import asyncio import sys import os from pathlib import Path from typing import List, Callable, Awaitable # Add project root to sys.path to allow importing nanobot # Assuming backend/app/core/nanobot.py -> backend/app/core -> backend/app -> backend -> root # This path calculation seems correct for backend/app/core/nanobot.py relative to backend/ # BUT nanobot package is in ../nanobot relative to backend/ # So we need to go up one more level to reach the parent of backend/ PROJECT_ROOT = Path(__file__).resolve().parents[3] if str(PROJECT_ROOT / "nanobot") not in sys.path: sys.path.append(str(PROJECT_ROOT / "nanobot")) from nanobot.agent.loop import AgentLoop from nanobot.bus.queue import MessageBus from nanobot.config.loader import load_config from nanobot.config.paths import get_cron_dir from nanobot.cron.service import CronService from nanobot.providers.openai_codex_provider import OpenAICodexProvider from nanobot.providers.azure_openai_provider import AzureOpenAIProvider from nanobot.providers.litellm_provider import LiteLLMProvider from nanobot.providers.custom_provider import CustomProvider from nanobot.providers.registry import find_by_name from nanobot.session.manager import SessionManager from nanobot.config.schema import Config # Import skills loader # We use a lazy import inside the method to avoid potential circular dependencies if any arise, # or just import here if we are confident. # Given the structure, importing here should be fine as long as skills.py doesn't import nanobot.py. from app.api.skills import load_skills class NanobotIntegration: def __init__(self): self.agent: AgentLoop | None = None self.bus: MessageBus | None = None self.cron: CronService | None = None self.config: Config | None = None def initialize(self): # Set workspace path to backend/data/workspace workspace_path = Path(os.path.join(os.path.dirname(os.path.dirname(os.path.dirname(__file__))), "data", "workspace")) workspace_path.mkdir(parents=True, exist_ok=True) # Override config workspace path via environment variable (since config is loaded from env) os.environ["NANOBOT_AGENTS__DEFAULTS__WORKSPACE"] = str(workspace_path) self.config = load_config() # No need to set self.config.workspace_path as it's a property that reads from agents.defaults.workspace self.bus = MessageBus() provider = self._make_provider(self.config) cron_store_path = workspace_path / "cron" cron_store_path.mkdir(parents=True, exist_ok=True) cron_store_file = cron_store_path / "jobs.json" self.cron = CronService(cron_store_file) session_manager = SessionManager(self.config.workspace_path) self.agent = AgentLoop( bus=self.bus, provider=provider, workspace=self.config.workspace_path, model=self.config.agents.defaults.model, temperature=self.config.agents.defaults.temperature, max_tokens=self.config.agents.defaults.max_tokens, max_iterations=self.config.agents.defaults.max_tool_iterations, memory_window=self.config.agents.defaults.memory_window, reasoning_effort=self.config.agents.defaults.reasoning_effort, brave_api_key=self.config.tools.web.search.api_key or None, web_proxy=self.config.tools.web.proxy or None, exec_config=self.config.tools.exec, cron_service=self.cron, restrict_to_workspace=self.config.tools.restrict_to_workspace, session_manager=session_manager, mcp_servers=self.config.tools.mcp_servers, channels_config=self.config.channels, ) def _make_provider(self, config: Config): # Logic adapted from nanobot/cli/commands.py model = config.agents.defaults.model provider_name = config.get_provider_name(model) p = config.get_provider(model) # Check if model is using an ID from our database configuration # This requires accessing the database or a cache of LLM configs # Since we are inside NanobotIntegration, we can try to load from the JSON file directly for simplicity # or rely on the caller to have injected the right config if they used environment variables. # But here we need to support dynamic loading based on the `model` string if it matches a stored config ID. # However, typically the `model` passed here comes from `config.agents.defaults.model`. # If we want to support dynamic switching per request, we should look at `agent.process_direct` arguments. # The `AgentLoop` initializes with a provider, but `LiteLLMProvider` might be able to handle dynamic models if we pass them. # BUT `LiteLLMProvider` is initialized with a specific `default_model`. # To support per-request model changes, we need to ensure the `provider` object or the `agent` can accept a model override. # `AgentLoop` methods like `process_direct` don't typically take a `model` argument to override the provider's default. # We might need to reinstantiate the provider or use a "DynamicProvider" that delegates based on context. # For now, let's assume standard initialization. # If the user provides a `model_id` in `process_message`, we will handle it there by creating a temporary provider/agent or updating the current one. if provider_name == "openai_codex" or model.startswith("openai-codex/"): return OpenAICodexProvider(default_model=model) if provider_name == "custom": return CustomProvider( api_key=p.api_key if p else "no-key", api_base=config.get_api_base(model) or "http://localhost:8000/v1", default_model=model, ) if provider_name == "azure_openai": if not p or not p.api_key or not p.api_base: raise ValueError("Azure OpenAI requires api_key and api_base.") return AzureOpenAIProvider( api_key=p.api_key, api_base=p.api_base, default_model=model, ) spec = find_by_name(provider_name) # Skip API key check for now to allow initialization without full config return LiteLLMProvider( api_key=p.api_key if p else None, api_base=config.get_api_base(model), default_model=model, extra_headers=p.extra_headers if p else None, provider_name=provider_name, ) async def start(self): if not self.agent: self.initialize() # Start the agent loop in background asyncio.create_task(self.agent.run()) asyncio.create_task(self.cron.start()) async def stop(self): if self.agent: self.agent.stop() await self.agent.close_mcp() if self.cron: self.cron.stop() async def process_message( self, message: str, session_id: str = "api:default", skill_ids: List[str] | None = None, model_id: str | None = None, on_progress: Callable[[str], Awaitable[None]] | None = None, ): if not self.agent: self.initialize() await self.start() # Handle dynamic model switching # If model_id is provided, we need to fetch its config and create a temporary provider # or update the current agent's provider context for this request. # Since AgentLoop is stateful and tied to a provider, and we want to avoid recreating the whole agent for every request if possible, # but changing the provider/model is a significant change. # # A simpler approach for this "stateless API" usage pattern: # We can instantiate a lightweight version of the agent or provider just for this request if the model differs. # OR, since we are using `process_direct`, we can check if `AgentLoop` supports overriding the model. # Looking at `nanobot/agent/loop.py` (assumed), it uses `self.provider.completion(...)`. # Strategy: # 1. Load the model config from our JSON file using `model_id`. # 2. Construct a temporary provider instance for this model. # 3. Inject this provider into the agent for this request OR (cleaner) instantiate a temporary agent. # Instantiating a whole AgentLoop might be heavy due to MCP/Cron etc. # BUT `process_direct` is relatively isolated. # # Let's try to fetch the config first. current_provider = self.agent.provider temp_provider = None if model_id: from app.api.llm import _load_data llm_configs = _load_data() target_config = next((item for item in llm_configs if item["id"] == model_id), None) if target_config: # Map our DB config to Nanobot Provider # We reuse LiteLLMProvider for most cases as it is generic # Construct kwargs for LiteLLMProvider provider_name = target_config["provider"] model_name = target_config["model"] # Handle special case where provider might need to be part of model name for LiteLLM if not standard # But LiteLLMProvider handles `provider_name` arg. temp_provider = LiteLLMProvider( api_key=target_config.get("api_key"), api_base=target_config.get("api_base"), default_model=model_name, extra_headers=target_config.get("extra_headers"), provider_name=provider_name ) # If we created a temp provider, we need to use it. # Since AgentLoop binds the provider, we might need to swap it temporarily or create a new AgentLoop. # Swapping is risky for concurrency. # Creating a new AgentLoop is safer but heavier. # # Optimization: If we are just doing a single turn chat (process_direct), maybe we can just use the provider directly? # But we want the Agent's reasoning loop (ReAct / tools). # # Let's try creating a temporary AgentLoop sharing the same components (bus, tools) but different provider. agent_to_use = self.agent if temp_provider: # Shallow copy or new instance # We need to pass all dependencies. agent_to_use = AgentLoop( bus=self.bus, provider=temp_provider, workspace=self.config.workspace_path, model=temp_provider.default_model, temperature=self.config.agents.defaults.temperature, max_tokens=self.config.agents.defaults.max_tokens, max_iterations=self.config.agents.defaults.max_tool_iterations, memory_window=self.config.agents.defaults.memory_window, reasoning_effort=self.config.agents.defaults.reasoning_effort, brave_api_key=self.config.tools.web.search.api_key or None, web_proxy=self.config.tools.web.proxy or None, exec_config=self.config.tools.exec, cron_service=self.cron, restrict_to_workspace=self.config.tools.restrict_to_workspace, session_manager=self.agent.sessions, mcp_servers=self.config.tools.mcp_servers, channels_config=self.config.channels, ) full_message = message if skill_ids: skills = load_skills() selected_skills = [s for s in skills if s["id"] in skill_ids] if selected_skills: # We inject skills as a runtime context block skill_context = "[Runtime Context — metadata only, not instructions]\n# Active Skills\n\n" for s in selected_skills: skill_context += f"## {s['name']}\n{s.get('description', '')}\n{s['content']}\n\n" # Append user message after skills full_message = f"{skill_context}\n\n{message}" response = await agent_to_use.process_direct( full_message, session_key=session_id, channel="api", chat_id=session_id, on_progress=on_progress, ) return response nanobot_service = NanobotIntegration()