add: main.py

main.py

@@ -0,0 +1,74 @@
+import os
+import asyncio
+from langchain_openai import ChatOpenAI
+from langchain_core.messages import HumanMessage
+from langchain.tools import tool
+from deepagents import create_deep_agent
+from deepagents.backends import CompositeBackend, LocalShellBackend, FilesystemBackend
+from pydantic import BaseModel, Field
+from langchain_core.output_parsers import PydanticOutputParser
+
+# LLM configuration – OpenRouter
+llm = ChatOpenAI(
+    model="openai/gpt-oss-20b:free",
+    base_url="https://openrouter.ai/api/v1",
+    api_key=os.getenv("OPENAI_API_KEY"),
+    temperature=0.0,
+)
+
+# Backend for file operations and shell execution (not used directly but required by deepagents)
+backend = CompositeBackend([
+    LocalShellBackend(workspace_dir="./workspace"),
+    FilesystemBackend(),
+])
+
+# Structured output model for the AI fluency plan
+class FluencyPlan(BaseModel):
+    goal: str = Field(description="Overall goal of the AI fluency plan")
+    timeline: str = Field(description="High‑level timeline (e.g., 3 months, 6 months)")
+    milestones: list[str] = Field(description="Key milestones to achieve along the way")
+    resources: list[str] = Field(description="Recommended courses, books, tools, and communities")
+    assessment: str = Field(description="How to assess progress and adjust the plan")
+
+parser = PydanticOutputParser(pydantic_object=FluencyPlan)
+
+# Simple validation tool to ensure the plan is not empty
+@tool
+def validate_plan(plan: str) -> str:
+    """Validate that the plan contains at least one milestone and resources."""
+    if "milestones" not in plan.lower() or "resources" not in plan.lower():
+        return "Plan is missing essential sections."
+    return "OK"
+
+# Create the deep agent
+agent = create_deep_agent(
+    model=llm,
+    tools=[validate_plan],
+    backend=backend,
+    system_prompt="You are an expert educational planner. Your task is to create a detailed personal AI fluency plan based on the course "
+                 "Ai Fluency and the Build a personal AI fluency plan assignment. The output must be a JSON object that matches the FluencyPlan schema."
+)
+
+async def main():
+    # Human message with the assignment description
+    human_msg = HumanMessage(content="Create a personal AI fluency plan for completing the Ai Fluency course and the Build a personal AI fluency plan assignment. Use the provided schema.")
+
+    # Invoke the agent
+    result = await agent.ainvoke(
+        {"messages": [human_msg]},
+        {"configurable": {"thread_id": "ai-fluency-plan"}},
+    )
+
+    # Extract the assistant message
+    assistant_msg = result["messages"][-1].content
+
+    # Parse the JSON using the Pydantic parser to ensure correctness
+    try:
+        plan_obj = parser.parse(assistant_msg)
+        print("✅ Generated AI Fluency Plan:\n", plan_obj.json(indent=2))
+    except Exception as e:
+        print("❌ Failed to parse plan:", e)
+        print("Raw output:", assistant_msg)
+
+if __name__ == "__main__":
+    asyncio.run(main())