# 💻🔗 LangCode
âš¡ Build code-executing autonomous agents âš¡
## Documentation
💻🔗 LangCode is a library which provides easy-to-use and reliable interface to a Python code-execution environment, such as Jupyter. LangCode can be used to build autonomous code-executing agents. LangCode supports text, image and code output of Jupyter. Also remote connection feature and jupyter server launcher is planned for the future versions.
### Example
```bash
pip install langcode
```
`INTERESTING`: You can find `claude_agent.ipynb` in `examples` which demonstrates a real-world application of LangCode using Claude 3.5 Sonnet
```python
from langcode.jupyter import Jupyter, ExecutionEvent, ExecutionResult
def process_execution_event(x: ExecutionEvent) -> None:
...
jupyter = Jupyter.local(
env="...", # Set the execution environment.
timeout=None, # Set global timeout (can be overriden later).
event_handler: lambda x: process_execution_event(x) # Pass event handler if you need.
)
result: ExecutionResult = jupyter.run_cell(code="x = 10; x", timeout=None) # Final result.
for event in jupyter.stream_cell(code="import time; time.sleep(5)", timeout=1000): # Or stream events in real time.
event: ExecutionEvent
# This will raise TimeoutError after 1 second.
jupyter.restart() # You can also restart kernel to clear the cell state.
jupyter.close() # Close the notebook and Jupyter kernel.
```
You can also get images outputted by Jupyter kernel:
```python
code = """
import matplotlib.pyplot as plt
import numpy as np
x = np.linspace(0, 2 * np.pi, 200)
y = np.sin(x)
fig, ax = plt.subplots()
ax.plot(x, y)
ax.set(xlabel='x', ylabel='sin(x)',
title='A simple plot')
plt.show()
"""
result = jupyter.run_cell(code)
result.images
```
```python
[Base64ImageString(content_format='png', content='iVBORw0KGg...')]
```
You can also get images in `ExecutionEvent` data transfer object when you use `stream_cell`.
Also you can stop the execution in the process if you need:
```python
jupyter.stop_execution()
```
Here are the data transfer objects which represent execution results:
```python
@dataclass
class ExecutionEvent:
"""
An execution event which can be outputted in real time into user UI iteratively.
"""
msg_type: Literal["stream", "error", "display_data", "execute_result"]
content_type: Literal["console", "image", "code"]
content_format: Literal["output", "base64/png", "base64/jpeg", "html", "javascript"]
content: str
@dataclass
class Base64ImageString:
"""Represents an image in `base64` form, either `png` or `jpeg`."""
content_format: Literal["png", "jpeg"]
content: str
@dataclass
class ExecutionResult:
"""
Final result of code execution inside Jupyter notebook.
"""
events: List[ExecutionEvent]
"""List of all `ExecutionEvent` events outputted during execution."""
error: bool
"""Signals whether an error has occured during execution, `True` if error occured."""
text: str
"""Final text, excluding images, which can be injected into LLM."""
images: List[ImageString]
"""Final list of `base64` images outputted during execution, can be injected into LLM."""
```
Got inspired and borrowed the code from [Open Interpreter](https://github.com/OpenInterpreter/open-interpreter) and [E2B Code Interpreter](https://github.com/e2b-dev/code-interpreter)
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"description": "# \ud83d\udcbb\ud83d\udd17 LangCode\n\n\u26a1 Build code-executing autonomous agents \u26a1\n\n## Documentation\n\n\ud83d\udcbb\ud83d\udd17 LangCode is a library which provides easy-to-use and reliable interface to a Python code-execution environment, such as Jupyter. LangCode can be used to build autonomous code-executing agents. LangCode supports text, image and code output of Jupyter. Also remote connection feature and jupyter server launcher is planned for the future versions.\n\n### Example\n\n```bash\npip install langcode\n```\n\n`INTERESTING`: You can find `claude_agent.ipynb` in `examples` which demonstrates a real-world application of LangCode using Claude 3.5 Sonnet\n\n```python\nfrom langcode.jupyter import Jupyter, ExecutionEvent, ExecutionResult\n\ndef process_execution_event(x: ExecutionEvent) -> None:\n ...\n\njupyter = Jupyter.local(\n env=\"...\", # Set the execution environment.\n timeout=None, # Set global timeout (can be overriden later).\n event_handler: lambda x: process_execution_event(x) # Pass event handler if you need.\n)\n\nresult: ExecutionResult = jupyter.run_cell(code=\"x = 10; x\", timeout=None) # Final result.\n\nfor event in jupyter.stream_cell(code=\"import time; time.sleep(5)\", timeout=1000): # Or stream events in real time.\n event: ExecutionEvent\n\n # This will raise TimeoutError after 1 second.\n\njupyter.restart() # You can also restart kernel to clear the cell state.\n\njupyter.close() # Close the notebook and Jupyter kernel.\n```\n\nYou can also get images outputted by Jupyter kernel:\n\n```python\ncode = \"\"\"\nimport matplotlib.pyplot as plt\nimport numpy as np\n\nx = np.linspace(0, 2 * np.pi, 200)\ny = np.sin(x)\n\nfig, ax = plt.subplots()\nax.plot(x, y)\nax.set(xlabel='x', ylabel='sin(x)',\n title='A simple plot')\n\nplt.show()\n\n\"\"\"\n\nresult = jupyter.run_cell(code)\n\nresult.images\n```\n\n```python\n[Base64ImageString(content_format='png', content='iVBORw0KGg...')]\n```\n\nYou can also get images in `ExecutionEvent` data transfer object when you use `stream_cell`.\n\nAlso you can stop the execution in the process if you need:\n\n```python\njupyter.stop_execution()\n```\n\nHere are the data transfer objects which represent execution results:\n\n```python\n@dataclass\nclass ExecutionEvent:\n \"\"\"\n An execution event which can be outputted in real time into user UI iteratively.\n \"\"\"\n\n msg_type: Literal[\"stream\", \"error\", \"display_data\", \"execute_result\"]\n content_type: Literal[\"console\", \"image\", \"code\"]\n content_format: Literal[\"output\", \"base64/png\", \"base64/jpeg\", \"html\", \"javascript\"]\n content: str\n\n\n@dataclass\nclass Base64ImageString:\n \"\"\"Represents an image in `base64` form, either `png` or `jpeg`.\"\"\"\n\n content_format: Literal[\"png\", \"jpeg\"]\n content: str\n\n\n@dataclass\nclass ExecutionResult:\n \"\"\"\n Final result of code execution inside Jupyter notebook.\n \"\"\"\n\n events: List[ExecutionEvent]\n \"\"\"List of all `ExecutionEvent` events outputted during execution.\"\"\"\n\n error: bool\n \"\"\"Signals whether an error has occured during execution, `True` if error occured.\"\"\"\n\n text: str\n \"\"\"Final text, excluding images, which can be injected into LLM.\"\"\"\n\n images: List[ImageString]\n \"\"\"Final list of `base64` images outputted during execution, can be injected into LLM.\"\"\"\n```\n\nGot inspired and borrowed the code from [Open Interpreter](https://github.com/OpenInterpreter/open-interpreter) and [E2B Code Interpreter](https://github.com/e2b-dev/code-interpreter)\n",
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