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Adding Agent Tools to Duckie

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Steps Overview

Agent tools enable Duckie to connect to internal systems like error logs, dashboards, databases, and more to perform troubleshooting, query systems, and take actions on behalf of a human agent, helping to resolve more complex tickets.

Here’s a loom video on the setup: Loom Video

Customers can create API wrappers for Duckie to connect and query internal systems:

  1. Create an API – 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.

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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