# Contributing to AutoGPT Agent Server: Creating and Testing Blocks

This guide will walk you through the process of creating and testing a new block for the AutoGPT Agent Server, using the WikipediaSummaryBlock as an example.

## Understanding Blocks and Testing

Blocks are reusable components that can be connected to form a graph representing an agent's behavior. Each block has inputs, outputs, and a specific function. Proper testing is crucial to ensure blocks work correctly and consistently.

## Creating and Testing a New Block

Follow these steps to create and test a new block:

1. **Create a new Python file** in the `autogpt_server/blocks` directory. Name it descriptively and use snake_case. For example: `get_wikipedia_summary.py`.

2. **Import necessary modules and create a class that inherits from `Block`**. Make sure to include all necessary imports for your block.

 Every block should contain the following:

   ```python
   from autogpt_server.data.block import Block, BlockSchema, BlockOutput
   ```

   Example for the Wikipedia summary block:

   ```python
   from autogpt_server.data.block import Block, BlockSchema, BlockOutput
   from autogpt_server.utils.get_request import GetRequest
   import requests

   class WikipediaSummaryBlock(Block, GetRequest):
       # Block implementation will go here
   ```

3. **Define the input and output schemas** using `BlockSchema`. These schemas specify the data structure that the block expects to receive (input) and produce (output).

   - The input schema defines the structure of the data the block will process. Each field in the schema represents a required piece of input data.
   - The output schema defines the structure of the data the block will return after processing. Each field in the schema represents a piece of output data.

   Example:

   ```python
   class Input(BlockSchema):
       topic: str  # The topic to get the Wikipedia summary for

   class Output(BlockSchema):
       summary: str  # The summary of the topic from Wikipedia
       error: str  # Any error message if the request fails
   ```

4. **Implement the `__init__` method, including test data and mocks:**

   ```python
   def __init__(self):
       super().__init__(
           # Unique ID for the block, used across users for templates
           # you can generate this with this python one liner
           # print(__import__('uuid').uuid4())
           id="h5e7f8g9-1b2c-3d4e-5f6g-7h8i9j0k1l2m",
           input_schema=WikipediaSummaryBlock.Input,  # Assign input schema
           output_schema=WikipediaSummaryBlock.Output,  # Assign output schema

            # Provide sample input, output and test mock for testing the block

           test_input={"topic": "Artificial Intelligence"},
           test_output=("summary", "summary content"),
           test_mock={"get_request": lambda url, json: {"extract": "summary content"}},
       )
   ```

   - `id`: A unique identifier for the block.

   - `input_schema` and `output_schema`: Define the structure of the input and output data.

   Let's break down the testing components:

   - `test_input`: This is a sample input that will be used to test the block. It should be a valid input according to your Input schema.

   - `test_output`: This is the expected output when running the block with the `test_input`. It should match your Output schema. For non-deterministic outputs or when you only want to assert the type, you can use Python types instead of specific values. In this example, `("summary", str)` asserts that the output key is "summary" and its value is a string.

   - `test_mock`: This is crucial for blocks that make network calls. It provides a mock function that replaces the actual network call during testing. 

     In this case, we're mocking the `get_request` method to always return a dictionary with an 'extract' key, simulating a successful API response. This allows us to test the block's logic without making actual network requests, which could be slow, unreliable, or rate-limited.

5. **Implement the `run` method with error handling:**, this should contain the main logic of the block:

   ```python
   def run(self, input_data: Input) -> BlockOutput:
       try:
           topic = input_data.topic
           url = f"https://en.wikipedia.org/api/rest_v1/page/summary/{topic}"

           response = self.get_request(url, json=True)
           yield "summary", response['extract']

       except requests.exceptions.HTTPError as http_err:
           yield "error", f"HTTP error occurred: {http_err}"
       except requests.RequestException as e:
           yield "error", f"Request to Wikipedia failed: {e}"
       except KeyError as e:
           yield "error", f"Error parsing Wikipedia response: {e}"
   ```

   - **Try block**: Contains the main logic to fetch and process the Wikipedia summary.
   - **API request**: Send a GET request to the Wikipedia API.
   - **Error handling**: Handle various exceptions that might occur during the API request and data processing.
   - **Yield**: Use `yield` to output the results.

## Key Points to Remember

- **Unique ID**: Give your block a unique ID in the **init** method.
- **Input and Output Schemas**: Define clear input and output schemas.
- **Error Handling**: Implement error handling in the `run` method.
- **Output Results**: Use `yield` to output results in the `run` method.
- **Testing**: Provide test input and output in the **init** method for automatic testing.

## Understanding the Testing Process

The testing of blocks is handled by `test_block.py`, which does the following:

1. It calls the block with the provided `test_input`.
2. If a `test_mock` is provided, it temporarily replaces the specified methods with the mock functions.
3. It then asserts that the output matches the `test_output`.

For the WikipediaSummaryBlock:

- The test will call the block with the topic "Artificial Intelligence".
- Instead of making a real API call, it will use the mock function, which returns `{"extract": "summary content"}`.
- It will then check if the output key is "summary" and its value is a string.

This approach allows us to test the block's logic comprehensively without relying on external services, while also accommodating non-deterministic outputs.

## Tips for Effective Block Testing

1. **Provide realistic test_input**: Ensure your test input covers typical use cases.

2. **Define appropriate test_output**: 
   - For deterministic outputs, use specific expected values.
   - For non-deterministic outputs or when only the type matters, use Python types (e.g., `str`, `int`, `dict`).
   - You can mix specific values and types, e.g., `("key1", str), ("key2", 42)`.

3. **Use test_mock for network calls**: This prevents tests from failing due to network issues or API changes.

4. **Consider omitting test_mock for blocks without external dependencies**: If your block doesn't make network calls or use external resources, you might not need a mock.

5. **Consider edge cases**: Include tests for potential error conditions in your `run` method.

6. **Update tests when changing block behavior**: If you modify your block, ensure the tests are updated accordingly.

By following these steps, you can create new blocks that extend the functionality of the AutoGPT Agent Server.

## Blocks we want to see

Below is a list of blocks that we would like to see implemented in the AutoGPT Agent Server. If you're interested in contributing, feel free to pick one of these blocks or suggest your own by editing [docs/content/server/new_blocks.md](https://github.com/Significant-Gravitas/AutoGPT/edit/master/docs/content/server/new_blocks.md) and opening a pull request.

If you would like to implement one of these blocks, open a pull request and we will start the review process.

### Consumer Services/Platforms

- Google sheets - Read/Append [Read in Progress](https://github.com/Significant-Gravitas/AutoGPT/pull/7521)
- Email - Read/Send with Gmail, Outlook, Yahoo, Proton, etc
- Calendar - Read/Write with Google Calendar, Outlook Calendar, etc
- Home Assistant - Call Service, Get Status
- Dominos - Order Pizza, Track Order
- Uber - Book Ride, Track Ride
- Notion - Create/Read Page, Create/Append/Read DB
- Google drive - read/write/overwrite file/folder

### Social Media

- Twitter - Post, Reply, Get Replies, Get Comments, Get Followers, Get Following, Get Tweets, Get Mentions
- Instagram - Post, Reply, Get Comments, Get Followers, Get Following, Get Posts, Get Mentions, Get Trending Posts
- TikTok - Post, Reply, Get Comments, Get Followers, Get Following, Get Videos, Get Mentions, Get Trending Videos
- LinkedIn - Post, Reply, Get Comments, Get Followers, Get Following, Get Posts, Get Mentions, Get Trending Posts
- YouTube - Transcribe Videos/Shorts, Post Videos/Shorts, Read/Reply/React to Comments, Update Thumbnails, Update Description, Update Tags, Update Titles, Get Views, Get Likes, Get Dislikes, Get Subscribers, Get Comments, Get Shares, Get Watch Time, Get Revenue, Get Trending Videos, Get Top Videos, Get Top Channels
- Reddit - Post, Reply, Get Comments, Get Followers, Get Following, Get Posts, Get Mentions, Get Trending Posts
- Treatwell (and related Platforms) - Book, Cancel, Review, Get Recommendations
- Substack - Read/Subscribe/Unsubscribe, Post/Reply, Get Recommendations
- Discord - Read/Post/Reply, Moderation actions
- GoodReads - Read/Post/Reply, Get Recommendations

### E-commerce

- Airbnb - Book, Cancel, Review, Get Recommendations
- Amazon - Order, Track Order, Return, Review, Get Recommendations
- eBay - Order, Track Order, Return, Review, Get Recommendations
- Upwork - Post Jobs, Hire Freelancer, Review Freelancer, Fire Freelancer

### Business Tools

- External Agents - Call other agents similar to AutoGPT
- Trello - Create/Read/Update/Delete Cards, Lists, Boards
- Jira - Create/Read/Update/Delete Issues, Projects, Boards
- Linear - Create/Read/Update/Delete Issues, Projects, Boards
- Excel - Read/Write/Update/Delete Rows, Columns, Sheets
- Slack - Read/Post/Reply to Messages, Create Channels, Invite Users
- ERPNext - Create/Read/Update/Delete Invoices, Orders, Customers, Products
- Salesforce - Create/Read/Update/Delete Leads, Opportunities, Accounts
- HubSpot - Create/Read/Update/Delete Contacts, Deals, Companies
- Zendesk - Create/Read/Update/Delete Tickets, Users, Organizations
- Odoo - Create/Read/Update/Delete Sales Orders, Invoices, Customers
- Shopify - Create/Read/Update/Delete Products, Orders, Customers
- WooCommerce - Create/Read/Update/Delete Products, Orders, Customers
- Squarespace - Create/Read/Update/Delete Pages, Products, Orders

## Agent Templates we want to see


### Data/Information

- Summarize top news of today, of this week, this month via Apple News or other large media outlets BBC, TechCrunch, hackernews, etc
- Create, read, and summarize substack newsletters or any newsletters (blog writer vs blog reader)
- Get/read/summarize the most viral Twitter, Instagram, TikTok (general social media accounts) of the day, week, month
- Get/Read any LinkedIn posts or profile that mention AI Agents
- Read/Summarize discord (might not be able to do this because you need access)
- Read / Get most read books in a given month, year, etc from GoodReads or Amazon Books, etc
- Get dates for specific shows across all streaming services
  - Suggest/Recommend/Get most watched shows in a given month, year, etc across all streaming platforms
- Data analysis from xlsx data set
   - Gather via Excel or Google Sheets data > Sample the data randomly (sample block takes top X, bottom X, randomly, etc) > pass that to LLM Block to generate a script for analysis of the full data > Python block to run the script> making a loop back through LLM Fix Block on error > create chart/visualization (potentially in the code block?) > show the image as output (this may require frontend changes to show)
- Tiktok video search and download

### Marketing 

- Portfolio site design and enhancements