fix import

Author: binary-husky

docs/en/tune_your_first_agent.md

@@ -284,6 +284,10 @@ ajet --conf tutorial/example_math_agent/math_agent.yaml
     import re
     from loguru import logger
     from agentscope.message import Msg
+    from agentscope.agent import ReActAgent
+    from agentscope.formatter import DashScopeChatFormatter
+    from agentscope.memory import InMemoryMemory
+    from agentscope.tool import Toolkit, execute_python_code
     from ajet import AjetTuner, Workflow, WorkflowOutput, WorkflowTask
 
 
@@ -340,7 +344,6 @@ ajet --conf tutorial/example_math_agent/math_agent.yaml
             if match: is_success = (match.group(1) == reference_answer)
             else:     is_success = False
             return WorkflowOutput(reward=(1.0 if is_success else 0.0), metadata={"final_answer": final_answer})
-
     ```
 
 === "`math_agent.yaml` - Configuration Yaml"

tutorial/example_math_agent/math_agent.yaml

@@ -19,6 +19,11 @@ ajet:
 
   rollout:
     user_workflow: "tutorial.example_math_agent.math_agent->ExampleMathLearn" # ✨✨✨✨ write and select workflow
+    # user_workflow: "tutorial.example_math_agent.math_agent_langchain->ExampleMathLearn"                     # ✨if you prefer langchain version
+    # user_workflow: "tutorial/example_math_agent/math_agent_oai_sdk.py->ExampleMathLearn_Simple_NoToolCall"  # ✨if you prefer openai sdk version without toolcall
+    # user_workflow: "tutorial/example_math_agent/math_agent_oai_sdk.py->ExampleMathLearn"                    # ✨if you prefer openai sdk version with toolcall
+    # user_workflow: "tutorial/example_math_agent/math_agent_raw_http.py->ExampleMathLearn"                   # ✨if you do not want to use any agentic framwork at all
+    # user_workflow: "tutorial/example_math_agent/math_agent_simplify.py->MathToolWorkflow"                   # ✨if you prefer to compute reward inside workflow
     temperature: 1.0
     max_env_worker: 64
     num_repeat: 6