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Documentation Index

Fetch the complete documentation index at: https://notes.kodekloud.com/llms.txt

Use this file to discover all available pages before exploring further.

In this guide, you’ll learn how to build a session-aware agent that leverages the Tavily Search API to answer questions step by step. We’ll set up message history, define a rich prompt template, instantiate an LLM and tools, and finally run the agent in a persistent session.

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Table of Contents

  1. Install Dependencies
  2. Import Libraries & Configure API Key
  3. Define the Prompt Template
  4. Initialize LLM, Tools, and History
  5. Create Agent & Executor
  6. Enable Session Management
  7. Invoke the Agent
  8. Component Reference Table
  9. Next Steps & Observations
  10. Links and References

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1. Install Dependencies

Install the core LangChain packages and the community search tool: 
pip install langchain_core langchain_openai langchain_community

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2. Import Libraries & Configure API Key

Load required modules and read your Tavily API key from the environment. 
import os

from langchain_core.prompts.chat import ChatPromptTemplate, MessagesPlaceholder
from langchain_community.chat_message_histories import ChatMessageHistory
from langchain_core.runnables.history import RunnableWithMessageHistory
from langchain_openai import ChatOpenAI
from langchain_community.tools.tavily_search import TavilySearchResults
from langchain.agents import create_tool_calling_agent, AgentExecutor

# Load your Tavily API key
TAVILY_API_KEY = os.getenv("TAVILY_API_KEY")
search = TavilySearchResults(api_key=TAVILY_API_KEY)
Ensure TAVILY_API_KEY is set in your environment (export TAVILY_API_KEY=your_key_here) before running the code.

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3. Define the Prompt Template

We use a chat prompt that includes:
  • A system instruction
  • A placeholder for chat history
  • The user’s input
  • A placeholder for the agent scratchpad
prompt = ChatPromptTemplate.from_messages([
    ("system", "You are a helpful assistant. Think step by step before responding."),
    ("placeholder", "{chat_history}"),
    ("human", "{input}"),
    ("placeholder", "{agent_scratchpad}")
])
The agent scratchpad holds intermediate reasoning or tool calls before the final response.

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4. Initialize LLM, Tools, and History

Instantiate the chat model, register the search tool, and prepare in-memory history. 
llm = ChatOpenAI()
tools = [search]
message_history = ChatMessageHistory()

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5. Create Agent & Executor

Build the tool-calling agent and wrap it in an executor for streamlined invocation. 
agent = create_tool_calling_agent(
    llm=llm,
    tools=tools,
    prompt=prompt
)

agent_executor = AgentExecutor(
    agent=agent,
    tools=tools,
    verbose=False
)

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6. Enable Session Management

To maintain context across calls, wrap the executor with RunnableWithMessageHistory. In production, you can replace the in-memory store with Redis. 
agent_chain = RunnableWithMessageHistory(
    agent_executor,
    # Map a session ID to the shared ChatMessageHistory instance
    lambda session_id: message_history,
    input_messages_key="input",
    history_messages_key="chat_history"
)
For scalable session storage, consider using a Redis-backed message history.

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7. Invoke the Agent

Pass a session_id with every call to preserve dialogue history and agent scratchpad. 
# 1. Start the session
result = agent_chain.invoke(
    {"input": "hi!"},
    config={"configurable": {"session_id": "session1"}}
)
print(result["output"])
# 2. Query the ICC Men's T20 2024 World Cup schedule
resp = agent_chain.invoke(
    {"input": "When is the ICC Men's T20 2024 World Cup scheduled?"},
    config={"configurable": {"session_id": "session1"}}
)
print(resp["output"])
# 3. Ask which countries are hosting
resp = agent_chain.invoke(
    {"input": "Which countries are hosting?"},
    config={"configurable": {"session_id": "session1"}}
)
print(resp["output"])
# 4. Ask for days until first match
resp = agent_chain.invoke(
    {"input": "How many days before the first match starts?"},
    config={"configurable": {"session_id": "session1"}}
)
print(resp["output"])
# → The LLM may approximate rather than calculate precisely.
The LLM’s arithmetic can be inaccurate. In the next section, we’ll add a Python REPL tool for exact calculations.

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8. Component Reference Table

ComponentPurposeInstantiation
ChatOpenAICore chat-based LLMChatOpenAI()
TavilySearchResultsExternal search integrationTavilySearchResults(api_key=...)
AgentExecutorExecutes the tool-calling agentAgentExecutor(agent=…, tools=…)
RunnableWithMessageHistoryManages per-session message historyRunnableWithMessageHistory(agent_executor, …)

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9. Next Steps & Observations

The agent effectively combines web search results and LLM reasoning for concise answers. To improve numeric accuracy (e.g., date calculations), integrate a Python REPL tool in the next lesson.

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