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feat: add MCP

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This commit is contained in:
qixinbo
2026-03-27 22:06:00 +08:00
parent 5d013231bc
commit b24aff956a
8 changed files with 600 additions and 69 deletions
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backend/app/core/nanobot.py
+55 -37
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@@ -44,7 +44,7 @@ class NanobotIntegration:
self.cron: CronService | None = None
self.config: Config | None = None
self._started = False
self._model_agent_cache: Dict[str, AgentLoop] = {}
self._model_agent_cache: Dict[tuple[str | None, int | None], AgentLoop] = {}
self._model_agent_lock = asyncio.Lock()
def initialize(self):
@@ -189,7 +189,7 @@ class NanobotIntegration:
self.cron.stop()
self._started = False
def _build_agent_for_provider(self, provider: Any) -> AgentLoop:
def _build_agent_for_provider(self, provider: Any, mcp_servers: dict | None = None) -> AgentLoop:
return AgentLoop(
bus=self.bus,
provider=provider,
@@ -205,26 +205,48 @@ class NanobotIntegration:
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,
session_manager=self.agent.sessions if self.agent else None,
mcp_servers=mcp_servers if mcp_servers is not None else self.config.tools.mcp_servers,
channels_config=self.config.channels,
)
async def _get_or_create_model_agent(self, model_id: str, target_config: Dict[str, Any]) -> AgentLoop:
async def _get_or_create_model_agent(self, model_id: str | None, target_config: Dict[str, Any] | None, project_id: int | None = None) -> AgentLoop:
cache_key = (model_id, project_id)
async with self._model_agent_lock:
cached = self._model_agent_cache.get(model_id)
cached = self._model_agent_cache.get(cache_key)
if cached:
return cached
provider = StreamingLiteLLMProvider(
api_key=target_config.get("api_key"),
api_base=target_config.get("api_base"),
default_model=target_config.get("model"),
extra_headers=target_config.get("extra_headers"),
provider_name=target_config.get("provider"),
)
agent = self._build_agent_for_provider(provider)
if target_config:
provider = StreamingLiteLLMProvider(
api_key=target_config.get("api_key"),
api_base=target_config.get("api_base"),
default_model=target_config.get("model"),
extra_headers=target_config.get("extra_headers"),
provider_name=target_config.get("provider"),
)
else:
provider = self._make_provider(self.config)
mcp_servers_dict = dict(self.config.tools.mcp_servers) if self.config.tools.mcp_servers else {}
if project_id is not None:
from app.api.mcp import list_mcp_servers
from nanobot.config.schema import MCPServerConfig
servers = list_mcp_servers(project_id=project_id)
for s in servers:
cfg = MCPServerConfig(
type=s.get("type"),
command=s.get("command") or "",
args=s.get("args") or [],
env=s.get("env") or {},
url=s.get("url") or "",
headers=s.get("headers") or {}
)
mcp_servers_dict[s["name"]] = cfg
agent = self._build_agent_for_provider(provider, mcp_servers=mcp_servers_dict)
self._register_custom_tools(agent)
self._model_agent_cache[model_id] = agent
self._model_agent_cache[cache_key] = agent
return agent
async def process_message(
@@ -233,6 +255,7 @@ class NanobotIntegration:
session_id: str = "api:default",
skill_ids: List[str] | None = None,
model_id: str | None = None,
project_id: int | None = None,
on_progress: Callable[[str], Awaitable[None]] | None = None,
):
if not self.agent:
@@ -240,32 +263,27 @@ class NanobotIntegration:
if not self._started:
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.
if project_id is None:
from app.core.session_alias_store import session_alias_store
alias_info = session_alias_store.get_alias(session_id)
if alias_info and alias_info.get("project_id"):
project_id = alias_info.get("project_id")
agent_to_use = self.agent
need_custom_agent = False
target_config = None
if model_id:
llm_configs = get_llm_configs()
target_config = next((item for item in llm_configs if item.get("id") == model_id), None)
if target_config:
if target_config.get("model") != self.agent.model:
agent_to_use = await self._get_or_create_model_agent(model_id, target_config)
if target_config and target_config.get("model") != self.agent.model:
need_custom_agent = True
if project_id is not None:
need_custom_agent = True
if need_custom_agent:
agent_to_use = await self._get_or_create_model_agent(model_id, target_config, project_id)
full_message = message
# We no longer inject the full skill content into the user's message here,