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Main agent implementation with integrated tool system and MCP support
"""
import asyncio
import json
import logging
import time
from dataclasses import dataclass, field
from typing import Any
from litellm import (
ChatCompletionMessageToolCall,
Message,
acompletion,
stream_chunk_builder,
)
from litellm.exceptions import ContextWindowExceededError
from agent.config import Config
from agent.core.approval_policy import (
is_scheduled_operation,
normalize_tool_operation,
)
from agent.core.cost_estimation import CostEstimate, estimate_tool_cost
from agent.messaging.gateway import NotificationGateway
from agent.core import telemetry
from agent.core.doom_loop import check_for_doom_loop
from agent.core.llm_params import _resolve_llm_params
from agent.core.prompt_caching import with_prompt_caching
from agent.core.session import Event, OpType, Session
from agent.core.tools import ToolRouter
from agent.tools.jobs_tool import CPU_FLAVORS
from agent.tools.sandbox_tool import DEFAULT_CPU_SANDBOX_HARDWARE
logger = logging.getLogger(__name__)
ToolCall = ChatCompletionMessageToolCall
_MALFORMED_TOOL_PREFIX = "ERROR: Tool call to '"
_MALFORMED_TOOL_SUFFIX = "' had malformed JSON arguments"
def _malformed_tool_name(message: Message) -> str | None:
"""Return the tool name for malformed-json tool-result messages."""
if getattr(message, "role", None) != "tool":
return None
content = getattr(message, "content", None)
if not isinstance(content, str):
return None
if not content.startswith(_MALFORMED_TOOL_PREFIX):
return None
end = content.find(_MALFORMED_TOOL_SUFFIX, len(_MALFORMED_TOOL_PREFIX))
if end == -1:
return None
return content[len(_MALFORMED_TOOL_PREFIX) : end]
def _detect_repeated_malformed(
items: list[Message],
threshold: int = 2,
) -> str | None:
"""Return the repeated malformed tool name if the tail contains a streak.
Walk backward over the current conversation tail. A streak counts only
consecutive malformed tool-result messages for the same tool; any other
tool result breaks it.
"""
if threshold <= 0:
return None
streak_tool: str | None = None
streak = 0
for item in reversed(items):
if getattr(item, "role", None) != "tool":
continue
malformed_tool = _malformed_tool_name(item)
if malformed_tool is None:
break
if streak_tool is None:
streak_tool = malformed_tool
streak = 1
elif malformed_tool == streak_tool:
streak += 1
else:
break
if streak >= threshold:
return streak_tool
return None
def _validate_tool_args(tool_args: dict) -> tuple[bool, str | None]:
"""
Validate tool arguments structure.
Returns:
(is_valid, error_message)
"""
args = tool_args.get("args", {})
# Sometimes LLM passes args as string instead of dict
if isinstance(args, str):
return (
False,
f"Tool call error: 'args' must be a JSON object, not a string. You passed: {repr(args)}",
)
if not isinstance(args, dict) and args is not None:
return (
False,
f"Tool call error: 'args' must be a JSON object. You passed type: {type(args).__name__}",
)
return True, None
_IMMEDIATE_HF_JOB_RUNS = {"run", "uv"}
@dataclass(frozen=True)
class ApprovalDecision:
requires_approval: bool
auto_approved: bool = False
auto_approval_blocked: bool = False
block_reason: str | None = None
estimated_cost_usd: float | None = None
remaining_cap_usd: float | None = None
billable: bool = False
def _operation(tool_args: dict) -> str:
return normalize_tool_operation(tool_args.get("operation"))
def _is_immediate_hf_job_run(tool_name: str, tool_args: dict) -> bool:
return tool_name == "hf_jobs" and _operation(tool_args) in _IMMEDIATE_HF_JOB_RUNS
def _is_scheduled_hf_job_run(tool_name: str, tool_args: dict) -> bool:
return tool_name == "hf_jobs" and is_scheduled_operation(_operation(tool_args))
def _is_budgeted_auto_approval_target(tool_name: str, tool_args: dict) -> bool:
return tool_name == "sandbox_create" or _is_immediate_hf_job_run(
tool_name, tool_args
)
def _base_needs_approval(
tool_name: str, tool_args: dict, config: Config | None = None
) -> bool:
"""Check if a tool call requires approval before YOLO policy is applied."""
# If args are malformed, skip approval (validation error will be shown later)
args_valid, _ = _validate_tool_args(tool_args)
if not args_valid:
return False
if tool_name == "sandbox_create":
hardware = tool_args.get("hardware") or DEFAULT_CPU_SANDBOX_HARDWARE
return hardware != DEFAULT_CPU_SANDBOX_HARDWARE
if tool_name == "hf_jobs":
operation = _operation(tool_args)
if is_scheduled_operation(operation):
return True
if operation not in _IMMEDIATE_HF_JOB_RUNS:
return False
# Check if this is a CPU-only job
# hardware_flavor is at top level of tool_args, not nested in args
hardware_flavor = (
tool_args.get("hardware_flavor")
or tool_args.get("flavor")
or tool_args.get("hardware")
or "cpu-basic"
)
is_cpu_job = hardware_flavor in CPU_FLAVORS
if is_cpu_job:
if config and not config.confirm_cpu_jobs:
return False
return True
return True
# Check for file upload operations (hf_private_repos or other tools)
if tool_name == "hf_private_repos":
operation = tool_args.get("operation", "")
if operation == "upload_file":
if config and config.auto_file_upload:
return False
return True
# Other operations (create_repo, etc.) always require approval
if operation in ["create_repo"]:
return True
# hf_repo_files: upload (can overwrite) and delete require approval
if tool_name == "hf_repo_files":
operation = tool_args.get("operation", "")
if operation in ["upload", "delete"]:
return True
# hf_repo_git: destructive operations require approval
if tool_name == "hf_repo_git":
operation = tool_args.get("operation", "")
if operation in [
"delete_branch",
"delete_tag",
"merge_pr",
"create_repo",
"update_repo",
]:
return True
return False
def _needs_approval(
tool_name: str, tool_args: dict, config: Config | None = None
) -> bool:
"""Legacy sync approval predicate used by tests and CLI display helpers."""
if _is_scheduled_hf_job_run(tool_name, tool_args):
return True
if config and config.yolo_mode:
return False
return _base_needs_approval(tool_name, tool_args, config)
def _session_auto_approval_enabled(session: Session | None) -> bool:
return bool(session and getattr(session, "auto_approval_enabled", False))
def _effective_yolo_enabled(session: Session | None, config: Config | None) -> bool:
return bool(
(config and config.yolo_mode) or _session_auto_approval_enabled(session)
)
def _remaining_budget_after_reservations(
session: Session | None, reserved_spend_usd: float
) -> float | None:
if not session or getattr(session, "auto_approval_cost_cap_usd", None) is None:
return None
cap = float(getattr(session, "auto_approval_cost_cap_usd") or 0.0)
spent = float(getattr(session, "auto_approval_estimated_spend_usd", 0.0) or 0.0)
return round(max(0.0, cap - spent - reserved_spend_usd), 4)
def _budget_block_reason(
estimate: CostEstimate,
*,
remaining_cap_usd: float | None,
) -> str | None:
if estimate.estimated_cost_usd is None:
return estimate.block_reason or "Could not estimate the cost safely."
if (
remaining_cap_usd is not None
and estimate.estimated_cost_usd > remaining_cap_usd
):
return (
f"Estimated cost ${estimate.estimated_cost_usd:.2f} exceeds "
f"remaining YOLO cap ${remaining_cap_usd:.2f}."
)
return None
async def _approval_decision(
tool_name: str,
tool_args: dict,
session: Session,
*,
reserved_spend_usd: float = 0.0,
) -> ApprovalDecision:
"""Return the approval decision for one parsed tool call."""
config = session.config
base_requires_approval = _base_needs_approval(tool_name, tool_args, config)
# Scheduled jobs are recurring/unbounded enough that YOLO never bypasses
# the human confirmation, including legacy config.yolo_mode.
if _is_scheduled_hf_job_run(tool_name, tool_args):
return ApprovalDecision(
requires_approval=True,
auto_approval_blocked=_effective_yolo_enabled(session, config),
block_reason="Scheduled HF jobs always require manual approval.",
)
yolo_enabled = _effective_yolo_enabled(session, config)
budgeted_target = _is_budgeted_auto_approval_target(tool_name, tool_args)
# Cost caps are a session-scoped web policy. Legacy config.yolo_mode
# remains uncapped for CLI/headless, except for scheduled jobs above.
session_yolo_enabled = _session_auto_approval_enabled(session)
if yolo_enabled and budgeted_target and session_yolo_enabled:
estimate = await estimate_tool_cost(tool_name, tool_args, session=session)
remaining = _remaining_budget_after_reservations(session, reserved_spend_usd)
reason = _budget_block_reason(estimate, remaining_cap_usd=remaining)
if reason:
return ApprovalDecision(
requires_approval=True,
auto_approval_blocked=True,
block_reason=reason,
estimated_cost_usd=estimate.estimated_cost_usd,
remaining_cap_usd=remaining,
billable=estimate.billable,
)
if base_requires_approval:
return ApprovalDecision(
requires_approval=False,
auto_approved=True,
estimated_cost_usd=estimate.estimated_cost_usd,
remaining_cap_usd=remaining,
billable=estimate.billable,
)
return ApprovalDecision(
requires_approval=False,
estimated_cost_usd=estimate.estimated_cost_usd,
remaining_cap_usd=remaining,
billable=estimate.billable,
)
if base_requires_approval and yolo_enabled:
return ApprovalDecision(requires_approval=False, auto_approved=True)
return ApprovalDecision(requires_approval=base_requires_approval)
def _record_estimated_spend(session: Session, decision: ApprovalDecision) -> None:
if not decision.billable or decision.estimated_cost_usd is None:
return
if hasattr(session, "add_auto_approval_estimated_spend"):
session.add_auto_approval_estimated_spend(decision.estimated_cost_usd)
else:
session.auto_approval_estimated_spend_usd = round(
float(getattr(session, "auto_approval_estimated_spend_usd", 0.0) or 0.0)
+ float(decision.estimated_cost_usd),
4,
)
async def _record_manual_approved_spend_if_needed(
session: Session,
tool_name: str,
tool_args: dict,
) -> None:
if not _session_auto_approval_enabled(session):
return
if not _is_budgeted_auto_approval_target(tool_name, tool_args):
return
estimate = await estimate_tool_cost(tool_name, tool_args, session=session)
_record_estimated_spend(
session,
ApprovalDecision(
requires_approval=False,
billable=estimate.billable,
estimated_cost_usd=estimate.estimated_cost_usd,
),
)
# -- LLM retry constants --------------------------------------------------
_MAX_LLM_RETRIES = 3
_LLM_RETRY_DELAYS = [5, 15, 30] # seconds between retries
_LLM_RATE_LIMIT_RETRY_DELAYS = [30, 60] # exceed Bedrock's ~60s TPM bucket window
def _is_rate_limit_error(error: Exception) -> bool:
"""Return True for rate-limit / quota-bucket style provider errors."""
err_str = str(error).lower()
rate_limit_patterns = [
"429",
"rate limit",
"rate_limit",
"too many requests",
"too many tokens",
"request limit",
"throttl",
]
return any(pattern in err_str for pattern in rate_limit_patterns)
def _is_context_overflow_error(error: Exception) -> bool:
"""Return True when the prompt exceeded the model's context window."""
if isinstance(error, ContextWindowExceededError):
return True
err_str = str(error).lower()
overflow_patterns = [
"context window exceeded",
"maximum context length",
"max context length",
"prompt is too long",
"context length exceeded",
"too many input tokens",
"input is too long",
]
return any(pattern in err_str for pattern in overflow_patterns)
def _retry_delay_for(error: Exception, attempt_index: int) -> int | None:
"""Return the delay for this retry attempt, or None if it should not retry."""
if _is_rate_limit_error(error):
schedule = _LLM_RATE_LIMIT_RETRY_DELAYS
elif _is_transient_error(error):
schedule = _LLM_RETRY_DELAYS
else:
return None
if attempt_index >= len(schedule):
return None
return schedule[attempt_index]
def _is_transient_error(error: Exception) -> bool:
"""Return True for errors that are likely transient and worth retrying."""
err_str = str(error).lower()
transient_patterns = [
"timeout",
"timed out",
"503",
"service unavailable",
"502",
"bad gateway",
"500",
"internal server error",
"overloaded",
"capacity",
"connection reset",
"connection refused",
"connection error",
"eof",
"broken pipe",
]
return _is_rate_limit_error(error) or any(
pattern in err_str for pattern in transient_patterns
)
def _is_effort_config_error(error: Exception) -> bool:
"""Catch the two 400s the effort probe also handles — thinking
unsupported for this model, or the specific effort level invalid.
This is our safety net for the case where ``/effort`` was changed
mid-conversation (which clears the probe cache) and the new level
doesn't work for the current model. We heal the cache and retry once.
"""
from agent.core.effort_probe import _is_invalid_effort, _is_thinking_unsupported
return _is_thinking_unsupported(error) or _is_invalid_effort(error)
async def _heal_effort_and_rebuild_params(
session: Session,
error: Exception,
llm_params: dict,
) -> dict:
"""Update the session's effort cache based on ``error`` and return new
llm_params. Called only when ``_is_effort_config_error(error)`` is True.
Two branches:
• thinking-unsupported → cache ``None`` for this model, next call
strips thinking entirely
• invalid-effort → re-run the full cascade probe; the result lands
in the cache
"""
from agent.core.effort_probe import (
ProbeInconclusive,
_is_thinking_unsupported,
probe_effort,
)
model = session.config.model_name
if _is_thinking_unsupported(error):
session.model_effective_effort[model] = None
logger.info("healed: %s doesn't support thinking — stripped", model)
else:
try:
outcome = await probe_effort(
model,
session.config.reasoning_effort,
session.hf_token,
session=session,
)
session.model_effective_effort[model] = outcome.effective_effort
logger.info(
"healed: %s effort cascade → %s",
model,
outcome.effective_effort,
)
except ProbeInconclusive:
# Transient during healing — strip thinking for safety, next
# call will either succeed or surface the real error.
session.model_effective_effort[model] = None
logger.info("healed: %s probe inconclusive — stripped", model)
return _resolve_llm_params(
model,
session.hf_token,
reasoning_effort=session.effective_effort_for(model),
)
def _friendly_error_message(error: Exception) -> str | None:
"""Return a user-friendly message for known error types, or None to fall back to traceback."""
err_str = str(error).lower()
if (
"authentication" in err_str
or "unauthorized" in err_str
or "invalid x-api-key" in err_str
):
return (
"Authentication failed — your API key is missing or invalid.\n\n"
"To fix this, set the API key for your model provider:\n"
" • Anthropic: export ANTHROPIC_API_KEY=sk-...\n"
" • OpenAI: export OPENAI_API_KEY=sk-...\n"
" • HF Router: export HF_TOKEN=hf_...\n\n"
"You can also add it to a .env file in the project root.\n"
"To switch models, use the /model command."
)
if "insufficient" in err_str and "credit" in err_str:
return (
"Insufficient API credits. Please check your account balance "
"at your model provider's dashboard."
)
if "not supported by provider" in err_str or "no provider supports" in err_str:
return (
"The model isn't served by the provider you pinned.\n\n"
"Drop the ':<provider>' suffix to let the HF router auto-pick a "
"provider, or use '/model' (no arg) to see which providers host "
"which models."
)
if "model_not_found" in err_str or (
"model" in err_str and ("not found" in err_str or "does not exist" in err_str)
):
return (
"Model not found. Use '/model' to list suggestions, or paste an "
"HF model id like 'MiniMaxAI/MiniMax-M2.7'. Availability is shown "
"when you switch."
)
return None
async def _compact_and_notify(session: Session) -> None:
"""Run compaction and send event if context was reduced.
Catches ``CompactionFailedError`` and ends the session cleanly instead
of letting the caller retry. Pre-2026-05-04 the caller looped on
ContextWindowExceededError → compact → re-trigger, burning Bedrock
budget at ~$3/Opus retry while the session never reached the upload
path (so the cost was invisible in the dataset).
"""
from agent.context_manager.manager import CompactionFailedError
cm = session.context_manager
old_usage = cm.running_context_usage
logger.debug(
"Compaction check: usage=%d, max=%d, threshold=%d, needs_compact=%s",
old_usage,
cm.model_max_tokens,
cm.compaction_threshold,
cm.needs_compaction,
)
try:
await cm.compact(
model_name=session.config.model_name,
tool_specs=session.tool_router.get_tool_specs_for_llm(),
hf_token=session.hf_token,
session=session,
)
except CompactionFailedError as e:
logger.error(
"Compaction failed for session %s: %s — terminating session",
session.session_id,
e,
)
# Persist the failure event so the dataset has a record of WHY this
# session ended (and the cost it incurred up to that point) even if
# save_and_upload_detached has issues downstream.
await session.send_event(
Event(
event_type="session_terminated",
data={
"reason": "compaction_failed",
"context_usage": cm.running_context_usage,
"context_threshold": cm.compaction_threshold,
"error": str(e)[:300],
"user_message": (
"Your conversation has grown too large to continue. "
"The work you've done is saved — start a new session to keep going."
),
},
)
)
# Stop the agent loop; the finally in _run_session will fire
# cleanup_sandbox + save_trajectory so the dataset captures
# everything that did happen.
session.is_running = False
return
new_usage = cm.running_context_usage
if new_usage != old_usage:
logger.warning(
"Context compacted: %d -> %d tokens (max=%d, %d messages)",
old_usage,
new_usage,
cm.model_max_tokens,
len(cm.items),
)
await session.send_event(
Event(
event_type="compacted",
data={"old_tokens": old_usage, "new_tokens": new_usage},
)
)
async def _cleanup_on_cancel(session: Session) -> None:
"""Kill sandbox processes and cancel HF jobs when the user interrupts."""
# Kill active sandbox processes
sandbox = getattr(session, "sandbox", None)
if sandbox:
try:
await asyncio.to_thread(sandbox.kill_all)
logger.info("Killed sandbox processes on cancel")
except Exception as e:
logger.warning("Failed to kill sandbox processes: %s", e)
# Cancel running HF jobs
job_ids = list(session._running_job_ids)
if job_ids:
from huggingface_hub import HfApi
api = HfApi(token=session.hf_token)
for job_id in job_ids:
try:
await asyncio.to_thread(api.cancel_job, job_id=job_id)
logger.info("Cancelled HF job %s on interrupt", job_id)
except Exception as e:
logger.warning("Failed to cancel HF job %s: %s", job_id, e)
session._running_job_ids.clear()
@dataclass
class LLMResult:
"""Result from an LLM call (streaming or non-streaming)."""
content: str | None
tool_calls_acc: dict[int, dict]
token_count: int
finish_reason: str | None
usage: dict = field(default_factory=dict)
thinking_blocks: list[dict[str, Any]] | None = None
reasoning_content: str | None = None
def _extract_thinking_state(
message: Any,
) -> tuple[list[dict[str, Any]] | None, str | None]:
"""Return provider reasoning fields that must be replayed after tool calls."""
provider_fields = getattr(message, "provider_specific_fields", None)
if not isinstance(provider_fields, dict):
provider_fields = {}
thinking_blocks = (
getattr(message, "thinking_blocks", None)
or provider_fields.get("thinking_blocks")
or None
)
reasoning_content = (
getattr(message, "reasoning_content", None)
or provider_fields.get("reasoning_content")
or None
)
return thinking_blocks, reasoning_content
def _should_replay_thinking_state(model_name: str | None) -> bool:
"""Only Anthropic's native adapter accepts replayed thinking metadata."""
return bool(model_name and model_name.startswith("anthropic/"))
def _is_invalid_thinking_signature_error(exc: Exception) -> bool:
"""Return True when Anthropic rejected replayed extended-thinking state."""
text = str(exc)
return (
"Invalid `signature` in `thinking` block" in text
or "Invalid signature in thinking block" in text
)
def _strip_thinking_state_from_messages(messages: list[Any]) -> int:
"""Remove replayed thinking metadata from assistant history messages."""
stripped = 0
for message in messages:
role = (
message.get("role")
if isinstance(message, dict)
else getattr(message, "role", None)
)
if role != "assistant":
continue
if isinstance(message, dict):
if message.pop("thinking_blocks", None) is not None:
stripped += 1
if message.pop("reasoning_content", None) is not None:
stripped += 1
provider_fields = message.get("provider_specific_fields")
content = message.get("content")
else:
if getattr(message, "thinking_blocks", None) is not None:
message.thinking_blocks = None
stripped += 1
if getattr(message, "reasoning_content", None) is not None:
message.reasoning_content = None
stripped += 1
provider_fields = getattr(message, "provider_specific_fields", None)
content = getattr(message, "content", None)
if isinstance(provider_fields, dict):
cleaned_fields = dict(provider_fields)
if cleaned_fields.pop("thinking_blocks", None) is not None:
stripped += 1
if cleaned_fields.pop("reasoning_content", None) is not None:
stripped += 1
if cleaned_fields != provider_fields:
if isinstance(message, dict):
message["provider_specific_fields"] = cleaned_fields
else:
message.provider_specific_fields = cleaned_fields
if isinstance(content, list):
cleaned_content = [
block
for block in content
if not (
isinstance(block, dict)
and block.get("type") in {"thinking", "redacted_thinking"}
)
]
if len(cleaned_content) != len(content):
stripped += len(content) - len(cleaned_content)
if isinstance(message, dict):
message["content"] = cleaned_content
else:
message.content = cleaned_content
return stripped
async def _maybe_heal_invalid_thinking_signature(
session: Session,
messages: list[Any],
exc: Exception,
*,
already_healed: bool,
) -> bool:
if already_healed or not _is_invalid_thinking_signature_error(exc):
return False
stripped = _strip_thinking_state_from_messages(messages)
if not stripped:
return False
await session.send_event(
Event(
event_type="tool_log",
data={
"tool": "system",
"log": (
"Anthropic rejected stale thinking signatures; retrying "
"without replayed thinking metadata."
),
},
)
)
return True
def _assistant_message_from_result(
llm_result: LLMResult,
*,
model_name: str | None,
tool_calls: list[ToolCall] | None = None,
) -> Message:
"""Build an assistant history message without dropping reasoning state."""
kwargs: dict[str, Any] = {
"role": "assistant",
"content": llm_result.content,
}
if tool_calls is not None:
kwargs["tool_calls"] = tool_calls
if _should_replay_thinking_state(model_name):
if llm_result.thinking_blocks:
kwargs["thinking_blocks"] = llm_result.thinking_blocks
if llm_result.reasoning_content:
kwargs["reasoning_content"] = llm_result.reasoning_content
return Message(**kwargs)
async def _call_llm_streaming(
session: Session, messages, tools, llm_params
) -> LLMResult:
"""Call the LLM with streaming, emitting assistant_chunk events."""
response = None
_healed_effort = False # one-shot safety net per call
_healed_thinking_signature = False
messages, tools = with_prompt_caching(messages, tools, llm_params.get("model"))
t_start = time.monotonic()
for _llm_attempt in range(_MAX_LLM_RETRIES):
try:
response = await acompletion(
messages=messages,
tools=tools,
tool_choice="auto",
stream=True,
stream_options={"include_usage": True},
timeout=600,
**llm_params,
)
break
except ContextWindowExceededError:
raise
except Exception as e:
if _is_context_overflow_error(e):
raise ContextWindowExceededError(str(e)) from e
if not _healed_effort and _is_effort_config_error(e):
_healed_effort = True
llm_params = await _heal_effort_and_rebuild_params(
session, e, llm_params
)
await session.send_event(
Event(
event_type="tool_log",
data={
"tool": "system",
"log": "Reasoning effort not supported for this model — adjusting and retrying.",
},
)
)
continue
if await _maybe_heal_invalid_thinking_signature(
session,
messages,
e,
already_healed=_healed_thinking_signature,
):
_healed_thinking_signature = True
continue
_delay = _retry_delay_for(e, _llm_attempt)
if _llm_attempt < _MAX_LLM_RETRIES - 1 and _delay is not None:
logger.warning(
"Transient LLM error (attempt %d/%d): %s — retrying in %ds",
_llm_attempt + 1,
_MAX_LLM_RETRIES,
e,
_delay,
)
await session.send_event(
Event(
event_type="tool_log",
data={
"tool": "system",
"log": f"LLM connection error, retrying in {_delay}s...",
},
)
)
await asyncio.sleep(_delay)
continue
raise
full_content = ""
tool_calls_acc: dict[int, dict] = {}
token_count = 0
finish_reason = None
final_usage_chunk = None
chunks = []
should_replay_thinking = _should_replay_thinking_state(llm_params.get("model"))
async for chunk in response:
chunks.append(chunk)
if session.is_cancelled:
tool_calls_acc.clear()
break
choice = chunk.choices[0] if chunk.choices else None
if not choice:
if hasattr(chunk, "usage") and chunk.usage:
token_count = chunk.usage.total_tokens
final_usage_chunk = chunk
continue
delta = choice.delta
if choice.finish_reason:
finish_reason = choice.finish_reason
if delta.content:
full_content += delta.content
await session.send_event(
Event(event_type="assistant_chunk", data={"content": delta.content})
)
if delta.tool_calls:
for tc_delta in delta.tool_calls:
idx = tc_delta.index
if idx not in tool_calls_acc:
tool_calls_acc[idx] = {
"id": "",
"type": "function",
"function": {"name": "", "arguments": ""},
}
if tc_delta.id:
tool_calls_acc[idx]["id"] = tc_delta.id
if tc_delta.function:
if tc_delta.function.name:
tool_calls_acc[idx]["function"]["name"] += (
tc_delta.function.name
)
if tc_delta.function.arguments:
tool_calls_acc[idx]["function"]["arguments"] += (
tc_delta.function.arguments
)
if hasattr(chunk, "usage") and chunk.usage:
token_count = chunk.usage.total_tokens
final_usage_chunk = chunk
usage = await telemetry.record_llm_call(
session,
model=llm_params.get("model", session.config.model_name),
response=final_usage_chunk,
latency_ms=int((time.monotonic() - t_start) * 1000),
finish_reason=finish_reason,
)
thinking_blocks = None
reasoning_content = None
if chunks and should_replay_thinking:
try:
rebuilt = stream_chunk_builder(chunks, messages=messages)
if rebuilt and getattr(rebuilt, "choices", None):
rebuilt_msg = rebuilt.choices[0].message
thinking_blocks, reasoning_content = _extract_thinking_state(
rebuilt_msg
)
except Exception:
logger.debug("Failed to rebuild streaming thinking state", exc_info=True)
return LLMResult(
content=full_content or None,
tool_calls_acc=tool_calls_acc,
token_count=token_count,
finish_reason=finish_reason,
usage=usage,
thinking_blocks=thinking_blocks,
reasoning_content=reasoning_content,
)
async def _call_llm_non_streaming(
session: Session, messages, tools, llm_params
) -> LLMResult:
"""Call the LLM without streaming, emit assistant_message at the end."""
response = None
_healed_effort = False
_healed_thinking_signature = False
messages, tools = with_prompt_caching(messages, tools, llm_params.get("model"))
t_start = time.monotonic()
for _llm_attempt in range(_MAX_LLM_RETRIES):
try:
response = await acompletion(
messages=messages,
tools=tools,
tool_choice="auto",
stream=False,
timeout=600,
**llm_params,
)
break
except ContextWindowExceededError:
raise
except Exception as e:
if _is_context_overflow_error(e):
raise ContextWindowExceededError(str(e)) from e
if not _healed_effort and _is_effort_config_error(e):
_healed_effort = True
llm_params = await _heal_effort_and_rebuild_params(
session, e, llm_params
)
await session.send_event(
Event(
event_type="tool_log",
data={
"tool": "system",
"log": "Reasoning effort not supported for this model — adjusting and retrying.",
},
)
)
continue
if await _maybe_heal_invalid_thinking_signature(
session,
messages,
e,
already_healed=_healed_thinking_signature,
):
_healed_thinking_signature = True
continue
_delay = _retry_delay_for(e, _llm_attempt)
if _llm_attempt < _MAX_LLM_RETRIES - 1 and _delay is not None:
logger.warning(
"Transient LLM error (attempt %d/%d): %s — retrying in %ds",
_llm_attempt + 1,
_MAX_LLM_RETRIES,
e,
_delay,
)
await session.send_event(
Event(
event_type="tool_log",
data={
"tool": "system",
"log": f"LLM connection error, retrying in {_delay}s...",
},
)
)
await asyncio.sleep(_delay)
continue
raise
choice = response.choices[0]
message = choice.message
content = message.content or None
finish_reason = choice.finish_reason
token_count = response.usage.total_tokens if response.usage else 0
thinking_blocks, reasoning_content = _extract_thinking_state(message)
# Build tool_calls_acc in the same format as streaming
tool_calls_acc: dict[int, dict] = {}
if message.tool_calls:
for idx, tc in enumerate(message.tool_calls):
tool_calls_acc[idx] = {
"id": tc.id,
"type": "function",
"function": {
"name": tc.function.name,
"arguments": tc.function.arguments,
},
}
# Emit the full message as a single event
if content:
await session.send_event(
Event(event_type="assistant_message", data={"content": content})
)
usage = await telemetry.record_llm_call(
session,
model=llm_params.get("model", session.config.model_name),
response=response,
latency_ms=int((time.monotonic() - t_start) * 1000),
finish_reason=finish_reason,
)
return LLMResult(
content=content,
tool_calls_acc=tool_calls_acc,
token_count=token_count,
finish_reason=finish_reason,
usage=usage,
thinking_blocks=thinking_blocks,
reasoning_content=reasoning_content,
)
class Handlers:
"""Handler functions for each operation type"""
@staticmethod
async def _abandon_pending_approval(session: Session) -> None:
"""Cancel pending approval tools when the user continues the conversation.
Injects rejection tool-result messages into the LLM context (so the
history stays valid) and notifies the frontend that those tools were
abandoned.
"""
tool_calls = session.pending_approval.get("tool_calls", [])
for tc in tool_calls:
tool_name = tc.function.name
abandon_msg = (
"Task abandoned — user continued the conversation without approving."
)
# Keep LLM context valid: every tool_call needs a tool result
tool_msg = Message(
role="tool",
content=abandon_msg,
tool_call_id=tc.id,
name=tool_name,
)
session.context_manager.add_message(tool_msg)
await session.send_event(
Event(
event_type="tool_state_change",
data={
"tool_call_id": tc.id,
"tool": tool_name,
"state": "abandoned",
},
)
)
session.pending_approval = None
logger.info("Abandoned %d pending approval tool(s)", len(tool_calls))
@staticmethod
async def run_agent(
session: Session,
text: str,
) -> str | None:
"""
Handle user input (like user_input_or_turn in codex.rs:1291)
Returns the final assistant response content, if any.
"""
# Clear any stale cancellation flag from a previous run
session.reset_cancel()
# If there's a pending approval and the user sent a new message,
# abandon the pending tools so the LLM context stays valid.
if text and session.pending_approval:
await Handlers._abandon_pending_approval(session)
# Add user message to history only if there's actual content
if text:
user_msg = Message(role="user", content=text)
session.context_manager.add_message(user_msg)
# Send event that we're processing
await session.send_event(
Event(event_type="processing", data={"message": "Processing user input"})
)
# Agentic loop - continue until model doesn't call tools or max iterations is reached
iteration = 0
final_response = None
errored = False
max_iterations = session.config.max_iterations
while max_iterations == -1 or iteration < max_iterations:
# ── Cancellation check: before LLM call ──
if session.is_cancelled:
break
# Compact before calling the LLM if context is near the limit.
# When _compact_and_notify catches CompactionFailedError it sets
# session.is_running = False; we MUST exit the loop here, otherwise
# the LLM call below fires with an over-threshold context, hits
# ContextWindowExceededError, and we end up looping again on the
# except path — exactly the bug this PR is supposed to fix.
await _compact_and_notify(session)
if not session.is_running:
break
# Doom-loop detection: break out of repeated tool call patterns
doom_prompt = check_for_doom_loop(session.context_manager.items)
if doom_prompt:
session.context_manager.add_message(
Message(role="user", content=doom_prompt)
)
malformed_tool = _detect_repeated_malformed(session.context_manager.items)
if malformed_tool:
recovery_prompt = (
"[SYSTEM: Repeated malformed tool arguments detected for "
f"'{malformed_tool}'. Stop retrying the same tool call shape. "
"Use a different strategy that produces smaller, valid JSON. "
"For large file writes, prefer bash with a heredoc or split the "
"edit into multiple smaller tool calls.]"
)
session.context_manager.add_message(
Message(role="user", content=recovery_prompt)
)
await session.send_event(
Event(
event_type="tool_log",
data={
"tool": "system",
"log": (
"Repeated malformed tool arguments detected — "
f"forcing a different strategy for {malformed_tool}"
),
},
)
)
messages = session.context_manager.get_messages()
tools = session.tool_router.get_tool_specs_for_llm()
try:
# ── Call the LLM (streaming or non-streaming) ──
# Pull the per-model probed effort from the session cache when
# available; fall back to the raw preference for models we
# haven't probed yet (e.g. research sub-model).
llm_params = _resolve_llm_params(
session.config.model_name,
session.hf_token,
reasoning_effort=session.effective_effort_for(
session.config.model_name
),
)
if session.stream:
llm_result = await _call_llm_streaming(
session, messages, tools, llm_params
)
else:
llm_result = await _call_llm_non_streaming(
session, messages, tools, llm_params
)
content = llm_result.content
tool_calls_acc = llm_result.tool_calls_acc
token_count = llm_result.token_count
finish_reason = llm_result.finish_reason
# If output was truncated, all tool call args are garbage.
# Inject a system hint so the LLM retries with smaller content.
if finish_reason == "length" and tool_calls_acc:
dropped_names = [
tc["function"]["name"]
for tc in tool_calls_acc.values()
if tc["function"]["name"]
]
logger.warning(
"Output truncated (finish_reason=length) — dropping tool calls: %s",
dropped_names,
)
tool_calls_acc.clear()
# Tell the agent what happened so it can retry differently
truncation_hint = (
"Your previous response was truncated because the output hit the "
"token limit. The following tool calls were lost: "
f"{dropped_names}. "
"IMPORTANT: Do NOT retry with the same large content. Instead:\n"
" • For 'write': use bash with cat<<'HEREDOC' to write the file, "
"or split into several smaller edit calls.\n"
" • For other tools: reduce the size of your arguments or use bash."
)
if content:
assistant_msg = _assistant_message_from_result(
llm_result,
model_name=llm_params.get("model"),
)
session.context_manager.add_message(assistant_msg, token_count)
session.context_manager.add_message(
Message(role="user", content=f"[SYSTEM: {truncation_hint}]")
)
if session.stream:
await session.send_event(
Event(event_type="assistant_stream_end", data={})
)
await session.send_event(
Event(
event_type="tool_log",
data={
"tool": "system",
"log": f"Output truncated — retrying with smaller content ({dropped_names})",
},
)
)
iteration += 1
continue # retry this iteration
# Build tool_calls list from accumulated deltas
tool_calls: list[ToolCall] = []
for idx in sorted(tool_calls_acc.keys()):
tc_data = tool_calls_acc[idx]
tool_calls.append(
ToolCall(
id=tc_data["id"],
type="function",
function={
"name": tc_data["function"]["name"],
"arguments": tc_data["function"]["arguments"],
},
)
)
# Signal end of streaming to the frontend
if session.stream:
await session.send_event(
Event(event_type="assistant_stream_end", data={})
)
# If no tool calls, add assistant message and we're done
if not tool_calls:
logger.debug(
"Agent loop ending: no tool calls. "
"finish_reason=%s, token_count=%d, "
"usage=%d, model_max_tokens=%d, "
"iteration=%d/%d, "
"response_text=%s",
finish_reason,
token_count,
session.context_manager.running_context_usage,
session.context_manager.model_max_tokens,
iteration,
max_iterations,
(content or "")[:500],
)
if content:
assistant_msg = _assistant_message_from_result(
llm_result,
model_name=llm_params.get("model"),
)
session.context_manager.add_message(assistant_msg, token_count)
final_response = content
break
# Validate tool call args (one json.loads per call, once)
# and split into good vs bad
good_tools: list[tuple[ToolCall, str, dict]] = []
bad_tools: list[ToolCall] = []
for tc in tool_calls:
try:
args = json.loads(tc.function.arguments)
good_tools.append((tc, tc.function.name, args))
except (json.JSONDecodeError, TypeError, ValueError):
logger.warning(
"Malformed arguments for tool_call %s (%s) — skipping",
tc.id,
tc.function.name,
)
tc.function.arguments = "{}"
bad_tools.append(tc)
# Add assistant message with all tool calls to context
assistant_msg = _assistant_message_from_result(
llm_result,
model_name=llm_params.get("model"),
tool_calls=tool_calls,
)
session.context_manager.add_message(assistant_msg, token_count)
# Add error results for bad tool calls so the LLM
# knows what happened and can retry differently
for tc in bad_tools:
error_msg = (
f"ERROR: Tool call to '{tc.function.name}' had malformed JSON "
f"arguments and was NOT executed. Retry with smaller content — "
f"for 'write', split into multiple smaller writes using 'edit'."
)
session.context_manager.add_message(
Message(
role="tool",
content=error_msg,
tool_call_id=tc.id,
name=tc.function.name,
)
)
await session.send_event(
Event(
event_type="tool_call",
data={
"tool": tc.function.name,
"arguments": {},
"tool_call_id": tc.id,
},
)
)
await session.send_event(
Event(
event_type="tool_output",
data={
"tool": tc.function.name,
"tool_call_id": tc.id,
"output": error_msg,
"success": False,
},
)
)
# ── Cancellation check: before tool execution ──
if session.is_cancelled:
break
# Separate good tools into approval-required vs auto-execute.
# Track reserved spend while classifying a batch so two
# auto-approved jobs in one model response cannot jointly
# exceed the remaining session cap.
approval_required_tools: list[
tuple[ToolCall, str, dict, ApprovalDecision]
] = []
non_approval_tools: list[
tuple[ToolCall, str, dict, ApprovalDecision]
] = []
reserved_auto_spend_usd = 0.0
for tc, tool_name, tool_args in good_tools:
decision = await _approval_decision(
tool_name,
tool_args,
session,
reserved_spend_usd=reserved_auto_spend_usd,
)
if decision.requires_approval:
approval_required_tools.append(
(tc, tool_name, tool_args, decision)
)
else:
non_approval_tools.append((tc, tool_name, tool_args, decision))
if (
decision.auto_approved
and decision.billable
and decision.estimated_cost_usd is not None
):
reserved_auto_spend_usd += decision.estimated_cost_usd
# Execute non-approval tools (in parallel when possible)
if non_approval_tools:
# 1. Validate args upfront
parsed_tools: list[
tuple[ToolCall, str, dict, ApprovalDecision, bool, str]
] = []
for tc, tool_name, tool_args, decision in non_approval_tools:
args_valid, error_msg = _validate_tool_args(tool_args)
parsed_tools.append(
(tc, tool_name, tool_args, decision, args_valid, error_msg)
)
# 2. Send all tool_call events upfront (so frontend shows them all)
for (
tc,
tool_name,
tool_args,
_decision,
args_valid,
_,
) in parsed_tools:
if args_valid:
await session.send_event(
Event(
event_type="tool_call",
data={
"tool": tool_name,
"arguments": tool_args,
"tool_call_id": tc.id,
},
)
)
# 3. Execute all valid tools in parallel, cancellable
async def _exec_tool(
tc: ToolCall,
name: str,
args: dict,
decision: ApprovalDecision,
valid: bool,
err: str,
) -> tuple[ToolCall, str, dict, str, bool]:
if not valid:
return (tc, name, args, err, False)
if decision.billable:
_record_estimated_spend(session, decision)
out, ok = await session.tool_router.call_tool(
name, args, session=session, tool_call_id=tc.id
)
return (tc, name, args, out, ok)
gather_task = asyncio.ensure_future(
asyncio.gather(
*[
_exec_tool(tc, name, args, decision, valid, err)
for tc, name, args, decision, valid, err in parsed_tools
]
)
)
cancel_task = asyncio.ensure_future(session._cancelled.wait())
done, _ = await asyncio.wait(
[gather_task, cancel_task],
return_when=asyncio.FIRST_COMPLETED,
)
if cancel_task in done:
gather_task.cancel()
try:
await gather_task
except asyncio.CancelledError:
pass
# Notify frontend that in-flight tools were cancelled
for tc, name, _args, _decision, valid, _ in parsed_tools:
if valid:
await session.send_event(
Event(
event_type="tool_state_change",
data={
"tool_call_id": tc.id,
"tool": name,
"state": "cancelled",
},
)
)
await _cleanup_on_cancel(session)
break
cancel_task.cancel()
results = gather_task.result()
# 4. Record results and send outputs (order preserved)
for tc, tool_name, tool_args, output, success in results:
tool_msg = Message(
role="tool",
content=output,
tool_call_id=tc.id,
name=tool_name,
)
session.context_manager.add_message(tool_msg)
await session.send_event(
Event(
event_type="tool_output",
data={
"tool": tool_name,
"tool_call_id": tc.id,
"output": output,
"success": success,
},
)
)
# If there are tools requiring approval, ask for batch approval
if approval_required_tools:
# Prepare batch approval data
tools_data = []
blocked_payloads = []
for tc, tool_name, tool_args, decision in approval_required_tools:
# Resolve sandbox file paths for hf_jobs scripts so the
# frontend can display & edit the actual file content.
if tool_name == "hf_jobs" and isinstance(
tool_args.get("script"), str
):
from agent.tools.sandbox_tool import resolve_sandbox_script
sandbox = getattr(session, "sandbox", None)
resolved, _ = await resolve_sandbox_script(
sandbox, tool_args["script"]
)
if resolved:
tool_args = {**tool_args, "script": resolved}
tool_payload = {
"tool": tool_name,
"arguments": tool_args,
"tool_call_id": tc.id,
}
if decision.auto_approval_blocked:
tool_payload.update(
{
"auto_approval_blocked": True,
"block_reason": decision.block_reason,
"estimated_cost_usd": decision.estimated_cost_usd,
"remaining_cap_usd": decision.remaining_cap_usd,
}
)
blocked_payloads.append(tool_payload)
tools_data.append(tool_payload)
event_data = {"tools": tools_data, "count": len(tools_data)}
if blocked_payloads:
first = blocked_payloads[0]
event_data.update(
{
"auto_approval_blocked": True,
"block_reason": first.get("block_reason"),
"estimated_cost_usd": first.get("estimated_cost_usd"),
"remaining_cap_usd": first.get("remaining_cap_usd"),
}
)
await session.send_event(
Event(
event_type="approval_required",
data=event_data,
)
)
# Store all approval-requiring tools (ToolCall objects for execution)
session.pending_approval = {
"tool_calls": [tc for tc, _, _, _ in approval_required_tools],
}
# Return early - wait for EXEC_APPROVAL operation
return None
iteration += 1
except ContextWindowExceededError:
# Force compact and retry this iteration.
cm = session.context_manager
logger.warning(
"ContextWindowExceededError at iteration %d — forcing compaction "
"(usage=%d, model_max_tokens=%d, messages=%d)",
iteration,
cm.running_context_usage,
cm.model_max_tokens,
len(cm.items),
)
cm.running_context_usage = cm.model_max_tokens + 1
await _compact_and_notify(session)
# Same guard as the top of the loop: if compaction couldn't
# bring us under threshold, _compact_and_notify has already
# emitted session_terminated and set is_running=False. Continue
# would just re-call the LLM with the same too-big context.
if not session.is_running:
break
continue
except Exception as e:
import traceback
error_msg = _friendly_error_message(e)
if error_msg is None:
error_msg = str(e) + "\n" + traceback.format_exc()
await session.send_event(
Event(
event_type="error",
data={"error": error_msg},
)
)
errored = True
break
if session.is_cancelled:
await _cleanup_on_cancel(session)
await session.send_event(Event(event_type="interrupted"))
elif not errored:
await session.send_event(
Event(
event_type="turn_complete",
data={
"history_size": len(session.context_manager.items),
"final_response": final_response
if isinstance(final_response, str)
else None,
},
)
)
# Increment turn counter and check for auto-save
session.increment_turn()
await session.auto_save_if_needed()
return final_response
@staticmethod
async def undo(session: Session) -> None:
"""Remove the last complete turn and notify the frontend."""
removed = session.context_manager.undo_last_turn()
if not removed:
logger.warning("Undo: no user message found to remove")
await session.send_event(Event(event_type="undo_complete"))
@staticmethod
async def exec_approval(session: Session, approvals: list[dict]) -> None:
"""Handle batch job execution approval"""
if not session.pending_approval:
await session.send_event(
Event(
event_type="error",
data={"error": "No pending approval to process"},
)
)
return
tool_calls = session.pending_approval.get("tool_calls", [])
if not tool_calls:
await session.send_event(
Event(
event_type="error",
data={"error": "No pending tool calls found"},
)
)
return
# Create a map of tool_call_id -> approval decision
approval_map = {a["tool_call_id"]: a for a in approvals}
for a in approvals:
if a.get("edited_script"):
logger.info(
f"Received edited script for tool_call {a['tool_call_id']} ({len(a['edited_script'])} chars)"
)
# Separate approved and rejected tool calls
approved_tasks = []
rejected_tasks = []
for tc in tool_calls:
tool_name = tc.function.name
try:
tool_args = json.loads(tc.function.arguments)
except (json.JSONDecodeError, TypeError) as e:
# Malformed arguments — treat as failed, notify agent
logger.warning(f"Malformed tool arguments for {tool_name}: {e}")
tool_msg = Message(
role="tool",
content=f"Malformed arguments: {e}",
tool_call_id=tc.id,
name=tool_name,
)
session.context_manager.add_message(tool_msg)
await session.send_event(
Event(
event_type="tool_output",
data={
"tool": tool_name,
"tool_call_id": tc.id,
"output": f"Malformed arguments: {e}",
"success": False,
},
)
)
continue
approval_decision = approval_map.get(tc.id, {"approved": False})
if approval_decision.get("approved", False):
edited_script = approval_decision.get("edited_script")
was_edited = False
if edited_script and "script" in tool_args:
tool_args["script"] = edited_script
was_edited = True
logger.info(f"Using user-edited script for {tool_name} ({tc.id})")
selected_namespace = approval_decision.get("namespace")
if selected_namespace and tool_name == "hf_jobs":
tool_args["namespace"] = selected_namespace
approved_tasks.append((tc, tool_name, tool_args, was_edited))
else:
rejected_tasks.append((tc, tool_name, approval_decision))
# Clear pending approval immediately so a page refresh during
# execution won't re-show the approval dialog.
session.pending_approval = None
# Notify frontend of approval decisions immediately (before execution)
for tc, tool_name, tool_args, _was_edited in approved_tasks:
await session.send_event(
Event(
event_type="tool_state_change",
data={
"tool_call_id": tc.id,
"tool": tool_name,
"state": "approved",
},
)
)
for tc, tool_name, approval_decision in rejected_tasks:
await session.send_event(
Event(
event_type="tool_state_change",
data={
"tool_call_id": tc.id,
"tool": tool_name,
"state": "rejected",
},
)
)
# Execute all approved tools concurrently
async def execute_tool(tc, tool_name, tool_args, was_edited):
"""Execute a single tool and return its result.
The TraceLog already exists on the frontend (created by
approval_required), so we send tool_state_change instead of
tool_call to avoid creating a duplicate.
"""
await session.send_event(
Event(
event_type="tool_state_change",
data={
"tool_call_id": tc.id,
"tool": tool_name,
"state": "running",
},
)
)
await _record_manual_approved_spend_if_needed(session, tool_name, tool_args)
output, success = await session.tool_router.call_tool(
tool_name, tool_args, session=session, tool_call_id=tc.id
)
return (tc, tool_name, output, success, was_edited)
# Execute all approved tools concurrently (cancellable)
if approved_tasks:
gather_task = asyncio.ensure_future(
asyncio.gather(
*[
execute_tool(tc, tool_name, tool_args, was_edited)
for tc, tool_name, tool_args, was_edited in approved_tasks
],
return_exceptions=True,
)
)
cancel_task = asyncio.ensure_future(session._cancelled.wait())
done, _ = await asyncio.wait(
[gather_task, cancel_task],
return_when=asyncio.FIRST_COMPLETED,
)
if cancel_task in done:
gather_task.cancel()
try:
await gather_task
except asyncio.CancelledError:
pass
# Notify frontend that approved tools were cancelled
for tc, tool_name, _args, _was_edited in approved_tasks:
await session.send_event(
Event(
event_type="tool_state_change",
data={
"tool_call_id": tc.id,
"tool": tool_name,
"state": "cancelled",
},
)
)
await _cleanup_on_cancel(session)
await session.send_event(Event(event_type="interrupted"))
session.increment_turn()
await session.auto_save_if_needed()
return
cancel_task.cancel()
results = gather_task.result()
# Process results and add to context
for result in results:
if isinstance(result, Exception):
# Handle execution error
logger.error(f"Tool execution error: {result}")
continue
tc, tool_name, output, success, was_edited = result
if was_edited:
output = f"[Note: The user edited the script before execution. The output below reflects the user-modified version, not your original script.]\n\n{output}"
# Add tool result to context
tool_msg = Message(
role="tool",
content=output,
tool_call_id=tc.id,
name=tool_name,
)
session.context_manager.add_message(tool_msg)
await session.send_event(
Event(
event_type="tool_output",
data={
"tool": tool_name,
"tool_call_id": tc.id,
"output": output,
"success": success,
},
)
)
# Process rejected tools
for tc, tool_name, approval_decision in rejected_tasks:
rejection_msg = "Job execution cancelled by user"
user_feedback = approval_decision.get("feedback")
if user_feedback:
# Ensure feedback is a string and sanitize any problematic characters
feedback_str = str(user_feedback).strip()
# Remove any control characters that might break JSON parsing
feedback_str = "".join(
char for char in feedback_str if ord(char) >= 32 or char in "\n\t"
)
rejection_msg += f". User feedback: {feedback_str}"
# Ensure rejection_msg is a clean string
rejection_msg = str(rejection_msg).strip()
tool_msg = Message(
role="tool",
content=rejection_msg,
tool_call_id=tc.id,
name=tool_name,
)
session.context_manager.add_message(tool_msg)
await session.send_event(
Event(
event_type="tool_output",
data={
"tool": tool_name,
"tool_call_id": tc.id,
"output": rejection_msg,
"success": False,
},
)
)
# Continue agent loop with empty input to process the tool results
await Handlers.run_agent(session, "")
@staticmethod
async def shutdown(session: Session) -> bool:
"""Handle shutdown (like shutdown in codex.rs:1329)"""
# Save session trajectory if enabled (fire-and-forget, returns immediately)
if session.config.save_sessions:
logger.info("Saving session...")
repo_id = session.config.session_dataset_repo
_ = session.save_and_upload_detached(repo_id)
session.is_running = False
await session.send_event(Event(event_type="shutdown"))
return True
async def process_submission(session: Session, submission) -> bool:
"""
Process a single submission and return whether to continue running.
Returns:
bool: True to continue, False to shutdown
"""
op = submission.operation
logger.debug("Received operation: %s", op.op_type.value)
if op.op_type == OpType.USER_INPUT:
text = op.data.get("text", "") if op.data else ""
await Handlers.run_agent(session, text)
return True
if op.op_type == OpType.COMPACT:
await _compact_and_notify(session)
return True
if op.op_type == OpType.UNDO:
await Handlers.undo(session)
return True
if op.op_type == OpType.EXEC_APPROVAL:
approvals = op.data.get("approvals", []) if op.data else []
await Handlers.exec_approval(session, approvals)
return True
if op.op_type == OpType.SHUTDOWN:
return not await Handlers.shutdown(session)
logger.warning(f"Unknown operation: {op.op_type}")
return True
async def submission_loop(
submission_queue: asyncio.Queue,
event_queue: asyncio.Queue,
config: Config,
tool_router: ToolRouter | None = None,
session_holder: list | None = None,
hf_token: str | None = None,
user_id: str | None = None,
local_mode: bool = False,
stream: bool = True,
notification_gateway: NotificationGateway | None = None,
notification_destinations: list[str] | None = None,
defer_turn_complete_notification: bool = False,
) -> None:
"""
Main agent loop - processes submissions and dispatches to handlers.
This is the core of the agent (like submission_loop in codex.rs:1259-1340)
"""
# Create session with tool router
session = Session(
event_queue,
config=config,
tool_router=tool_router,
hf_token=hf_token,
user_id=user_id,
local_mode=local_mode,
stream=stream,
notification_gateway=notification_gateway,
notification_destinations=notification_destinations,
defer_turn_complete_notification=defer_turn_complete_notification,
)
if session_holder is not None:
session_holder[0] = session
logger.info("Agent loop started")
# Retry any failed uploads from previous sessions (fire-and-forget).
# Includes the personal trace repo when enabled so a session that failed
# to publish to the user's HF dataset gets a fresh attempt on next run.
if config and config.save_sessions:
Session.retry_failed_uploads_detached(
directory="session_logs",
repo_id=config.session_dataset_repo,
personal_repo_id=session._personal_trace_repo_id(),
)
try:
# Main processing loop
async with tool_router:
# Emit ready event after initialization
await session.send_event(
Event(
event_type="ready",
data={
"message": "Agent initialized",
"tool_count": len(tool_router.tools),
},
)
)
while session.is_running:
submission = await submission_queue.get()
try:
should_continue = await process_submission(session, submission)
if not should_continue:
break
except asyncio.CancelledError:
logger.warning("Agent loop cancelled")
break
except Exception as e:
logger.error(f"Error in agent loop: {e}")
await session.send_event(
Event(event_type="error", data={"error": str(e)})
)
logger.info("Agent loop exited")
finally:
# Emergency save if session saving is enabled and shutdown wasn't called properly
if session.config.save_sessions and session.is_running:
logger.info("Emergency save: preserving session before exit...")
try:
local_path = session.save_and_upload_detached(
session.config.session_dataset_repo
)
if local_path:
logger.info("Emergency save successful, upload in progress")
except Exception as e:
logger.error(f"Emergency save failed: {e}")
|