https://github.com/ghackenberg/agentbrick
Build and refine LDraw models using LangChain agents and LangGraph workflows.
https://github.com/ghackenberg/agentbrick
Last synced: 29 days ago
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Build and refine LDraw models using LangChain agents and LangGraph workflows.
- Host: GitHub
- URL: https://github.com/ghackenberg/agentbrick
- Owner: ghackenberg
- License: other
- Created: 2026-02-06T14:11:41.000Z (5 months ago)
- Default Branch: main
- Last Pushed: 2026-02-12T12:57:49.000Z (5 months ago)
- Last Synced: 2026-02-12T18:52:59.897Z (5 months ago)
- Language: Python
- Homepage:
- Size: 203 KB
- Stars: 1
- Watchers: 0
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- Changelog: CHANGELOG.md
- Contributing: CONTRIBUTING.md
- License: LICENSE.md
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README
# **AgentBrick** - From Chat to CAD
**AgentBrick** is an open-source framework designed to bridge the gap between natural language and the **LDraw** standard. By leveraging **LangChain agents** and **LangGraph workflows**, AgentBrick transforms user prompts into valid, structured LDraw models, allowing for iterative refinement through conversational AI.
## User guide
```python
from agentbrick.workflows import main_workflow
main_workflow.invoke({
"prompt": "House", # Initial model description
"size_x": 5, # Size of the target voxel grid in x direction
"size_y": 5, # Size of the target voxel grid in y direction
"size_z": 5 # Size of the target voxel grid in z direction
})
```
## Main workflow
### Step 1: Model Description
Based on the initial user input (including the size of the discrete voxel grid), the agent derives a detailed description of the target model.
```mermaid
flowchart LR
Promt["Prompt"] --> LLM
Size["Grid size (x, y, z)"] --> LLM
LLM --> Description["Detailed model description"]
LLM@{ shape: cloud }
```
### Step 2: Component Graph
Then, the agent extracts a list of logical components (i.e. groups of connected voxels) and their interfaces with other components (i.e. voxels).
**Step 2.1: Extract components**
```mermaid
flowchart LR
Promt["Prompt"] --> LLM
Size["Grid size (x, y, z)"] --> LLM
Description[Detailed model description] --> LLM
LLM --> Components[List of components]
LLM@{ shape: cloud }
```
**Step 2.2: Extract interfaces**
```mermaid
flowchart LR
Promt["Prompt"] --> LLM
Size["Grid size (x, y, z)"] --> LLM
Description[Detailed model description] --> LLM
Components[List of components] --> LLM
LLM --> Interfaces[List of interfaces]
LLM@{ shape: cloud }
```
**Step 2.3: Visualize components and interfaces**
### Step 3: Voxel Model
Finally, the agent iterates over the individual voxels of the voxel grid and determines the contents of each voxel (i.e. empty or assigned to component).
**Step 3.1: Define voxel model**
```mermaid
flowchart LR
subgraph X["Define voxel content"]
direction LR
Promt["Prompt"] --> LLM
Size["Grid size (x, y, z)"] --> LLM
Description[Detailed model description] --> LLM
Components[List of components] --> LLM
Interfaces[List of interfaces] --> LLM
Grid[Current voxel model] --> LLM
Cell["Next voxel coordinate (x, y, z)"] --> LLM
LLM --> Content["Voxel content (empty | component)"]
end
X --repeat--> X
LLM@{ shape: cloud }
style Cell white-space:nowrap;
style Content white-space:nowrap;
```
**Step 3.2: Visualize voxel model**
## Project structure
- [assets/](./assets/) - Project assets (icon, logo, social preview image, ...)
- [sources/](./sources/) - Source code
- [agentbrick/](./sources/agentbrick/) - AgentBrick library
- [agents/](./sources/agentbrick/agents/) - Agent package
- [middlewares.py](./sources/agentbrick/agents/middlewares.py) - Middleware definitions (e.g. `log_model_call`)
- [responses.py](./sources/agentbrick/agents/responses.py) - Response definitions (i.e. pydantic schema classes)
- [tools.py](./sources/agentbrick/agents/tools.py) - Tool definitions (e.g. `calculate_sum`)
- [\_\_init__.py](./sources/agentbrick/agents/__init__.py) - Agent definitions (e.g. `generate_description_agent`)
- [workflows/](./sources/agentbrick/workflows/) - Workflow package
- [states.py](./sources/agentbrick/workflows/states.py) - State definitions
- [\_\_init__.py](./sources/agentbrick/workflows/__init__.py) - Workflow definitions
- [models.py](./sources/agentbrick/models.py) - Model definitions (e.g. llama 3.2)
- [main.py](./sources/main.py) - Main program
- [requirements.txt](./requirements.txt) - Project dependencies
## Developer guide
Install dependencies
```sh
pip install -r requirements.txt
```
Linting
```sh
black .
```
Static type checking
```sh
pyright
```