add agent.py

agent.py

@@ -0,0 +1,145 @@
+"""
+Agent implementation for the Planning Agent assignment.
+
+This module contains all logic for building a LangGraph agent that:
+1. Plans a task into discrete steps using an LLM.
+2. Executes each step sequentially, collecting results.
+3. Returns a final summary of all results.
+
+The graph is built in :func:`build_agent` and returned as a compiled object.
+"""
+
+import os
+from typing import TypedDict, List
+from langgraph.graph import StateGraph, START, END
+from langchain_openai import ChatOpenAI
+from langchain_core.messages import HumanMessage, SystemMessage
+
+# ---------------------------------------------------------------------------
+# 1. State definition
+# ---------------------------------------------------------------------------
+class PlanningState(TypedDict):
+    task: str
+    plan: List[str] | None
+    current_step: int
+    results: List[str]
+
+# ---------------------------------------------------------------------------
+# 2. LLM configuration – BroJS provider
+# ---------------------------------------------------------------------------
+llm = ChatOpenAI(
+    model="openai/gpt-oss-20b:free",
+    base_url="https://platform.brojs.ru/jrnl-bh/api/inference/v1",
+    api_key=os.getenv("JOURNAL_MCP_PAT"),
+    temperature=0.2,
+)
+
+# ---------------------------------------------------------------------------
+# 3. Planning node – split the task into steps
+# ---------------------------------------------------------------------------
+def planning(state: PlanningState) -> PlanningState:
+    """Ask LLM to produce a numbered list of steps.
+
+    The prompt asks for JSON output with a single key ``steps`` containing an array
+    of strings.  This guarantees deterministic parsing.
+    """
+    task = state["task"]
+    system_prompt = (
+        "You are a helpful assistant that breaks down a user request into a list of concrete, actionable steps."
+    )
+    user_prompt = (
+        f"Task: {task}\n\nReturn a JSON object with a single key 'steps' containing an array of strings.  Each string should be a concise step.  Provide between 3 and 6 steps.")
+
+    response = llm.invoke([SystemMessage(content=system_prompt), HumanMessage(content=user_prompt)])
+    # Parse JSON safely
+    import json, re
+    try:
+        data = json.loads(response.content)
+        steps = data.get("steps", [])
+    except Exception:
+        # Fallback: extract numbered list via regex
+        pattern = r"\d+\.\s*(.+)"
+        steps = [m.group(1).strip() for m in re.finditer(pattern, response.content)]
+    return {
+        "task": task,
+        "plan": steps,
+        "current_step": 0,
+        "results": [],
+    }
+
+# ---------------------------------------------------------------------------
+# 4. Execution node – run one step and record result
+# ---------------------------------------------------------------------------
+def execution(state: PlanningState) -> PlanningState:
+    idx = state["current_step"]
+    plan = state["plan"] or []
+    if idx >= len(plan):
+        return state
+    step_text = plan[idx]
+    # For demonstration, we simply echo the step as result.
+    result = f"Result of step {idx+1}: {step_text}"
+    new_results = state["results"] + [result]
+    return {
+        "task": state["task"],
+        "plan": plan,
+        "current_step": idx + 1,
+        "results": new_results,
+    }
+
+# ---------------------------------------------------------------------------
+# 5. Decision node – continue or finish
+# ---------------------------------------------------------------------------
+def should_continue(state: PlanningState) -> str:
+    if state["current_step"] >= len(state.get("plan", [])):
+        return "finish"
+    return "execute"
+
+# ---------------------------------------------------------------------------
+# 6. Build graph and compile
+# ---------------------------------------------------------------------------
+
+def build_agent() -> StateGraph:
+    graph = StateGraph(PlanningState)
+    graph.add_node("planning", planning)
+    graph.add_node("execution", execution)
+    graph.add_conditional_edges(
+        "planning",
+        lambda _: "execute" if _["plan"] else "finish",
+    )
+    graph.add_edge("execution", "should_continue")
+    graph.add_conditional_edges(
+        "should_continue",
+        should_continue,
+        {"execute": "execution", "finish": END},
+    )
+    graph.set_entry_point("planning")
+    return graph
+
+# ---------------------------------------------------------------------------
+# 7. Public helper to get compiled agent
+# ---------------------------------------------------------------------------
+def get_agent():
+    """Return a compiled LangGraph agent ready for invocation."""
+    return build_agent().compile()
+
+# ---------------------------------------------------------------------------
+# If run as script, demonstrate usage with one example task.
+# ---------------------------------------------------------------------------
+if __name__ == "__main__":
+    from rich.console import Console
+    console = Console()
+    agent = get_agent()
+    task_text = "Compare Python and JavaScript in terms of performance, syntax simplicity, and ecosystem support."
+    result_state = agent.invoke({"task": task_text})
+    console.print("\n[bold cyan]Plan:\n[/bold cyan]")
+    for i, step in enumerate(result_state.get("plan", []), 1):
+        console.print(f"{i}. {step}")
+    console.print("\n[blue]Execution results:\n[/blue]")
+    for r in result_state.get("results", []):
+        console.print(r)
+    console.print("\n[bold magenta]Final summary:[/bold magenta]\n")
+    # Final LLM summarization
+    summary_prompt = (
+        "You have executed the following steps: \n" + "\n".join(result_state.get("results", [])) + "\nProvide a concise final answer.")
+    summary_response = llm.invoke([HumanMessage(content=summary_prompt)])
+    console.print(summary_response.content)