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feat: harden agent runtime for long-running tasks

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This commit is contained in:
Xubin Ren
2026-04-01 19:12:49 +00:00
parent 63d646f731
commit fbedf7ad77
25 changed files with 1348 additions and 185 deletions
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nanobot/agent/runner.py
+280 -25
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@@ -4,20 +4,29 @@ from __future__ import annotations
import asyncio
from dataclasses import dataclass, field
from pathlib import Path
from typing import Any
from loguru import logger
from nanobot.agent.hook import AgentHook, AgentHookContext
from nanobot.agent.tools.registry import ToolRegistry
from nanobot.providers.base import LLMProvider, ToolCallRequest
from nanobot.utils.helpers import build_assistant_message
from nanobot.utils.helpers import (
build_assistant_message,
estimate_message_tokens,
estimate_prompt_tokens_chain,
find_legal_message_start,
maybe_persist_tool_result,
truncate_text,
)
_DEFAULT_MAX_ITERATIONS_MESSAGE = (
"I reached the maximum number of tool call iterations ({max_iterations}) "
"without completing the task. You can try breaking the task into smaller steps."
)
_DEFAULT_ERROR_MESSAGE = "Sorry, I encountered an error calling the AI model."
_SNIP_SAFETY_BUFFER = 1024
@dataclass(slots=True)
class AgentRunSpec:
"""Configuration for a single agent execution."""
@@ -26,6 +35,7 @@ class AgentRunSpec:
tools: ToolRegistry
model: str
max_iterations: int
max_tool_result_chars: int
temperature: float | None = None
max_tokens: int | None = None
reasoning_effort: str | None = None
@@ -34,6 +44,13 @@ class AgentRunSpec:
max_iterations_message: str | None = None
concurrent_tools: bool = False
fail_on_tool_error: bool = False
workspace: Path | None = None
session_key: str | None = None
context_window_tokens: int | None = None
context_block_limit: int | None = None
provider_retry_mode: str = "standard"
progress_callback: Any | None = None
checkpoint_callback: Any | None = None
@dataclass(slots=True)
@@ -66,12 +83,25 @@ class AgentRunner:
tool_events: list[dict[str, str]] = []
for iteration in range(spec.max_iterations):
try:
messages = self._apply_tool_result_budget(spec, messages)
messages_for_model = self._snip_history(spec, messages)
except Exception as exc:
logger.warning(
"Context governance failed on turn {} for {}: {}; using raw messages",
iteration,
spec.session_key or "default",
exc,
)
messages_for_model = messages
context = AgentHookContext(iteration=iteration, messages=messages)
await hook.before_iteration(context)
kwargs: dict[str, Any] = {
"messages": messages,
"messages": messages_for_model,
"tools": spec.tools.get_definitions(),
"model": spec.model,
"retry_mode": spec.provider_retry_mode,
"on_retry_wait": spec.progress_callback,
}
if spec.temperature is not None:
kwargs["temperature"] = spec.temperature
@@ -104,13 +134,25 @@ class AgentRunner:
if hook.wants_streaming():
await hook.on_stream_end(context, resuming=True)
messages.append(build_assistant_message(
assistant_message = build_assistant_message(
response.content or "",
tool_calls=[tc.to_openai_tool_call() for tc in response.tool_calls],
reasoning_content=response.reasoning_content,
thinking_blocks=response.thinking_blocks,
))
)
messages.append(assistant_message)
tools_used.extend(tc.name for tc in response.tool_calls)
await self._emit_checkpoint(
spec,
{
"phase": "awaiting_tools",
"iteration": iteration,
"model": spec.model,
"assistant_message": assistant_message,
"completed_tool_results": [],
"pending_tool_calls": [tc.to_openai_tool_call() for tc in response.tool_calls],
},
)
await hook.before_execute_tools(context)
@@ -125,13 +167,31 @@ class AgentRunner:
context.stop_reason = stop_reason
await hook.after_iteration(context)
break
completed_tool_results: list[dict[str, Any]] = []
for tool_call, result in zip(response.tool_calls, results):
messages.append({
tool_message = {
"role": "tool",
"tool_call_id": tool_call.id,
"name": tool_call.name,
"content": result,
})
"content": self._normalize_tool_result(
spec,
tool_call.id,
result,
),
}
messages.append(tool_message)
completed_tool_results.append(tool_message)
await self._emit_checkpoint(
spec,
{
"phase": "tools_completed",
"iteration": iteration,
"model": spec.model,
"assistant_message": assistant_message,
"completed_tool_results": completed_tool_results,
"pending_tool_calls": [],
},
)
await hook.after_iteration(context)
continue
@@ -143,6 +203,7 @@ class AgentRunner:
final_content = clean or spec.error_message or _DEFAULT_ERROR_MESSAGE
stop_reason = "error"
error = final_content
self._append_final_message(messages, final_content)
context.final_content = final_content
context.error = error
context.stop_reason = stop_reason
@@ -154,6 +215,17 @@ class AgentRunner:
reasoning_content=response.reasoning_content,
thinking_blocks=response.thinking_blocks,
))
await self._emit_checkpoint(
spec,
{
"phase": "final_response",
"iteration": iteration,
"model": spec.model,
"assistant_message": messages[-1],
"completed_tool_results": [],
"pending_tool_calls": [],
},
)
final_content = clean
context.final_content = final_content
context.stop_reason = stop_reason
@@ -163,6 +235,7 @@ class AgentRunner:
stop_reason = "max_iterations"
template = spec.max_iterations_message or _DEFAULT_MAX_ITERATIONS_MESSAGE
final_content = template.format(max_iterations=spec.max_iterations)
self._append_final_message(messages, final_content)
return AgentRunResult(
final_content=final_content,
@@ -179,16 +252,17 @@ class AgentRunner:
spec: AgentRunSpec,
tool_calls: list[ToolCallRequest],
) -> tuple[list[Any], list[dict[str, str]], BaseException | None]:
if spec.concurrent_tools:
tool_results = await asyncio.gather(*(
self._run_tool(spec, tool_call)
for tool_call in tool_calls
))
else:
tool_results = [
await self._run_tool(spec, tool_call)
for tool_call in tool_calls
]
batches = self._partition_tool_batches(spec, tool_calls)
tool_results: list[tuple[Any, dict[str, str], BaseException | None]] = []
for batch in batches:
if spec.concurrent_tools and len(batch) > 1:
tool_results.extend(await asyncio.gather(*(
self._run_tool(spec, tool_call)
for tool_call in batch
)))
else:
for tool_call in batch:
tool_results.append(await self._run_tool(spec, tool_call))
results: list[Any] = []
events: list[dict[str, str]] = []
@@ -205,8 +279,28 @@ class AgentRunner:
spec: AgentRunSpec,
tool_call: ToolCallRequest,
) -> tuple[Any, dict[str, str], BaseException | None]:
_HINT = "\n\n[Analyze the error above and try a different approach.]"
prepare_call = getattr(spec.tools, "prepare_call", None)
tool, params, prep_error = None, tool_call.arguments, None
if callable(prepare_call):
try:
prepared = prepare_call(tool_call.name, tool_call.arguments)
if isinstance(prepared, tuple) and len(prepared) == 3:
tool, params, prep_error = prepared
except Exception:
pass
if prep_error:
event = {
"name": tool_call.name,
"status": "error",
"detail": prep_error.split(": ", 1)[-1][:120],
}
return prep_error + _HINT, event, RuntimeError(prep_error) if spec.fail_on_tool_error else None
try:
result = await spec.tools.execute(tool_call.name, tool_call.arguments)
if tool is not None:
result = await tool.execute(**params)
else:
result = await spec.tools.execute(tool_call.name, params)
except asyncio.CancelledError:
raise
except BaseException as exc:
@@ -219,14 +313,175 @@ class AgentRunner:
return f"Error: {type(exc).__name__}: {exc}", event, exc
return f"Error: {type(exc).__name__}: {exc}", event, None
if isinstance(result, str) and result.startswith("Error"):
event = {
"name": tool_call.name,
"status": "error",
"detail": result.replace("\n", " ").strip()[:120],
}
if spec.fail_on_tool_error:
return result + _HINT, event, RuntimeError(result)
return result + _HINT, event, None
detail = "" if result is None else str(result)
detail = detail.replace("\n", " ").strip()
if not detail:
detail = "(empty)"
elif len(detail) > 120:
detail = detail[:120] + "..."
return result, {
"name": tool_call.name,
"status": "error" if isinstance(result, str) and result.startswith("Error") else "ok",
"detail": detail,
}, None
return result, {"name": tool_call.name, "status": "ok", "detail": detail}, None
async def _emit_checkpoint(
self,
spec: AgentRunSpec,
payload: dict[str, Any],
) -> None:
callback = spec.checkpoint_callback
if callback is not None:
await callback(payload)
@staticmethod
def _append_final_message(messages: list[dict[str, Any]], content: str | None) -> None:
if not content:
return
if (
messages
and messages[-1].get("role") == "assistant"
and not messages[-1].get("tool_calls")
):
if messages[-1].get("content") == content:
return
messages[-1] = build_assistant_message(content)
return
messages.append(build_assistant_message(content))
def _normalize_tool_result(
self,
spec: AgentRunSpec,
tool_call_id: str,
result: Any,
) -> Any:
try:
content = maybe_persist_tool_result(
spec.workspace,
spec.session_key,
tool_call_id,
result,
max_chars=spec.max_tool_result_chars,
)
except Exception as exc:
logger.warning(
"Tool result persist failed for {} in {}: {}; using raw result",
tool_call_id,
spec.session_key or "default",
exc,
)
content = result
if isinstance(content, str) and len(content) > spec.max_tool_result_chars:
return truncate_text(content, spec.max_tool_result_chars)
return content
def _apply_tool_result_budget(
self,
spec: AgentRunSpec,
messages: list[dict[str, Any]],
) -> list[dict[str, Any]]:
updated = messages
for idx, message in enumerate(messages):
if message.get("role") != "tool":
continue
normalized = self._normalize_tool_result(
spec,
str(message.get("tool_call_id") or f"tool_{idx}"),
message.get("content"),
)
if normalized != message.get("content"):
if updated is messages:
updated = [dict(m) for m in messages]
updated[idx]["content"] = normalized
return updated
def _snip_history(
self,
spec: AgentRunSpec,
messages: list[dict[str, Any]],
) -> list[dict[str, Any]]:
if not messages or not spec.context_window_tokens:
return messages
provider_max_tokens = getattr(getattr(self.provider, "generation", None), "max_tokens", 4096)
max_output = spec.max_tokens if isinstance(spec.max_tokens, int) else (
provider_max_tokens if isinstance(provider_max_tokens, int) else 4096
)
budget = spec.context_block_limit or (
spec.context_window_tokens - max_output - _SNIP_SAFETY_BUFFER
)
if budget <= 0:
return messages
estimate, _ = estimate_prompt_tokens_chain(
self.provider,
spec.model,
messages,
spec.tools.get_definitions(),
)
if estimate <= budget:
return messages
system_messages = [dict(msg) for msg in messages if msg.get("role") == "system"]
non_system = [dict(msg) for msg in messages if msg.get("role") != "system"]
if not non_system:
return messages
system_tokens = sum(estimate_message_tokens(msg) for msg in system_messages)
remaining_budget = max(128, budget - system_tokens)
kept: list[dict[str, Any]] = []
kept_tokens = 0
for message in reversed(non_system):
msg_tokens = estimate_message_tokens(message)
if kept and kept_tokens + msg_tokens > remaining_budget:
break
kept.append(message)
kept_tokens += msg_tokens
kept.reverse()
if kept:
for i, message in enumerate(kept):
if message.get("role") == "user":
kept = kept[i:]
break
start = find_legal_message_start(kept)
if start:
kept = kept[start:]
if not kept:
kept = non_system[-min(len(non_system), 4) :]
start = find_legal_message_start(kept)
if start:
kept = kept[start:]
return system_messages + kept
def _partition_tool_batches(
self,
spec: AgentRunSpec,
tool_calls: list[ToolCallRequest],
) -> list[list[ToolCallRequest]]:
if not spec.concurrent_tools:
return [[tool_call] for tool_call in tool_calls]
batches: list[list[ToolCallRequest]] = []
current: list[ToolCallRequest] = []
for tool_call in tool_calls:
get_tool = getattr(spec.tools, "get", None)
tool = get_tool(tool_call.name) if callable(get_tool) else None
can_batch = bool(tool and tool.concurrency_safe)
if can_batch:
current.append(tool_call)
continue
if current:
batches.append(current)
current = []
batches.append([tool_call])
if current:
batches.append(current)
return batches