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What are Agent Tools?

Agent Tools let you define specific actions and functions for your Duckie agents to perform. They are API endpoints that can be embedded into the Duckie response process to perform company-specific tasks.

Common tools include connecting to internal logging systems, gathering user data from dashboards and databasaes, creating engineering tickets, and more.

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Adding Agent Tools to Duckie

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Steps

To create an Agne Tool:

  1. Create an API endpoing – Define endpoints for data retrieval and processing (i.e. using FastAPI).
  2. Deploy the API – Use Docker, Railway, Fly.io, or AWS to make it publicly accessible.
  3. Add it to Duckie’s Agent Tool – Input the APIs to Duckie’s agent tools, and give it detailed description on when this API/tool shall be invoked.

For a live demonstration of creating a new Agent Tool, see the following Loom Video

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Example: Build and Deploy an API Wrapper for Datadog

  1. Create the API wrappers
    1. Retrieve a container ID using a caller_id.
    2. Fetch logs based on the container ID, filtering with specific keywords.
  2. Deploy the API for external use and expose it publicly.

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1. Setup and Prerequisites

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Install Dependencies

Ensure you have Python installed, then install the required packages:

pip install fastapi uvicorn requests datadog-api-client

### Datadog API Credentials

Obtain your **Datadog API Key** and **Application Key** from the [Datadog API Key Management](https://app.datadoghq.com/organization-settings/api-keys).

## 2. Writing the API Wrapper

**Please note that these are templates for references only, the logic will need to be modified for your particular use case.** 

Create a file `api.py`:

```python
from fastapi import FastAPI, HTTPException, Header, Query
import requests

app = FastAPI()

DATADOG_API_KEY = "your_api_key"
DATADOG_APP_KEY = "your_app_key"
DATADOG_LOGS_URL = "https://api.datadoghq.com/api/v2/logs/events/search"

# Step 1: Retrieve container ID
@app.get("/get-container")
def get_container_id(caller_id: str, x_api_key: str = Header(None)):
    response = requests.get(DATADOG_LOGS_URL, headers={
        "DD-API-KEY": DATADOG_API_KEY,
        "DD-APPLICATION-KEY": DATADOG_APP_KEY
    }, params={
        "query": f"caller_id:{caller_id}",
        "sort": "desc"
    })

    logs = response.json().get("data", [])
    if not logs:
        raise HTTPException(status_code=404, detail="No logs found for caller_id")

    container_id = logs[0].get("attributes", {}).get("container_id", "Unknown")
    return {"container_id": container_id}

# Step 2: Fetch logs with filters
@app.get("/get-logs")
def get_logs(container_id: str, tool_keyword: str = Query(None), x_api_key: str = Header(None)):
    response = requests.get(DATADOG_LOGS_URL, headers={
        "DD-API-KEY": DATADOG_API_KEY,
        "DD-APPLICATION-KEY": DATADOG_APP_KEY
    }, params={
        "query": f"container_id:{container_id} {tool_keyword if tool_keyword else ''}",
        "sort": "desc"
    })

    logs = response.json().get("data", [])
    return {"logs": logs}

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3. Deploying the API

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Option 1: Deploy with Docker

Create a Dockerfile:

FROM python:3.9
WORKDIR /app
COPY . /app
RUN pip install fastapi uvicorn requests datadog-api-client
CMD ["uvicorn", "api:app", "--host", "0.0.0.0", "--port", "8000"]

Build and run locally:

docker build -t datadog-api-wrapper .
docker run -p 8000:8000 datadog-api-wrapper

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Option 2: Deploy on Railway or Fly.io (Easiest)

For Railway:

railway init
railway up

For Fly.io:

fly launch
fly deploy

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Option 3: Deploy on AWS with API Gateway for Public Access

  1. Deploy FastAPI as an AWS Lambda function with API Gateway.
  2. Use Mangum to convert FastAPI into an AWS Lambda-compatible app.
  3. Open API Gateway settings and enable public access.
pip install mangum

Modify api.py:

from mangum import Mangum
handler = Mangum(app)

Then deploy using AWS SAM or Terraform.

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4. Add it to Duckie’s Agent Tool

Next, you will input the APIs to Duckie’s agent tools, and give it detailed description on when this API/tool shall be invoked.

Here’s a loom video on the setup: https://www.loom.com/share/d189881c1dc34b388697ae60e6948134?sid=acdefd34-59d2-4cf0-a57f-028754d21b9a