swe_bench_verified_agentic: multi-turn SWE agent (mini-swe-agent protocol)

Ports es's swe_bench_agentic_adapter into our plugin architecture:
- env swe_agentic: per-sample LONG-RUNNING container (official sweb
  image, /testbed, bash -lc like the testbed startup files expect),
  single bash tool via function calling, sentinel-submission protocol
  (COMPLETE_TASK_AND_SUBMIT_FINAL_OUTPUT + patch), git-diff fallback;
  observations capped at 30k chars
- dataset swe_bench_verified_agentic: same princeton source/converter,
  separate bench name so both variants coexist
- recipe: recovered patch + OFFICIAL test_patch applied in-container,
  FAIL_TO_PASS + capped PASS_TO_PASS via conda testbed pytest, 1800s
- config: max_turns 250, env swe_agentic

Single-turn swe_bench_verified is untouched.

Co-Authored-By: Claude <noreply@anthropic.com>

evalharness/agent/envs/swe_agentic.py

@@ -0,0 +1,219 @@
+"""swe_bench_verified_agentic: multi-turn SWE agent (mini-swe-agent protocol).
+
+Per sample: start a LONG-RUNNING per-instance Docker container (the official
+swebench image, /testbed workdir), give the model a single `bash` tool whose
+execs run inside it, loop until the sentinel submission or max_turns, then
+recover the patch (sentinel payload, else `git diff` in /testbed).
+
+Ports es's swe_bench_agentic_adapter (which itself mirrors mini-swe-agent's
+swebench.yaml). Scoring stays in the recipe (official swebench harness).
+"""
+
+import asyncio
+import json
+import re
+import subprocess
+import uuid
+from typing import Any, Dict
+
+from ...data.sample import ChatMessage, Sample
+from ..loop import Environment, register_env
+
+SENTINEL = 'COMPLETE_TASK_AND_SUBMIT_FINAL_OUTPUT'
+
+# mini-swe-agent swebench.yaml contract (verbatim sections that matter)
+INSTANCE_TEMPLATE = """<pr_description>
+Consider the following PR description:
+{problem_statement}
+</pr_description>
+
+<instructions>
+# Task Instructions
+
+## Overview
+
+You're a software engineer interacting continuously with a computer by submitting commands.
+You'll be helping implement necessary changes to meet requirements in the PR description.
+Your task is specifically to make changes to non-test files in the current directory in order to fix the issue described in the PR description in a way that is general and consistent with the codebase.
+<IMPORTANT>This is an interactive process where you will think and issue AT LEAST ONE command, see the result, then think and issue your next command(s).</important>
+
+For each response:
+1. Include a THOUGHT section explaining your reasoning and what you're trying to accomplish
+2. Provide one or more bash tool calls to execute
+
+## Important Boundaries
+
+- MODIFY: Regular source code files in /testbed (this is the working directory for all your subsequent commands)
+- DO NOT MODIFY: Tests, configuration files (pyproject.toml, setup.cfg, etc.)
+
+## Recommended Workflow
+
+1. Analyze the codebase by finding and reading relevant files
+2. Create a script to reproduce the issue
+3. Edit the source code to resolve the issue
+4. Verify your fix works by running your script again
+5. Test edge cases to ensure your fix is robust
+
+## Command Execution Rules
+
+- Directory or environment variable changes are not persistent; every action runs in a new subshell
+- Prefix actions with `cd /testbed && ...` when needed
+- Always use non-interactive flags (-y, -f); avoid vi/nano
+
+## Submission
+
+When you've completed your work, you MUST submit your changes as a git patch:
+
+Step 1: Create the patch file
+Run `git diff -- path/to/file1 path/to/file2 > patch.txt` listing only the source files you modified.
+Do NOT commit your changes.
+
+Step 2: Verify the patch
+Inspect patch.txt to confirm it only contains your intended changes and headers show `--- a/` and `+++ b/` paths.
+
+Step 3: Submit (EXACT command required)
+You MUST use this EXACT command to submit:
+
+```bash
+echo {sentinel} && cat patch.txt
+```
+
+If the command fails (nonzero exit status), it will not submit.
+
+<CRITICAL>
+- Creating/viewing the patch and submitting MUST be separate commands (not combined with &&).
+- You CANNOT continue working after submitting.
+</CRITICAL>
+</instructions>"""
+
+# OpenAI function-calling bash tool (mini-swe-agent mainline protocol)
+BASH_TOOL = {
+    'type': 'function',
+    'function': {
+        'name': 'bash',
+        'description': 'Execute a bash command in /testbed (persistent working '
+                       'tree, fresh subshell per call). Use for exploring, '
+                       'editing, testing.',
+        'parameters': {'type': 'object',
+                       'properties': {'command': {'type': 'string',
+                                                  'description': 'the bash command to run'}},
+                       'required': ['command']},
+    },
+}
+
+
+def _docker(args, timeout=300):
+    return subprocess.run(['docker'] + args, capture_output=True, text=True,
+                          timeout=timeout)
+
+
+@register_env('swe_agentic')
+class SWEAgenticEnvironment(Environment):
+    """Self-running env: one persistent container per sample."""
+
+    def __init__(self):
+        self.container = ''
+
+    # ---- container lifecycle ----
+    def _start(self, image: str) -> str:
+        name = f'eh-swe-{uuid.uuid4().hex[:10]}'
+        r = _docker(['run', '-d', '--name', name,
+                     '-w', '/testbed',
+                     '-e', 'PAGER=cat', '-e', 'MANPAGER=cat',
+                     '-e', 'LESS=-R', '-e', 'PIP_PROGRESS_BAR=off',
+                     '-e', 'TQDM_DISABLE=1',
+                     image, 'tail', '-f', '/dev/null'], timeout=120)
+        if r.returncode != 0:
+            raise RuntimeError(f'start container failed: {r.stderr[:200]}')
+        return name
+
+    def _exec(self, cmd: str, timeout: int = 600) -> Dict[str, Any]:
+        # bash -lc: swebench images activate the per-instance testbed via
+        # shell startup files (es parity: _SWE_BENCH_INTERPRETER)
+        r = _docker(['exec', self.container, 'bash', '-lc', cmd], timeout=timeout)
+        out = (r.stdout or '') + (r.stderr or '')
+        # cap observation: agents choke on 100k-char dumps
+        if len(out) > 30000:
+            out = out[:15000] + '\n...[truncated]...\n' + out[-15000:]
+        return {'exit': r.returncode, 'out': out}
+
+    def _stop(self):
+        if self.container:
+            _docker(['rm', '-f', self.container], timeout=60)
+            self.container = ''
+
+    # ---- the agent loop ----
+    async def run_task(self, adapter, sample: Sample, max_turns: int = 250,
+                       system: str = '', user_adapter=None, gen_kwargs=None,
+                       **kw) -> Dict[str, Any]:
+        image = (sample.sandbox and sample.sandbox.image) or ''
+        if not image:
+            raise RuntimeError('swe_agentic: sample has no sandbox image '
+                               '(dataset plugin must declare it)')
+        self.container = self._start(image)
+        ps = (sample.metadata or {}).get('problem_statement') or sample.input_text
+        messages = [ChatMessage(role='user', content=INSTANCE_TEMPLATE.format(
+            problem_statement=ps, sentinel=SENTINEL))]
+        tools = [{'name': BASH_TOOL['function']['name'],
+                  'description': BASH_TOOL['function']['description'],
+                  'parameters': BASH_TOOL['function']['parameters']['properties']}]
+
+        from ...model.output import Usage
+        total_usage = Usage()
+        patch = ''
+        turns = 0
+        try:
+            for turns in range(1, max_turns + 1):
+                out = await adapter.generate(messages, tools=tools,
+                                             **(gen_kwargs or {}))
+                total_usage = total_usage + out.usage
+                text = out.text or ''
+                if SENTINEL in text:
+                    # payload after the sentinel echo = `cat patch.txt` output
+                    patch = text.split(SENTINEL, 1)[1].strip()
+                    break
+                if out.tool_calls:
+                    messages.append(ChatMessage(role='assistant', content=text))
+                    for tc in out.tool_calls:
+                        cmd = (tc.arguments_dict or {}).get('command', '')
+                        res = await asyncio.to_thread(self._exec, cmd)
+                        messages.append(ChatMessage(
+                            role='user',
+                            content=f"exit={res['exit']}\n{res['out']}"))
+                        if SENTINEL in res['out']:
+                            # sentinel appeared in command output: capture patch
+                            patch = res['out'].split(SENTINEL, 1)[1].strip()
+                else:
+                    # no tool call: nudge per protocol
+                    messages.append(ChatMessage(role='assistant', content=text))
+                    messages.append(ChatMessage(
+                        role='user',
+                        content='Continue: issue a bash tool call (or submit '
+                                f'via `echo {SENTINEL} && cat patch.txt`).'))
+            if not patch:
+                # fallback (es parity): recover from the working tree
+                res = await asyncio.to_thread(
+                    self._exec, 'cd /testbed && git diff')
+                patch = res['out'].strip()
+        finally:
+            self._stop()
+
+        md = sample.metadata or {}
+        return {
+            'raw': patch or '(no patch produced)',
+            'usage': total_usage.model_dump(),
+            'env_state': {
+                'patch': patch,
+                'instance_id': md.get('instance_id'),
+                'repo': md.get('repo'),
+                'base_commit': md.get('base_commit'),
+                'test_patch': md.get('test_patch'),
+                'FAIL_TO_PASS': md.get('FAIL_TO_PASS'),
+                'PASS_TO_PASS': md.get('PASS_TO_PASS'),
+                'image': image,
+                'turns_used': turns,
+            },
+            'trajectory': [{'role': m.role, 'content': (m.content or '')[:2000]}
+                           for m in messages],
+            'group_key': str(md.get('instance_id') or ''),
+        }

evalharness/config/default.yaml

@@ -66,3 +66,9 @@ tau2_bench:
 swe_bench_verified:
   max_tokens: 32768       # patch 生成预算（diff 可能较长）
   temperature: 0.0        # es 口径：确定性生成 patch
+
+swe_bench_verified_agentic:
+  max_tokens: 4096           # 单轮 bash 命令生成预算（mini-swe-agent 口径）
+  temperature: 0.0
+  env: swe_agentic           # 多轮 agent：bash 探索 /testbed + sentinel 提交
+  max_turns: 250             # mini-swe-agent 默认步数

evalharness/data/datasets/swe_bench_verified.py

@@ -17,8 +17,7 @@ from ..spec import DatasetSpec
         description='SWE-bench Verified; per-instance docker image carried in Sample.sandbox.',
     )
 )
-def swe_bench_verified():
-    def to_sample(record: dict) -> Sample:
+def _record_to_sample(record: dict) -> Sample:
     instance_id = record['instance_id']
     # EXACT Docker Hub naming (verified by es's pull state: 500/500):
     # swebench/sweb.eval.x86_64.{instance_id.lower() with __->_1776_}:latest

evalharness/data/datasets/swe_bench_verified_agentic.py

@@ -0,0 +1,26 @@
+"""swe_bench_verified_agentic: same data as swe_bench_verified, agentic bench.
+
+Separate bench name so both variants coexist (single-turn oracle vs
+multi-turn agent); source/split/fields identical — the difference lives
+in the recipe (env loop + official harness scoring) and config."""
+
+from ..registry import register_dataset
+from ..spec import DatasetSpec
+
+
+@register_dataset(
+    DatasetSpec(
+        name='swe_bench_verified_agentic',
+        source='princeton-nlp/SWE-bench_Verified',
+        split='test',
+        task_type='agent',
+        tags=['code', 'agent', 'swe'],
+        requires=['docker'],
+        description='SWE-bench Verified (agentic): multi-turn bash agent in '
+                    'the per-instance /testbed container.',
+    )
+)
+def swe_bench_verified_agentic():
+    from .swe_bench_verified import _record_to_sample
+
+    return _record_to_sample

evalharness/eval/recipes/agent.py

@@ -261,6 +261,60 @@ def swe_bench_verified():
     )
 
 
+def _swe_agentic_harness(sample, pred: str):
+    """Agentic scoring: pred is the recovered patch (from env_state); same
+    in-container test protocol as the single-turn variant."""
+    md = sample.metadata or {}
+    es = md.get('env_state') or {}
+    patch = es.get('patch') or pred or ''
+    f2p = _json.loads(es.get('FAIL_TO_PASS') or '[]') if isinstance(
+        es.get('FAIL_TO_PASS'), str) else (es.get('FAIL_TO_PASS') or [])
+    p2p = _json.loads(es.get('PASS_TO_PASS') or '[]') if isinstance(
+        es.get('PASS_TO_PASS'), str) else (es.get('PASS_TO_PASS') or [])
+    test_patch = es.get('test_patch') or md.get('test_patch') or ''
+    tests = f2p + p2p[:20]
+    script = f'''set -e
+cd /testbed
+cat > /work/model_patch.diff << 'EOFP'
+{patch}
+EOFP
+cat > /work/test_patch.diff << 'EOFT'
+{test_patch}
+EOFT
+git apply --whitespace=fix /work/model_patch.diff || {{ echo PATCH_FAILED; exit 2; }}
+git apply --whitespace=fix /work/test_patch.diff || {{ echo TESTPATCH_FAILED; exit 2; }}
+FAIL=0
+while IFS= read -r t; do
+  [ -z "$t" ] && continue
+  if ! (conda run -n testbed python -m pytest -x -q "$t" > /dev/null 2>&1); then
+    echo "TEST_FAILED $t"; FAIL=1
+  fi
+done <<'EOF'
+{chr(10).join(tests)}
+EOF
+[ "$FAIL" = 0 ] && echo RESOLVED
+exit $FAIL
+'''
+    return {'run.sh': script}
+
+
+@register_eval('swe_bench_verified_agentic')
+def swe_bench_verified_agentic():
+    return EvalRecipe(
+        name='swe_bench_verified_agentic',
+        extract='identity',
+        scorers={'resolved': {'name': 'execution',
+                              'harness': _swe_agentic_harness,
+                              'entry': 'run.sh', 'sandbox': 'docker',
+                              'timeout_s': 1800}},
+        exec_workers=2,
+        description='SWE-bench Verified AGENTIC (mini-swe-agent protocol): '
+                    'multi-turn bash agent explores /testbed in the '
+                    'per-instance container, submits a git diff; scored by '
+                    'FAIL_TO_PASS(+P2P) with the official test patch applied.',
+    )
+
+
 def _tau2_reward(pred, target, sample, ctx):
     """Score from the official engine's reward_info (env_state).