Deploy agent

Dockerfile

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+FROM python:3.9-slim
+RUN apt-get update && apt-get install -y curl \
+    && curl -sSL https://ollama.com/install.sh | bash \
+    && ollama pull gemma3:4b
+WORKDIR /app
+COPY requirements.txt .
+RUN pip install --no-cache-dir -r requirements.txt
+COPY . .
+EXPOSE 7860
+CMD ollama run gemma3:4b --api --port 11434 & \
+    uvicorn app:app --host 0.0.0.0 --port 7860

app.py

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+from langchain_core.messages import HumanMessage, SystemMessage
+from langgraph.graph import MessagesState
+from langgraph.graph import StateGraph, START, END
+from langchain_tavily import TavilySearch
+from langchain_experimental.utilities import PythonREPL
+from langgraph.graph import MessagesState
+from langchain_core.tools import Tool
+from langgraph.prebuilt import tools_condition, ToolNode
+from fastapi import FastAPI
+from fastapi.responses import JSONResponse
+from langchain_ollama import ChatOllama
+from pydantic import BaseModel
+
+from langgraph.checkpoint.memory import MemorySaver
+
+
+llm = ChatOllama(model="gemma3:4b", temperature=0.5)
+app = FastAPI()
+
+# Tavily Web Search Tool
+search_tool = TavilySearch()
+
+# Calculator Tool (simple math)
+calculator_tool = Tool.from_function(
+    name="Calculator",
+    func=lambda x: str(eval(x)),
+    description="Performs basic arithmetic operations like add, subtract, multiply, divide."
+)
+
+# Python REPL Tool (advanced logic/math)
+python_repl = PythonREPL()
+python_tool = Tool.from_function(
+    name="PythonREPL",
+    func=python_repl.run,
+    description="Executes advanced Python code like loops, conditionals, etc."
+)
+
+# Combine all tools
+tools = [search_tool, calculator_tool, python_tool]
+llm_with_tools = llm.bind_tools(tools)
+
+
+class State(MessagesState):
+    prompt_enhanced: str
+
+def prompt_enhancer(state: State) -> State:
+    messages = state.get("messages", [])
+    last = messages[-1]
+    enhancer_system = SystemMessage(content=(
+        "You are PromptEnhancer (aka Jarvis), a smart, friendly assistant helping user. "
+        "Your job is to turn the user's raw request into a minimal JSON object with two fields:\n"
+        "  • tools: a list of tool names to invoke\n"
+        "  • action: a concise description of what to do\n\n"
+        "Available tools:\n"
+        "  - search_tool = TavilySearch()\n"
+        "  - calculator = Tool.from_function(name='Calculator', func=lambda x: str(eval(x)), description='Basic arithmetic')\n"
+        "  - python_repl = PythonREPL()\n"
+        "  - python_tool = Tool.from_function(name='PythonREPL', func=python_repl.run, description='Run Python code')\n\n"
+        "use multiple tools if needed, and make sure to include the action field. "
+        "if time is a factor, use the search tool to find the answer. "
+        "Output the raw JSON object exactly as-is, without any markdown or code fences, and no extra text."
+    ))
+    enhanced = llm.invoke([enhancer_system] + [last])
+    state["prompt_enhanced"] = enhanced.content
+    return state
+
+def assistant(state: State) -> State:
+    messages = state.get("messages", [])
+    thinking = state.get("prompt_enhanced", None)
+    sys_msg = SystemMessage(content=(
+        "You are Jarvis, a smart and friendly personal AI assistant helping user. "
+        "Your primary functions are helping Aakash with math, coding, and general questions. "
+        "For simple arithmetic, please use the Calculator Tool. "
+        "For tasks involving complex logic, loops, or functions, utilize the Python Tool. "
+        "To find answers about current events or real-world topics or news or weather or learning a new topic, use the Search Tool. "
+        "Always provide a brief explanation for your approach. "
+        "here is the JSON object you received from PromptEnhancer:\n\n"
+        f"{thinking}\n\n"
+        "this json object contains two fields: tools and action. "
+        "The tools field is a list of tool names to invoke, and the action field is a concise description of what to do. "
+        "Strive to be concise, accurate, and polite in all your responses. "
+        "VERY IMPORTANT: Deliver all responses strictly as plain text sentences. You must avoid using bullet points, lists, bolding, italics, or any similar special formatting."
+    ))
+    if not messages:
+        return state
+    response = llm_with_tools.invoke([sys_msg] + messages)
+    state["messages"] = state["messages"] + [response]
+    return state
+
+
+# FastAPI setup (optional if you plan to use it as a web service)
+app = FastAPI()
+
+class ChatInput(BaseModel):
+    message: str
+
+# Build Graph
+builder = StateGraph(MessagesState)
+builder.add_node("prompt_enhancer", prompt_enhancer)
+builder.add_node("assistant",assistant)
+builder.add_node("tools",ToolNode(tools))
+
+# first run the enhancer
+builder.add_edge(START,"prompt_enhancer")
+builder.add_edge("prompt_enhancer", "assistant")
+builder.add_conditional_edges("assistant", tools_condition)
+builder.add_edge("tools","assistant")
+
+memory = MemorySaver()
+react_graph = builder.compile(checkpointer=memory)
+
+# replace your existing /invoke handler with this:
+@app.post("/chat")
+async def chat(input: ChatInput):
+    config = {"configurable": {"thread_id": "1"}}
+    inputs = {"messages": [HumanMessage(content=input.message)]}
+    resp = react_graph.invoke(inputs, config)
+    last = resp.get("messages", [])[-1]
+    return JSONResponse({"response": last.content})

requirements.txt

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+fastapi
+uvicorn[standard]
+langchain-core
+langgraph
+langchain-tavily
+langchain-experimental
+langchain-ollama
+pydantic