Monitor LangChain Agents with Splunk Agent Observability

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Overview

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What you’ll build

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What you’ll learn

• How to configure a LangChain Agent
• How to integrate Splunk Agent Observability for observability and monitoring
• How to structure tools and environment for scalable development

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Requirements

• Python package manager + some familiarity with Python (for the sake of this cookbook, we’ll use uv)
• A Splunk Agent Observability Developer Account. If you don’t have one, sign up for free.
• OpenAI Key to assist get one here

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Environment setup

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Ingredients

• git
• Python environment tools
• Package manager (pip or uv)

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Steps

1. Clone the repository:
   git clone https://github.com/splunk/splunk-ao-python.git
   cd sdk-examples/python/agent/langchain-agent
   
2. Create a virtual environment: on Windows
   python -m venv venv
   venv\Scripts\activate
   
   on Mac Using a standard virtual environment
   python -m venv venv
   source venv/bin/activate
   
   Or using uv (faster)
   uv venv venv
   source venv/bin/activate
   
3. Install dependencies: Using pip
   pip install -r requirements.txt
   
   OR using uv
   uv pip install -r requirements.txt
   
4. Set up your environment variables: Copy the existing .env.example file, and rename it to .env in your project directory. Set your Splunk Agent Observability and OpenAI environment variables:
   .env

   # Provide your API key and deployment URL if you are
   # using an on-premises, standalone, or custom deployment
   # SPLUNK_AO_API_KEY="your-splunk-ao-api-key"
   # SPLUNK_AO_CONSOLE_URL="your-splunk-ao-url"
   
   # Your Splunk Agent Observability project name
   SPLUNK_AO_PROJECT="your-splunk-ao-project-name"
   
   # The name of the Agent Stream you want to use for logging
   SPLUNK_AO_AGENT_STREAM="your-splunk-ao-agent-stream"
   
   # OpenAI properties
   OPENAI_API_KEY="your-openai-api-key"
   
   # Optional. The base URL of your OpenAI deployment.
   # Leave this commented out if you are using the default OpenAI API.
   # OPENAI_BASE_URL="your-openai-base-url-here"
   
   # Optional. Your OpenAI organization.
   # OPENAI_ORGANIZATION="your-openai-organization-here"
   
   • Replace the values with your actual keys. This keeps your credentials secure and out of your code.

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Understand the agent architecture

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🧠 Agent core (main.py)

• Loading of secrets
• Tool declaration
• Agent instantiation
• Splunk Agent Observability

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🛠️ Tools

@tool
def greet(name: str) -> str:
    """Say hello to someone."""
    return f"Hello, {name}! 👋"

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🔍 Instrumentation (splunk_ao_context + SplunkAOCallback)

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Main agent workflow

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Key ingredients

• LangChain agent
• OpenAI model
• Splunk Agent Observability integration

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How it works

1. Load .env variables.
2. Declare tools.
3. Wrap agent execution in splunk_ao_context.
4. Use SplunkAOCallback to trace the run.
5. Print the agent’s response.

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Running the agent

python main.py

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Expected output

Agent Response:
Hello, Erin! 👋

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View traces in Splunk Agent Observability

1. Log into Splunk Agent Observability.
2. Open the langchain-docs project and my_agent_stream.
3. Inspect:
   • Prompts
   • Reasoning steps
   • Tool invocations
   • Outputs

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Extending the agent

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Add new tools

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Change models

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Update context

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Conclusion

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Key takeaways

• LangChain + Splunk Agent Observability makes AI agents traceable and observable
• Using tools and context managers helps modularize and organize agent behavior
• Monitoring enables better debugging and optimization

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Next steps

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Common issues and solutions

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API key issues

• Double-check your .env file

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Splunk Agent Observability connection issues

• Confirm your API key is valid
• Check internet connectivity
• Ensure flush() is being called at the end of execution