new Op，but SimpleChatOp not found

[magicbrighter]

🛠️ Step1 Build Op
from flowllm.core.context import C
from flowllm.core.op import BaseAsyncOp
from flowllm.core.schema import Message
from flowllm.core.enumeration import Role

@C.register_op()
class SimpleChatOp(BaseAsyncOp):
async def async_execute(self):
query = self.context.get("query", "")
messages = [Message(role=Role.USER, content=query)]

    # Use token_count method to calculate token count
    token_num = self.token_count(messages)
    print(f"Input tokens: {token_num}")

    response = await self.llm.achat(messages=messages)
    self.context.response.answer = response.content.strip()

For details, refer to the Simple Op Guide, LLM Op Guide, and Advanced Op Guide (including Embedding, VectorStore, and concurrent execution).

📝 Step2 Configure Config
The following example demonstrates building an MCP (Model Context Protocol) service. Create a configuration file my_mcp_config.yaml:

backend: mcp

mcp:
transport: sse
host: "0.0.0.0"
port: 8001

flow:
demo_mcp_flow:
flow_content: SimpleChatOp()
description: "Search results for a given query."
input_schema:
query:
type: string
description: "User query"
required: true

llm:
default:
backend: openai_compatible
model_name: qwen3-30b-a3b-instruct-2507
params:
temperature: 0.6
token_count: # Optional, configure token counting backend
model_name: Qwen/Qwen3-30B-A3B-Instruct-2507
backend: hf # Supports base, openai, hf, etc.
params:
use_mirror: true
🚀 Step3 Start MCP Service
flowllm
config=my_mcp_config
backend=mcp \ # Optional, overrides config
mcp.transport=sse \ # Optional, overrides config
mcp.port=8001 \ # Optional, overrides config
llm.default.model_name=qwen3-30b-a3b-thinking-2507 # Optional, overrides config

╭─ ReMe ────────────────────────────────────────────╮
│                                                   │
│        ____       __  ___                         │
│       / __ \___  /  |/  /__                       │
│      / /_/ / _ \/ /|_/ / _ \                      │
│     / _, _/  __/ /  / /  __/                      │
│    /_/ |_|\___/_/  /_/\___/                       │
│                                                   │
│                                                   │
│                                                   │
│    📦 Backend:         mcp                        │
│    📚 Transport:       sse                        │
│    🔗 URL:             http://0.0.0.0:8001/sse    │
│                                                   │
│    🚀 FlowLLM version: 0.2.0.3                    │
│    📚 FastMCP version: 2.13.1                     │
│                                                   │
╰───────────────────────────────────────────────────╯


Traceback (most recent call last):
  File "/Users/banma-3420/Downloads/reme_ai_test_4/.venv/bin/flowllm", line 8, in <module>
    sys.exit(main())
             ^^^^^^
  File "/Users/banma-3420/Downloads/reme_ai_test_4/.venv/lib/python3.12/site-packages/flowllm/main.py", line 87, in main
    app.run_service()
  File "/Users/banma-3420/Downloads/reme_ai_test_4/.venv/lib/python3.12/site-packages/flowllm/core/application.py", line 384, in run_service
    service.run()
  File "/Users/banma-3420/Downloads/reme_ai_test_4/.venv/lib/python3.12/site-packages/flowllm/core/service/mcp_service.py", line 50, in run
    super().run()
  File "/Users/banma-3420/Downloads/reme_ai_test_4/.venv/lib/python3.12/site-packages/flowllm/core/service/base_service.py", line 67, in run
    if self.integrate_tool_flow(flow):
       ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/banma-3420/Downloads/reme_ai_test_4/.venv/lib/python3.12/site-packages/flowllm/core/service/mcp_service.py", line 29, in integrate_tool_flow
    request_model = create_pydantic_model(flow.name, flow.tool_call.input_schema)
                                                     ^^^^^^^^^^^^^^
  File "/Users/banma-3420/Downloads/reme_ai_test_4/.venv/lib/python3.12/site-packages/flowllm/core/flow/base_tool_flow.py", line 28, in tool_call
    self._tool_call = self.build_tool_call()
                      ^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/banma-3420/Downloads/reme_ai_test_4/.venv/lib/python3.12/site-packages/flowllm/core/flow/expression_tool_flow.py", line 43, in build_tool_call
    if hasattr(self.flow_op, "tool_call"):
               ^^^^^^^^^^^^
  File "/Users/banma-3420/Downloads/reme_ai_test_4/.venv/lib/python3.12/site-packages/flowllm/core/flow/base_flow.py", line 164, in flow_op
    self._flow_op = self.build_flow()
                    ^^^^^^^^^^^^^^^^^
  File "/Users/banma-3420/Downloads/reme_ai_test_4/.venv/lib/python3.12/site-packages/flowllm/core/flow/expression_tool_flow.py", line 35, in build_flow
    return parse_flow_expression(self.flow_config.flow_content)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/banma-3420/Downloads/reme_ai_test_4/.venv/lib/python3.12/site-packages/flowllm/core/utils/common_utils.py", line 221, in parse_flow_expression
    result = eval(last_line_expr, {"__builtins__": {}}, env)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "<string>", line 1, in <module>
NameError: name 'SimpleChatOp' is not defined

[jinliyl]

Thank you very much for your question.
It's probably because the file hasn't been imported. Before running ReMeApp, you need to register it in the global context via an import statement.
I'll add this clarification to the guide documentation later.

[jinliyl]

@magicbrighter