src/backend/agents/example/info.md

How to Run the LangGraph Reasoning Monitoring Demo Agent

1. Make sure to have the follwijg installed

pip install -r requriements/dev.txt

2. Set TAVILY_API_KEY:

• link: https://www.tavily.com

3. Run the following from repo root:

export PYTHONPATH=./src
langgraph dev

This loads the root-level langgraph.json and makes all agents available in LangGraph Studio.

4 Open the Studio UI After the server starts, open:

https://smith.langchain.com/studio/?baseUrl=http://127.0.0.1:2024

NOTE: Open it in anything, but safari!

Select the agent named react_agent (or whichever your config specifies).

---

Demo Prompt to Use

Paste the following into the Studio console:

First search for the current temperature in Fahrenheit in Cape Town, South Africa.
Then convert that temperature to Celsius using the conversion tool.

This triggers:

1. A Tavily search for the current Fahrenheit temperature
2. A tool call to convert Fahrenheit → Celsius
3. Full ReAct reasoning + tool trace in the UI

---

⚙️ Multiple Agents in langgraph.json

You can expose multiple agents to LangGraph Studio by listing them under the graphs section of your root langgraph.json.

Example:

{
  "dependencies": ["src"],
  "graphs": {
    "react_agent": "agents.example.react_agent:agent",
    "cv_screener": "agents.cv_screening.screener:agent",
    "supervisor": "agents.supervisor.supervisor:agent"
  }
}

Each entry maps:

"graph_name": "module.path:object_name"

Where:

• graph_name → appears in LangGraph Studio
• module.path → Python import path under src/
• object_name → the variable that contains the graph/agent This allows one project to host many agents simultaneously (e.g., supervisor, tools agent, CV-screening agent, etc.).