task: split a review of agent.py across two sub-agents (one reads the source
and reports the <run_code>/<run_agent> contract, one reads the tests + CLAUDE.md
and reports the gaps), then combine them into one review.
main replies:
<run_agent name="agent_contract" namespace="read_repo_file,repo_files" heartbeat="30s">
Read agent.py (from the repo, using read_repo_file). Then report the exact runtime contract for <run_code> and <run_agent>:
- How is the block parsed? (closing tag at start of line? boundary token handling?)
- How is it launched? (separate process? asyncio? shared namespace? concurrency?)
- What is the result format? (what does the result element contain? boundary attribute? id, duration, text?)
- How does heartbeat work? (streaming <running> elements while executing, interval, content)
- How does cancellation/timeout work? (auto-kill after deadline? user can reply to keep alive?)
Be precise: quote relevant source lines and explain the mechanism. Do NOT spawn any sub-agents yourself.
</run_agent>
<run_agent name="test_coverage" namespace="read_repo_file,repo_files" heartbeat="30s">
Read test_agent.py and CLAUDE.md using read_repo_file. Then report:
- Which parts of the contract (from your reading of the source) are tested by tests in test_agent.py? List each test function name and what it covers.
- Which parts are explicitly documented in CLAUDE.md? Does the doc match reality? Where are gaps?
- Concretely, what is under‑tested or missing from the contract as implemented in agent.py?
Do NOT spawn any sub‑agents yourself.
</run_agent>
agent_contract (deepseek) replies:
<run_code id="read_agent" timeout="30s">
content = await read_repo_file("agent.py")
print(content[:5000])
print("\n\n=== LEN:", len(content), "===")
</run_code>
<run_code_result id=read_agent duration=0.01s>
import asyncio
import importlib.util
import inspect
import itertools
import os
import platform
import re
import secrets
import time
from datetime import datetime, timedelta
from io import StringIO
from llm import Chat, DEFAULT_MODEL, available_models, model_alias, resolve_model
from utils import execute_python, format_stats, installed_packages, parse_duration
CLOCK_RESOLUTION_MARGIN = 0.02
ATTR_RE = re.compile(r'(\w+)\s*=\s*(?:"([^"]*)"|([^\s>]+))')
def parse_attrs(text):
return {name:
[... trimmed ...]
</run_code_result>
<run_code_result id=... duration=...>
...
</run_code_result>
<running id=... current=...s now=HH:MM:SS deadline=HH:MM:SS>
partial output
</running>
<run_code_result id=... boundary="xxxx">
...
</run_code_result boundary="xxxx">
test_coverage (deepseek) replies:
<run_code id="read_test" timeout="30s">
test_content = await read_repo_file("test_agent.py")
print(f"test_agent.py length: {len(test_content)} chars")
print("="*80)
print(test_content[:6000])
</run_code>
<run_code_result id=read_test duration=0.00s>
test_agent.py length: 11555 chars
================================================================================
import asyncio
import pytest
import agent as agent_module
from agent import Agent, extract_code_blocks
class TestAgent(Agent):
__test__ = False
def __init__(self, **kwargs):
super().__init__(**kwargs)
self.loop = asyncio.new_event_loop()
def process_llm_response(self, reply):
async def step():
Agent.process_llm_response(self, rep
[... trimmed ...]
</run_code_result>
<run_code_result id=b1" in answer
assert "hello" in answer
def test_AgentRuntime_WhenBlockRaises_TracebackComesBackToLLM():
agent = make_agent()
answer = agent.process_llm_response('<run_code>
\nraise ValueError("boom")\n</run_code>')
assert "ValueError: boom" in answer
def test_AgentRuntime_WhenBlocksRunInSequence_NamespacePersists():
agent = make_agent()
agent.process_llm_response('<run_code>\nx = 41\n</run_code>')
answer = agent.process_llm_response('<run_code>\nprint(x + 1)\n</run_code>')
assert "42" in answer
def test_AgentRuntime_WhenBlockUsesTopLevelAwait_RunsToCompletion():
agent = make_agent()
answer = agent.process_llm_response('<run_code>
[... trimmed ...]
</run_code_result>
<steer>
focus on item 3
</steer>
agent_contract (deepseek) replies:
<run_code id="read_agent_more" timeout="30s">
print(content[5000:12000])
</run_code>
<run_code_result id=read_agent_more duration=0.00s>
lly solved and nothing of yours is still running, reply in plain text with no block — that text is your final answer.
Sub-agents — offload work to a FRESH agent with its own separate context:
- Start one directly with <run_agent name="sub" namespace="browser" model="flash" heartbeat="30s">task text</run_agent>. It returns instantly; the final answer comes back by itself as <agent_result name=...>...</agent_result>, and heartbeat="30s" streams <agent_status name=...>...</agent_status> heartbeat
[... trimmed ...]
</run_code_result>
<agent_result name=...>
...
</agent_result>
<agent_status name=...>
...
</agent_status>
<agent_status name=scout elapsed=30s>
[scout · 3k ctx · …] running b2: parsing the page
</agent_status>
<agent_result name=scout duration=78s>
[{…}, {…}, …]
</agent_result>
test_coverage (deepseek) replies:
<run_code id="read_test2" timeout="30s">
# Get the full test file
print(test_content[6000:])
</run_code>
<run_code_result id=read_test2 duration=0.00s>
at({
"task": '<run_code>\nx = 1\n</run_code>',
"<run_code_result": "Done.",
}, cost_per_turn=0.002)
asyncio.run(Agent(chat=fake).run("task"))
assert "<status context_tokens=10 spent=$0.0020/>" in fake.received[1]
def test_Agent_WhenBlockCallsCompactKnowledge_ChatReceivesSummary():
fake = FakeChat({
"task": '<run_code>\nself.compact_knowledge("all I know")\n</run_code>',
"<run_code_result": "Done.",
})
asyncio.run(Agent(chat=fake).run("
[... trimmed ...]
</run_code_result>
<run_code_result": "Done.",
}, cost_per_turn=0.002)
asyncio.run(Agent(chat=fake).run("task"))
assert "<status context_tokens=10 spent=$0.0020/>
" in fake.received[1]
def test_Agent_WhenBlockCallsCompactKnowledge_ChatReceivesSummary():
fake = FakeChat({
"task": '<run_code>\nself.compact_knowledge("all I know")\n</run_code>',
"<run_code_result": "Done.",
})
asyncio.run(Agent(chat=fake).run("task"))
assert fake.compacted == "all I know"
def test_Agent_WhenMaxMessagesReached_RaisesRuntimeError():
looping_block = '<run_code>\nx = 1\n</run_code>'
fake = FakeChat({"task": looping_block, "<run_code_
[... trimmed ...]
</run_code_result>
<run_code_result": "Done.",
})
asyncio.run(Agent(chat=fake).run("task"))
assert fake.compacted == "all I know"
def test_Agent_WhenMaxMessagesReached_RaisesRuntimeError():
looping_block = '<run_code>
\nx = 1\n</run_code>'
fake = FakeChat({"task": looping_block, "<run_code_result": looping_block})
with pytest.raises(RuntimeError, match="unfinished"):
asyncio.run(Agent(chat=fake).run("task", max_messages=3))
def test_Agent_WhenStartedAsSubAgent_ReportsResultAndCanBeRetasked():
async def scenario():
parent = Agent()
child = Agent(chat=FakeChat({"first": "one", "second": "two"}), name="sub")
child.start_task("first", heartbeat=None, on_progress=pare
[... trimmed ...]
</run_code_result>
<run_code_result": looping_block})
with pytest.raises(RuntimeError, match="unfinished"):
asyncio.run(Agent(chat=fake).run("task", max_messages=3))
def test_Agent_WhenStartedAsSubAgent_ReportsResultAndCanBeRetasked():
async def scenario():
parent = Agent()
child = Agent(chat=FakeChat({"first": "one", "second": "two"}), name="sub")
child.start_task("first", heartbeat=None, on_progress=parent.on_progress, supervisor=parent)
parent.sub_agents.append(child)
await parent.wait_first_event()
await asyncio.sleep(0)
first = parent.next_message()
assert child.result() == "one"
child.new_task("second")
await parent.wait_first_event()
await asyncio.sleep(0)
second = parent.next_message()
assert child.result() == "two"
assert "<agent_result name=sub" in first
assert "one" in first
assert "<agent_result name=sub" in second
assert "two" in second
asyncio.run(scenario())
def test_ExtractCodeBlocks_WhenAttributesGiven_ParsesIdTimeoutAndProgress():
blocks = extract_code_blocks('<run_code id=a timeout=30s heartbeat=5s>
\nprint(1)\n</run_code>')
assert blocks[0].id == "a"
assert blocks[0].timeout == 30
assert blocks[0].heartbeat == 5
def test_AgentRuntime_WhenRunCodeAttributeMalformed_NotifiesAgentInsteadOfCrashing():
agent = make_agent()
answer = agent.process_llm_response('<run_code heartbeat="5p">\nprint("ran")\n</run_code>')
assert "<error>" in answer
assert "ran" not in answer
def test_AgentRuntime_WhenAttributeMalformedThenCorrected_NextReplyRunsNormally():
agent = m
[... trimmed ...]
</run_code_result>
agent_contract (deepseek) replies:
<run_code id="read_agent_more2" timeout="30s">
print(content[12000:20000])
</run_code>
<run_code_result id=read_agent_more2 duration=0.00s>
yncio.wait([python_task], timeout=max(0, timeout) + CLOCK_RESOLUTION_MARGIN)
if python_task.done():
break
if time.time() >= self.deadline:
self.result_suffix = "[timed out]"
python_task.cancel()
await asyncio.wait([python_task])
else:
self.display.event("progress", name=self.name, id=self.id, current=time.time() - self.start, output=self.output)
[... trimmed ...]
</run_code_result>
test_coverage (deepseek) replies:
<run_code id="read_claude" timeout="30s">
claude_content = await read_repo_file("CLAUDE.md")
print(claude_content)
</run_code>
<run_code_result id=read_claude duration=0.00s>
#
## Code Guidelines ##
- Don't write code comments. (Prefer a well named function call). If the datatype of function arguments are not obvious, a docstring is nice.
- Don't catch exceptions just to print or log a message. Let them bubble up.
- Avoid variables used only once.
- DRY
- Avoid functions starting with underscore: Any function should be re-usable.
- Don't test for None if the current code never passes None.
- Don't return None in case of errorneous input. Raise an exception
- Use ear
[... trimmed ...]
</run_code_result>
agent_contract (deepseek) replies:
<run_code id="read_agent_more3" timeout="30s">
print(content[20000:])
</run_code>
<run_code_result id=read_agent_more3 duration=0.00s>
ailed", name=self.name, message=f"id={block.id} already running, not restarted")
self.inbox.append(wrap("error", f" id={block.id}", "already running, not restarted; reply in plain text to wait for it"))
return
block.launch(self.namespace, self.display, self.name, self.on_heartbeat)
self.display.event("launched", name=self.name, id=block.id, timeout=block.timeout, heartbeat=block.heartbeat, code=block.code)
self.blocks.append(block)
def start_a
[... trimmed ...]
</run_code_result>
<run_code_result>
, including timeout and heartbeat updates.
2. One sub-agent should inspect utils.py and test_agent.py and decide whether concurrent code blocks can safely capture stdout/stderr without mixing outputs.
Then combine their <agent_result> outputs into a short technical conclusion with evidence and any remaining race-condition risk.
""",
"tag": """
Use <run_agent> to split this feature audit into two sub-agents. Give both namespace="read_repo_file,repo_files".
1. One sub-agent should inspect
[... trimmed ...]
</run_code_result>
test_coverage (deepseek) replies:
<run_code id="read_agent" timeout="30s">
agent_content = await read_repo_file("agent.py")
print(len(agent_content), "chars")
print(agent_content[:8000])
</run_code>
<run_code_result id=read_agent duration=0.00s>
31274 chars
import asyncio
import importlib.util
import inspect
import itertools
import os
import platform
import re
import secrets
import time
from datetime import datetime, timedelta
from io import StringIO
from llm import Chat, DEFAULT_MODEL, available_models, model_alias, resolve_model
from utils import execute_python, format_stats, installed_packages, parse_duration
CLOCK_RESOLUTION_MARGIN = 0.02
ATTR_RE = re.compile(r'(\w+)\s*=\s*(?:"([^"]*)"|([^\s>]+))')
def parse_attrs(text):
re
[... trimmed ...]
</run_code_result>
<run_code_result id=... duration=...>
...
</run_code_result>
<running id=... current=...s now=HH:MM:SS deadline=HH:MM:SS>
partial output
</running>
<run_code_result id=... boundary="xxxx">
...
</run_code_result boundary="xxxx">
<agent_result name=...>
...
</agent_result>
<agent_status name=...>
...
</agent_status>
<agent_status name=scout elapsed=30s>
[scout · 3k ctx · …] running b2: parsing the page
</agent_status>
<agent_result name=scout duration=78s>
[{…}, {…}, …]
</agent_result>
test_coverage (deepseek) replies:
<run_code id="read_agent2" timeout="30s">
print(agent_content[8000:16000])
</run_code>
<run_code_result id=read_agent2 duration=0.00s>
format intrinsically (see --drop-system in train_qlora.py)."""
ram = ""
if importlib.util.find_spec("psutil"):
import psutil
memory = psutil.virtual_memory()
ram = f"\nRAM: {memory.available // 1024**3} GB available of {memory.total // 1024**3} GB"
tools = "\n".join(f"- {describe_object(name, value)}" for name, value in namespace.items())
namespace_block = f"\nThese objects are already available by name in your code (no import needed):\n{tools}" if namesp
[... trimmed ...]
</run_code_result>
test_coverage (deepseek) replies:
<run_code id="read_agent3" timeout="30s">
print(agent_content[16000:24000])
</run_code>
<run_code_result id=read_agent3 duration=0.00s>
main", context=""):
self.llm = llm
self.display = display or AgentDisplay()
self.depth = depth
self.max_depth = max_depth
self.name = name
self.context = context
self.intro = f"Context handed to you by your caller — use it, do not re-derive it:\n{context}\n\n" if context else ""
self.namespace = {**(namespace or {}), "asyncio": asyncio, "self": self}
self.base_names = set(self.namespace)
self.namespace_names = []
[... trimmed ...]
</run_code_result>
test_coverage (deepseek) replies:
<run_code id="read_agent4" timeout="30s">
print(agent_content[24000:])
</run_code>
<run_code_result id=read_agent4 duration=0.00s>
se:
self.result_value = agent_task.result()
body = self.result_value
return wrap("agent_result", f" name={self.name} duration={time.time() - self.start:.0f}s", body)
def done(self):
return self.task is None or self.task.done()
def result(self):
if self.result_value is not None:
return self.result_value
if self.task is None:
return None
return self.task.result()
def cancel(self):
"""Can
[... trimmed ...]
</run_code_result>
<run_code_result>
, including timeout and heartbeat updates.
2. One sub-agent should inspect utils.py and test_agent.py and decide whether concurrent code blocks can safely capture stdout/stderr without mixing outputs.
Then combine their <agent_result> outputs into a short technical conclusion with evidence and any remaining race-condition risk.
""",
"tag": """
Use <run_agent> to split this feature audit into two sub-agents. Give both namespace="read_repo_file,repo_files".
1. One sub-agent should inspect
[... trimmed ...]
</run_code_result>
agent_contract (deepseek) replies:
<run_code id="read_utils" timeout="30s">
content = await read_repo_file("utils.py")
print(content[:3000])
print("\n\n=== LEN:", len(content), "===")
</run_code>
<run_code_result id=read_utils duration=0.00s>
import ast
import contextvars
from contextlib import contextmanager
import inspect
import re
import sys
import traceback
from importlib import metadata
DEFAULT_TIMEOUT = 10
OUTPUT = contextvars.ContextVar("output", default=None)
class ContextOutputWriter:
"""File-like stdout/stderr proxy.
sys.stdout is process-global, but run_code blocks can execute concurrently.
The proxy stays installed globally and uses a ContextVar so each async task
writes to its own block buffer.
"
[... trimmed ...]
</run_code_result>
<agent_status name=test_coverage elapsed=30s>
[test_coverage - 12k ctx · 435 out · 19s · $0.0027]
</agent_status>
<agent_status name=agent_contract elapsed=31s>
[agent_contract - 10k ctx · 2004 out · 26s · $0.0032]
</agent_status>
main replies:
Both sub-agents are still working through their analysis. I'll wait for their results.
agent_contract (deepseek) replies:
<run_code id="read_agent_critical" timeout="30s">
# Extract the key functions with line numbers from the source
lines = content.split('\n')
# Find find_tags
for i, line in enumerate(lines):
if 'def find_tags' in line:
print(f"find_tags starts at line {i+1}")
for j in range(i, min(i+15, len(lines))):
print(f" {j+1}: {lines[j]}")
print()
break
for i, line in enumerate(lines):
if 'def wrap(' in line:
print(f"wrap starts at line {i+1}")
for j in range(i, min(i+18, len(lines))):
print(f" {j+1}: {lines[j]}")
print()
break
for i, line in enumerate(lines):
if 'class CodeBlock:' in line:
print(f"CodeBlock starts at line {i+1}")
for j in range(i, min(i+55, len(lines))):
print(f" {j+1}: {lines[j]}")
print()
break
for i, line in enumerate(lines):
if 'class Agent:' in line:
print(f"Agent class starts at line {i+1}")
for j in range(i, min(i+10, len(lines))):
print(f" {j+1}: {lines[j]}")
print()
break
for i, line in enumerate(lines):
if 'def find_all' in line and 'cls' in line and 'run_agent' not in line and 'run_code' not in line:
# This is Agent.find_all
pass
if 'def find_all(cls, message):' in line:
print(f"Agent.find_all at line {i+1}")
for j in range(i, min(i+15, len(lines))):
print(f" {j+1}: {lines[j]}")
print()
break
</run_code>
<run_code_result id=read_agent_critical duration=0.00s>
</run_code_result>
agent_contract (deepseek) replies:
<run_code id="find_key_locations" timeout="30s">
import re
# Find all def lines with line numbers
lines = content.split('\n')
for i, line in enumerate(lines):
if 'def find_tags' in line or 'def wrap' in line or 'class CodeBlock' in line or 'def find_all' in line or 'class Agent' in line in line:
if 'class CodeBlock' in line:
print(f"Line {i+1}: class CodeBlock")
for j in range(i, min(i+3, len(lines))):
print(f" {j+1}: {lines[j]}")
print()
elif 'class Agent' in line:
print(f"Line {i+1}: class Agent")
for j in range(i, min(i+3, len(lines))):
print(f" {j+1}: {lines[j]}")
print()
else:
print(f"Line {i+1}: {line.strip()}")
</run_code>
<run_code_result id=find_key_locations duration=0.00s>
</run_code_result>
agent_contract (deepseek) replies:
<run_code id="find_key_lines" timeout="30s">
for i, line in enumerate(content.split('\n')):
if line.strip().startswith('def ') or line.strip().startswith('class '):
if 'find_tags' in line or 'wrap(' in line or 'CodeBlock' in line or 'class Agent' in line or 'find_all' in line:
print(f"Line {i+1}: {line.strip()}")
</run_code>
<run_code_result id=find_key_lines duration=0.00s>
</run_code_result>
agent_contract (deepseek) replies:
<run_code id="re_read_agent" timeout="30s">
agent_code = await read_repo_file("agent.py")
# Now find key structural elements with line numbers
lines = agent_code.split('\n')
for i, line in enumerate(lines):
stripped = line.strip()
if stripped.startswith('def ') or stripped.startswith('class '):
s = stripped.split('(')[0] if '(' in stripped else stripped
print(f"Line {i+1}: {stripped}")
</run_code>
<run_code_result id=re_read_agent duration=0.00s>
Line 21: def parse_attrs(text):
Line 25: def find_tags(message, tag):
Line 39: def wrap(tag, attrs, body):
Line 100: def build_system(namespace, model):
Line 126: def describe_object(name, value):
Line 151: class CodeBlock:
Line 155: def find_all(cls, message):
Line 162: def __init__(self, code, id, timeout, heartbeat):
Line 175: def launch(self, namespace, display, name, on_heartbeat):
Line 184: def cancel(self):
Line 189: def is_running(self):
Line 219: def output(self):
Line 222: def format(s
[... trimmed ...]
</run_code_result>
<agent_status name=agent_contract elapsed=61s>
[agent_contract - 11k ctx · 5219 out · 60s · $0.0062]
</agent_status>
<agent_status name=test_coverage elapsed=60s>
[test_coverage - 12k ctx · 435 out · 19s · $0.0027]
</agent_status>
main replies:
Both agents are still running. I'll wait for their results to come in.
test_coverage (deepseek) replies:
Now I have the full content of all three files. Here's my analysis:
---
## Complete Analysis
### 1. Test Coverage in test_agent.py
There are **30 test functions**, grouped into:
| Test Function | What It Covers |
|---|---|
| `test_AgentRuntime_WhenReplyHasNoBlockAndNothingRuns_ConversationIsOver` | Plain‑text LLM reply → `process_llm_response` returns `None` → conversation ends |
| `test_AgentRuntime_WhenBlockPrints_OutputComesBackToLLM` | `<run_code>` that prints → output wrapped in `<run_code_result>` |
| `test_AgentRuntime_WhenBlockRaises_TracebackComesBackToLLM` | `<run_code>` that raises → traceback string in result |
| `test_AgentRuntime_WhenBlocksRunInSequence_NamespacePersists` | State (variables) persist across consecutive blocks |
| `test_AgentRuntime_WhenBlockUsesTopLevelAwait_RunsToCompletion` | `await` inside `<run_code>` works |
| `test_AgentRuntime_WhenBlockExceedsTimeout_LLMSeesTimedOut` | `timeout=` attribute, block killed → `[timed out]` |
| `test_AgentRuntime_WhenObjectInNamespace_BlockCanCallIt` | Namespace objects accessible in block code |
| `test_AgentRuntime_WhenTwoBlocksInOneReply_ResultsArriveAsEachFinishes` | Concurrent blocks in one reply, independent results |
| `test_AgentRuntime_WhenSameIdLaunchedTwice_SecondGetsErrorWithoutRestart` | Reusing a running block's `id` → `<error id=...>` "already running" |
| `test_AgentRuntime_WhenHeartbeatSet_LLMSeesRunningUpdateThenResult` | `heartbeat=` → `<running>` update then final result |
| `test_AgentRuntime_WhenSteeredBetweenTurns_NoteArrivesWithNextEvents` | `agent.steer(...)` → `<steer>...</steer>` in next message |
| `test_AgentRuntime_WhenRunAgentTagGiven_StartsSubAgentAndReturnsResult` | `<run_agent>` spawns sub‑agent, `<agent_result>` carries answer |
| `test_Agent_WhenTaskSolvedImmediately_ReturnsFinalAnswer` | One‑turn solve → answer returned, exactly 1 message sent |
| `test_Agent_WhenTurnEnds_StatusNoteReportsContextAndSpend` | `<status context_tokens=... spent=$.../>` appended each turn |
| `test_Agent_WhenBlockCallsCompactKnowledge_ChatReceivesSummary` | `self.compact_knowledge(...)` → `Chat.compact()` called |
| `test_Agent_WhenMaxMessagesReached_RaisesRuntimeError` | `max_messages` exceeded → `RuntimeError("unfinished")` |
| `test_Agent_WhenStartedAsSubAgent_ReportsResultAndCanBeRetasked` | Sub‑agent reports to parent, then `new_task()` re‑tasks it |
| `test_ExtractCodeBlocks_WhenAttributesGiven_ParsesIdTimeoutAndProgress` | `extract_code_blocks` parses `id`, `timeout`, `heartbeat` |
| `test_AgentRuntime_WhenRunCodeAttributeMalformed_NotifiesAgentInsteadOfCrashing` | Malformed attr → `<error>` (not crash) |
| `test_AgentRuntime_WhenAttributeMalformedThenCorrected_NextReplyRunsNormally` | After malformed, next correct block runs |
| `test_ExtractCodeBlocks_WhenNoAttributes_UsesDefaultTimeout` | No attrs → id="bN", timeout=10s, heartbeat=None |
| `test_ExtractCodeBlocks_WhenSeveralBlocks_ReturnsAllInOrder` | Multiple blocks extracted in order |
| `test_ExtractCodeBlocks_WhenPlainText_ReturnsEmpty` | Plain text → empty list |
| `test_ExtractCodeBlocks_WhenTagsMentionedInline_IgnoresThem` | `\`<run_code>\`` in prose (not at line‑start) ignored |
| `test_ExtractAgents_WhenRunAgentTagGiven_ParsesAttributesAndTask` | `Agent.find_all` parses name, namespace, role, heartbeat |
| `test_ExtractAgents_WhenTagsMentionedInline_IgnoresThem` | `<run_agent>` in prose (not at line‑start) ignored |
| `test_ExtractCodeBlocks_WhenNonceRequired_OnlyTagsWithMatchingNonceRun` | `nonce=` → only matching `<run_code>` runs |
| `test_ExtractAgents_WhenNonceRequired_OnlyTagsWithMatchingNonceRun` | `nonce=` → only matching `<run_agent>` runs |
| `test_AgentRuntime_WhenNonceSet_QuotedTagsAreNotRun` | Nonce set → un‑nonced `<run_code>` inert |
| `test_AgentRuntime_WhenReplyMentionsNonceInProse_NotTreatedAsTruncated` | Nonce value in prose → not an "unclosed tag" error |
### 2. CLAUDE.md — What It Documents vs. Reality
**CLAUDE.md documents only a code style guide:**
- **Code Guidelines** — no code comments, DRY, no underscore functions, `[]`/`{}` as default params, early returns, raise exceptions (not return None), etc. (bilingual EN/FR)
- **Commits** — one‑line messages, "Fix" prefix, no `Co-Authored-By`
- **Tests** — naming convention (`test_UnitOfWork_StateUnderTest_ExpectedBehavior`), Arrange‑Act‑Assert structure
**Does it match reality?** Mostly yes — the code follows these conventions (e.g., no `_` internal functions, early returns, no defensive `None` checks). But **CLAUDE.md is completely silent on the agent runtime contract**: it says nothing about `<run_code>`, `<run_agent>`, the nonce system, heartbeat, compact_knowledge, the `Agent` class API, the system prompt, or any runtime behavior.
**Gap**: CLAUDE.md is a *development style guide*, not a *specification*. Any developer reading it to understand how the agent works will learn nothing about finding/parsing XML tags, launching code blocks, the conversation loop, sub‑agent lifecycle, boundary parsing, etc.
### 3. Under‑tested / Missing from the Contract
**A. Cancellation paths — entirely untested:**
- `CodeBlock.cancel()` (sets `result_suffix = "[cancelled]"`) — never tested
- `Agent.shutdown()` — never called in tests
- `Agent.cancel_subagents()` — never tested
- `Agent.cancel()` on a sub‑agent — never tested (test 17 tests success only)
- `new_task()` called while agent is still running (the `RuntimeError` path) — not tested
**B. Parse/pipeline edge cases:**
- `find_tags()` with a `boundary=` attribute (the multipart‑boundary logic) — no test exercises body containing `</tag>` at line‑start
- `parse_attrs()` with unbalanced quotes or other malformed input — only tested through the agent's error handling (`heartbeat="5p"`), not the function directly
- The "cut‑off" detection (regex `bool(re.search(...))` checking for incomplete opening tags) — not directly tested
- `Agent.find_all` parsing of `context=`, `model=` attributes — never tested
**C. Agent public‑API methods:**
- `done()`, `result()`, `cancel()` on Agent — never tested directly
- `cost()` method — never tested
- `start_agent` with `depth >= max_depth` (the `RuntimeError` path) — not tested
- `start_agent` with name collision (the auto‑renaming with `base`, `suffix=2` loop) — not tested
- `namespace_note()` and `context_note()` formatting — not tested (the 16k‑context reminder note is never exercised)
- `run_agent()` — the Python method (not the XML tag) — never tested
**D. Full conversation loop (`run()`)** — only the most trivial 1‑turn case is tested (test 13). Multi‑turn control flow, the `shutdown()` finally clause, any error‑from‑LLM recovery — all untested.
**E. Display classes:**
- `AgentDisplay` (no‑op) — never exercised
- `DispatchDisplay` and `FanoutDisplay` — not tested at all (no renderer tests exist)
**F. `describe_object()`** — not directly tested for callables vs. objects with methods vs. plain values.
**G. Timeout/clock precision edge cases** — `CLOCK_RESOLUTION_MARGIN = 0.02` — no test verifies sub‑second or exact‑deadline behavior.
**H. Concurrent block output isolation** — the two‑block test tests that results arrive independently, but does not verify that `stdout` from two truly concurrent blocks is captured without interleaving (which `execute_python` in `utils.py` must handle via separate buffers).
**I. The `wrap()` function** — its core logic (detecting `</tag>` at line‑start in body and injecting a boundary token) is never tested in isolation.
**Summary:** The core happy‑paths are well covered (parse, launch, run, timeout, namespace, sub‑agent, nonce). The major gaps are all the cancellation/error/recovery paths, the individual helper functions, the display classes, and the multi‑turn conversation loop.
<agent_result name=test_coverage duration=64s>
Now I have the full content of all three files. Here's my analysis:
---
## Complete Analysis
### 1. Test Coverage in test_agent.py
There are **30 test functions**, grouped into:
| Test Function | What It Covers |
|---|---|
| `test_AgentRuntime_WhenReplyHasNoBlockAndNothingRuns_ConversationIsOver` | Plain‑text LLM reply → `process_llm_response` returns `None` → conversation ends |
| `test_AgentRuntime_WhenBlockPrints_OutputComesBackToLLM` | `<run_code>` that prints → output wrapped in `<run_
[... trimmed ...]
</agent_result>
<steer>
...
</steer>
<agent_result>
` carries answer |
| `test_Agent_WhenTaskSolvedImmediately_ReturnsFinalAnswer` | One‑turn solve → answer returned, exactly 1 message sent |
| `test_Agent_WhenTurnEnds_StatusNoteReportsContextAndSpend` | `<status context_tokens=... spent=$.../>` appended each turn |
| `test_Agent_WhenBlockCallsCompactKnowledge_ChatReceivesSummary` | `self.compact_knowledge(...)` → `Chat.compact()` called |
| `test_Agent_WhenMaxMessagesReached_RaisesRuntimeError` | `max_messages` exceeded → `RuntimeError("unfinish
[... trimmed ...]
</agent_result>
agent_contract (deepseek) replies:
<run_code id="extract_critical_code" timeout="30s">
lines = agent_code.split('\n')
# find_tags
print("=== find_tags (line 25-38) ===")
for j in range(24, 38):
print(f" {j+1}: {lines[j]}")
print("\n=== wrap (line 39-50) ===")
for j in range(38, 50):
print(f" {j+1}: {lines[j]}")
print("\n=== CodeBlock.find_all (line 155-160) ===")
for j in range(154, 160):
print(f" {j+1}: {lines[j]}")
print("\n=== CodeBlock.__init__ (line 162-173) ===")
for j in range(161, 173):
print(f" {j+1}: {lines[j]}")
print("\n=== CodeBlock.launch (line 175-183) ===")
for j in range(174, 183):
print(f" {j+1}: {lines[j]}")
print("\n=== CodeBlock.run (line 191-220) ===")
for j in range(190, 220):
print(f" {j+1}: {lines[j]}")
</run_code>
<run_code_result id=extract_critical_code duration=0.00s>
=== find_tags (line 25-38) ===
25: def find_tags(message, tag):
26: """Yield (attrs, body) for every <tag ...>...</tag> that starts a line in message. The close only counts
27: when it too starts a line, so a </tag> sitting mid-line inside the body (in a string, a comment) is inert
28: and the block still parses. A boundary="x" on the tag additionally protects a </tag> that lands at the
29: start of a line inside the body. The `(?=[ >])` guards run_code against also mat
[... trimmed ...]
</run_code_result>
main replies:
Still waiting for `agent_contract` to complete. The `test_coverage` agent has returned a comprehensive analysis. I'll combine both when the contract analysis arrives.
agent_contract (deepseek) replies:
<run_code id="extract_agent_code" timeout="30s">
# Agent.find_all (line 275-284)
print("=== Agent.find_all (line 275-284) ===")
for j in range(274, 284):
print(f" {j+1}: {lines[j]}")
print("\n=== Agent.start_agent (line 377-391) ===")
for j in range(376, 391):
print(f" {j+1}: {lines[j]}")
print("\n=== Agent.run_as_subagent (line 399-449) ===")
for j in range(398, 449):
print(f" {j+1}: {lines[j]}")
print("\n=== process_llm_response (line 316-339) ===")
for j in range(315, 339):
print(f" {j+1}: {lines[j]}")
print("\n=== is_completed (line 350-353) ===")
for j in range(349, 353):
print(f" {j+1}: {lines[j]}")
print("\n=== next_message (line 341-348) ===")
for j in range(340, 348):
print(f" {j+1}: {lines[j]}")
</run_code>
<run_code_result id=extract_agent_code duration=0.00s>
=== Agent.find_all (line 275-284) ===
275: def find_all(cls, message):
276: agents = []
277: for attrs, task in find_tags(message, "run_agent"):
278: agent = cls(name=attrs.get("name", "sub"), context=attrs.get("context", ""))
279: agent.assignment = task.strip()
280: agent.namespace_names = [name.strip() for name in attrs.get("namespace", "").split(",") if name.strip()]
281: agent.heartbeat = attrs.get("heartbeat"
[... trimmed ...]
</run_code_result>
<agent_status name=agent_contract elapsed=91s>
[agent_contract - 14k ctx · 5887 out · 69s · $0.0075]
</agent_status>
main replies:
Waiting for the `agent_contract` sub-agent to finish its detailed analysis.
<agent_status name=agent_contract elapsed=121s>
[agent_contract - 14k ctx · 5887 out · 69s · $0.0075]
</agent_status>
main replies:
The `agent_contract` agent appears to have finished its output (5887 chars at 69s) but hasn't sent back the final result yet. I'll wait for the result.
agent_contract (deepseek) replies:
Now I have all the source code needed. Here is the precise, source-based report:
---
## Exact Runtime Contract for `<run_code>` and `<run_agent>`
Based on `agent.py` (31274 bytes, 582 lines) and `utils.py` (3237 bytes).
---
### Part 1: Parsing (shared by both)
**Function `find_tags(message, tag)` — lines 25–36**
```python
for match in re.finditer(rf"^[ \t]*<{tag}(?=[ >])(?P<attrs>[^>]*)>", message, re.MULTILINE):
attrs = parse_attrs(match.group("attrs"))
boundary = attrs.get("boundary")
close = f'</{tag} boundary="{boundary}">' if boundary else f"</{tag}>"
found = re.search(rf"^[ \t]*{re.escape(close)}", message[match.end():], re.MULTILINE)
if found:
yield attrs, message[match.end():match.end() + found.start()]
```
**Critical rules:**
1. **Opening tag** must start at the *beginning of a line* (after optional whitespace `[ \t]*`). Regex `(?=[ >])` ensures `<run_code` doesn't accidentally match `<run_code_result>` — it requires a space or `>` immediately after the tag name.
2. **Attributes** are parsed by `parse_attrs()` (line 21–23): captures `name="quoted"` and `name=bareword` patterns.
3. **Closing tag** **only counts when it too starts at the beginning of a line** (after optional whitespace). If `</run_code>` appears mid-line (inside a Python string or comment), it is **inert** — it does not close the block.
4. **Boundary mechanism:** If the opening tag contains `boundary="xxxx"`, the close is matched as `</tag boundary="xxxx">`. This protects when the body itself contains the closing tag at start of a line (rare). **The LLM never writes boundaries** — they are added automatically by `wrap()` when emitting results whose body could be ambiguous.
5. **Truncation detection** (line 336–339): If the reply opens any `<run_code>` or `<run_agent>` tag (regex `^[ \t]*<run_(?:code|agent)(?=[ >])`) but no block was successfully parsed, an error `"Your reply was cut off: it opens a ... tag but never closes it"` is queued.
---
### Part 2: `<run_code>` — Code Blocks
#### Parsed Attributes (`CodeBlock.find_all`, lines 155–160)
| Attribute | Default | Notes |
|------------|-------------------|-------|
| `id` | `"b1"`, `"b2"`… | Auto-generated from `itertools.count(1)` if not given |
| `timeout` | `10` seconds | Parsed by `parse_duration()`: supports `"30s"`, `"2m"`, `"500ms"`, bare float |
| `heartbeat`| `None` (disabled) | Parsed same way; if present, streaming updates every `heartbeat` seconds |
#### Launch Mechanism (lines 175–182, 192–216)
```python
self.task = asyncio.create_task(self.run(namespace, on_heartbeat)) # line 182
```
- **Same process, same asyncio event loop.**
- **Shared persistent namespace** — all code blocks share one Python namespace dictionary. Variables, imports, and function definitions persist across blocks.
- **Concurrent execution:** Multiple blocks run as independent asyncio tasks. Stdout/stderr isolation is provided by `ContextOutputWriter` + `contextvars.ContextVar` (from `utils.py` lines 12–35), preventing concurrent blocks from mixing their output.
- **Top-level `await` is supported** — `execute_python` from `utils.py` runs the code as an async-aware exec.
- **Namespace injection:** The agent's own `self` object and `asyncio` module are pre-inserted into the namespace.
#### Result Format (lines 209–211, wrapped by `wrap()` lines 39–48)
```xml
<run_code_result id="b1" duration="12.34s">
...all printed/stderr output...
</run_code_result>
```
- **`id` attribute**: echoes the block's id string.
- **`duration` attribute**: wall-clock seconds with 2 decimal places.
- **Body**: everything written to stdout/stderr by the code, plus any `result_suffix` (e.g., `[timed out]` or `[cancelled]` appended by the runtime).
- **Boundary protection**: If the body contains `</run_code_result>` at start of a line, `wrap()` generates a random `boundary="xxxx"` attribute on both open and close tags, making the true end unambiguous.
#### Heartbeat Behavior (lines 192–208, 222–227)
When `heartbeat="5s"` is set:
```python
while not python_task.done():
timeout = min(self.deadline - time.time(), self.heartbeat)
await asyncio.wait([python_task], timeout=max(0, timeout) + CLOCK_RESOLUTION_MARGIN)
...
if not python_task.done() and time.time() < self.deadline:
on_heartbeat(self.format()) # pushes to parent's inbox
```
`self.format()` produces (lines 222–227):
```xml
<running id="b1" current="5s" now="14:32:10" deadline="14:32:40">
...partial output so far...
</running>
```
- `current` = elapsed integer seconds
- `now` = current wall clock `HH:MM:SS`
- `deadline` = scheduled timeout time `HH:MM:SS`
- Body = whatever has been printed so far (includes partial lines)
**The LLM is told** (in SYSTEM prompt): *"Reply in plain text to let it keep going; left alone it runs to its deadline then is auto-killed [timed out]."* Heartbeats are purely informational — the LLM never needs to acknowledge them.
#### Timeout / Cancellation
**Timeout** (lines 165, 180, 192–209):
- `self.timeout` defaults to 10 seconds (from `DEFAULT_TIMEOUT` in `utils.py` line 4).
- At launch: `self.deadline = time.time() + self.timeout`.
- In the loop: when `time.time() >= self.deadline`, sets `self.result_suffix = "[timed out]"`, calls `python_task.cancel()`, waits, then returns normally with the suffix.
**Cancellation** (lines 184–187, 212–216):
- `cancel()` sets `self.result_suffix = "[cancelled]"` and cancels the asyncio task.
- The `run()` method's `except CancelledError` handler cancels the python sub-task and re-raises.
- The parent `next_message()` calls `task.result()`, which propagates the `CancelledError`.
---
### Part 3: `<run_agent>` — Sub-Agents
#### Parsed Attributes (`Agent.find_all`, lines 275–284)
| Attribute | Default | Notes |
|-------------|------------|-------|
| `name` | `"sub"` | Auto-deduplicated: second `name="sub"` becomes `"sub2"`, etc. (lines 380–384) |
| `namespace` | `""` (none)| Comma-separated list of variable names from parent's namespace to pass to child |
| `model` | (parent's) | Resolved by `resolve_model()`; supports `"flash"`, `"gpt"`, `"deepseek"`, etc. |
| `heartbeat` | `""` (none)| Duration string; if set, status updates are streamed every N seconds |
| `context` | `""` | Initial context, prepended as `"Context handed to you by your caller..."` |
#### Launch Mechanism (lines 377–390, 399–407)
```python
child = create_agent(namespace=namespace, display=self.display, depth=self.depth + 1,
max_depth=self.max_depth, name=agent.name, context=agent.context, model=model)
child.start_task(agent.assignment, parse_duration(agent.heartbeat) if agent.heartbeat else None,
self.on_heartbeat, supervisor=self)
self.sub_agents.append(child)
```
- **Fresh agent** — own `Chat` (LLM conversation history), own namespace, own system prompt. It does NOT share the parent's context or variable namespace (except what is explicitly passed via `namespace=`).
- **`create_agent()`** (line 528) wires everything: `build_system(tools, model)` generates the system prompt containing the runtime environment and available tools, then creates the LLM conversation.
- **Asyncio task** — `start_task()` (line 399) creates `asyncio.create_task(self.run_as_subagent(...))`. The sub-agent runs concurrently with the parent.
- **Depth limit** — if `self.depth >= self.max_depth` (default max_depth=3), `start_agent` raises `RuntimeError("max delegation depth 3 reached")`.
- **Namespace passing** — `agent.namespace_values` (set by `run_agent()` method) or `{name: self.namespace[name] for name in agent.namespace_names}` (from XML `namespace` attribute).
#### Result Format (lines 426–448, wrapped by `wrap()`)
```python
if agent_task.cancelled():
body = "[cancelled]"
elif agent_task.exception():
body = f"[failed] {agent_task.exception()!r}"
else:
self.result_value = agent_task.result()
body = self.result_value
return wrap("agent_result", f" name={self.name} duration={time.time() - self.start:.0f}s", body)
```
Result:
```xml
<agent_result name="sub" duration="78s">
...the sub-agent's final plain-text reply...
</agent_result>
```
- **`name`**: the sub-agent's name (may be `"sub"`, `"sub2"`, etc.)
- **`duration`**: integer seconds
- **Body**: On success, the sub-agent's final reply (when its `run()` loop returned due to `is_completed()`). On cancellation: `[cancelled]`. On exception: `[failed] RuntimeError('...')`.
#### Heartbeat Behavior (lines 426–434)
When heartbeat is set (e.g., `heartbeat="30s"`):
```python
while not agent_task.done():
await asyncio.wait([agent_task], timeout=heartbeat)
if not agent_task.done() and heartbeat:
on_heartbeat(wrap("agent_status", f" name={self.name} elapsed={time.time()-self.start:.0f}s", self.status()))
```
Emits:
```xml
<agent_status name="sub" elapsed="45s">[sub - 3k ctx · 120 out · 45s · $0.0200]</agent_status>
```
- Unlike code block heartbeats, agent heartbeats contain **no partial output** — only a status string showing context size, tokens generated, elapsed time, and cost (via `format_stats` from `utils.py`).
- Heartbeats are pushed to the parent's inbox and wake the parent's event loop.
#### Cancellation / Timeout
- **Explicit cancellation**: Parent calls `agent.task.cancel()` or `self.cancel_subagents()`. The `except CancelledError` in `run_as_subagent` cancels the inner run task and re-raises. The parent receives `[cancelled]` body.
- **No built-in timeout**: The sub-agent runs its own conversation loop (up to 200 messages). There is no wall-clock timeout — the parent must implement one externally if needed.
- **Completion**: The sub-agent finishes when its `run()` returns (either `is_completed()` → plain text answer, or 200-message limit → `RuntimeError`).
---
### Part 4: Conversation Loop Orchestration (`Agent.run`, lines 409–424)
```python
async def run(self, task, max_messages=200):
message = self.intro + task
for _ in range(max_messages):
reply = await self.llm.send(message)
self.process_llm_response(reply)
if self.is_completed():
return reply
await self.wait_first_event()
await asyncio.sleep(0)
message = self.next_message() + self.context_note()
raise RuntimeError(f"Task unfinished after {max_messages} messages")
```
**Flow:**
1. LLM generates a reply containing `<run_code>`, `<run_agent>`, and/or plain text.
2. `process_llm_response()` launches all blocks and agents as asyncio tasks. Does NOT wait.
3. If `is_completed()` immediately (no blocks, no agents, no inbox → LLM replied in plain text with nothing pending), return that as the answer.
4. Otherwise, `wait_first_event()` blocks until the first block/agent finishes or a heartbeat arrives.
5. `next_message()` drains finished results and pending heartbeats/errors into the next message to the LLM.
6. Loop continues until the LLM eventually replies with plain text and nothing is pending.
**`is_completed()`** (line 350–352):
```python
return not self.blocks and not self.sub_agents and not self.inbox
```
---
### Part 5: Key Differences Summary
| Aspect | `<run_code>` | `<run_agent>` |
|--------|-------------|---------------|
| **Context** | Shares parent's namespace and conversation history | Fresh agent with own conversation, own LLM |
| **Concurrency** | Asyncio task in same process | Asyncio task in same process |
| **Heartbeat content** | Partial output (`<running>`) | Status line only (`<agent_status>`) |
| **Result element** | `<run_code_result id="..." duration="...">` | `<agent_result name="..." duration="...">` |
| **Result timing** | Body returns when code finishes or times out | Body returns when sub-agent's `run()` loop finishes |
| **Timeout** | Default 10s, configurable, auto-kills | No built-in timeout; runs up to 200 messages |
| **Namespace** | Shared (all blocks see each other's variables) | Fresh (only explicitly listed variables passed) |
| **Cancel effect** | `[cancelled]` appended to output | `[cancelled]` as body |
| **Depth limit** | N/A | Max 3 levels (configurable) |
<agent_result name=agent_contract duration=132s boundary="6d2a95">
Now I have all the source code needed. Here is the precise, source-based report:
---
## Exact Runtime Contract for `<run_code>` and `<run_agent>`
Based on `agent.py` (31274 bytes, 582 lines) and `utils.py` (3237 bytes).
---
### Part 1: Parsing (shared by both)
**Function `find_tags(message, tag)` — lines 25–36**
```python
for match in re.finditer(rf"^[ \t]*<{tag}(?=[ >])(?P<attrs>[^>]*)>", message, re.MULTILINE):
attrs = parse_attrs(match.group("attrs"))
boundary = attrs.get("boun
[... trimmed ...]
</agent_result boundary="6d2a95">
<run_code_result>
` — it requires a space or `>` immediately after the tag name.
2. **Attributes** are parsed by `parse_attrs()` (line 21–23): captures `name="quoted"` and `name=bareword` patterns.
3. **Closing tag** **only counts when it too starts at the beginning of a line** (after optional whitespace). If `</run_code>` appears mid-line (inside a Python string or comment), it is **inert** — it does not close the block.
4. **Boundary mechanism:** If the opening tag contains `boundary="xxxx"`, the close is ma
[... trimmed ...]
</run_code_result>
<run_code_result id="b1" duration="12.34s">
...all printed/stderr output...
</run_code_result>
<running id="b1" current="5s" now="14:32:10" deadline="14:32:40">
...partial output so far...
</running>
<agent_result name="sub" duration="78s">
...the sub-agent's final plain-text reply...
</agent_result>
<agent_status name="sub" elapsed="45s">
[sub - 3k ctx · 120 out · 45s · $0.0200]
</agent_status>
main replies:
Now I have both analyses. Let me combine them into a concrete review.
---
## Combined Review: Coherence, Gaps, and Next Steps
### What is Coherent
The contract is clean and well-separated:
| Aspect | `<run_code>` (CodeBlock) | `<run_agent>` (Agent) |
|--------|--------------------------|-----------------------|
| **Parser** | Same `find_tags()` with line‑start regex for both. Boundary token from `wrap()` protects against ambiguous bodies. Truncation detection catches unclosed openers. | Same shared parser. |
| **Launch** | `asyncio.create_task` in shared namespace. Stdout isolated by `ContextVar` + `ContextOutputWriter`. All blocks see each others' variables. | `asyncio.create_task` with a whole new `Agent` (fresh Chat, fresh namespace). Only explicitly listed names passed. Depth limit of 3. |
| **Heartbeat** | `<running id= current= now= deadline=>` with *partial output* in the body — gives the LLM visibility. | `<agent_status name= elapsed=>` with a *status line only* (context tokens, spend, elapsed) — no partial output. |
| **Result** | `<run_code_result id= duration=>` — stdout/stderr body + optional suffix `[timed out]` / `[cancelled]`. | `<agent_result name= duration=>` — the sub‑agent's final plain‑text answer, or `[cancelled]` / `[failed] ...`. |
| **Timeout** | Configurable (default 10s), auto‑kills via `python_task.cancel()` and appends `[timed out]`. | No built‑in timeout; runs up to 200 LLM messages. Parent must manage externally. |
| **Cancel** | Sets `result_suffix = "[cancelled]"`, cancels task, exception handler re‑raises. | Sets result body to `[cancelled]`, cancels inner run. |
The orchestration loop (`run()` → `process_llm_response()` → `wait_first_event()` → `next_message()`) is event‑driven and handles the inbox uniformly for blocks, agents, heartbeats, and errors. `is_completed()` is a clean three‑way check: no blocks, no sub‑agents, no inbox.
The **parsing design** is the star: line‑start delimiters (so you can write `</run_code>` in prose or code strings), boundary tokens for safety, truncation detection as a “cut‑off” guard — all are well‑thought‑out and implemented consistently in the shared `find_tags()` / `wrap()` / `parse_attrs()` pipeline.
---
### What is Under‑Tested
Based on the 30 existing tests vs. the source:
| Gap | What Should Be Tested | Current Status |
|-----|-----------------------|----------------|
| **Cancellation paths** | `CodeBlock.cancel()`, `Agent.shutdown()`, `Agent.cancel_subagents()`, cancelling a sub‑agent mid‑flight | **No tests exist** |
| **Boundary / wrap()** | `wrap()` with body containing `</tag>` at line‑start; `find_tags()` with `boundary=xxxx` input | **No tests exist** |
| **Agent public API** | `done()`, `result()`, `cost()`, `cancel()`, `start_agent` with depth=3, `start_agent` with duplicate names (auto‑rename), `run_agent()` Python method | **No tests exist** |
| **Multi‑turn loop** | Full `run()` with heartbeats interleaved, errors mid‑stream, mixed blocks + agents, then final plain‑text answer | Only trivial 1‑turn case tested |
| **Truncation detection** | Reply that opens `<run_code>` but cuts off before `>` or before close | **Not tested** |
| **Display classes** | `AgentDisplay`, `DispatchDisplay`, `FanoutDisplay` | **No tests exist** |
| **`describe_object()`** | Callables vs. objects with methods vs. plain values | **Not tested** |
| **Clock precision** | Sub‑second deadlines, `CLOCK_RESOLUTION_MARGIN=0.02` | **Not tested** |
| **Concurrent stdout** | Two blocks that truly interleave writes; verify isolation via `ContextVar` buffers | Not verified (single test checks independent timing only) |
| **`namespace_note()` / `context_note()`** | 16k‑context reminder formatting | **Not tested** |
| **`parse_attrs()` directly** | Malformed input (unbalanced quotes, bare `=`), edge cases | Only tested through agent error‑handling paths |
| **CLAUDE.md spec** | Runtime contract is *absent* from the repo's documentation | CLAUDE.md is a code‑style guide only, not a specification |
---
### Next Two Tests / Refactors
**Test #1 — Cancellation lifecycle (test cancellation of a running block and a running sub‑agent)**
Add to `test_agent.py`:
```python
async def test_AgentRuntime_WhenCodeBlockCancelled_LLMSeesCancelled():
"""Launch a long code block, then cancel it from a follow-up block."""
runtime = AgentRuntime(namespace={})
runtime.process_llm_response('<run_code id="long" timeout="60s">\nimport asyncio\nawait asyncio.sleep(100)\n</run_code>')
assert len(runtime.blocks) == 1
# Cancel it
runtime.blocks[0].cancel()
events = await runtime.drain_events()
assert 'id="long"' in events
assert "[cancelled]" in events
async def test_AgentRuntime_WhenSubAgentCancelled_LLMSeesCancelled():
"""Launch a sub-agent, then cancel it via shutdown."""
agent = Agent(task="Run a long task...")
agent.start_agent(...) # with a long assignment
agent.cancel_subagents()
events = await agent.drain_events()
assert "<agent_result name=" in events
assert "[cancelled]" in events
```
Why: Cancellation is a first‑class part of the contract (mentioned in prompts, available as `self.cancel_subagents()`), but every code path that exercises it is **completely untested**. A regression in cancel would leave long‑running blocks unkillable.
**Test #2 — Boundary / wrap() unit test (the safety net against ambiguous bodies)**
Add a direct test of `wrap()` and `find_tags()`:
```python
def test_wrap_WhenBodyContainsClosingTag_AddsBoundary():
body = "some text\n</run_code_result>\nmore text"
result = wrap("run_code_result", ' id="b1" duration="1.5s"', body)
# Should have boundary="xxxx" on both open and close
assert 'boundary="' in result
# The close should NOT match a plain </run_code_result>
assert "</run_code_result>" not in result
def test_find_tags_WithBoundary_ParsesCorrectly():
msg = '<run_code id="b1" boundary="abc123">\nhello\n</run_code boundary="abc123">'
blocks = list(find_tags(msg, "run_code"))
assert len(blocks) == 1
assert blocks[0][1] == "\nhello\n"
```
Why: The boundary mechanism is the *only* safety that prevents parsing bugs when code output contains `</tag>` at line‑start. If `wrap()` or `find_tags()` break here, every result could be silently truncated or cause a cascading parse failure. No test even touches this path.
---
**Summary:** The runtime contract is internally coherent and well‑implemented. The biggest risks are **(1) untested cancellation** (could leave orphan tasks consuming resources) and **(2) untested boundary handling** (could silently swallow results). Fixing those two with the tests above would remove the most dangerous blind spots. The CLAUDE.md should also document the contract so future maintainers don't have to read 582 lines of `agent.py` to understand the parsing rules.