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Merge branch 'main' into pr-2304

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This commit is contained in:
Xubin Ren
2026-03-21 04:14:40 +00:00
parent 71a88da186 32f4e60145
commit 834f1e3a9f
26 changed files with 2536 additions and 143 deletions
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13
nanobot/agent/loop.py
View File

@@ -120,12 +120,13 @@ class AgentLoop:
self.tools.register(ReadFileTool(workspace=self.workspace, allowed_dir=allowed_dir, extra_allowed_dirs=extra_read))
for cls in (WriteFileTool, EditFileTool, ListDirTool):
self.tools.register(cls(workspace=self.workspace, allowed_dir=allowed_dir))
self.tools.register(ExecTool(
working_dir=str(self.workspace),
timeout=self.exec_config.timeout,
restrict_to_workspace=self.restrict_to_workspace,
path_append=self.exec_config.path_append,
))
if self.exec_config.enable:
self.tools.register(ExecTool(
working_dir=str(self.workspace),
timeout=self.exec_config.timeout,
restrict_to_workspace=self.restrict_to_workspace,
path_append=self.exec_config.path_append,
))
self.tools.register(WebSearchTool(config=self.web_search_config, proxy=self.web_proxy))
self.tools.register(WebFetchTool(proxy=self.web_proxy))
self.tools.register(MessageTool(send_callback=self.bus.publish_outbound))

4
nanobot/agent/tools/spawn.py
View File

@@ -32,7 +32,9 @@ class SpawnTool(Tool):
return (
"Spawn a subagent to handle a task in the background. "
"Use this for complex or time-consuming tasks that can run independently. "
"The subagent will complete the task and report back when done."
"The subagent will complete the task and report back when done. "
"For deliverables or existing projects, inspect the workspace first "
"and use a dedicated subdirectory when helpful."
)
@property

69
nanobot/channels/email.py
View File

@@ -80,6 +80,21 @@ class EmailChannel(BaseChannel):
"Nov",
"Dec",
)
_IMAP_RECONNECT_MARKERS = (
"disconnected for inactivity",
"eof occurred in violation of protocol",
"socket error",
"connection reset",
"broken pipe",
"bye",
)
_IMAP_MISSING_MAILBOX_MARKERS = (
"mailbox doesn't exist",
"select failed",
"no such mailbox",
"can't open mailbox",
"does not exist",
)
@classmethod
def default_config(cls) -> dict[str, Any]:
@@ -267,8 +282,37 @@ class EmailChannel(BaseChannel):
dedupe: bool,
limit: int,
) -> list[dict[str, Any]]:
"""Fetch messages by arbitrary IMAP search criteria."""
messages: list[dict[str, Any]] = []
cycle_uids: set[str] = set()
for attempt in range(2):
try:
self._fetch_messages_once(
search_criteria,
mark_seen,
dedupe,
limit,
messages,
cycle_uids,
)
return messages
except Exception as exc:
if attempt == 1 or not self._is_stale_imap_error(exc):
raise
logger.warning("Email IMAP connection went stale, retrying once: {}", exc)
return messages
def _fetch_messages_once(
self,
search_criteria: tuple[str, ...],
mark_seen: bool,
dedupe: bool,
limit: int,
messages: list[dict[str, Any]],
cycle_uids: set[str],
) -> None:
"""Fetch messages by arbitrary IMAP search criteria."""
mailbox = self.config.imap_mailbox or "INBOX"
if self.config.imap_use_ssl:
@@ -278,8 +322,15 @@ class EmailChannel(BaseChannel):
try:
client.login(self.config.imap_username, self.config.imap_password)
status, _ = client.select(mailbox)
try:
status, _ = client.select(mailbox)
except Exception as exc:
if self._is_missing_mailbox_error(exc):
logger.warning("Email mailbox unavailable, skipping poll for {}: {}", mailbox, exc)
return messages
raise
if status != "OK":
logger.warning("Email mailbox select returned {}, skipping poll for {}", status, mailbox)
return messages
status, data = client.search(None, *search_criteria)
@@ -299,6 +350,8 @@ class EmailChannel(BaseChannel):
continue
uid = self._extract_uid(fetched)
if uid and uid in cycle_uids:
continue
if dedupe and uid and uid in self._processed_uids:
continue
@@ -341,6 +394,8 @@ class EmailChannel(BaseChannel):
}
)
if uid:
cycle_uids.add(uid)
if dedupe and uid:
self._processed_uids.add(uid)
# mark_seen is the primary dedup; this set is a safety net
@@ -356,7 +411,15 @@ class EmailChannel(BaseChannel):
except Exception:
pass
return messages
@classmethod
def _is_stale_imap_error(cls, exc: Exception) -> bool:
message = str(exc).lower()
return any(marker in message for marker in cls._IMAP_RECONNECT_MARKERS)
@classmethod
def _is_missing_mailbox_error(cls, exc: Exception) -> bool:
message = str(exc).lower()
return any(marker in message for marker in cls._IMAP_MISSING_MAILBOX_MARKERS)
@classmethod
def _format_imap_date(cls, value: date) -> str:

6
nanobot/channels/feishu.py
View File

@@ -191,6 +191,10 @@ def _extract_post_content(content_json: dict) -> tuple[str, list[str]]:
texts.append(el.get("text", ""))
elif tag == "at":
texts.append(f"@{el.get('user_name', 'user')}")
elif tag == "code_block":
lang = el.get("language", "")
code_text = el.get("text", "")
texts.append(f"\n```{lang}\n{code_text}\n```\n")
elif tag == "img" and (key := el.get("image_key")):
images.append(key)
return (" ".join(texts).strip() or None), images
@@ -1039,7 +1043,7 @@ class FeishuChannel(BaseChannel):
event = data.event
message = event.message
sender = event.sender
# Deduplication check
message_id = message.message_id
if message_id in self._processed_message_ids:

57
nanobot/channels/telegram.py
View File

@@ -11,6 +11,7 @@ from typing import Any, Literal
from loguru import logger
from pydantic import Field
from telegram import BotCommand, ReplyParameters, Update
from telegram.error import TimedOut
from telegram.ext import Application, CommandHandler, ContextTypes, MessageHandler, filters
from telegram.request import HTTPXRequest
@@ -151,6 +152,10 @@ def _markdown_to_telegram_html(text: str) -> str:
return text
_SEND_MAX_RETRIES = 3
_SEND_RETRY_BASE_DELAY = 0.5 # seconds, doubled each retry
class TelegramConfig(Base):
"""Telegram channel configuration."""
@@ -160,6 +165,8 @@ class TelegramConfig(Base):
proxy: str | None = None
reply_to_message: bool = False
group_policy: Literal["open", "mention"] = "mention"
connection_pool_size: int = 32
pool_timeout: float = 5.0
class TelegramChannel(BaseChannel):
@@ -226,15 +233,29 @@ class TelegramChannel(BaseChannel):
self._running = True
# Build the application with larger connection pool to avoid pool-timeout on long runs
req = HTTPXRequest(
connection_pool_size=16,
pool_timeout=5.0,
proxy = self.config.proxy or None
# Separate pools so long-polling (getUpdates) never starves outbound sends.
api_request = HTTPXRequest(
connection_pool_size=self.config.connection_pool_size,
pool_timeout=self.config.pool_timeout,
connect_timeout=30.0,
read_timeout=30.0,
proxy=self.config.proxy if self.config.proxy else None,
proxy=proxy,
)
poll_request = HTTPXRequest(
connection_pool_size=4,
pool_timeout=self.config.pool_timeout,
connect_timeout=30.0,
read_timeout=30.0,
proxy=proxy,
)
builder = (
Application.builder()
.token(self.config.token)
.request(api_request)
.get_updates_request(poll_request)
)
builder = Application.builder().token(self.config.token).request(req).get_updates_request(req)
self._app = builder.build()
self._app.add_error_handler(self._on_error)
@@ -365,7 +386,8 @@ class TelegramChannel(BaseChannel):
ok, error = validate_url_target(media_path)
if not ok:
raise ValueError(f"unsafe media URL: {error}")
await sender(
await self._call_with_retry(
sender,
chat_id=chat_id,
**{param: media_path},
reply_parameters=reply_params,
@@ -401,6 +423,21 @@ class TelegramChannel(BaseChannel):
else:
await self._send_text(chat_id, chunk, reply_params, thread_kwargs)
async def _call_with_retry(self, fn, *args, **kwargs):
"""Call an async Telegram API function with retry on pool/network timeout."""
for attempt in range(1, _SEND_MAX_RETRIES + 1):
try:
return await fn(*args, **kwargs)
except TimedOut:
if attempt == _SEND_MAX_RETRIES:
raise
delay = _SEND_RETRY_BASE_DELAY * (2 ** (attempt - 1))
logger.warning(
"Telegram timeout (attempt {}/{}), retrying in {:.1f}s",
attempt, _SEND_MAX_RETRIES, delay,
)
await asyncio.sleep(delay)
async def _send_text(
self,
chat_id: int,
@@ -411,7 +448,8 @@ class TelegramChannel(BaseChannel):
"""Send a plain text message with HTML fallback."""
try:
html = _markdown_to_telegram_html(text)
await self._app.bot.send_message(
await self._call_with_retry(
self._app.bot.send_message,
chat_id=chat_id, text=html, parse_mode="HTML",
reply_parameters=reply_params,
**(thread_kwargs or {}),
@@ -419,7 +457,8 @@ class TelegramChannel(BaseChannel):
except Exception as e:
logger.warning("HTML parse failed, falling back to plain text: {}", e)
try:
await self._app.bot.send_message(
await self._call_with_retry(
self._app.bot.send_message,
chat_id=chat_id,
text=text,
reply_parameters=reply_params,

101
nanobot/cli/commands.py
View File

@@ -21,12 +21,11 @@ if sys.platform == "win32":
pass
import typer
from prompt_toolkit import print_formatted_text
from prompt_toolkit import PromptSession
from prompt_toolkit import PromptSession, print_formatted_text
from prompt_toolkit.application import run_in_terminal
from prompt_toolkit.formatted_text import ANSI, HTML
from prompt_toolkit.history import FileHistory
from prompt_toolkit.patch_stdout import patch_stdout
from prompt_toolkit.application import run_in_terminal
from rich.console import Console
from rich.markdown import Markdown
from rich.table import Table
@@ -39,6 +38,7 @@ from nanobot.utils.helpers import sync_workspace_templates
app = typer.Typer(
name="nanobot",
context_settings={"help_option_names": ["-h", "--help"]},
help=f"{__logo__} nanobot - Personal AI Assistant",
no_args_is_help=True,
)
@@ -265,6 +265,7 @@ def main(
def onboard(
workspace: str | None = typer.Option(None, "--workspace", "-w", help="Workspace directory"),
config: str | None = typer.Option(None, "--config", "-c", help="Path to config file"),
wizard: bool = typer.Option(False, "--wizard", help="Use interactive wizard"),
):
"""Initialize nanobot configuration and workspace."""
from nanobot.config.loader import get_config_path, load_config, save_config, set_config_path
@@ -284,42 +285,69 @@ def onboard(
# Create or update config
if config_path.exists():
console.print(f"[yellow]Config already exists at {config_path}[/yellow]")
console.print(" [bold]y[/bold] = overwrite with defaults (existing values will be lost)")
console.print(" [bold]N[/bold] = refresh config, keeping existing values and adding new fields")
if typer.confirm("Overwrite?"):
config = _apply_workspace_override(Config())
save_config(config, config_path)
console.print(f"[green]✓[/green] Config reset to defaults at {config_path}")
else:
if wizard:
config = _apply_workspace_override(load_config(config_path))
save_config(config, config_path)
console.print(f"[green]✓[/green] Config refreshed at {config_path} (existing values preserved)")
else:
console.print(f"[yellow]Config already exists at {config_path}[/yellow]")
console.print(" [bold]y[/bold] = overwrite with defaults (existing values will be lost)")
console.print(" [bold]N[/bold] = refresh config, keeping existing values and adding new fields")
if typer.confirm("Overwrite?"):
config = _apply_workspace_override(Config())
save_config(config, config_path)
console.print(f"[green]✓[/green] Config reset to defaults at {config_path}")
else:
config = _apply_workspace_override(load_config(config_path))
save_config(config, config_path)
console.print(f"[green]✓[/green] Config refreshed at {config_path} (existing values preserved)")
else:
config = _apply_workspace_override(Config())
save_config(config, config_path)
console.print(f"[green]✓[/green] Created config at {config_path}")
console.print("[dim]Config template now uses `maxTokens` + `contextWindowTokens`; `memoryWindow` is no longer a runtime setting.[/dim]")
# In wizard mode, don't save yet - the wizard will handle saving if should_save=True
if not wizard:
save_config(config, config_path)
console.print(f"[green]✓[/green] Created config at {config_path}")
# Run interactive wizard if enabled
if wizard:
from nanobot.cli.onboard_wizard import run_onboard
try:
result = run_onboard(initial_config=config)
if not result.should_save:
console.print("[yellow]Configuration discarded. No changes were saved.[/yellow]")
return
config = result.config
save_config(config, config_path)
console.print(f"[green]✓[/green] Config saved at {config_path}")
except Exception as e:
console.print(f"[red]✗[/red] Error during configuration: {e}")
console.print("[yellow]Please run 'nanobot onboard' again to complete setup.[/yellow]")
raise typer.Exit(1)
_onboard_plugins(config_path)
# Create workspace, preferring the configured workspace path.
workspace = get_workspace_path(config.workspace_path)
if not workspace.exists():
workspace.mkdir(parents=True, exist_ok=True)
console.print(f"[green]✓[/green] Created workspace at {workspace}")
workspace_path = get_workspace_path(config.workspace_path)
if not workspace_path.exists():
workspace_path.mkdir(parents=True, exist_ok=True)
console.print(f"[green]✓[/green] Created workspace at {workspace_path}")
sync_workspace_templates(workspace)
sync_workspace_templates(workspace_path)
agent_cmd = 'nanobot agent -m "Hello!"'
gateway_cmd = "nanobot gateway"
if config:
agent_cmd += f" --config {config_path}"
gateway_cmd += f" --config {config_path}"
console.print(f"\n{__logo__} nanobot is ready!")
console.print("\nNext steps:")
console.print(f" 1. Add your API key to [cyan]{config_path}[/cyan]")
console.print(" Get one at: https://openrouter.ai/keys")
console.print(f" 2. Chat: [cyan]{agent_cmd}[/cyan]")
if wizard:
console.print(f" 1. Chat: [cyan]{agent_cmd}[/cyan]")
console.print(f" 2. Start gateway: [cyan]{gateway_cmd}[/cyan]")
else:
console.print(f" 1. Add your API key to [cyan]{config_path}[/cyan]")
console.print(" Get one at: https://openrouter.ai/keys")
console.print(f" 2. Chat: [cyan]{agent_cmd}[/cyan]")
console.print("\n[dim]Want Telegram/WhatsApp? See: https://github.com/HKUDS/nanobot#-chat-apps[/dim]")
@@ -363,9 +391,9 @@ def _onboard_plugins(config_path: Path) -> None:
def _make_provider(config: Config):
"""Create the appropriate LLM provider from config."""
from nanobot.providers.azure_openai_provider import AzureOpenAIProvider
from nanobot.providers.base import GenerationSettings
from nanobot.providers.openai_codex_provider import OpenAICodexProvider
from nanobot.providers.azure_openai_provider import AzureOpenAIProvider
model = config.agents.defaults.model
provider_name = config.get_provider_name(model)
@@ -434,21 +462,30 @@ def _load_runtime_config(config: str | None = None, workspace: str | None = None
console.print(f"[dim]Using config: {config_path}[/dim]")
loaded = load_config(config_path)
_warn_deprecated_config_keys(config_path)
if workspace:
loaded.agents.defaults.workspace = workspace
return loaded
def _print_deprecated_memory_window_notice(config: Config) -> None:
"""Warn when running with old memoryWindow-only config."""
if config.agents.defaults.should_warn_deprecated_memory_window:
def _warn_deprecated_config_keys(config_path: Path | None) -> None:
"""Hint users to remove obsolete keys from their config file."""
import json
from nanobot.config.loader import get_config_path
path = config_path or get_config_path()
try:
raw = json.loads(path.read_text(encoding="utf-8"))
except Exception:
return
if "memoryWindow" in raw.get("agents", {}).get("defaults", {}):
console.print(
"[yellow]Hint:[/yellow] Detected deprecated `memoryWindow` without "
"`contextWindowTokens`. `memoryWindow` is ignored; run "
"[cyan]nanobot onboard[/cyan] to refresh your config template."
"[dim]Hint: `memoryWindow` in your config is no longer used "
"and can be safely removed.[/dim]"
)
# ============================================================================
# Gateway / Server
# ============================================================================
@@ -476,7 +513,6 @@ def gateway(
logging.basicConfig(level=logging.DEBUG)
config = _load_runtime_config(config, workspace)
_print_deprecated_memory_window_notice(config)
port = port if port is not None else config.gateway.port
console.print(f"{__logo__} Starting nanobot gateway version {__version__} on port {port}...")
@@ -667,7 +703,6 @@ def agent(
from nanobot.cron.service import CronService
config = _load_runtime_config(config, workspace)
_print_deprecated_memory_window_notice(config)
sync_workspace_templates(config.workspace_path)
bus = MessageBus()

231
nanobot/cli/model_info.py Normal file
View File

@@ -0,0 +1,231 @@
"""Model information helpers for the onboard wizard.
Provides model context window lookup and autocomplete suggestions using litellm.
"""
from __future__ import annotations
from functools import lru_cache
from typing import Any
def _litellm():
"""Lazy accessor for litellm (heavy import deferred until actually needed)."""
import litellm as _ll
return _ll
@lru_cache(maxsize=1)
def _get_model_cost_map() -> dict[str, Any]:
"""Get litellm's model cost map (cached)."""
return getattr(_litellm(), "model_cost", {})
@lru_cache(maxsize=1)
def get_all_models() -> list[str]:
"""Get all known model names from litellm.
"""
models = set()
# From model_cost (has pricing info)
cost_map = _get_model_cost_map()
for k in cost_map.keys():
if k != "sample_spec":
models.add(k)
# From models_by_provider (more complete provider coverage)
for provider_models in getattr(_litellm(), "models_by_provider", {}).values():
if isinstance(provider_models, (set, list)):
models.update(provider_models)
return sorted(models)
def _normalize_model_name(model: str) -> str:
"""Normalize model name for comparison."""
return model.lower().replace("-", "_").replace(".", "")
def find_model_info(model_name: str) -> dict[str, Any] | None:
"""Find model info with fuzzy matching.
Args:
model_name: Model name in any common format
Returns:
Model info dict or None if not found
"""
cost_map = _get_model_cost_map()
if not cost_map:
return None
# Direct match
if model_name in cost_map:
return cost_map[model_name]
# Extract base name (without provider prefix)
base_name = model_name.split("/")[-1] if "/" in model_name else model_name
base_normalized = _normalize_model_name(base_name)
candidates = []
for key, info in cost_map.items():
if key == "sample_spec":
continue
key_base = key.split("/")[-1] if "/" in key else key
key_base_normalized = _normalize_model_name(key_base)
# Score the match
score = 0
# Exact base name match (highest priority)
if base_normalized == key_base_normalized:
score = 100
# Base name contains model
elif base_normalized in key_base_normalized:
score = 80
# Model contains base name
elif key_base_normalized in base_normalized:
score = 70
# Partial match
elif base_normalized[:10] in key_base_normalized:
score = 50
if score > 0:
# Prefer models with max_input_tokens
if info.get("max_input_tokens"):
score += 10
candidates.append((score, key, info))
if not candidates:
return None
# Return the best match
candidates.sort(key=lambda x: (-x[0], x[1]))
return candidates[0][2]
def get_model_context_limit(model: str, provider: str = "auto") -> int | None:
"""Get the maximum input context tokens for a model.
Args:
model: Model name (e.g., "claude-3.5-sonnet", "gpt-4o")
provider: Provider name for informational purposes (not yet used for filtering)
Returns:
Maximum input tokens, or None if unknown
Note:
The provider parameter is currently informational only. Future versions may
use it to prefer provider-specific model variants in the lookup.
"""
# First try fuzzy search in model_cost (has more accurate max_input_tokens)
info = find_model_info(model)
if info:
# Prefer max_input_tokens (this is what we want for context window)
max_input = info.get("max_input_tokens")
if max_input and isinstance(max_input, int):
return max_input
# Fall back to litellm's get_max_tokens (returns max_output_tokens typically)
try:
result = _litellm().get_max_tokens(model)
if result and result > 0:
return result
except (KeyError, ValueError, AttributeError):
# Model not found in litellm's database or invalid response
pass
# Last resort: use max_tokens from model_cost
if info:
max_tokens = info.get("max_tokens")
if max_tokens and isinstance(max_tokens, int):
return max_tokens
return None
@lru_cache(maxsize=1)
def _get_provider_keywords() -> dict[str, list[str]]:
"""Build provider keywords mapping from nanobot's provider registry.
Returns:
Dict mapping provider name to list of keywords for model filtering.
"""
try:
from nanobot.providers.registry import PROVIDERS
mapping = {}
for spec in PROVIDERS:
if spec.keywords:
mapping[spec.name] = list(spec.keywords)
return mapping
except ImportError:
return {}
def get_model_suggestions(partial: str, provider: str = "auto", limit: int = 20) -> list[str]:
"""Get autocomplete suggestions for model names.
Args:
partial: Partial model name typed by user
provider: Provider name for filtering (e.g., "openrouter", "minimax")
limit: Maximum number of suggestions to return
Returns:
List of matching model names
"""
all_models = get_all_models()
if not all_models:
return []
partial_lower = partial.lower()
partial_normalized = _normalize_model_name(partial)
# Get provider keywords from registry
provider_keywords = _get_provider_keywords()
# Filter by provider if specified
allowed_keywords = None
if provider and provider != "auto":
allowed_keywords = provider_keywords.get(provider.lower())
matches = []
for model in all_models:
model_lower = model.lower()
# Apply provider filter
if allowed_keywords:
if not any(kw in model_lower for kw in allowed_keywords):
continue
# Match against partial input
if not partial:
matches.append(model)
continue
if partial_lower in model_lower:
# Score by position of match (earlier = better)
pos = model_lower.find(partial_lower)
score = 100 - pos
matches.append((score, model))
elif partial_normalized in _normalize_model_name(model):
score = 50
matches.append((score, model))
# Sort by score if we have scored matches
if matches and isinstance(matches[0], tuple):
matches.sort(key=lambda x: (-x[0], x[1]))
matches = [m[1] for m in matches]
else:
matches.sort()
return matches[:limit]
def format_token_count(tokens: int) -> str:
"""Format token count for display (e.g., 200000 -> '200,000')."""
return f"{tokens:,}"

1023
nanobot/cli/onboard_wizard.py Normal file
View File

File diff suppressed because it is too large Load Diff

12
nanobot/config/loader.py
View File

@@ -3,8 +3,10 @@
import json
from pathlib import Path
from nanobot.config.schema import Config
import pydantic
from loguru import logger
from nanobot.config.schema import Config
# Global variable to store current config path (for multi-instance support)
_current_config_path: Path | None = None
@@ -41,9 +43,9 @@ def load_config(config_path: Path | None = None) -> Config:
data = json.load(f)
data = _migrate_config(data)
return Config.model_validate(data)
except (json.JSONDecodeError, ValueError) as e:
print(f"Warning: Failed to load config from {path}: {e}")
print("Using default configuration.")
except (json.JSONDecodeError, ValueError, pydantic.ValidationError) as e:
logger.warning(f"Failed to load config from {path}: {e}")
logger.warning("Using default configuration.")
return Config()
@@ -59,7 +61,7 @@ def save_config(config: Config, config_path: Path | None = None) -> None:
path = config_path or get_config_path()
path.parent.mkdir(parents=True, exist_ok=True)
data = config.model_dump(by_alias=True)
data = config.model_dump(mode="json", by_alias=True)
with open(path, "w", encoding="utf-8") as f:
json.dump(data, f, indent=2, ensure_ascii=False)

15
nanobot/config/schema.py
View File

@@ -38,14 +38,7 @@ class AgentDefaults(Base):
context_window_tokens: int = 65_536
temperature: float = 0.1
max_tool_iterations: int = 40
# Deprecated compatibility field: accepted from old configs but ignored at runtime.
memory_window: int | None = Field(default=None, exclude=True)
reasoning_effort: str | None = None # low / medium / high — enables LLM thinking mode
@property
def should_warn_deprecated_memory_window(self) -> bool:
"""Return True when old memoryWindow is present without contextWindowTokens."""
return self.memory_window is not None and "context_window_tokens" not in self.model_fields_set
reasoning_effort: str | None = None # low / medium / high - enables LLM thinking mode
class AgentsConfig(Base):
@@ -85,8 +78,8 @@ class ProvidersConfig(Base):
volcengine_coding_plan: ProviderConfig = Field(default_factory=ProviderConfig) # VolcEngine Coding Plan
byteplus: ProviderConfig = Field(default_factory=ProviderConfig) # BytePlus (VolcEngine international)
byteplus_coding_plan: ProviderConfig = Field(default_factory=ProviderConfig) # BytePlus Coding Plan
openai_codex: ProviderConfig = Field(default_factory=ProviderConfig) # OpenAI Codex (OAuth)
github_copilot: ProviderConfig = Field(default_factory=ProviderConfig) # Github Copilot (OAuth)
openai_codex: ProviderConfig = Field(default_factory=ProviderConfig, exclude=True) # OpenAI Codex (OAuth)
github_copilot: ProviderConfig = Field(default_factory=ProviderConfig, exclude=True) # Github Copilot (OAuth)
class HeartbeatConfig(Base):
@@ -125,10 +118,10 @@ class WebToolsConfig(Base):
class ExecToolConfig(Base):
"""Shell exec tool configuration."""
enable: bool = True
timeout: int = 60
path_append: str = ""
class MCPServerConfig(Base):
"""MCP server connection configuration (stdio or HTTP)."""

43
nanobot/cron/service.py
View File

@@ -10,7 +10,7 @@ from typing import Any, Callable, Coroutine
from loguru import logger
from nanobot.cron.types import CronJob, CronJobState, CronPayload, CronSchedule, CronStore
from nanobot.cron.types import CronJob, CronJobState, CronPayload, CronRunRecord, CronSchedule, CronStore
def _now_ms() -> int:
@@ -63,10 +63,12 @@ def _validate_schedule_for_add(schedule: CronSchedule) -> None:
class CronService:
"""Service for managing and executing scheduled jobs."""
_MAX_RUN_HISTORY = 20
def __init__(
self,
store_path: Path,
on_job: Callable[[CronJob], Coroutine[Any, Any, str | None]] | None = None
on_job: Callable[[CronJob], Coroutine[Any, Any, str | None]] | None = None,
):
self.store_path = store_path
self.on_job = on_job
@@ -113,6 +115,15 @@ class CronService:
last_run_at_ms=j.get("state", {}).get("lastRunAtMs"),
last_status=j.get("state", {}).get("lastStatus"),
last_error=j.get("state", {}).get("lastError"),
run_history=[
CronRunRecord(
run_at_ms=r["runAtMs"],
status=r["status"],
duration_ms=r.get("durationMs", 0),
error=r.get("error"),
)
for r in j.get("state", {}).get("runHistory", [])
],
),
created_at_ms=j.get("createdAtMs", 0),
updated_at_ms=j.get("updatedAtMs", 0),
@@ -160,6 +171,15 @@ class CronService:
"lastRunAtMs": j.state.last_run_at_ms,
"lastStatus": j.state.last_status,
"lastError": j.state.last_error,
"runHistory": [
{
"runAtMs": r.run_at_ms,
"status": r.status,
"durationMs": r.duration_ms,
"error": r.error,
}
for r in j.state.run_history
],
},
"createdAtMs": j.created_at_ms,
"updatedAtMs": j.updated_at_ms,
@@ -248,9 +268,8 @@ class CronService:
logger.info("Cron: executing job '{}' ({})", job.name, job.id)
try:
response = None
if self.on_job:
response = await self.on_job(job)
await self.on_job(job)
job.state.last_status = "ok"
job.state.last_error = None
@@ -261,8 +280,17 @@ class CronService:
job.state.last_error = str(e)
logger.error("Cron: job '{}' failed: {}", job.name, e)
end_ms = _now_ms()
job.state.last_run_at_ms = start_ms
job.updated_at_ms = _now_ms()
job.updated_at_ms = end_ms
job.state.run_history.append(CronRunRecord(
run_at_ms=start_ms,
status=job.state.last_status,
duration_ms=end_ms - start_ms,
error=job.state.last_error,
))
job.state.run_history = job.state.run_history[-self._MAX_RUN_HISTORY:]
# Handle one-shot jobs
if job.schedule.kind == "at":
@@ -366,6 +394,11 @@ class CronService:
return True
return False
def get_job(self, job_id: str) -> CronJob | None:
"""Get a job by ID."""
store = self._load_store()
return next((j for j in store.jobs if j.id == job_id), None)
def status(self) -> dict:
"""Get service status."""
store = self._load_store()

10
nanobot/cron/types.py
View File

@@ -29,6 +29,15 @@ class CronPayload:
to: str | None = None # e.g. phone number
@dataclass
class CronRunRecord:
"""A single execution record for a cron job."""
run_at_ms: int
status: Literal["ok", "error", "skipped"]
duration_ms: int = 0
error: str | None = None
@dataclass
class CronJobState:
"""Runtime state of a job."""
@@ -36,6 +45,7 @@ class CronJobState:
last_run_at_ms: int | None = None
last_status: Literal["ok", "error", "skipped"] | None = None
last_error: str | None = None
run_history: list[CronRunRecord] = field(default_factory=list)
@dataclass

6
nanobot/providers/custom_provider.py
View File

@@ -51,6 +51,12 @@ class CustomProvider(LLMProvider):
try:
return self._parse(await self._client.chat.completions.create(**kwargs))
except Exception as e:
# JSONDecodeError.doc / APIError.response.text may carry the raw body
# (e.g. "unsupported model: xxx") which is far more useful than the
# generic "Expecting value …" message. Truncate to avoid huge HTML pages.
body = getattr(e, "doc", None) or getattr(getattr(e, "response", None), "text", None)
if body and body.strip():
return LLMResponse(content=f"Error: {body.strip()[:500]}", finish_reason="error")
return LLMResponse(content=f"Error: {e}", finish_reason="error")
def _parse(self, response: Any) -> LLMResponse: