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An Agno agent can double as a launch strategist—fetching Product Hunt rankings, answering questions, and firing confetti when it is time to celebrate.

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What You’ll Build

  • A FastAPI service that wraps an Agno agent with Product Hunt tools.
  • Tooling for Algolia search, GraphQL leaderboards, natural timeframe parsing, and confetti payloads.
  • /api/chat and /stream endpoints that share CometChat-compatible newline-delimited JSON payloads.
  • Optional Product Hunt GraphQL integration (falls back gracefully when no token is provided).

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Prerequisites

  • Python 3.10 or newer.
  • OPENAI_API_KEY with GPT-4o (or similar) access.
  • Optional PRODUCTHUNT_API_TOKEN for live leaderboard queries.
  • curl or an API client to verify endpoints.

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How it works

  • Agent — create_product_hunt_agent configures an OpenAIChat model, system prompt, and five tools (getTopProducts, getTopProductsThisWeek, getTopProductsByTimeframe, searchProducts, triggerConfetti).
  • Data — services.py wraps Product Hunt’s GraphQL API (with token), Algolia search, timezone-aware timeframe parsing, and safe fallbacks when the API token is missing.
  • API — FastAPI routes in main.py expose REST endpoints for ranked lists and search, plus /api/chat and /stream for conversations and newline-delimited event streaming.
  • Parity — The streaming payload mirrors CometChat’s Bring Your Own Agent format (text_delta, tool_*, text_done, done, error), so existing UI logic can be reused.

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Setup

1

Clone & install

From your workspace: git clone https://github.com/cometchat/ai-agent-agno-examples.git. Inside the repo: python3 -m venv .venv && source .venv/bin/activate && pip install -e .
2

Configure environment

Create .env with OPENAI_API_KEY. Add PRODUCTHUNT_API_TOKEN for GraphQL access. Optional knobs: PRODUCTHUNT_DEFAULT_TIMEZONE, OPENAI_BASE_URL, PRODUCT_OPENAI_MODEL.
3

Run the server

Start FastAPI: uvicorn product_hunt_agent.main:app —host 0.0.0.0 —port 8001 —reload. Health check: GET /healthz; streaming lives at POST /stream.

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Project Structure

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Step 1 - Configure Settings

ProductHuntSettings centralizes configuration (API keys, timeouts, default timezone). The FastAPI app loads it once and injects it via Depends, so each route reuses the same validated settings.

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Step 2 - Understand the Agent Tools

agent_builder.py clamps incoming arguments, calls service helpers, and returns structured dictionaries the UI can render. Highlights:
  • getTopProducts — All-time votes.
  • getTopProductsThisWeek — Rolling window controlled by days.
  • getTopProductsByTimeframe — Natural language inputs such as yesterday, last-week, or ISO dates.
  • searchProducts — Uses Product Hunt’s public Algolia index (no token required).
  • triggerConfetti — Returns payloads that UI Kit Builder variants can pass to your frontend celebration handler.
The system prompt instructs the agent to cite sources and explain data gaps whenever a tool returns empty results.

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Step 3 - Product Hunt Data Services

services.py wraps the external APIs with resilient defaults:
  • parse_timeframe translates natural strings into UTC ranges using pendulum.
  • fetch_graphql only runs when PRODUCTHUNT_API_TOKEN is present; otherwise, helper functions return empty lists so the agent can respond gracefully.
  • search_products hits the public Algolia index (Post_production) with rate-limit friendly defaults.

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Step 4 - Test the API

List top launches from the past week: 
curl "http://localhost:8001/api/top-week?limit=5&days=7"
Ask the agent a question (non-streaming): 
curl -X POST http://localhost:8001/api/chat \
  -H "Content-Type: application/json" \
  -d '{
        "messages": [
          { "role": "user", "content": "What should I highlight in my launch post?" }
        ]
      }'
Stream responses (newline-delimited JSON): 
curl -N http://localhost:8001/stream \
  -H "Content-Type: application/json" \
  -d '{
        "thread_id": "launch-room",
        "run_id": "run-457",
        "messages": [
          { "role": "user", "content": "Show me the top launches last week." }
        ]
      }'
Each line is a JSON object with type (e.g., text_delta, tool_call_start, tool_result, text_done, done) plus the echoed thread_id and run_id.

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Step 5 - Connect to CometChat

  • Deploy the service behind HTTPS and protect it with auth headers (use the agent’s Headers field when registering in CometChat).
  • Point the Agno agent variant at your /stream endpoint; reuse the same Agent ID from UI Kit Builder.
  • When the agent triggers triggerConfetti, listen for the tool event in your frontend to launch celebratory animations.
  • Echo thread_id and run_id in every streamed line so CometChat can correlate partial tool events and message chunks.
You now have an Agno Product Hunt agent ready to ship inside CometChat-powered experiences.