---
# System prepended metadata

title: Building a Developer Community Management Tool with AI Agents

---

# Building a Developer Community Management Tool with AI Agents

Managing a developer community can be a challenging task. From onboarding new members to fostering engagement and building partnerships, there’s a lot to keep track of. What if you could automate some of these tasks using AI? That’s exactly what we’ve built with the **Developer Community Agents** app—a Streamlit-based tool powered by AI agents to help you manage and grow your developer community.

In this blog post, I’ll walk you through how this app works, how we used **Phidata’s OpenAILike function** to integrate with Gaia’s API, and how you can extend this tool to suit your needs. Let’s dive in!

## What Does the App Do?

The **Developer Community Agents** app is designed to help community managers, developer advocates, and community leads streamline their workflows. It uses four AI agents, each with a specific role:

1. **Developer Relations Agent:** Creates tutorials, SDKs, and sample projects to educate developers.
2. **Community Manager Agent:** Onboards new members, facilitates discussions, and moderates community spaces.
3. **Partnerships Manager Agent:** Identifies and manages partnerships with companies, universities, and influencers.
4. **Community Lead Agent:** Strategizes the vision and direction of the community’s growth and initiatives.

Users can input a topic (e.g., “Increase Discord Engagement” or “Design a Community Roadmap”), and the app generates a detailed response. The response can also be exported as a PDF for easy sharing and documentation.

## How Does It Work?

The app is built using Streamlit, a popular framework for creating web apps with Python. It leverages Phidata’s Agent framework to create and manage the AI agents. Here’s a breakdown of the key components:

### 1. Using Phidata’s OpenAILike Function
The app uses Phidata’s `OpenAILike` function to interact with Gaia’s API. Gaia provides a powerful language model that can generate human-like responses. Here’s how we set it up:

```python
from phi.model.openai.like import OpenAILike

# Create an agent using Gaia's API
devrel_agent = Agent(
    name="Developer Relations Agent",
    role="Educates developers about the platform via webinars, blogs, and tutorials.",
    model=OpenAILike(
        id="llama",
        api_key=st.session_state['gaia_api_key'],  # Gaia API key
        base_url="https://llama8b.gaia.domains/v1"  # Gaia's API endpoint
    ),
    instructions=[
        "Create engaging tutorials, SDKs, and sample projects for the given topic.",
        "Engage with the developer community through forums, Discord, GitHub, and other platforms.",
        "Create troubleshooting guides and ensure active communication channels.",
        "Respond with a detailed text response that includes all the necessary information.",
    ],
    show_tool_calls=False,
    markdown=True,
)
```

In this snippet:

We use `OpenAILike` to connect to Gaia’s API.
The `api_key` is provided by the user in the Streamlit sidebar.
The `base_url` points to Gaia’s API endpoint.

### 2. Combining Agents into a Team
The app combines all four agents into a single team using Phidata’s `Agent` framework. This allows the agents to collaborate and generate comprehensive responses:

```python
# Combine all agents into a team
developer_community_team = Agent(
    team=[devrel_agent, community_manager_agent, partnerships_manager_agent, community_lead_agent],
    instructions=[
        "Collaborate to generate a comprehensive response for the given topic.",
        "Ensure the response includes detailed information from all agents.",
        "Use clear and structured formatting.",
    ],
    show_tool_calls=False,
    markdown=True,
)
```
### 3. Generating Responses
When the user enters a topic and clicks “Start,” the app generates a response using the team of agents:

```python
if st.button("Start"):
    if not st.session_state['topic']:
        st.error("Please enter a topic.")
    else:
        with st.spinner("Generating Developer Community Resources..."):
            response = developer_community_team.run(
                f"the topic is: {st.session_state['topic']}",
                stream=False
            )
        
        # Store response in session state
        st.session_state['response'] = response.content
```
The response is displayed on the screen, and users can export it as a PDF.

## How to Use the App
Enter Your Gaia API Key:

When you launch the app, enter your Gaia API key in the sidebar. You can get a free API key from [Gaia](https://docs.gaianet.ai/getting-started/authentication).

1. Choose a Topic:
    - Enter a topic in the input field (e.g., “Increase Discord Engagement”) or click one of the predefined task buttons.
2. Generate a Response:
    - Click “Start” to generate a response from the team of agents.
3. Export as PDF:
    - Click “Export to PDF” to download the response as a PDF.

## Extending the App
This app is just the beginning! Here are some ideas for extending its functionality:

1. Integrate with Discord:
    - Use Composio.dev to connect the Community Manager Agent with Discord’s API. Automatically post engagement strategies or onboarding plans directly to your Discord server.
2. Google Docs Integration:
    - Save generated responses as Google Docs for easy collaboration.
3. GitHub Integration:
    - Connect the Developer Relations Agent with GitHub’s API to automatically create repositories or issues for sample projects and tutorials.
4. Slack Integration:
    - Send notifications or updates about community activities directly to your Slack channels.

## Try It Out!
You can find the full source code for this project on GitHub:
👉[ Developer Community Agents Repository](https://github.com/harishkotra/developer-community-agents)

Feel free to clone the repository, experiment with the code, and extend it to suit your needs.

Give it a try, and let me know what you think! 🚀