Files
Piotr Wilkin (ilintar) f3363593f4 Add more benchmarks to llama-eval (#26174)
* llama-eval : add sandboxed execution for code suites

Adds a lightweight replacement for the Docker containers usually used to run
generated code, so a benchmark can be executed without a daemon, an image or
root.

A cached venv is provisioned per suite and reused; candidate programs run in a
throwaway working directory as their own process group, under the strongest
isolation the host offers:

  bwrap    read-only root, tmpfs /tmp and $HOME, no network
  unshare  no network, via --map-current-user
  rlimit   cpu/memory/file-size/process caps only

Candidate code must not run as uid 0, which is why "unshare -r" is a
last-resort tier that warns: root bypasses file permission bits, so a test
asserting that a write to a chmod 0444 file fails would instead see it succeed.

Two details worth noting for the bwrap tier. Only paths hidden by the $HOME
tmpfs are bound back, because bwrap cannot mount onto a symlink and
version-manager alias directories usually are one. And the venv's base
interpreter tree is resolved by walking the symlink chain, since a
version-managed interpreter is commonly reached through an unversioned alias
that startup resolves through.

Suites can declare provisioning steps, which run once at build time while the
network is still up, so test time never needs it.

Assisted-by: Claude Opus 4.8 <noreply@anthropic.com>

* llama-eval : add HumanEval and ClassEval code suites

Adds two execution-graded suites alongside the existing text-graded ones. The
model's reply is parsed for a python code block, combined with the task's own
unit tests, and run in the sandbox.

  --dataset humaneval   164 function-level problems, scored pass@k
  --dataset classeval   100 class-level tasks, scored at class level (feeding
                        pass@k) and method level, matching upstream

pass@k reuses the existing chunk mechanism: --n_cases at a multiple of the
dataset size gives that many samples per problem, aggregated with the unbiased
estimator from the Codex paper.

Program construction and result interpretation are dataset-owned hooks, because
the two suites differ: one HumanEval problem is a function judged by a single
assert, whereas one ClassEval task is a class judged by many independent test
methods, and reducing that to pass/fail would throw away partial credit. Suites
also declare their own package set, provisioning steps, interpreter and timeout.

The package set is derived from the dataset's own imports rather than a
requirements file. HumanEval turns out to need no third-party packages at all;
ClassEval needs thirteen, two of which must be declared because no import
mentions them -- lxml is selected by string in ClassEval_44, and scipy is a
gensim requirement.

Notes on ClassEval specifically:

* gensim publishes no wheel past cp313, so 3.13 is preferred; on 3.14+ the two
  pure-Python APIs it uses are provided by a small shim instead.
* The three nltk corpora it needs are pre-seeded at venv build time. These are
  the modern names -- the dataset asks for the since-renamed punkt and
  averaged_perceptron_tagger.
* It defaults to a patched fork, ilintar/ClassEval, because three upstream
  tests cannot be passed by anything: a hard-coded 2024 date checked against
  datetime.now(), an assertion on the dataset author's own hostname, and an
  exact float comparison. Reference solutions go from 96/100 to 100/100 classes
  and 2182/2196 to 2196/2196 test methods. No skeleton is touched, so what the
  model is asked to write is unchanged; --dataset-source selects the original.

--exec-seed (default 0) seeds the RNG before each test, so suites whose tests
draw random inputs score reproducibly. ClassEval_58 fails its own reference
solution about 30% of the time without it.

The simulator gains both suites, so the harness can be exercised without a
model.

Assisted-by: Claude Opus 4.8 <noreply@anthropic.com>

* llama-eval : fix silent isolation downgrade, generalise the sandbox runner

The isolation probe ran the interpreter without binding it back, so whenever
this tool is itself started from a venv under $HOME -- the usual case, since it
needs `datasets` -- bwrap's tmpfs hid the interpreter, the probe failed, and the
sandbox quietly fell back to a tier that does not hide $HOME at all. Nothing in
the output looked wrong when that happened, and the existing suites have been
running that way. The probe now uses the same interpreter and the same binds the
suite will actually get.

Two additions the agentic suite needs, neither of which changes the existing
suites:

* run_argv() runs an arbitrary command in a caller-owned directory that survives
  the call. run() is now a thin wrapper over it, so HumanEval and ClassEval take
  exactly the same path as before. A multi-step agent needs its edits to persist
  across many invocations, and a suite for a language other than Python needs to
  drive a linter and a test runner rather than a single source string.

* RLIMIT_AS is now per-sandbox, defaulting to what it was. It caps reserved
  address space rather than resident memory, which is the wrong instrument for a
  runtime that reserves a large region and commits little of it: node's
  TypeScript type-stripping goes through WebAssembly, reserves gigabytes per
  instance, and aborts at 4 GiB with an out-of-memory error that reads like a
  fault in the code under test.

HumanEval stays at 164/164 and ClassEval at 100/100.

Assisted-by: Claude Opus 5 <noreply@anthropic.com>

* llama-eval : add SACB, a multi-turn agentic coding suite

`--dataset agentic` is a different shape from the other code suites. Rather than
answering a single prompt, the model is given a small but real repository, a
narrow tool set and a bug report, drives its own multi-turn conversation, and is
graded by running a test suite it was never allowed to see.

The corpus is SACB (Simple Agent Coding Benchmark), 60 tasks over three
hand-written repositories in Python and TypeScript, hosted at ilintar/SACB.
Grading needs no container: a cached venv gives ruff and mypy for Python, and
node's own test runner plus tsc for TypeScript, so the whole dependency set is
four packages.

Three constraints are deliberate:

* No shell, and no way to run the tests. The only feedback channel is `lint`. A
  syntax or type error can be driven out mechanically; a logic error has to be
  reasoned about. A harness that lets an agent iterate against the test suite
  measures something closer to search than to understanding.
* Two edit tools, one line-addressed and one content-addressed. Which one a
  model reaches for, and whether it keeps line numbers straight after its own
  earlier edit, is itself a signal.
* The tests never touch the working tree. They are held outside it and copied
  into a throwaway copy only once the agent has stopped, so they can be neither
  read nor edited. Every tool path is resolved and confinement-checked against
  the repository root, so a symlink planted inside the tree cannot reach out of
  it.

A task counts as resolved when every test that was failing passes and nothing
that already passed broke. Both sets are derived by running the tests against
the defective tree and against the reference fix, never written by hand, so a
task whose defect no test exercises cannot enter the corpus. Many tasks carry
several independent defects, so the fraction of target tests passed is reported
alongside, separating "fixed two of the three faults" from "changed nothing".

An episode that arrives with no tool call and no `finish` is nudged rather than
ended. Models routinely narrate their analysis mid-task, and treating that as
completion ends the episode with no edits and scores a reasoning failure that
never happened; about one episode in four exits that way.

Nothing here is installed unless this suite is selected, and then only for the
language being run: agentic_eval is imported lazily and each toolchain is keyed
per language.

Assisted-by: Claude Opus 5 <noreply@anthropic.com>

* llama-eval : make the agentic hooks typed rather than discovered

`ty` flagged nine unresolved attributes, of which the seven in main() predate
the agentic suite: `EvalState.dataset` is Optional and was dereferenced without
narrowing.

* `loaded_dataset` turns the Optional into a value in one place, raising rather
  than handing back None, so callers do not each have to narrow it.
* `is_agentic`, `prepare()` and `run()` are declared on BaseDataset. They were
  reached through getattr with a default at three call sites, which no checker
  can see through; `run()` now raises a clear error naming the suite instead of
  failing as a missing attribute.
* The sandbox setup is one branch on `is_agentic` rather than two tests of
  `grader_type`, which also drops a duplicated condition.

No behaviour change: HumanEval stays at 164/164, and `ty check` on
examples/llama-eval is clean.

Assisted-by: Claude Opus 5 <noreply@anthropic.com>

* feat: API key

* fix reasoning and response length for agentic runs, add option to save entire transcripts

* llama-eval : fix ty unresolved-attribute on loaded_dataset.ae

Assisted-by: AI coding agent

* llama-eval : trim comments and remove non-ASCII from README

Assisted-by: AI coding agent
2026-10-16 11:52:18 +02:00

919 lines
37 KiB
Python

#!/usr/bin/env python3
"""Multi-turn agentic coding eval: tools over a real working tree.
The existing code suites are single-shot -- one prompt, one reply, execute,
grade. This one is not. The model is given a small but real repository and a
deliberately narrow tool set, and has to find its way to the defect before it
can fix it. That shape is what puts a run in the tens-of-thousands-of-tokens
range without any artificial padding: the context grows because the model
chose to read things.
Three deliberate constraints:
* No shell, and no way to run the tests. The only feedback channel is `lint`.
A syntax or type error can be driven out mechanically; a logic error has to
be reasoned about. Suites that let an agent iterate against the test suite
measure something closer to search than to understanding.
* Two edit tools, one line-addressed and one content-addressed. Which one a
model reaches for, and whether it keeps line numbers straight after its own
earlier edit, is itself a signal.
* Tests never touch the working tree. They are held outside it and copied in
after the agent has stopped, so they can be neither read nor edited.
Everything here is imported lazily by llama-eval.py, and every dependency it
needs is scoped to the language actually under test, so none of it is
installed for anyone running the other suites.
"""
import html
import json
import os
import re
import shutil
import tempfile
from dataclasses import dataclass, field
from pathlib import Path
from typing import Any, Dict, List, Optional, Sequence, Tuple
MAX_READ_LINES = 400
MAX_TOOL_CHARS = 12000
MAX_SEARCH_HITS = 60
@dataclass
class LangSpec:
"""Everything language-specific: how to lint, how to test, what to install.
Kept as data so adding a language is a corpus concern rather than a harness
change, and so a run only ever provisions the toolchain for the language it
is actually evaluating.
"""
name: str
exts: Tuple[str, ...]
packages: Tuple[str, ...] = ()
provision: Tuple[str, ...] = ()
env: Dict[str, str] = field(default_factory=dict)
address_space_limit: Optional[int] = 4 * 1024 * 1024 * 1024
def source_files(self, root: Path) -> List[Path]:
return sorted(p for p in root.rglob("*")
if p.is_file() and p.suffix in self.exts)
PYTHON = LangSpec(
name="python",
exts=(".py",),
packages=("ruff==0.14.2", "mypy==1.18.2"),
)
TYPESCRIPT = LangSpec(
name="typescript",
exts=(".ts",),
address_space_limit=16 * 1024 * 1024 * 1024,
provision=(
"import os, subprocess, shutil, sys\n"
"npm = shutil.which('npm')\n"
"if not npm: sys.exit('npm not found on PATH; needed for the typescript suite')\n"
"dest = os.path.join(os.environ['SANDBOX_VENV'], 'node')\n"
"os.makedirs(dest, exist_ok=True)\n"
"r = subprocess.run([npm, 'install', '--silent', '--no-audit', '--no-fund',\n"
" '--prefix', dest, 'typescript@5.9.3'],\n"
" capture_output=True, text=True)\n"
"sys.exit(0 if r.returncode == 0 else (r.stderr or r.stdout)[-2000:])\n",
),
)
LANGS: Dict[str, LangSpec] = {"python": PYTHON, "typescript": TYPESCRIPT}
class PathEscape(Exception):
"""A tool was handed a path outside the repository."""
class Workspace:
"""A private, writable copy of one task's repository.
Every tool path is resolved through here. Confinement is checked on the
fully-resolved path, so a symlink planted inside the tree cannot be used to
read or write outside it.
"""
def __init__(self, root: Path):
self.root = root.resolve()
self.escape_attempts = 0
def resolve(self, rel: str) -> Path:
if rel is None or not str(rel).strip():
raise PathEscape("no path given")
raw = Path(str(rel))
base = raw if raw.is_absolute() else (self.root / raw)
candidate = base.resolve()
if candidate != self.root and self.root not in candidate.parents:
self.escape_attempts += 1
raise PathEscape(f"path {rel!r} is outside the repository")
return candidate
def rel(self, path: Path) -> str:
return str(path.relative_to(self.root))
def files(self) -> List[Path]:
return sorted(p for p in self.root.rglob("*")
if p.is_file() and "__pycache__" not in p.parts)
def snapshot(self) -> Dict[str, str]:
out = {}
for p in self.files():
try:
out[self.rel(p)] = p.read_text()
except (UnicodeDecodeError, OSError):
pass
return out
def _clip(text: str, limit: int = MAX_TOOL_CHARS) -> str:
if len(text) <= limit:
return text
return text[:limit] + f"\n... [truncated, {len(text) - limit} more characters]"
def _numbered(lines: Sequence[str], start: int) -> str:
width = len(str(start + len(lines) - 1))
return "\n".join(f"{start + i:>{width}}\t{ln}" for i, ln in enumerate(lines))
def tool_schemas() -> List[Dict[str, Any]]:
"""OpenAI-format function schemas for the whole tool set."""
def fn(name, desc, props, required=()):
return {"type": "function", "function": {
"name": name, "description": desc,
"parameters": {"type": "object", "properties": props,
"required": list(required)}}}
return [
fn("list_files", "List files in the repository.",
{"path": {"type": "string",
"description": "Directory relative to the repository root. "
"Omit for the whole tree."}}),
fn("read_file",
"Read a file. Output is prefixed with 1-based line numbers. Give "
"start_line/end_line to read only part of a large file.",
{"path": {"type": "string", "description": "File path relative to the repository root."},
"start_line": {"type": "integer", "description": "First line to read, 1-based, inclusive."},
"end_line": {"type": "integer", "description": "Last line to read, 1-based, inclusive."}},
["path"]),
fn("search",
"Search the repository with a regular expression. Returns matching "
"lines with their file and line number.",
{"pattern": {"type": "string", "description": "Python regular expression."},
"path": {"type": "string", "description": "Limit the search to this subdirectory."},
"glob": {"type": "string", "description": "Limit to files matching this glob, e.g. '*.py'."}},
["pattern"]),
fn("edit_lines",
"Replace an inclusive 1-based line range with new text. Line numbers "
"refer to the file as it is now, so re-read it after any edit that "
"changed the number of lines.",
{"path": {"type": "string", "description": "File path relative to the repository root."},
"start_line": {"type": "integer", "description": "First line to replace, 1-based, inclusive."},
"end_line": {"type": "integer", "description": "Last line to replace, 1-based, inclusive."},
"new_text": {"type": "string", "description": "Replacement text. Use an empty string to delete the range."}},
["path", "start_line", "end_line", "new_text"]),
fn("edit_replace",
"Replace an exact snippet of text. The snippet must occur exactly "
"once in the file unless replace_all is set.",
{"path": {"type": "string", "description": "File path relative to the repository root."},
"old_text": {"type": "string", "description": "Exact text to find, including indentation."},
"new_text": {"type": "string", "description": "Replacement text."},
"replace_all": {"type": "boolean", "description": "Replace every occurrence instead of requiring exactly one."}},
["path", "old_text", "new_text"]),
fn("write_file",
"Write a whole file, creating or overwriting it. Prefer the edit "
"tools for changes to existing files.",
{"path": {"type": "string", "description": "File path relative to the repository root."},
"content": {"type": "string", "description": "Full new file contents."}},
["path", "content"]),
fn("lint",
"Run the language's linter and type checker over the repository and "
"return the diagnostics. This is the only feedback available; the "
"test suite cannot be run.",
{"path": {"type": "string", "description": "Limit to this file or directory."}}),
fn("finish",
"Declare the task complete and end the session. This is the only "
"way to end it; call this once the change is made.",
{"summary": {"type": "string", "description": "Brief description of the change made."}}),
]
class ToolBox:
"""Executes tool calls against one workspace. Returns plain text results.
Failures are reported as ordinary text rather than raised, because how a
model recovers from a rejected edit is part of what is being measured.
"""
def __init__(self, ws: Workspace, linter=None):
self.ws = ws
self.linter = linter
self.calls = 0
self.errors = 0
self.edits = 0
self.finished = False
self.finish_summary: Optional[str] = None
self.per_tool: Dict[str, int] = {}
def dispatch(self, name: str, args: Dict[str, Any]) -> str:
self.calls += 1
self.per_tool[name] = self.per_tool.get(name, 0) + 1
handler = getattr(self, f"_t_{name}", None)
if handler is None:
result = f"Error: unknown tool {name!r}."
else:
try:
result = handler(args)
except PathEscape as e:
result = f"Error: {e}"
except KeyError as e:
result = f"Error: missing required argument {e}."
except Exception as e:
result = f"Error: {type(e).__name__}: {e}"
if result.startswith("Error:"):
self.errors += 1
return _clip(result)
def _t_list_files(self, a):
base = self.ws.resolve(a.get("path") or ".")
if not base.exists():
return f"Error: {a.get('path')!r} does not exist."
paths = [p for p in self.ws.files() if p == base or base in p.parents]
if not paths:
return "(no files)"
return "\n".join(f"{self.ws.rel(p)}\t{p.stat().st_size} bytes" for p in paths)
def _t_read_file(self, a):
path = self.ws.resolve(a["path"])
if not path.is_file():
return f"Error: {a['path']!r} is not a file."
lines = path.read_text().splitlines()
start = max(1, int(a.get("start_line") or 1))
end = int(a.get("end_line") or len(lines))
end = min(end, len(lines))
if start > len(lines):
return f"Error: {a['path']!r} has only {len(lines)} lines."
window = lines[start - 1:end]
note = ""
if len(window) > MAX_READ_LINES:
window = window[:MAX_READ_LINES]
note = (f"\n... [showing {MAX_READ_LINES} of {end - start + 1} requested "
f"lines; re-read with start_line/end_line for the rest]")
header = f"{a['path']} ({len(lines)} lines total)\n"
return header + _numbered(window, start) + note
def _t_search(self, a):
try:
rx = re.compile(a["pattern"])
except re.error as e:
return f"Error: bad regular expression: {e}"
base = self.ws.resolve(a.get("path") or ".")
glob = a.get("glob")
hits, scanned = [], 0
for p in self.ws.files():
if not (p == base or base in p.parents):
continue
if glob and not p.match(glob):
continue
try:
text = p.read_text()
except (UnicodeDecodeError, OSError):
continue
scanned += 1
for i, line in enumerate(text.splitlines(), 1):
if rx.search(line):
hits.append(f"{self.ws.rel(p)}:{i}:{line.strip()[:200]}")
if len(hits) >= MAX_SEARCH_HITS:
return ("\n".join(hits) +
f"\n... [stopped at {MAX_SEARCH_HITS} matches; narrow the pattern]")
if not hits:
return f"No matches for {a['pattern']!r} in {scanned} file(s)."
return "\n".join(hits)
def _t_edit_lines(self, a):
path = self.ws.resolve(a["path"])
if not path.is_file():
return f"Error: {a['path']!r} is not a file."
lines = path.read_text().splitlines()
start, end = int(a["start_line"]), int(a["end_line"])
if start < 1 or start > len(lines):
return (f"Error: start_line {start} out of range; "
f"{a['path']!r} has {len(lines)} lines.")
if end < start or end > len(lines):
return (f"Error: end_line {end} out of range; must be between "
f"{start} and {len(lines)}.")
new = str(a["new_text"]).splitlines()
path.write_text("\n".join(lines[:start - 1] + new + lines[end:]) + "\n")
self.edits += 1
delta = len(new) - (end - start + 1)
return (f"Replaced lines {start}-{end} of {a['path']} "
f"({end - start + 1} -> {len(new)} lines, {delta:+d}). "
f"Later line numbers have shifted." if delta else
f"Replaced lines {start}-{end} of {a['path']}.")
def _t_edit_replace(self, a):
path = self.ws.resolve(a["path"])
if not path.is_file():
return f"Error: {a['path']!r} is not a file."
text = path.read_text()
old, new = str(a["old_text"]), str(a["new_text"])
if not old:
return "Error: old_text must not be empty."
n = text.count(old)
if n == 0:
return (f"Error: old_text not found in {a['path']}. It must match "
f"exactly, including indentation.")
if n > 1 and not a.get("replace_all"):
return (f"Error: old_text occurs {n} times in {a['path']}. Include "
f"more surrounding context to make it unique, or set "
f"replace_all.")
path.write_text(text.replace(old, new))
self.edits += 1
return f"Replaced {n} occurrence(s) in {a['path']}."
def _t_write_file(self, a):
path = self.ws.resolve(a["path"])
path.parent.mkdir(parents=True, exist_ok=True)
existed = path.is_file()
path.write_text(str(a["content"]))
self.edits += 1
return f"{'Overwrote' if existed else 'Created'} {a['path']}."
def _t_lint(self, a):
if self.linter is None:
return "Error: no linter is configured for this task."
target = a.get("path")
if target:
self.ws.resolve(target)
return self.linter(target)
def _t_finish(self, a):
self.finished = True
self.finish_summary = a.get("summary")
return "Task marked complete."
PY_TEST_RUNNER = r'''
import json, os, random, sys, unittest
sys.path.insert(0, os.getcwd())
random.seed(0)
class Collect(unittest.TestResult):
def __init__(self):
super().__init__()
self.outcomes = {}
def addSuccess(self, t): self.outcomes[t.id()] = "pass"
def addFailure(self, t, e): self.outcomes[t.id()] = "fail"
def addError(self, t, e): self.outcomes[t.id()] = "error"
def addSkip(self, t, r): self.outcomes[t.id()] = "skip"
def addExpectedFailure(self, t, e): self.outcomes[t.id()] = "pass"
def addUnexpectedSuccess(self, t): self.outcomes[t.id()] = "fail"
def flatten(s):
for t in s:
if isinstance(t, unittest.TestSuite):
yield from flatten(t)
else:
yield t
out = {"tests": {}}
try:
suite = unittest.TestLoader().discover(os.environ.get("TEST_DIR", "tests"),
top_level_dir=".")
res = Collect()
for t in flatten(suite):
tid = t.id()
try:
t.run(res)
except Exception:
pass
res.outcomes.setdefault(tid, "error")
out["tests"] = res.outcomes
except Exception as e:
out["error"] = f"{type(e).__name__}: {e}"
print("###RESULT###")
print(json.dumps(out))
'''
def _node_roots() -> List[Path]:
"""Directories that must stay visible for node to start under bwrap."""
node = shutil.which("node")
if not node:
return []
real = Path(node).resolve()
return [Path(node).parent.parent, real.parent.parent]
class Runner:
"""Runs the linter and the hidden tests for one language, in the sandbox."""
def __init__(self, sandbox, lang: LangSpec, timeout: float = 90.0):
self.sandbox = sandbox
self.lang = lang
self.timeout = timeout
def _binds(self) -> List[Path]:
return _node_roots() if self.lang.name == "typescript" else []
def _env(self) -> Dict[str, str]:
if self.lang.name != "typescript":
return {}
node = shutil.which("node")
return {"PATH": f"{Path(node).parent}:/usr/bin:/bin"} if node else {}
def lint(self, workdir: Path, target: Optional[str] = None) -> str:
if self.lang.name == "python":
return self._lint_python(workdir, target)
return self._lint_typescript(workdir, target)
def _lint_python(self, workdir: Path, target: Optional[str]) -> str:
venv = self.sandbox.venv_dir
tgt = target or "."
chunks = []
r = self.sandbox.run_argv(
[str(venv / "bin" / "ruff"), "check", "--no-cache",
"--output-format", "concise", tgt],
workdir, timeout=self.timeout)
text = (r.stdout or r.stderr).strip()
if text and "All checks passed" not in text:
chunks.append(text)
r = self.sandbox.run_argv(
[str(venv / "bin" / "mypy"), "--no-color-output", "--no-error-summary",
"--cache-dir", "/dev/null", "--ignore-missing-imports", tgt],
workdir, timeout=self.timeout)
text = (r.stdout or r.stderr).strip()
if text and not text.startswith("Success:"):
chunks.append(text)
return "\n".join(chunks) if chunks else "No problems found."
def _lint_typescript(self, workdir: Path, target: Optional[str]) -> str:
tsc = self.sandbox.venv_dir / "node" / "node_modules" / "typescript" / "bin" / "tsc"
files = [str(p.relative_to(workdir)) for p in sorted(workdir.rglob("*.ts"))
if "node_modules" not in p.parts]
if target:
t = (workdir / target).resolve()
files = [f for f in files if (workdir / f).resolve() == t
or t in (workdir / f).resolve().parents]
if not files:
return "No TypeScript files to check."
r = self.sandbox.run_argv(
["node", str(tsc), "--noEmit", "--strict", "--target", "es2022",
"--module", "esnext", "--moduleResolution", "bundler",
"--allowImportingTsExtensions", *files],
workdir, timeout=self.timeout, env=self._env(), ro_binds=self._binds())
out = (r.stdout or "") + (r.stderr or "")
return out.strip() or "No problems found."
def test(self, workdir: Path) -> Dict[str, Any]:
if self.lang.name == "python":
return self._test_python(workdir)
return self._test_typescript(workdir)
def _test_python(self, workdir: Path) -> Dict[str, Any]:
runner = workdir / "__run_tests.py"
runner.write_text(PY_TEST_RUNNER)
r = self.sandbox.run_argv(
[str(self.sandbox.python), "-I", "-B", str(runner)],
workdir, timeout=self.timeout, env={"TEST_DIR": "tests"})
return self._parse_json_result(r)
def _parse_json_result(self, r) -> Dict[str, Any]:
if r.status == "timeout":
return {"tests": {}, "error": "timeout"}
marker = "###RESULT###"
if marker not in (r.stdout or ""):
return {"tests": {},
"error": f"no result: {(r.stderr or r.stdout or '')[-400:]}"}
try:
return json.loads(r.stdout.split(marker, 1)[1].strip())
except Exception as e:
return {"tests": {}, "error": f"unparseable result: {e}"}
def _test_typescript(self, workdir: Path) -> Dict[str, Any]:
tests = sorted(p for p in (workdir / "tests").rglob("*.ts")) \
if (workdir / "tests").is_dir() else []
if not tests:
return {"tests": {}, "error": "no test files"}
r = self.sandbox.run_argv(
["node", "--test", "--experimental-strip-types",
"--test-reporter=tap",
*[str(p.relative_to(workdir)) for p in tests]],
workdir, timeout=self.timeout, env=self._env(), ro_binds=self._binds())
return self._parse_tap(r)
@staticmethod
def _parse_tap(r) -> Dict[str, Any]:
if r.status == "timeout":
return {"tests": {}, "error": "timeout"}
text = (r.stdout or "") + "\n" + (r.stderr or "")
outcomes: Dict[str, str] = {}
for line in text.splitlines():
m = re.match(r"\s*(not )?ok\s+\d+\s*-\s*(.+?)\s*$", line)
if not m:
continue
name = m.group(2).strip()
if name.endswith(" # SKIP") or " # SKIP" in name:
outcomes[name.split(" # ")[0]] = "skip"
continue
verdict = "fail" if m.group(1) else "pass"
if outcomes.get(name) != "fail":
outcomes[name] = verdict
if not outcomes:
return {"tests": {},
"error": f"no TAP output: {text.strip()[-400:]}"}
return {"tests": outcomes}
SYSTEM_PROMPT = """You are an expert {lang} engineer working in an existing repository.
Resolve the user's request by editing files with the tools provided.
How this session works:
- You are working autonomously. There is no human watching; nobody will answer
questions, approve a plan, or read progress reports.
- Every reply you send must contain at least one tool call until the task is
done. A reply that contains only text does nothing: no work happens and the
turn is wasted. Do not stop to describe what you are about to do; do it.
- The session ends only when you call `finish`. Call it once the change is
made, and not before. Only the files as you leave them are evaluated.
Important constraints:
- You cannot run the code, the tests, or any shell command. The only feedback
available is the `lint` tool, which reports syntax and type problems.
- The repository already has a test suite that will be run against your work
after you finish, but you cannot see it or run it. Do not create test files.
- Read enough of the code to understand it before editing. A change that only
makes the symptom go away will usually fail the tests.
- Make the smallest correct change. Do not reformat or refactor unrelated code.
Keep calling tools until the change is complete, then call `finish`."""
@dataclass
class AgenticTask:
task_id: str
lang: str
category: str
difficulty: int
instruction: str
files: Dict[str, str]
tests: Dict[str, str]
gold: Dict[str, str] = field(default_factory=dict)
fail_to_pass: List[str] = field(default_factory=list)
pass_to_pass: List[str] = field(default_factory=list)
@classmethod
def from_record(cls, rec: Dict[str, Any]) -> "AgenticTask":
def as_map(v):
return v if isinstance(v, dict) else json.loads(v or "{}")
def as_list(v):
return v if isinstance(v, list) else json.loads(v or "[]")
return cls(
task_id=rec["task_id"], lang=rec["lang"], category=rec["category"],
difficulty=int(rec.get("difficulty", 3)),
instruction=rec["instruction"],
files=as_map(rec["files"]), tests=as_map(rec["tests"]),
gold=as_map(rec.get("gold")),
fail_to_pass=as_list(rec.get("fail_to_pass")),
pass_to_pass=as_list(rec.get("pass_to_pass")),
)
@property
def spec(self) -> LangSpec:
return LANGS[self.lang]
def materialise(files: Dict[str, str], dest: Path) -> Path:
"""Write a file map out as a real directory tree."""
dest.mkdir(parents=True, exist_ok=True)
for rel, content in files.items():
p = dest / rel
p.parent.mkdir(parents=True, exist_ok=True)
p.write_text(content)
return dest
def repo_overview(files: Dict[str, str]) -> str:
"""A compact tree, so a run is not lost merely to not finding the files."""
lines = []
for rel in sorted(files):
n = files[rel].count("\n") + 1
lines.append(f" {rel} ({n} lines)")
return "\n".join(lines)
def user_prompt(task: AgenticTask) -> str:
return (f"Repository files:\n{repo_overview(task.files)}\n\n"
f"Task:\n{task.instruction}")
@dataclass
class EpisodeResult:
stop_reason: str = "unknown"
turns: int = 0
tool_calls: int = 0
tool_errors: int = 0
edits: int = 0
escape_attempts: int = 0
finished: bool = False
bad_tool_json: int = 0
nudges: int = 0
prompt_tokens: int = 0
completion_tokens: int = 0
peak_context: int = 0
truncated_turns: int = 0
per_tool: Dict[str, int] = field(default_factory=dict)
error: Optional[str] = None
NUDGE = ("Your last reply contained no tool call, so nothing happened. There is "
"no human in this session to respond to text. If your change is "
"complete, call `finish`. Otherwise continue working by calling the "
"tools; do not reply with text alone.")
def run_episode(task: AgenticTask, toolbox: ToolBox, chat,
max_turns: int = 50,
context_limit: Optional[int] = None,
max_nudges: int = 3) -> Tuple[EpisodeResult, List[Dict]]:
"""Drive one multi-turn tool-calling episode.
`chat(messages, tools) -> response dict` is supplied by the caller so this
module stays free of any HTTP concerns.
"""
tools = tool_schemas()
messages: List[Dict[str, Any]] = [
{"role": "system", "content": SYSTEM_PROMPT.format(lang=task.spec.name)},
{"role": "user", "content": user_prompt(task)},
]
res = EpisodeResult()
for turn in range(max_turns):
res.turns = turn + 1
try:
reply = chat(messages, tools)
except Exception as e:
res.stop_reason, res.error = "request_failed", f"{type(e).__name__}: {e}"
break
usage = reply.get("usage") or {}
pt = int(usage.get("prompt_tokens") or 0)
ct = int(usage.get("completion_tokens") or 0)
res.prompt_tokens += pt
res.completion_tokens += ct
res.peak_context = max(res.peak_context, pt + ct)
choices = reply.get("choices") or []
if not choices:
res.stop_reason, res.error = "no_choices", json.dumps(reply)[:300]
break
msg = choices[0].get("message") or {}
if choices[0].get("finish_reason") == "length":
res.truncated_turns += 1
calls = msg.get("tool_calls") or []
reasoning = msg.get("reasoning_content")
messages.append({
"role": "assistant",
"content": msg.get("content") or "",
**({"reasoning_content": reasoning} if reasoning else {}),
**({"tool_calls": calls} if calls else {}),
})
if not calls:
if toolbox.finished:
res.stop_reason = "finished"
break
if res.nudges >= max_nudges:
res.stop_reason = "no_tool_calls"
break
res.nudges += 1
messages.append({"role": "user", "content": NUDGE})
continue
for call in calls:
fn = call.get("function") or {}
name = fn.get("name") or ""
raw = fn.get("arguments")
if isinstance(raw, dict):
args = raw
else:
try:
args = json.loads(raw or "{}")
except (json.JSONDecodeError, TypeError):
res.bad_tool_json += 1
args = None
output = (toolbox.dispatch(name, args) if args is not None else
f"Error: arguments for {name!r} were not valid JSON.")
messages.append({"role": "tool",
"tool_call_id": call.get("id") or f"call_{res.tool_calls}",
"content": output})
if toolbox.finished:
res.stop_reason = "finished"
break
if context_limit and res.peak_context > context_limit:
res.stop_reason = "context_limit"
break
else:
res.stop_reason = "max_turns"
res.tool_calls = toolbox.calls
res.tool_errors = toolbox.errors
res.edits = toolbox.edits
res.escape_attempts = toolbox.ws.escape_attempts
res.finished = toolbox.finished
res.per_tool = dict(toolbox.per_tool)
return res, messages
def run_tests_against(files: Dict[str, str], task: AgenticTask, runner: Runner,
workroot: Path, label: str) -> Dict[str, str]:
"""Materialise a tree, drop the hidden tests in, and run them.
The tests are only ever written into this throwaway copy, never into the
tree the agent worked in, so there is no window in which they could have
been read or edited.
"""
dest = workroot / f"grade-{label}"
shutil.rmtree(dest, ignore_errors=True)
materialise(files, dest)
materialise(task.tests, dest)
return runner.test(dest).get("tests", {})
def score(task: AgenticTask, outcomes: Dict[str, str]) -> Dict[str, Any]:
"""Resolved means every target test passes and nothing previously passing broke."""
def passing(tid):
return outcomes.get(tid) == "pass"
f2p_ok = [t for t in task.fail_to_pass if passing(t)]
p2p_ok = [t for t in task.pass_to_pass if passing(t)]
regressions = [t for t in task.pass_to_pass if not passing(t)]
resolved = (len(f2p_ok) == len(task.fail_to_pass)
and not regressions and bool(task.fail_to_pass))
return {
"resolved": resolved,
"f2p_passed": len(f2p_ok), "f2p_total": len(task.fail_to_pass),
"p2p_passed": len(p2p_ok), "p2p_total": len(task.pass_to_pass),
"regressions": regressions[:10],
"n_regressions": len(regressions),
"partial": (len(f2p_ok) / len(task.fail_to_pass)) if task.fail_to_pass else 0.0,
}
def make_sandbox(lang_name: str, sandbox_cls, cache_root=None, base_python=None):
"""A sandbox carrying only the toolchain for `lang_name`.
Keyed per language rather than per suite: running the Python tasks must
never provision node, and vice versa.
"""
lang = LANGS[lang_name]
return sandbox_cls(
name=f"agentic-{lang_name}",
packages=list(lang.packages),
provision=list(lang.provision),
env=dict(lang.env),
address_space_limit=lang.address_space_limit,
cache_root=cache_root,
base_python=base_python,
)
def run_task(task: AgenticTask, chat, sandbox, workroot: Optional[Path] = None,
max_turns: int = 50, context_limit: Optional[int] = None,
test_timeout: float = 90.0) -> Dict[str, Any]:
"""One full attempt: fresh workspace, agent episode, then hidden tests."""
root = Path(tempfile.mkdtemp(prefix=f"agentic-{task.task_id}-",
dir=str(workroot) if workroot else None))
try:
ws_root = root / "repo"
materialise(task.files, ws_root)
ws = Workspace(ws_root)
runner = Runner(sandbox, task.spec, timeout=test_timeout)
toolbox = ToolBox(ws, linter=lambda t: runner.lint(ws_root, t))
episode, messages = run_episode(task, toolbox, chat,
max_turns=max_turns,
context_limit=context_limit)
final = ws.snapshot()
changed = sorted(k for k in final
if final[k] != task.files.get(k)) + \
sorted(k for k in task.files if k not in final)
outcomes = run_tests_against(final, task, runner, root, "final")
result = score(task, outcomes)
result.update({
"task_id": task.task_id, "lang": task.lang,
"category": task.category, "difficulty": task.difficulty,
"changed_files": changed,
"episode": {k: v for k, v in vars(episode).items()},
"messages": messages,
})
return result
finally:
shutil.rmtree(root, ignore_errors=True)
TRANSCRIPT_CSS = """
body { font-family: system-ui, sans-serif; margin: 0; padding: 16px; background: #fff; color: #222; max-width: 1100px; }
h1 { font-size: 16px; margin: 0 0 4px; }
.meta { font-size: 13px; color: #555; font-family: 'SF Mono', 'Menlo', 'Consolas', monospace; margin-bottom: 12px; white-space: pre-wrap; }
.msg { border: 1px solid #e1e4e8; border-left-width: 4px; margin: 8px 0; padding: 6px 10px; background: #fafbfc; }
.msg .role { font-size: 12px; font-weight: 600; color: #555; margin-bottom: 4px; }
.system { border-left-color: #888; }
.user { border-left-color: #0969da; }
.nudge { border-left-color: #9a6700; }
.assistant { border-left-color: #1a7f37; background: #fff; }
.tool { border-left-color: #8250df; }
.tool.err { border-left-color: #cf222e; }
pre { font-family: 'SF Mono', 'Menlo', 'Consolas', monospace; font-size: 12px; background: #fff; border: 1px solid #e1e4e8; padding: 6px 8px; margin: 4px 0; white-space: pre-wrap; word-wrap: break-word; }
.reasoning pre { color: #57606a; background: #f6f8fa; }
.call { font-size: 12px; font-weight: 600; color: #8250df; margin-top: 6px; }
details > summary { cursor: pointer; font-size: 12px; color: #555; }
.empty { font-size: 12px; color: #888; font-style: italic; }
"""
def _pre(text: str) -> str:
return f"<pre>{html.escape(text)}</pre>"
def _render_call(call: Dict[str, Any]) -> str:
fn = call.get("function") or {}
raw = fn.get("arguments")
try:
args = raw if isinstance(raw, dict) else json.loads(raw or "{}")
shown = json.dumps(args, indent=2, ensure_ascii=False)
except (json.JSONDecodeError, TypeError):
shown = f"(invalid JSON)\n{raw}"
return (f'<div class="call">&rarr; {html.escape(fn.get("name") or "?")} '
f'<span style="font-weight:400;color:#888">{html.escape(call.get("id") or "")}</span></div>'
+ _pre(shown))
def render_transcript_html(messages: List[Dict[str, Any]], title: str,
meta: str = "") -> str:
"""One page per episode: every turn, with reasoning, tool calls and results."""
names = {}
for m in messages:
for c in m.get("tool_calls") or []:
names[c.get("id")] = (c.get("function") or {}).get("name") or "?"
parts = []
turn = 0
for m in messages:
role = m.get("role") or "?"
content = m.get("content") or ""
if role == "assistant":
turn += 1
body = ""
if m.get("reasoning_content"):
body += ('<details class="reasoning" open><summary>reasoning '
f'({len(m["reasoning_content"])} chars)</summary>'
+ _pre(m["reasoning_content"]) + "</details>")
body += _pre(content) if content.strip() else ""
body += "".join(_render_call(c) for c in m.get("tool_calls") or [])
if not body:
body = '<div class="empty">(empty reply)</div>'
parts.append(f'<div class="msg assistant"><div class="role">assistant '
f'&middot; turn {turn}</div>{body}</div>')
elif role == "tool":
name = names.get(m.get("tool_call_id"), "?")
err = " err" if content.startswith("Error") else ""
parts.append(f'<div class="msg tool{err}"><div class="role">tool result '
f'&middot; {html.escape(name)}</div>{_pre(content)}</div>')
elif role == "system":
parts.append('<div class="msg system"><details><summary>system prompt'
f'</summary>{_pre(content)}</details></div>')
else:
cls = "nudge" if content == NUDGE else role
label = "user (nudge)" if content == NUDGE else role
parts.append(f'<div class="msg {html.escape(cls)}"><div class="role">'
f'{html.escape(label)}</div>{_pre(content)}</div>')
return (f'<!DOCTYPE html>\n<html>\n<head>\n<meta charset="UTF-8">\n'
f'<title>{html.escape(title)}</title>\n<style>{TRANSCRIPT_CSS}</style>\n'
f'</head>\n<body>\n<h1>{html.escape(title)}</h1>\n'
f'<div class="meta">{html.escape(meta)}</div>\n'
+ "\n".join(parts) + "\n</body>\n</html>\n")
def write_transcript(messages: List[Dict[str, Any]], dest: Path, title: str,
meta: str = "") -> Path:
"""Write `<dest>.json` (the raw message history) and `<dest>.html`."""
dest.parent.mkdir(parents=True, exist_ok=True)
json_path = dest.with_name(dest.name + ".json")
with open(json_path, "w") as f:
json.dump(messages, f, indent=2, ensure_ascii=False)
with open(dest.with_name(dest.name + ".html"), "w") as f:
f.write(render_transcript_html(messages, title, meta))
return json_path