# feat(backend/blocks): add ConcatenateListsBlock
## Description
This PR implements a new block `ConcatenateListsBlock` that concatenates
multiple lists into a single list. This addresses the "good first issue"
for implementing a list concatenation block in the platform/blocks area.
The block takes a list of lists as input and combines all elements in
order into a single concatenated list. This is useful for workflows that
need to merge data from multiple sources or combine results from
different operations.
### Changes 🏗️
- **Added `ConcatenateListsBlock` class** in
`autogpt_platform/backend/backend/blocks/data_manipulation.py`
- Input: `lists: List[List[Any]]` - accepts a list of lists to
concatenate
- Output: `concatenated_list: List[Any]` - returns a single concatenated
list
- Error output: `error: str` - provides clear error messages for invalid
input types
- Block ID: `3cf9298b-5817-4141-9d80-7c2cc5199c8e`
- Category: `BlockCategory.BASIC` (consistent with other list
manipulation blocks)
- **Added comprehensive test suite** in
`autogpt_platform/backend/test/blocks/test_concatenate_lists.py`
- Tests using built-in `test_input`/`test_output` validation
- Manual test cases covering edge cases (empty lists, single list, empty
input)
- Error handling tests for invalid input types
- Category consistency verification
- All tests passing
- **Implementation details:**
- Uses `extend()` method for efficient list concatenation
- Preserves element order from all input lists
- **Runtime type validation**: Explicitly checks `isinstance(lst, list)`
before calling `extend()` to prevent:
- Strings being iterated character-by-character (e.g., `extend("abc")` →
`['a', 'b', 'c']`)
- Non-iterable types causing `TypeError` (e.g., `extend(1)`)
- Clear error messages indicating which index has invalid input
- Handles edge cases: empty lists, empty input, single list, None values
- Follows existing block patterns and conventions
### Checklist 📋
#### For code changes:
- [x] I have clearly listed my changes in the PR description
- [x] I have made a test plan
- [x] I have tested my changes according to the test plan:
- [x] Run `poetry run pytest test/blocks/test_concatenate_lists.py -v` -
all tests pass
- [x] Verified block can be imported and instantiated
- [x] Tested with built-in test cases (4 test scenarios)
- [x] Tested manual edge cases (empty lists, single list, empty input)
- [x] Tested error handling for invalid input types
- [x] Verified category is `BASIC` for consistency
- [x] Verified no linting errors
- [x] Confirmed block follows same patterns as other blocks in
`data_manipulation.py`
#### Code Quality:
- [x] Code follows existing patterns and conventions
- [x] Type hints are properly used
- [x] Documentation strings are clear and descriptive
- [x] Runtime type validation implemented
- [x] Error handling with clear error messages
- [x] No linting errors
- [x] Prisma client generated successfully
### Testing
**Test Results:**
```
test/blocks/test_concatenate_lists.py::test_concatenate_lists_block_builtin_tests PASSED
test/blocks/test_concatenate_lists.py::test_concatenate_lists_manual PASSED
============================== 2 passed in 8.35s ==============================
```
**Test Coverage:**
- Basic concatenation: `[[1, 2, 3], [4, 5, 6]]` → `[1, 2, 3, 4, 5, 6]`
- Mixed types: `[["a", "b"], ["c"], ["d", "e", "f"]]` → `["a", "b", "c",
"d", "e", "f"]`
- Empty list handling: `[[1, 2], []]` → `[1, 2]`
- Empty input: `[]` → `[]`
- Single list: `[[1, 2, 3]]` → `[1, 2, 3]`
- Error handling: Invalid input types (strings, non-lists) produce clear
error messages
- Category verification: Confirmed `BlockCategory.BASIC` for consistency
### Review Feedback Addressed
- **Category Consistency**: Changed from `BlockCategory.DATA` to
`BlockCategory.BASIC` to match other list manipulation blocks
(`AddToListBlock`, `FindInListBlock`, etc.)
- **Type Robustness**: Added explicit runtime validation with
`isinstance(lst, list)` check before calling `extend()` to prevent:
- Strings being iterated character-by-character
- Non-iterable types causing `TypeError`
- **Error Handling**: Added `error` output field with clear, descriptive
error messages indicating which index has invalid input
- **Test Coverage**: Added test case for error handling with invalid
input types
### Related Issues
- Addresses: "Implement block to concatenate lists" (good first issue,
platform/blocks, hacktoberfest)
### Notes
- This is a straightforward data manipulation block that doesn't require
external dependencies
- The block will be automatically discovered by the block loading system
- No database or configuration changes required
- Compatible with existing workflow system
- All review feedback has been addressed and incorporated
<!-- CURSOR_SUMMARY -->
---
> [!NOTE]
> Adds a new list utility and updates docs.
>
> - **New block**: `ConcatenateListsBlock` in
`backend/blocks/data_manipulation.py`
> - Input `lists: List[List[Any]]`; outputs `concatenated_list` or
`error`
> - Skips `None` entries; emits error for non-list items; preserves
order
> - **Docs**: Adds "Concatenate Lists" section to
`docs/integrations/basic.md` and links it in
`docs/integrations/README.md`
> - **Contributor guide**: New `docs/CLAUDE.md` with manual doc section
guidelines
>
> <sup>Written by [Cursor
Bugbot](https://cursor.com/dashboard?tab=bugbot) for commit
4f56dd86c2. This will update automatically
on new commits. Configure
[here](https://cursor.com/dashboard?tab=bugbot).</sup>
<!-- /CURSOR_SUMMARY -->
---------
Co-authored-by: Zamil Majdy <zamil.majdy@agpt.co>
Co-authored-by: Nicholas Tindle <nicholas.tindle@agpt.co>
Co-authored-by: Claude Opus 4.5 <noreply@anthropic.com>
AutoGPT Platform
Welcome to the AutoGPT Platform - a powerful system for creating and running AI agents to solve business problems. This platform enables you to harness the power of artificial intelligence to automate tasks, analyze data, and generate insights for your organization.
Getting Started
Prerequisites
- Docker
- Docker Compose V2 (comes with Docker Desktop, or can be installed separately)
Running the System
To run the AutoGPT Platform, follow these steps:
-
Clone this repository to your local machine and navigate to the
autogpt_platformdirectory within the repository:git clone <https://github.com/Significant-Gravitas/AutoGPT.git | git@github.com:Significant-Gravitas/AutoGPT.git> cd AutoGPT/autogpt_platform -
Run the following command:
cp .env.default .envThis command will copy the
.env.defaultfile to.env. You can modify the.envfile to add your own environment variables. -
Run the following command:
docker compose up -dThis command will start all the necessary backend services defined in the
docker-compose.ymlfile in detached mode. -
After all the services are in ready state, open your browser and navigate to
http://localhost:3000to access the AutoGPT Platform frontend.
Running Just Core services
You can now run the following to enable just the core services.
# For help
make help
# Run just Supabase + Redis + RabbitMQ
make start-core
# Stop core services
make stop-core
# View logs from core services
make logs-core
# Run formatting and linting for backend and frontend
make format
# Run migrations for backend database
make migrate
# Run backend server
make run-backend
# Run frontend development server
make run-frontend
Docker Compose Commands
Here are some useful Docker Compose commands for managing your AutoGPT Platform:
docker compose up -d: Start the services in detached mode.docker compose stop: Stop the running services without removing them.docker compose rm: Remove stopped service containers.docker compose build: Build or rebuild services.docker compose down: Stop and remove containers, networks, and volumes.docker compose watch: Watch for changes in your services and automatically update them.
Sample Scenarios
Here are some common scenarios where you might use multiple Docker Compose commands:
-
Updating and restarting a specific service:
docker compose build api_srv docker compose up -d --no-deps api_srvThis rebuilds the
api_srvservice and restarts it without affecting other services. -
Viewing logs for troubleshooting:
docker compose logs -f api_srv ws_srvThis shows and follows the logs for both
api_srvandws_srvservices. -
Scaling a service for increased load:
docker compose up -d --scale executor=3This scales the
executorservice to 3 instances to handle increased load. -
Stopping the entire system for maintenance:
docker compose stop docker compose rm -f docker compose pull docker compose up -dThis stops all services, removes containers, pulls the latest images, and restarts the system.
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Developing with live updates:
docker compose watchThis watches for changes in your code and automatically updates the relevant services.
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Checking the status of services:
docker compose psThis shows the current status of all services defined in your docker-compose.yml file.
These scenarios demonstrate how to use Docker Compose commands in combination to manage your AutoGPT Platform effectively.
Persisting Data
To persist data for PostgreSQL and Redis, you can modify the docker-compose.yml file to add volumes. Here's how:
-
Open the
docker-compose.ymlfile in a text editor. -
Add volume configurations for PostgreSQL and Redis services:
services: postgres: # ... other configurations ... volumes: - postgres_data:/var/lib/postgresql/data redis: # ... other configurations ... volumes: - redis_data:/data volumes: postgres_data: redis_data: -
Save the file and run
docker compose up -dto apply the changes.
This configuration will create named volumes for PostgreSQL and Redis, ensuring that your data persists across container restarts.
API Client Generation
The platform includes scripts for generating and managing the API client:
pnpm fetch:openapi: Fetches the OpenAPI specification from the backend service (requires backend to be running on port 8006)pnpm generate:api-client: Generates the TypeScript API client from the OpenAPI specification using Orvalpnpm generate:api: Runs both fetch and generate commands in sequence
Manual API Client Updates
If you need to update the API client after making changes to the backend API:
-
Ensure the backend services are running:
docker compose up -d -
Generate the updated API client:
pnpm generate:api
This will fetch the latest OpenAPI specification and regenerate the TypeScript client code.