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1020 1021 1022 1023 1024 1025 1026 1027 1028 1029 1030 1031 1032 1033 1034 1035 1036 1037 1038 1039 1040 1041 1042 1043 1044 1045 1046 1047 1048 1049 1050 1051 1052 1053 1054 1055 1056 1057 1058 1059 1060 1061 1062 1063 1064 1065 1066 1067 1068 1069 1070 1071 1072 1073 1074 1075 1076 1077 1078 1079 1080 1081 1082 1083 1084 1085 1086 1087 1088 1089 1090 1091 1092 1093 1094 1095 1096 1097 1098 1099 1100 1101 1102 1103 1104 1105 1106 1107 1108 1109 1110 1111 1112 1113 1114 1115 1116 1117 1118 1119 1120 1121 1122 1123 1124 1125 1126 1127 1128 1129 1130 1131 1132 1133 1134 1135 1136 1137 1138 1139 1140 1141 1142 1143 1144 1145 1146 1147 1148 1149 1150 1151 1152 1153 1154 1155 1156 1157 1158 1159 1160 1161 1162 1163 1164 1165 1166 1167 1168 1169 1170 1171 1172 1173 1174 1175 1176 1177 1178 1179 1180 1181 1182 1183 1184 1185 1186 1187 1188 1189 1190 1191 1192 1193 1194 1195 1196 1197 1198 1199 1200 1201 1202 1203 1204 1205 1206 1207 1208 1209 1210 | """E07 live search/read/edit/test agent experiment.
Unlike E03, this runner does not prepack evidence. The fixed local Qwen model
chooses and invokes repository tools over an isolated base-commit worktree.
"""
from __future__ import annotations
from dataclasses import asdict, dataclass
import difflib
from hashlib import sha256
import json
from pathlib import Path
import resource
import time
from typing import Any, Callable, Sequence
from .components import Candidate
from .fusion import reciprocal_rank_fusion
from .lm_studio import LMStudioClient, LMStudioTransportError
from .lm_studio_embeddings import LMStudioEmbeddingClient
from .lm_studio_management import (
LMStudioManagementError,
LMStudioResidencyManager,
LMStudioServer,
ResidencyTransition,
)
from .pilot import research_code_revision, retrieval_metrics
from .repair_experiment import (
PatchOutputError,
_apply_patch,
isolated_source_tree,
isolated_git_tree,
modified_paths,
run_test_command,
validate_generated_patch,
)
from .repository import GitSnapshot, SourceChunk, chunk_snapshot
from .retrieval import (
BM25FuzzyRetriever,
DenseRetriever,
ExactRetriever,
SQLiteEmbeddingCache,
)
from .specs import (
AgentSystemSpec,
EmbeddingSpec,
ExperimentSpec,
HarnessSpec,
ModelSpec,
TaskSpec,
load_embeddings,
load_experiments,
load_harnesses,
load_models,
load_task_split,
load_tasks,
)
from .syntax_index import SymbolGraph, SyntaxRetriever, parse_snapshot
from .syntax_index import parse_source_file
from .telemetry import EventWriter, RunIdentity, load_completed_or_archive_incomplete
from .tokenization import QwenTokenCounter
MAX_MODEL_CALLS = 12
SEARCH_LIMIT = 5
SEARCH_SNIPPET_LINES = 24
SEARCH_SNIPPET_CHARS = 1_200
READ_MAX_LINES = 200
READ_MAX_CHARS = 16_000
TEST_OUTPUT_CHARS = 12_000
CONVERSATION_TOKEN_LIMIT = 58_000
LIVE_AGENT_SYSTEM = """You are a coding agent repairing one issue in a large Go repository.
The repository is much larger than the context window. Use the provided tools to locate the
implementation, read exact source, apply the smallest correct production-code patch, and run
an allowed public test when useful. End by calling finish.
Rules:
- Never invent file contents; read before editing.
- Hidden tests, gold patches, and gold symbols are unavailable.
- Do not add or modify test files.
- apply_patch accepts a standard unified diff with a/ and b/ paths.
- Tool errors are observations: correct the request instead of pretending it succeeded.
- Stay within the tool and test budgets. Prefer focused queries and reads.
- The only durable edit is one accepted by apply_patch. Text in a normal assistant message is
not an edit.
"""
def live_agent_system(language: str, swe_agent_style: bool = False) -> str:
if language == "go" and not swe_agent_style:
return LIVE_AGENT_SYSTEM
label = {"go": "Go", "python": "Python"}.get(language, language)
interface = (
"Use the controlled interactive agent-computer interface to find files, search text, "
"read source, edit with a patch, and run an allowed test."
if swe_agent_style
else "Use the provided retrieval tools to locate the implementation, read exact source, "
"apply the smallest correct production-code patch, and run an allowed public test when useful."
)
return f"""You are a coding agent repairing one issue in a large {label} repository.
The repository is much larger than the context window. {interface} End by calling finish.
Rules:
- Never invent file contents; read before editing.
- Hidden tests, gold patches, and gold symbols are unavailable.
- Do not add or modify test files.
- apply_patch accepts a standard unified diff with a/ and b/ paths.
- Tool errors are observations: correct the request instead of pretending it succeeded.
- Stay within the tool and test budgets. Prefer focused queries and reads.
- The only durable edit is one accepted by apply_patch. Text in a normal assistant message is
not an edit.
"""
class LiveAgentExperimentError(RuntimeError):
"""Raised when E07 infrastructure cannot preserve its frozen protocol."""
@dataclass(slots=True)
class TaskRetrieval:
chunks: tuple[SourceChunk, ...]
exact: ExactRetriever
lexical: BM25FuzzyRetriever
syntax: SyntaxRetriever
graph: SymbolGraph
dense: DenseRetriever
dense_index_stats: dict[str, Any]
def _safe_relative_path(value: str) -> str:
path = Path(value)
if not value or path.is_absolute() or ".." in path.parts:
raise ValueError(f"unsafe repository path: {value!r}")
return path.as_posix()
def _trim(value: str, limit: int) -> str:
if len(value) <= limit:
return value
return value[:limit] + f"\n...[truncated {len(value) - limit} characters]"
class AgentWorkspace:
def __init__(self, tree: Path, tracked_paths: Sequence[str], task: TaskSpec, max_test_runs: int):
self.tree = tree
self.tracked_paths = set(tracked_paths)
self.task = task
self.max_test_runs = max_test_runs
self.test_runs: list[dict[str, Any]] = []
self.original: dict[str, str] = {}
self.edited_paths: set[str] = set()
self.patch_attempts = 0
@property
def language_name(self) -> str:
return {"go": "Go", "python": "Python"}.get(self.task.language, self.task.language)
def is_test_path(self, path: str) -> bool:
if self.task.language == "go":
return path.endswith("_test.go")
if self.task.language == "python":
return path.startswith("tests/") or Path(path).name.startswith("test_")
return False
def read_file(self, path: str, line_start: int = 1, line_end: int | None = None) -> dict[str, Any]:
safe = _safe_relative_path(path)
if safe not in self.tracked_paths:
raise ValueError(
f"path is not a tracked {self.language_name} source file at the frozen base commit: {safe}"
)
target = self.tree / safe
text = target.read_text(encoding="utf-8", errors="replace")
lines = text.splitlines()
start = max(int(line_start), 1)
requested_end = len(lines) if line_end is None else int(line_end)
end = min(max(requested_end, start), len(lines), start + READ_MAX_LINES - 1)
numbered = "\n".join(
f"{number:>6}: {lines[number - 1]}" for number in range(start, end + 1)
)
return {
"path": safe,
"line_start": start,
"line_end": end,
"total_lines": len(lines),
"content": _trim(numbered, READ_MAX_CHARS),
}
def apply_patch(self, patch: str) -> dict[str, Any]:
if not isinstance(patch, str) or not patch.strip():
raise ValueError("patch must be non-empty unified-diff text")
paths = modified_paths(patch)
for path in paths:
safe = _safe_relative_path(path)
if safe not in self.tracked_paths:
raise ValueError(
f"patch may modify only tracked {self.language_name} source files: {safe}"
)
if self.is_test_path(safe):
raise ValueError(f"test edits are forbidden: {safe}")
for path in paths:
if path not in self.original:
self.original[path] = (self.tree / path).read_text(
encoding="utf-8", errors="replace"
)
self.patch_attempts += 1
patch_path = self.tree.parent / f"agent-edit-{self.patch_attempts:02d}.patch"
patch_path.write_text(patch.rstrip() + "\n", encoding="utf-8")
result = _apply_patch(self.tree, patch_path)
if result["returncode"] == 0:
self.edited_paths.update(paths)
return {**result, "paths": paths, "accepted": result["returncode"] == 0}
def run_tests(self, command: str) -> dict[str, Any]:
if command not in set((*self.task.fail_to_pass_tests, *self.task.pass_to_pass_tests)):
raise ValueError(
"command is not in the frozen public-test allowlist: "
+ repr(command)
)
if len(self.test_runs) >= self.max_test_runs:
raise ValueError(f"test budget exhausted ({self.max_test_runs})")
result = run_test_command(self.tree, command)
self.test_runs.append(result)
return result
def final_patch(self) -> str:
blocks: list[str] = []
for path in sorted(self.edited_paths):
before = self.original[path].splitlines(keepends=True)
after = (self.tree / path).read_text(
encoding="utf-8", errors="replace"
).splitlines(keepends=True)
blocks.extend(
difflib.unified_diff(
before,
after,
fromfile=f"a/{path}",
tofile=f"b/{path}",
lineterm="\n",
)
)
patch = "".join(blocks)
return patch if not patch or patch.endswith("\n") else patch + "\n"
def _candidate_record(candidate: Candidate) -> dict[str, Any]:
lines = candidate.text.splitlines()
selected = lines[:SEARCH_SNIPPET_LINES]
snippet = "\n".join(
f"{candidate.line_start + offset:>6}: {line}"
for offset, line in enumerate(selected)
)
return {
"path": candidate.path,
"line_start": candidate.line_start,
"line_end": min(candidate.line_end, candidate.line_start + len(selected) - 1),
"source": candidate.source,
"score": candidate.score,
"symbol": candidate.symbol,
"snippet": _trim(snippet, SEARCH_SNIPPET_CHARS),
}
def _unique_candidates(candidates: Sequence[Candidate], limit: int) -> tuple[Candidate, ...]:
seen: set[str] = set()
result: list[Candidate] = []
for candidate in candidates:
if candidate.path in seen:
continue
seen.add(candidate.path)
result.append(candidate)
if len(result) >= limit:
break
return tuple(result)
class LiveToolHarness:
def __init__(
self,
harness: HarnessSpec,
retrieval: TaskRetrieval,
workspace: AgentWorkspace,
residency: LMStudioResidencyManager,
model: ModelSpec,
embedding: EmbeddingSpec,
transition_callback: Callable[[ResidencyTransition], None],
):
self.harness = harness
self.retrieval = retrieval
self.workspace = workspace
self.residency = residency
self.model = model
self.embedding = embedding
self.transition_callback = transition_callback
self.search_paths: list[str] = []
self.read_paths: list[str] = []
self.finished = False
self.finish_summary = ""
self.search_call_count = 0
def _packed_records(self, candidates: Sequence[Candidate]) -> list[dict[str, Any]]:
"""Render search observations according to the immutable packing treatment.
Live studies before Study 5 always returned ranked snippets. Study 5
prospectively operationalizes the catalogued packing field at the search
observation boundary while retaining the same ranked candidate paths.
"""
if self.harness.packing == "ranked_snippets":
return [_candidate_record(item) for item in candidates]
records: list[dict[str, Any]] = []
for candidate in candidates:
path = candidate.path
source = (self.workspace.tree / path).read_text(
encoding="utf-8", errors="replace"
)
base = {
"path": path,
"line_start": candidate.line_start,
"line_end": candidate.line_end,
"source": candidate.source,
"score": candidate.score,
"symbol": candidate.symbol,
}
if self.harness.packing == "whole_files":
observation = _trim(source, 12_000)
else:
symbols = parse_source_file(path, source, self.workspace.task.language)
if self.harness.packing == "skeletons":
observation = "\n".join(
f"{item.kind} {item.name} lines {item.line_start}-{item.line_end}: "
f"{item.signature}"
for item in symbols
)
elif self.harness.packing == "role_summaries":
kinds: dict[str, list[str]] = {}
for item in symbols:
kinds.setdefault(item.kind, []).append(item.name)
observation = "\n".join(
f"{kind}: {', '.join(names[:40])}"
for kind, names in sorted(kinds.items())
)
if not observation:
observation = "No indexed declarations; read the file for details."
else: # guarded by HarnessSpec validation
raise ValueError(f"unsupported packing strategy: {self.harness.packing}")
records.append({**base, "packing": self.harness.packing, "snippet": observation})
return records
def _begin_search(self) -> None:
if self.harness.query_policy == "one_shot" and self.search_call_count >= 1:
raise ValueError(
"one-shot query policy permits exactly one repository search; "
"use read_file on an observed path"
)
self.search_call_count += 1
def _dense(self, query: str, limit: int) -> Sequence[Candidate]:
transition = self.residency.ensure_exclusive(
self.embedding.model_key, self.embedding.loaded_context_length
)
self.transition_callback(transition)
return self.retrieval.dense.retrieve(query, limit)
def _base_rankings(self, query: str, include_dense: bool) -> list[Sequence[Candidate]]:
rankings: list[Sequence[Candidate]] = []
if self.harness.exact_search:
rankings.append(self.retrieval.exact.retrieve(query, 50))
if self.harness.lexical:
rankings.append(self.retrieval.lexical.retrieve(query, 50))
if self.harness.syntax == "tree_sitter":
rankings.append(self.retrieval.syntax.retrieve(query, 50))
if include_dense and self.harness.dense:
rankings.append(self._dense(query, 50))
return rankings
def unified_search(self, query: str) -> tuple[Candidate, ...]:
if self.harness.control != "none":
raise ValueError("this control harness has no search capability")
rankings = self._base_rankings(query, include_dense=True)
if not rankings:
return ()
if self.harness.fusion == "rrf" and len(rankings) >= 2:
candidates = reciprocal_rank_fusion(rankings, limit=50)
elif self.harness.harness_id == "H003" and len(rankings) == 2:
# H003 is dense-primary with literal-search backfill; it does not
# introduce the RRF treatment used by H007-H011.
candidates = _unique_candidates((*rankings[-1], *rankings[0]), 50)
else:
candidates = tuple(rankings[-1])
if self.harness.graph_hops:
candidates = self.retrieval.graph.expand(
candidates, self.harness.graph_hops, limit=50
)
return _unique_candidates(candidates, SEARCH_LIMIT)
def specialized_search(self, name: str, query: str) -> tuple[Candidate, ...]:
if name == "search_exact":
values = self.retrieval.exact.retrieve(query, 50)
elif name == "search_lexical":
values = self.retrieval.lexical.retrieve(query, 50)
elif name == "search_syntax":
values = self.retrieval.syntax.retrieve(query, 50)
elif name == "search_dense":
values = self._dense(query, 50)
elif name == "search_graph":
rankings = self._base_rankings(query, include_dense=True)
fused = reciprocal_rank_fusion(rankings, limit=50)
values = self.retrieval.graph.expand(fused, 1, limit=50)
else:
raise ValueError(f"unknown specialized search tool: {name}")
return _unique_candidates(values, SEARCH_LIMIT)
def execute(self, name: str, arguments: dict[str, Any]) -> tuple[dict[str, Any], dict[str, Any]]:
if name == "search_code":
self._begin_search()
candidates = self.unified_search(str(arguments.get("query", "")))
records = self._packed_records(candidates)
self.search_paths.extend(item.path for item in candidates)
return {"results": records}, {"query": arguments.get("query"), "results": records}
if name.startswith("search_"):
self._begin_search()
candidates = self.specialized_search(name, str(arguments.get("query", "")))
records = self._packed_records(candidates)
self.search_paths.extend(item.path for item in candidates)
return {"results": records}, {"query": arguments.get("query"), "results": records}
if name == "read_file":
result = self.workspace.read_file(
str(arguments.get("path", "")),
int(arguments.get("line_start", 1)),
int(arguments["line_end"]) if arguments.get("line_end") is not None else None,
)
self.read_paths.append(result["path"])
return result, result
if name == "apply_patch":
result = self.workspace.apply_patch(str(arguments.get("patch", "")))
compact = {
"accepted": result["accepted"],
"paths": result["paths"],
"returncode": result["returncode"],
"stdout": _trim(result["stdout"], 2_000),
"stderr": _trim(result["stderr"], 4_000),
"elapsed_seconds": result["elapsed_seconds"],
}
return compact, result
if name == "run_tests":
result = self.workspace.run_tests(str(arguments.get("command", "")))
compact = {
**result,
"stdout": _trim(str(result.get("stdout", "")), TEST_OUTPUT_CHARS),
"stderr": _trim(str(result.get("stderr", "")), TEST_OUTPUT_CHARS),
}
return compact, result
if name == "finish":
self.finished = True
self.finish_summary = str(arguments.get("summary", ""))
result = {"accepted": True, "message": "agent finished"}
return result, result
raise ValueError(f"unknown tool: {name}")
class SWEAgentStyleToolHarness:
"""Controlled search/read/edit/test interface inspired by SWE-agent's ACI."""
def __init__(self, retrieval: TaskRetrieval, workspace: AgentWorkspace):
self.retrieval = retrieval
self.workspace = workspace
self.search_paths: list[str] = []
self.read_paths: list[str] = []
self.finished = False
self.finish_summary = ""
def _find_files(self, query: str) -> tuple[Candidate, ...]:
terms = tuple(item.lower() for item in query.split() if item.strip())
scored: list[tuple[int, str]] = []
for path in sorted(self.workspace.tracked_paths):
lowered = path.lower()
score = sum(term in lowered for term in terms)
if score:
scored.append((score, path))
scored.sort(key=lambda item: (-item[0], item[1]))
values: list[Candidate] = []
for score, path in scored[:SEARCH_LIMIT]:
text = (self.workspace.tree / path).read_text(
encoding="utf-8", errors="replace"
)
selected = text.splitlines()[:SEARCH_SNIPPET_LINES]
values.append(
Candidate(
path=path,
line_start=1,
line_end=max(len(selected), 1),
text="\n".join(selected),
source="find_files",
score=float(score),
)
)
return tuple(values)
def execute(self, name: str, arguments: dict[str, Any]) -> tuple[dict[str, Any], dict[str, Any]]:
if name == "find_files":
candidates = self._find_files(str(arguments.get("query", "")))
records = [_candidate_record(item) for item in candidates]
self.search_paths.extend(item.path for item in candidates)
return {"results": records}, {"query": arguments.get("query"), "results": records}
if name == "search_text":
candidates = _unique_candidates(
self.retrieval.exact.retrieve(str(arguments.get("query", "")), 50),
SEARCH_LIMIT,
)
records = [_candidate_record(item) for item in candidates]
self.search_paths.extend(item.path for item in candidates)
return {"results": records}, {"query": arguments.get("query"), "results": records}
if name == "read_file":
result = self.workspace.read_file(
str(arguments.get("path", "")),
int(arguments.get("line_start", 1)),
int(arguments["line_end"]) if arguments.get("line_end") is not None else None,
)
self.read_paths.append(result["path"])
return result, result
if name == "apply_patch":
result = self.workspace.apply_patch(str(arguments.get("patch", "")))
compact = {
"accepted": result["accepted"],
"paths": result["paths"],
"returncode": result["returncode"],
"stdout": _trim(result["stdout"], 2_000),
"stderr": _trim(result["stderr"], 4_000),
"elapsed_seconds": result["elapsed_seconds"],
}
return compact, result
if name == "run_tests":
result = self.workspace.run_tests(str(arguments.get("command", "")))
compact = {
**result,
"stdout": _trim(str(result.get("stdout", "")), TEST_OUTPUT_CHARS),
"stderr": _trim(str(result.get("stderr", "")), TEST_OUTPUT_CHARS),
}
return compact, result
if name == "finish":
self.finished = True
self.finish_summary = str(arguments.get("summary", ""))
result = {"accepted": True, "message": "agent finished"}
return result, result
raise ValueError(f"unknown tool: {name}")
def _search_schema(name: str, description: str) -> dict[str, Any]:
return {
"type": "function",
"function": {
"name": name,
"description": description,
"parameters": {
"type": "object",
"properties": {
"query": {"type": "string", "description": "Focused code search query"}
},
"required": ["query"],
"additionalProperties": False,
},
},
}
def tool_definitions(harness: HarnessSpec, task: TaskSpec) -> list[dict[str, Any]]:
tools: list[dict[str, Any]] = []
language = {"go": "Go", "python": "Python"}.get(task.language, task.language)
if harness.control == "none" and harness.interface == "unified":
tools.append(_search_schema("search_code", "Search the repository using this harness's fixed retrieval stack."))
elif harness.control == "none" and harness.interface == "specialized":
tools.extend(
[
_search_schema("search_exact", "Literal identifier, substring, and path-term search."),
_search_schema("search_lexical", "BM25 code search with fuzzy path/name matching."),
_search_schema("search_syntax", "Tree-sitter declaration and symbol search."),
_search_schema("search_dense", "Code-embedding semantic search."),
_search_schema("search_graph", "Full fused retrieval followed by one static graph hop."),
]
)
tools.extend(
[
{
"type": "function",
"function": {
"name": "read_file",
"description": f"Read a bounded line range from a known tracked {language} file.",
"parameters": {
"type": "object",
"properties": {
"path": {"type": "string"},
"line_start": {"type": "integer", "minimum": 1},
"line_end": {"type": "integer", "minimum": 1},
},
"required": ["path"],
"additionalProperties": False,
},
},
},
{
"type": "function",
"function": {
"name": "apply_patch",
"description": f"Apply a unified diff to production {language} files in the isolated worktree.",
"parameters": {
"type": "object",
"properties": {"patch": {"type": "string"}},
"required": ["patch"],
"additionalProperties": False,
},
},
},
{
"type": "function",
"function": {
"name": "run_tests",
"description": f"Run one frozen public {language} test command.",
"parameters": {
"type": "object",
"properties": {
"command": {
"type": "string",
"enum": list(dict.fromkeys((*task.fail_to_pass_tests, *task.pass_to_pass_tests))),
}
},
"required": ["command"],
"additionalProperties": False,
},
},
},
{
"type": "function",
"function": {
"name": "finish",
"description": "Finish after the best patch has been applied.",
"parameters": {
"type": "object",
"properties": {"summary": {"type": "string"}},
"required": ["summary"],
"additionalProperties": False,
},
},
},
]
)
return tools
def swe_agent_style_tool_definitions(task: TaskSpec) -> list[dict[str, Any]]:
tools = [
_search_schema("find_files", "Find tracked source files by path/name terms."),
_search_schema("search_text", "Literal or regular-expression search over source text."),
]
tools.extend(
item
for item in tool_definitions(load_harnesses()["H000"], task)
if item["function"]["name"] != "search_code"
)
return tools
def _parse_tool_arguments(call: dict[str, Any]) -> tuple[str, dict[str, Any]]:
function = call.get("function", {})
name = function.get("name")
raw = function.get("arguments", "{}")
if not isinstance(name, str) or not name:
raise ValueError("tool call has no function name")
if isinstance(raw, dict):
arguments = raw
elif isinstance(raw, str):
arguments = json.loads(raw)
else:
raise ValueError("tool arguments must be JSON text or an object")
if not isinstance(arguments, dict):
raise ValueError("tool arguments must decode to an object")
return name, arguments
def _compact_conversation(
messages: list[dict[str, Any]], tokenizer: QwenTokenCounter
) -> tuple[int, int]:
before = tokenizer.count(json.dumps(messages, ensure_ascii=False, sort_keys=True))
if before <= CONVERSATION_TOKEN_LIMIT:
return before, before
# Preserve the system/task, assistant decisions, tool-call arguments, and the
# two newest tool outputs. Older bulky observations are replaced deterministically.
tool_indices = [index for index, item in enumerate(messages) if item.get("role") == "tool"]
for index in tool_indices[:-2]:
content = str(messages[index].get("content", ""))
if len(content) > 240:
messages[index]["content"] = json.dumps(
{"notice": "older tool output compacted", "original_sha256": sha256(content.encode()).hexdigest()}
)
current = tokenizer.count(json.dumps(messages, ensure_ascii=False, sort_keys=True))
if current <= CONVERSATION_TOKEN_LIMIT:
return before, current
return before, tokenizer.count(json.dumps(messages, ensure_ascii=False, sort_keys=True))
def _assistant_message(response: dict[str, Any]) -> tuple[dict[str, Any], list[dict[str, Any]]]:
try:
message = response["choices"][0]["message"]
except (KeyError, IndexError, TypeError) as exc:
raise LiveAgentExperimentError("chat completion has no assistant message") from exc
if not isinstance(message, dict):
raise LiveAgentExperimentError("chat completion assistant message is malformed")
result: dict[str, Any] = {
"role": "assistant",
"content": message.get("content") if isinstance(message.get("content"), str) else "",
}
calls = message.get("tool_calls", [])
if calls is None:
calls = []
if not isinstance(calls, list) or not all(isinstance(item, dict) for item in calls):
raise LiveAgentExperimentError("assistant tool_calls field is malformed")
if calls:
result["tool_calls"] = calls
return result, calls
def _usage_totals(responses: Sequence[dict[str, Any]]) -> dict[str, int]:
keys = ("prompt_tokens", "completion_tokens", "total_tokens")
return {
key: sum(
int(response.get("usage", {}).get(key, 0) or 0)
for response in responses
if isinstance(response.get("usage", {}), dict)
)
for key in keys
}
def _failure_validation(stage: str) -> dict[str, Any]:
return {
"hidden_test_patch_apply": None,
"model_patch_apply": None,
"tests": [],
"fail_to_pass": False,
"pass_to_pass": False,
"resolved_at_1": False,
"failure_stage": stage,
}
def _identity(
experiment: ExperimentSpec,
task: TaskSpec,
harness: HarnessSpec,
model: ModelSpec,
revision: str,
agent_system: AgentSystemSpec | None = None,
seed: int | None = None,
repetition: int = 0,
) -> RunIdentity:
treatment_id = agent_system.system_id if agent_system else harness.harness_id
treatment_hash = agent_system.config_hash if agent_system else harness.config_hash
return RunIdentity(
experiment_id=experiment.experiment_id,
task_id=task.task_id,
harness_id=treatment_id,
harness_hash=treatment_hash,
model_id=model.model_id,
model_key=model.expected_inference_key,
model_config_hash=model.config_hash,
context_budget=experiment.context_budgets[0],
seed=experiment.seeds[0] if seed is None else seed,
repetition=repetition,
repository_sha=task.base_commit,
code_revision=revision,
)
def _build_task_retrieval(
snapshot: GitSnapshot,
task: TaskSpec,
embedding: EmbeddingSpec,
embedding_client: LMStudioEmbeddingClient,
cache: SQLiteEmbeddingCache,
) -> TaskRetrieval:
chunks = chunk_snapshot(
snapshot,
task.base_commit,
embedding.chunk_lines,
embedding.chunk_overlap_lines,
embedding.chunk_char_limit,
suffixes={"go": (".go",), "python": (".py",)}[task.language],
)
symbols = parse_snapshot(snapshot, task.base_commit, task.language)
dense, stats = DenseRetriever.build(chunks, embedding, embedding_client, cache)
return TaskRetrieval(
chunks=chunks,
exact=ExactRetriever(chunks),
lexical=BM25FuzzyRetriever(chunks),
syntax=SyntaxRetriever(symbols),
graph=SymbolGraph(symbols),
dense=dense,
dense_index_stats=asdict(stats),
)
def run_live_agent_cell(
root: Path,
repository: Path,
experiment: ExperimentSpec,
task: TaskSpec,
harness: HarnessSpec,
model: ModelSpec,
embedding: EmbeddingSpec,
retrieval: TaskRetrieval,
residency: LMStudioResidencyManager,
server: LMStudioServer,
tokenizer: QwenTokenCounter,
revision: str,
agent_system: AgentSystemSpec | None = None,
seed: int | None = None,
repetition: int = 0,
preserve_git_metadata: bool = False,
) -> dict[str, Any]:
if agent_system is not None and agent_system.system_id != "A002":
raise LiveAgentExperimentError("interactive cell supports only controlled system A002")
identity = _identity(
experiment,
task,
harness,
model,
revision,
agent_system=agent_system,
seed=seed,
repetition=repetition,
)
treatment_id = identity.harness_id
completed = load_completed_or_archive_incomplete(root / "results", identity)
if completed is not None:
return completed
initial_transition = residency.ensure_exclusive(model.expected_inference_key, model.context_length)
client = LMStudioClient(model, timeout_seconds=experiment.timeout_seconds)
discovery, resolved = client.resolve()
source_suffixes = {"go": (".go",), "python": (".py",)}
if task.language not in source_suffixes:
raise LiveAgentExperimentError(f"unsupported Study 2 language: {task.language}")
tracked_paths = GitSnapshot(repository).tracked_paths(
task.base_commit, source_suffixes[task.language]
)
resolved_model = {
"agent_model": asdict(model),
"embedding_model": asdict(embedding),
"agent_runtime": resolved.to_dict(),
"tokenizer_path": str(tokenizer.path),
"tokenizer_sha256": tokenizer.sha256,
"initial_residency_transition": initial_transition.to_dict(),
}
source_context = (
isolated_git_tree(repository, task.base_commit, task.repository_url)
if preserve_git_metadata
else isolated_source_tree(repository, task.base_commit)
)
resolved_treatment = asdict(agent_system) if agent_system else asdict(harness)
with source_context as tree, EventWriter(
root / "results", identity, resolved_treatment, resolved_model
) as writer:
transitions: list[dict[str, Any]] = [initial_transition.to_dict()]
responses: list[dict[str, Any]] = []
protocol_violations: list[str] = []
tool_counts: dict[str, int] = {}
tool_call_count = 0
model_elapsed = 0.0
finished_reason = "model_turn_budget"
max_test_runs = agent_system.max_test_runs if agent_system else experiment.max_test_runs
workspace = AgentWorkspace(tree, tracked_paths, task, max_test_runs)
def record_transition(transition: ResidencyTransition) -> None:
value = transition.to_dict()
transitions.append(value)
writer.emit("resource_sample", {"kind": "model_residency_transition", **value})
live_tools: LiveToolHarness | SWEAgentStyleToolHarness
if agent_system:
live_tools = SWEAgentStyleToolHarness(retrieval, workspace)
else:
live_tools = LiveToolHarness(
harness, retrieval, workspace, residency, model, embedding, record_transition
)
task_prompt = f"ISSUE:\n{task.statement}"
if not agent_system and harness.control == "oracle_file":
task_prompt += "\n\nORACLE FILE LOCATIONS (names only):\n" + "\n".join(task.gold_files)
elif not agent_system and harness.control == "no_search":
task_prompt += "\n\nThis treatment intentionally provides no repository search tool."
messages: list[dict[str, Any]] = [
{
"role": "system",
"content": live_agent_system(task.language, swe_agent_style=bool(agent_system)),
},
{"role": "user", "content": task_prompt},
]
definitions = (
swe_agent_style_tool_definitions(task)
if agent_system
else tool_definitions(harness, task)
)
writer.emit(
"run_started",
{
"confirmatory": True,
"blinded": True,
"task_config_hash": task.config_hash,
"tool_names": [item["function"]["name"] for item in definitions],
"budgets": {
"model_calls": agent_system.model_calls if agent_system else MAX_MODEL_CALLS,
"tool_calls": agent_system.max_tool_calls if agent_system else experiment.max_tool_calls,
"test_runs": max_test_runs,
"timeout_seconds": experiment.timeout_seconds,
},
},
)
cell_started = time.monotonic()
model_call_budget = agent_system.model_calls if agent_system else MAX_MODEL_CALLS
tool_call_budget = agent_system.max_tool_calls if agent_system else experiment.max_tool_calls
for model_turn in range(1, model_call_budget + 1):
if time.monotonic() - cell_started > experiment.timeout_seconds:
finished_reason = "cell_timeout"
break
before_tokens, after_tokens = _compact_conversation(messages, tokenizer)
if before_tokens != after_tokens:
writer.emit(
"resource_sample",
{"kind": "context_compaction", "before_tokens": before_tokens, "after_tokens": after_tokens},
)
if after_tokens > CONVERSATION_TOKEN_LIMIT:
finished_reason = "context_budget_exhausted"
break
transition = residency.ensure_exclusive(model.expected_inference_key, model.context_length)
record_transition(transition)
# Re-resolve after every potential embedding->agent transition. This
# makes a wrong variant, context, or reasoning mode a fatal invariant.
call_started = time.monotonic()
try:
discovery, resolved = client.resolve()
response = client.chat_completions(
resolved.inference_key,
messages,
tools=definitions,
max_tokens=model.max_tokens,
seed=identity.seed,
)
except LMStudioTransportError as first_error:
# A single transparent transport recovery is allowed only because
# no valid model response was observed.
recovery = server.ensure_running()
writer.emit(
"resource_sample",
{"kind": "server_recovery", "error": str(first_error), "recovery": recovery},
)
transition = residency.ensure_exclusive(model.expected_inference_key, model.context_length)
record_transition(transition)
_, resolved = client.resolve()
response = client.chat_completions(
resolved.inference_key,
messages,
tools=definitions,
max_tokens=model.max_tokens,
seed=identity.seed,
)
elapsed = time.monotonic() - call_started
model_elapsed += elapsed
responses.append(response)
writer.write_artifact(
f"model_response_{model_turn:02d}.json", json.dumps(response, indent=2, sort_keys=True) + "\n"
)
writer.emit(
"model_call",
{
"turn": model_turn,
"elapsed_seconds": elapsed,
"usage": response.get("usage", {}),
"finish_reason": response.get("choices", [{}])[0].get("finish_reason"),
"input_conversation_tokens": after_tokens,
},
)
assistant, calls = _assistant_message(response)
messages.append(assistant)
if not calls:
finished_reason = "assistant_stop_without_finish"
if assistant.get("content"):
protocol_violations.append("assistant stopped without calling finish")
break
for call in calls:
if tool_call_count >= tool_call_budget:
finished_reason = "tool_budget_exhausted"
break
tool_call_count += 1
call_id = str(call.get("id") or f"tool-{tool_call_count}")
try:
name, arguments = _parse_tool_arguments(call)
tool_counts[name] = tool_counts.get(name, 0) + 1
compact_result, raw_result = live_tools.execute(name, arguments)
is_error = False
except (ValueError, PatchOutputError, json.JSONDecodeError) as exc:
name = str(call.get("function", {}).get("name", "invalid_tool"))
tool_counts[name] = tool_counts.get(name, 0) + 1
compact_result = {"error": str(exc)}
raw_result = compact_result
is_error = True
protocol_violations.append(f"{name}: {exc}")
messages.append(
{
"role": "tool",
"tool_call_id": call_id,
"content": json.dumps(compact_result, sort_keys=True, default=str),
}
)
event_payload = {
"tool_call_id": call_id,
"name": name,
"arguments_sha256": sha256(
json.dumps(call.get("function", {}).get("arguments", ""), sort_keys=True).encode()
).hexdigest(),
"is_error": is_error,
"result": raw_result,
}
writer.emit("tool_call", event_payload)
if (name.startswith("search_") or name == "find_files") and not is_error:
for candidate in raw_result.get("results", []):
writer.emit("retrieval_candidate", candidate)
elif name == "read_file" and not is_error:
writer.emit("file_read", raw_result)
elif name == "apply_patch":
writer.emit("edit", raw_result)
elif name == "run_tests" and not is_error:
writer.emit("test_run", raw_result)
if live_tools.finished:
finished_reason = "finish_tool"
break
if live_tools.finished or finished_reason == "tool_budget_exhausted":
break
patch = workspace.final_patch()
if patch:
validation = validate_generated_patch(
root,
repository,
task,
patch,
preserve_git_metadata=preserve_git_metadata,
)
else:
validation = _failure_validation("empty_patch")
edited_paths = tuple(sorted(workspace.edited_paths))
localization = retrieval_metrics(edited_paths, task.gold_files)
search_metrics = retrieval_metrics(tuple(dict.fromkeys(live_tools.search_paths)), task.gold_files)
read_metrics = retrieval_metrics(tuple(dict.fromkeys(live_tools.read_paths)), task.gold_files)
usage = _usage_totals(responses)
elapsed = time.monotonic() - cell_started
switch_seconds = sum(
float(item["elapsed_seconds"])
for item in transitions
if not item.get("reused")
)
final = {
"run_id": identity.run_id,
"experiment_id": experiment.experiment_id,
"task_id": task.task_id,
"harness_id": treatment_id,
"resolved_at_1": validation["resolved_at_1"],
"failure_stage": validation["failure_stage"],
"patch_applied": bool(
validation.get("model_patch_apply")
and validation["model_patch_apply"].get("returncode") == 0
),
"fail_to_pass": validation["fail_to_pass"],
"pass_to_pass": validation["pass_to_pass"],
"modified_files": edited_paths,
"localization_metrics": localization,
"search_localization_metrics": search_metrics,
"read_localization_metrics": read_metrics,
"finished_reason": finished_reason,
"finish_summary": live_tools.finish_summary,
"protocol_violations": protocol_violations,
"model_calls": len(responses),
"tool_calls": tool_call_count,
"tool_counts": tool_counts,
"test_runs": len(workspace.test_runs),
"usage": usage,
"elapsed_seconds": elapsed,
"model_elapsed_seconds": model_elapsed,
"model_switch_count": sum(not item.get("reused") for item in transitions),
"model_switch_seconds": switch_seconds,
"peak_process_rss_platform_units": resource.getrusage(resource.RUSAGE_SELF).ru_maxrss,
"dense_index_stats": retrieval.dense_index_stats,
"trajectory_sha256": sha256(
json.dumps(messages, sort_keys=True, separators=(",", ":")).encode()
).hexdigest(),
"patch_sha256": sha256(patch.encode()).hexdigest() if patch else None,
"residency_transitions": transitions,
"test_results": validation["tests"],
}
writer.write_artifact("messages.json", json.dumps(messages, indent=2, sort_keys=True) + "\n")
writer.write_artifact("model.patch", patch)
writer.write_artifact("validation.json", json.dumps(validation, indent=2, sort_keys=True) + "\n")
writer.write_artifact("final_metrics.json", json.dumps(final, indent=2, sort_keys=True) + "\n")
writer.emit(
"run_finished",
{
"status": "completed",
"resolved_at_1": validation["resolved_at_1"],
"failure_stage": validation["failure_stage"],
"finished_reason": finished_reason,
},
)
return final
def run_live_agent_experiment(
root: Path,
repository: Path,
experiment_id: str = "E07",
task_filter: set[str] | None = None,
harness_filter: set[str] | None = None,
) -> dict[str, Any]:
revision = research_code_revision(root)
experiment = load_experiments(root)[experiment_id]
if experiment.mode != "live_agent_repair":
raise LiveAgentExperimentError("live-agent runner requires mode=live_agent_repair")
catalog = load_harnesses(root)
task_catalog = load_tasks(root)
model = load_models(root)[experiment.model_ids[0]]
embedding = load_embeddings(root)[experiment.embedding_id]
split = load_task_split(root / "tasks" / "splits" / f"{experiment.task_split}.txt")
tasks = [task_catalog[item] for item in split if task_filter is None or item in task_filter]
harnesses = [catalog[item] for item in experiment.harness_ids if harness_filter is None or item in harness_filter]
if not tasks or not harnesses:
raise LiveAgentExperimentError("task or harness filters selected no E07 cells")
if any(task.validation_status != "end_to_end_ready" for task in tasks):
raise LiveAgentExperimentError("E07 split includes a task without frozen hidden-test validation")
server = LMStudioServer(port=1234)
server_state = server.ensure_running()
residency = LMStudioResidencyManager(
model.base_url, model.api_token_env, timeout_seconds=experiment.timeout_seconds
)
snapshot = GitSnapshot(repository)
tokenizer = QwenTokenCounter()
embedding_client = LMStudioEmbeddingClient(
embedding, timeout_seconds=experiment.timeout_seconds
)
cache_path = root / "indexes" / "embeddings" / f"{embedding.config_hash}.sqlite3"
rows: list[dict[str, Any]] = []
task_summaries: list[dict[str, Any]] = []
with SQLiteEmbeddingCache(cache_path, embedding) as cache:
for task_index, task in enumerate(tasks):
snapshot.verify_commit(task.base_commit)
index_transition = residency.ensure_exclusive(
embedding.model_key, embedding.loaded_context_length
)
embedding_client.resolve()
index_started = time.monotonic()
retrieval = _build_task_retrieval(snapshot, task, embedding, embedding_client, cache)
index_elapsed = time.monotonic() - index_started
# Cyclic treatment order counterbalances systematic thermal/time drift.
offset = task_index % len(harnesses)
ordered_harnesses = harnesses[offset:] + harnesses[:offset]
task_rows: list[dict[str, Any]] = []
for harness in ordered_harnesses:
row = run_live_agent_cell(
root,
repository,
experiment,
task,
harness,
model,
embedding,
retrieval,
residency,
server,
tokenizer,
revision,
)
rows.append(row)
task_rows.append(row)
task_summaries.append(
{
"task_id": task.task_id,
"harness_order": [item.harness_id for item in ordered_harnesses],
"embedding_index_transition": index_transition.to_dict(),
"index_elapsed_seconds": index_elapsed,
"dense_index_stats": retrieval.dense_index_stats,
"cells": len(task_rows),
"resolved": sum(bool(item["resolved_at_1"]) for item in task_rows),
}
)
final_transition = residency.unload_all()
report = {
"experiment_id": experiment.experiment_id,
"code_revision": revision,
"server_lifecycle": server_state,
"run_count": len(rows),
"resolved_count": sum(bool(item["resolved_at_1"]) for item in rows),
"task_summaries": task_summaries,
"final_residency_transition": final_transition.to_dict(),
"rows": rows,
}
report_dir = root / "results" / "reports"
report_dir.mkdir(parents=True, exist_ok=True)
report_path = report_dir / f"E07_{revision[:12]}_{int(time.time())}.json"
report_path.write_text(json.dumps(report, indent=2, sort_keys=True) + "\n", encoding="utf-8")
return {**report, "report_path": str(report_path)}
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