Pythonreact_agent.py
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import os
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import re
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import math
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import openai
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client = openai.OpenAI(api_key=os.environ["OPENAI_API_KEY"])
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REACT_SYSTEM_PROMPT = """You are a helpful assistant that solves problems step by step.
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Use this format:
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Thought: <reasoning about what to do next>
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Action: <tool_name>[<input>]
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Observation: <result of the action>
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... (repeat as needed)
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Final Answer: <final answer to the question>
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Available tools:
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- search[query]: Search for information
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- calculate[expression]: Evaluate a math expression
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- lookup[topic]: Look up facts about a topic
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Begin!"""
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# ── Simulated tools ─────────────────────────────────────────────────
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def search(query: str) -> str:
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    database = {
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        "eiffel tower": "The Eiffel Tower is 330 meters tall, built 1887-1889, located in Paris.",
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        "python": "Python was created by Guido van Rossum, first released in 1991.",
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        "population of france": "France has a population of approximately 68 million people.",
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    }
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    for key, value in database.items():
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        if key in query.lower():
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            return value
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    return f"Search results for '{query}': Found limited information."
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def calculate(expression: str) -> str:
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    try:
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        allowed_names = {"sqrt": math.sqrt, "pi": math.pi, "e": math.e}
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        result = eval(expression, {"__builtins__": {}}, allowed_names)
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        return f"{expression} = {result}"
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    except Exception as e:
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        return f"Error: {e}"
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def lookup(topic: str) -> str:
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    facts = {
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        "gravity": "Earth's gravitational acceleration is 9.81 m/s².",
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        "speed of light": "Speed of light is 299,792,458 m/s.",
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        "pi": f"Pi = {math.pi}",
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    }
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    for key, value in facts.items():
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        if key in topic.lower():
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            return value
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    return f"No specific facts found for '{topic}'."
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TOOL_MAP = {"search": search, "calculate": calculate, "lookup": lookup}
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# ── ReAct loop ───────────────────────────────────────────────────────
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def parse_action(text: str) -> tuple[str, str] | None:
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    """Extract tool name and input from Action: tool[input]."""
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    match = re.search(r'Action:s*(w+)[([^]]*)]', text, re.IGNORECASE)
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    if match:
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        return match.group(1).lower(), match.group(2)
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    return None
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def react_agent(question: str, max_steps: int = 6) -> str:
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    messages = [
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        {"role": "system", "content": REACT_SYSTEM_PROMPT},
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        {"role": "user", "content": question},
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    ]
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    for step in range(max_steps):
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        response = client.chat.completions.create(
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            model="gpt-4o-mini",
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            messages=messages,
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            max_tokens=300,
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            temperature=0,
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            stop=["Observation:"],  # Stop at observation — we fill it in
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        )
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        text = response.choices[0].message.content
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        print(f"  Step {step + 1}:\n{text}")
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        # Check for final answer
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        if "Final Answer:" in text:
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            return re.search(r"Final Answer:\s*(.+)", text, re.DOTALL).group(1).strip()
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        # Parse and execute action
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        action = parse_action(text)
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        if action:
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            tool_name, tool_input = action
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            if tool_name in TOOL_MAP:
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                observation = TOOL_MAP[tool_name](tool_input)
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            else:
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                observation = f"Tool '{tool_name}' not found."
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            # Append to conversation
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            messages.append({"role": "assistant", "content": text})
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            messages.append({"role": "user", "content": f"Observation: {observation}"})
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            print(f"  Observation: {observation}\n")
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        else:
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            # Model didn't call a tool — it may have final answer without the label
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            messages.append({"role": "assistant", "content": text})
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    return "Max steps reached without final answer."
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print("=== ReAct Agent ===")
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question = "How tall is the Eiffel Tower in feet? (1 meter = 3.28084 feet)"
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print(f"Q: {question}\n")
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answer = react_agent(question)
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print(f"\nFinal Answer: {answer}")
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