.
    config = json.loads(req.config_json) if req.config_json and req.config_json != "{}" else {}
    if req.model:
        config["model"] = req.model
    agent = Agent(name=req.name, adapter_type=req.adapter_type, config_json=json.dumps(config))
    db.add(agent)
    db.commit()
    db.refresh(agent)
    return {"id": agent.id, "name": agent.name}


@app.post("/api/runs")
async def start_run(req: RunRequest, db: Session = Depends(get_db)):
    task = db.query(Task).filter(Task.slug == req.task_slug).first()
    if not task:
        return JSONResponse({"error": "Task not found"}, status_code=404)

    # Auto-create agent if it doesn't exist (race-safe)
    agent = db.query(Agent).filter(Agent.name == req.agent_name).first()
    if not agent:
        try:
            config = {"model": req.model} if req.model else {}
            agent = Agent(
                name=req.agent_name,
                adapter_type=req.adapter_type,
                config_json=json.dumps(config),
            )
            db.add(agent)
            db.commit()
            db.refresh(agent)
        except IntegrityError:
            db.rollback()
            agent = db.query(Agent).filter(Agent.name == req.agent_name).first()

    if req.k > 1:
        task_id, agent_id = task.id, agent.id
        async def _run_k():
            run_db = SessionLocal()
            try:
                t = run_db.query(Task).filter(Task.id == task_id).first()
                a = run_db.query(Agent).filter(Agent.id == agent_id).first()
                await runner.run_pass_at_k(run_db, t, a, k=req.k)
            finally:
                run_db.close()
        bg_task = asyncio.create_task(_run_k())
        bg_task.add_done_callback(_log_task_exception)
        return {"status": "started", "k": req.k, "agent": agent.name}

    # Single run — fire in background (runner.run_single creates its own Run record)
    task_id, agent_id = task.id, agent.id

    async def _run_single():
        run_db = SessionLocal()
        try:
            t = run_db.query(Task).filter(Task.id == task_id).first()
            a = run_db.query(Agent).filter(Agent.id == agent_id).first()
            await runner.run_single(run_db, t, a)
        finally:
            run_db.close()
    bg_task = asyncio.create_task(_run_single())
    bg_task.add_done_callback(_log_task_exception)
    return {"status": "started", "agent": agent.name, "task": task.slug}


@app.get("/api/runs/{run_id}/stream")
async def stream_run(run_id: int):
    async def generate():
        while True:
            db = SessionLocal()
            try:
                run = db.query(Run).filter(Run.id == run_id).first()
                if not run:
                    yield f"event: error\ndata: {{\"error\": \"not found\"}}\n\n"
                    return
                data = {
                    "status": run.status.value,
                    "duration_s": run.duration_s,
                    "agent_duration_s": run.agent_duration_s,
                    "end_to_end_duration_s": run.end_to_end_duration_s,
                    "tokens_input": run.tokens_input,
                    "tokens_output": run.tokens_output,
                    "cost_usd": run.cost_usd,
                    "tests_passed": run.tests_passed,
                    "tests_total": run.tests_total,
                    "functional_success": run.functional_success,
                    "alignment_score_llm": run.alignment_score_llm,
                }
                yield f"event: update\ndata: {json.dumps(data)}\n\n"
                if run.status not in (RunStatus.pending, RunStatus.running):
                    yield f"event: done\ndata: {json.dumps(data)}\n\n"
                    return
            finally:
                db.close()
            await asyncio.sleep(2)

    return StreamingResponse(
        generate(),
        media_type="text/event-stream",
        headers={"Cache-Control": "no-cache", "X-Accel-Buffering": "no"},
    )


@app.post("/api/tasks/import")
async def import_task(request: Request, db: Session = Depends(get_db)):
    body = await request.body()
    try:
        data = yaml.safe_load(body)
    except yaml.YAMLError as e:
        return JSONResponse({"error": f"Invalid YAML: {e}"}, status_code=400)

    existing = db.query(Task).filter(Task.slug == data.get("slug", "")).first()
    if existing:
        return JSONResponse({"error": "Task slug already exists"}, status_code=409)

    task = Task(
        slug=data["slug"],
        title=data["title"],
        description=data.get("description", ""),
        repo_url=data["repo"]["url"],
        repo_ref=data["repo"].get("ref", "main"),
        success_cmd=data["success"]["command"],
        time_limit_s=data.get("limits", {}).get("time_seconds", 300),
        token_limit=data.get("limits", {}).get("max_tokens", 200000),
        difficulty=data.get("difficulty", "medium"),
    )
    db.add(task)
    db.commit()
    db.refresh(task)
    return {"task_id": task.id, "slug": task.slug}


@app.get("/api/leaderboard")
def leaderboard_json(db: Session = Depends(get_db)):
    stats = _agent_stats(db)
    return [
        {
            "rank": i + 1,
            "agent_name": s["agent"].name,
            "agent_id": s["agent"].id,
            "tasks_solved": s["tasks_solved"],
            "functional_success_rate": s["functional_success_rate"],
            "functional_ci_low": s["functional_ci_low"],
            "functional_ci_high": s["functional_ci_high"],
            "pass_at_1": s["pass_at_1"],
            "pass_at_3": s["pass_at_3"],
            "avg_alignment": s["avg_alignment"],
            "alignment_ci_low": s["alignment_ci_low"],
            "alignment_ci_high": s["alignment_ci_high"],
            "avg_composite_score": s["avg_composite"],
            "avg_cost_usd": s["avg_cost"],
            "avg_duration_s": s["avg_duration"],
            "total_runs": s["total_runs"],
        }
        for i, s in enumerate(stats)
    ]


@app.get("/api/tasks")
def list_tasks(db: Session = Depends(get_db)):
    tasks = db.query(Task).order_by(Task.created_at.desc()).all()
    return [
        {
            "id": t.id,
            "slug": t.slug,
            "title": t.title,
            "difficulty": t.difficulty,
            "time_limit_s": t.time_limit_s,
        }
        for t in tasks
    ]


@app.get("/api/agents")
def list_agents(db: Session = Depends(get_db)):
    agents = db.query(Agent).all()
    return [{"id": a.id, "name": a.name, "adapter_type": a.adapter_type} for a in agents]


@app.get("/api/experiments/{experiment_id}/compare")
def compare_experiment(experiment_id: str, left_arm: str, right_arm: str, db: Session = Depends(get_db)):
    runs = (
        db.query(Run)
        .filter(
            Run.experiment_id == experiment_id,
            Run.arm_id.in_([left_arm, right_arm]),
            Run.status.in_([RunStatus.success, RunStatus.failure, RunStatus.error, RunStatus.timeout]),
        )
        .all()
    )
    if not runs:
        return JSONResponse({"error": "No runs found for experiment"}, status_code=404)

    by_arm_task: dict[tuple[str, int], list[Run]] = {}
    for run in runs:
        by_arm_task.setdefault((run.arm_id, run.task_id), []).append(run)

    shared_tasks = sorted({
        task_id for arm_id, task_id in by_arm_task
        if (left_arm, task_id) in by_arm_task and (right_arm, task_id) in by_arm_task
    })
    if not shared_tasks:
        return JSONResponse({"error": "No shared tasks between arms"}, status_code=400)

    left_func = []
    right_func = []
    left_align = []
    right_align = []
    for task_id in shared_tasks:
        left_runs = by_arm_task[(left_arm, task_id)]
        right_runs = by_arm_task[(right_arm, task_id)]
        left_func.append(sum((r.functional_success or 0) for r in left_runs) / len(left_runs))
        right_func.append(sum((r.functional_success or 0) for r in right_runs) / len(right_runs))

        left_align_runs = [r.alignment_score_llm for r in left_runs if r.alignment_score_llm is not None]
        right_align_runs = [r.alignment_score_llm for r in right_runs if r.alignment_score_llm is not None]
        if left_align_runs and right_align_runs:
            left_align.append(sum(left_align_runs) / len(left_align_runs))
            right_align.append(sum(right_align_runs) / len(right_align_runs))

    response = {
        "experiment_id": experiment_id,
        "left_arm": left_arm,
        "right_arm": right_arm,
        "shared_tasks": len(shared_tasks),
        "functional_success_delta": paired_bootstrap_delta(left_func, right_func),
    }
    if left_align and right_align and len(left_align) == len(right_align):
        response["alignment_delta"] = paired_bootstrap_delta(left_align, right_align)
    return response


# ── Matrix View ──────────────────────────────────────────────────────────────

def _matrix_data(db: Session) -> dict:
    """Build harness x model matrix from all runs."""
    agents = db.query(Agent).all()

    cells: dict[tuple, list] = {}  # (adapter_type, model) -> list of runs
    all_models: set[str] = set()
    all_harnesses: set[str] = set()

    for agent in agents:
        config = json.loads(agent.config_json or "{}")
        model = config.get("model", "unknown")
        harness = agent.adapter_type
        all_models.add(model)
        all_harnesses.add(harness)

        key = (harness, model)
        if key not in cells:
            cells[key] = []

        finished = [
            r for r in agent.runs
            if r.status in (RunStatus.success, RunStatus.failure, RunStatus.error, RunStatus.timeout)
        ]
        cells[key].extend(finished)

    matrix = {}
    for key, runs in cells.items():
        if not runs:
            continue
        n = len(runs)
        c = sum(1 for r in runs if (r.functional_success or 0) >= 1)
        pass_1 = compute_pass_at_k(n, c, 1)
        avg_cost = sum(r.cost_usd or 0 for r in runs) / n
        avg_duration = sum((r.agent_duration_s or r.duration_s or 0) for r in runs) / n
        avg_alignment = (
            sum(r.alignment_score_llm for r in runs if r.alignment_score_llm is not None)
            / max(1, sum(1 for r in runs if r.alignment_score_llm is not None))
        ) if any(r.alignment_score_llm is not None for r in runs) else None

        composite_scores = []
        for r in runs:
            try:
                m = json.loads(r.metrics_json or "{}")
                cs = m.get("composite_score")
                if cs is not None:
                    composite_scores.append(float(cs))
            except (json.JSONDecodeError, TypeError, ValueError):
                pass
        avg_composite = (sum(composite_scores) / len(composite_scores)) if composite_scores else None

        matrix[key] = {
            "pass_at_1": round(pass_1, 3),
            "avg_composite": round(avg_composite, 3) if avg_composite else None,
            "avg_cost": round(avg_cost, 6),
            "avg_duration": round(avg_duration, 1),
            "avg_alignment": round(avg_alignment, 3) if avg_alignment is not None else None,
            "total_runs": n,
            "tasks_solved": len(set(r.task_id for r in runs if (r.functional_success or 0) >= 1)),
        }

    return {
        "models": sorted(all_models),
        "harnesses": sorted(all_harnesses),
        "cells": {f"{h}|{m}": v for (h, m), v in matrix.items()},
    }


@app.get("/matrix", response_class=HTMLResponse)
def matrix_view(request: Request, db: Session = Depends(get_db)):
    data = _matrix_data(db)
    return templates.TemplateResponse("matrix.html", {
        "request": request,
        "models": data["models"],
        "harnesses": data["harnesses"],
        "cells": data["cells"],
    })


@app.get("/api/matrix")
def matrix_json(db: Session = Depends(get_db)):
    return _matrix_data(db)


# ── Harness Report ───────────────────────────────────────────────────────────

def _harness_report(db: Session, adapter_type: str) -> dict | None:
    """Generate comprehensive report for a harness type."""
    agents = db.query(Agent).filter(Agent.adapter_type == adapter_type).all()
    if not agents:
        return None

    all_runs = []
    for agent in agents:
        all_runs.extend(agent.runs)

    finished = [
        r for r in all_runs
        if r.status in (RunStatus.success, RunStatus.failure, RunStatus.error, RunStatus.timeout)
    ]
    successful = [r for r in all_runs if (r.functional_success or 0) >= 1]

    if not finished:
        return {"adapter_type": adapter_type, "total_runs": 0}

    n = len(finished)
    c = len(successful)

    pass_at_1 = compute_pass_at_k(n, c, 1)
    avg_cost = sum(r.cost_usd or 0 for r in finished) / n
    cost_per_solved = (sum(r.cost_usd or 0 for r in successful) / c) if c > 0 else None
    avg_duration = sum((r.agent_duration_s or r.duration_s or 0) for r in finished) / n
    functional_ci = wilson_interval(c, n)

    diff_minimalities = []
    lint_deltas = []
    complexity_deltas = []
    directness_scores = []
    exploration_ratios = []
    phase_totals = {"exploration": 0, "implementation": 0, "testing": 0, "debugging": 0}
    composite_scores = []
    alignment_task_scores = []
    runs_per_task: dict[int, list] = {}

    for r in finished:
        runs_per_task.setdefault(r.task_id, []).append(r)
        try:
            m = json.loads(r.metrics_json or "{}")
        except (json.JSONDecodeError, TypeError, ValueError):
            continue

        diff = m.get("diff", {})
        churn = diff.get("churn")
        if churn is not None:
            diff_minimalities.append(1.0 / (1.0 + churn / 50.0))

        judge = m.get("judge", {})
        if "lint_delta" in judge:
            lint_deltas.append(judge["lint_delta"])
        if "complexity_delta" in judge:
            complexity_deltas.append(judge["complexity_delta"])

        traj = m.get("trajectory", {})
        if "directness_score" in traj:
            directness_scores.append(traj["directness_score"])
        if "exploration_ratio" in traj:
            exploration_ratios.append(traj["exploration_ratio"])
        phases = traj.get("phase_breakdown", {})
        for pk, pv in phases.items():
            phase_totals[pk] = phase_totals.get(pk, 0) + pv

        cs = m.get("composite_score")
        if cs is not None:
            composite_scores.append(float(cs))

    for task_runs in runs_per_task.values():
        per_task_alignment = [r.alignment_score_llm for r in task_runs if r.alignment_score_llm is not None]
        if per_task_alignment:
            alignment_task_scores.append(sum(per_task_alignment) / len(per_task_alignment))

    weak_tasks = []
    for task_id, task_runs in runs_per_task.items():
        tn = len(task_runs)
        tc = sum(1 for r in task_runs if (r.functional_success or 0) >= 1)
        task_pass = compute_pass_at_k(tn, tc, 1)
        if task_pass < 0.5:
            task_obj = task_runs[0].task
            weak_tasks.append({
                "task_slug": task_obj.slug if task_obj else f"task_{task_id}",
                "task_title": task_obj.title if task_obj else f"Task {task_id}",
                "pass_at_1": round(task_pass, 3),
                "runs": tn,
            })

    avg_composite = (sum(composite_scores) / len(composite_scores)) if composite_scores else None
    cost_benefit = (avg_composite / avg_cost) if avg_composite and avg_cost > 0 else None

    def _safe_avg(lst):
        return round(sum(lst) / len(lst), 3) if lst else None

    return {
        "adapter_type": adapter_type,
        "total_runs": n,
        "total_agents": len(agents),
        "tasks_attempted": len(runs_per_task),
        "tasks_solved": len(set(r.task_id for r in successful)),
        "reliability": {
            "pass_at_1": round(pass_at_1, 3),
            "success_rate": round(c / n, 3) if n > 0 else 0,
            "success_ci_low": round(functional_ci[0], 3),
            "success_ci_high": round(functional_ci[1], 3),
        },
        "efficiency": {
            "avg_cost_usd": round(avg_cost, 6),
            "cost_per_solved_task": round(cost_per_solved, 6) if cost_per_solved else None,
            "avg_duration_s": round(avg_duration, 1),
        },
        "cleanliness": {
            "avg_diff_minimality": _safe_avg(diff_minimalities),
            "avg_lint_delta": _safe_avg(lint_deltas),
            "avg_complexity_delta": _safe_avg(complexity_deltas),
        },
        "work_style": {
            "avg_directness": _safe_avg(directness_scores),
            "avg_exploration_ratio": _safe_avg(exploration_ratios),
            "phase_breakdown": phase_totals,
        },
        "alignment": {
            "avg_alignment": round(sum(alignment_task_scores) / len(alignment_task_scores), 3) if alignment_task_scores else None,
            "ci": (
                [round(v, 3) for v in task_bootstrap_ci(alignment_task_scores)]
                if alignment_task_scores else None
            ),
        },
        "weaknesses": sorted(weak_tasks, key=lambda x: x["pass_at_1"]),
        "cost_benefit": {
            "avg_composite": round(avg_composite, 3) if avg_composite else None,
            "composite_per_dollar": round(cost_benefit, 2) if cost_benefit else None,
        },
    }


@app.get("/harness/{adapter_type}", response_class=HTMLResponse)
def harness_report_view(adapter_type: str, request: Request, db: Session = Depends(get_db)):
    report = _harness_report(db, adapter_type)
    if report is None:
        return HTMLResponse("Harness not found", status_code=404)
    return templates.TemplateResponse("harness_report.html", {
        "request": request,
        "report": report,
    })


@app.get("/api/harness/{adapter_type}/report")
def harness_report_json(adapter_type: str, db: Session = Depends(get_db)):
    report = _harness_report(db, adapter_type)
    if report is None:
        return JSONResponse({"error": "No agents found for this adapter type"}, status_code=404)
    return report