Add File System Support via pyodide.FS

README.md

@@ -19,7 +19,6 @@ LangChain Sandbox provides a secure environment for executing untrusted Python c
 ## Limitations
 
 - **Latency**: There is a few seconds of latency when starting the sandbox per run
-- **File access**: Currently not supported. You will not be able to access the files written by the sandbox.
 - **Network requests**: If you need to make network requests please use `httpx.AsyncClient` instead of `requests`.
 
 ## 🚀 Quick Install
@@ -35,9 +34,6 @@ LangChain Sandbox provides a secure environment for executing untrusted Python c
 ## 💡 Example Usage
 
 
-> [!warning]
-> Use `alllow_net` to limit the network requests that can be made by the sandboxed code to avoid SSRF attacks
-> https://docs.deno.com/runtime/fundamentals/security/#network-access
 
 ```python
 from langchain_sandbox import PyodideSandbox
@@ -141,6 +137,49 @@ result = await agent.ainvoke(
 )
 ```
 
+### File System Support
+
+You can now attach files to the sandbox environment and perform data analysis:
+
+```python
+import asyncio
+
+from langchain_sandbox import PyodideSandboxTool
+from langgraph.prebuilt import create_react_agent
+
+# Define the sandbox tool with filesystem support
+sandbox_tool = PyodideSandboxTool(
+    enable_filesystem=True,
+    allow_net=True,
+)
+
+sales_data = """...csv_data"""
+
+sandbox_tool.attach_file("sales.csv", sales_data)
+
+# Create an agent with the sandbox tool
+agent = create_react_agent(
+    "anthropic:claude-3-7-sonnet-latest", [sandbox_tool]
+)
+
+query = """Please analyze the sales data and tell me:
+1. What is the total revenue by category?
+2. Which region has the highest sales?
+3. What are the top 3 best-selling products by revenue?
+
+Use pandas to read the CSV file and perform the analysis."""
+
+async def run_agent(query: str):
+    # Stream agent outputs
+    async for chunk in agent.astream({"messages": query}):
+        print(chunk)
+        print("\n")
+
+if __name__ == "__main__":
+    # Run the agent
+    asyncio.run(run_agent(query))
+```
+
 #### Stateful Tool
 
 > [!important]
@@ -192,16 +231,15 @@ second_result = await agent.ainvoke(
 )
 ```
 
-
-
 See full examples here:
 
 * [ReAct agent](examples/react_agent.py)
 * [CodeAct agent](examples/codeact_agent.py)
+* [ReAct agent with csv](examples/react_agent_with_csv.py)
 
 ## 🧩 Components
 
 The sandbox consists of two main components:
 
 - **`pyodide-sandbox-js`**: JavaScript/TypeScript module using Deno to provide the core sandboxing functionality.
-- **`sandbox-py`**: Contains `PyodideSandbox` which just wraps the JavaScript/TypeScript module and executes it as a subprocess.
+- **`sandbox-py`**: Contains `PyodideSandbox` which just wraps the JavaScript/TypeScript module and executes it as a subprocess.

examples/react_agent_with_csv.py

@@ -0,0 +1,48 @@
+# pip install langgraph-codeact "langchain[anthropic]"
+import asyncio
+
+from langchain_sandbox import PyodideSandboxTool
+from langgraph.prebuilt import create_react_agent
+
+
+# Define the sandbox tool with filesystem support
+sandbox_tool = PyodideSandboxTool(
+    enable_filesystem=True,
+    allow_net=True,
+)
+
+sales_data = """date,product,category,quantity,price,region
+2024-01-15,Laptop,Electronics,2,1299.99,North
+2024-01-16,Chair,Furniture,1,249.50,South
+2024-01-16,T-shirt,Clothing,5,29.99,East
+2024-01-17,Laptop,Electronics,1,1299.99,West
+2024-01-18,Phone,Electronics,3,799.99,North
+2024-01-19,Desk,Furniture,2,399.99,South
+2024-01-20,Jeans,Clothing,4,79.99,East
+2024-01-21,Tablet,Electronics,2,499.99,West
+2024-01-22,Sofa,Furniture,1,899.99,North
+2024-01-23,Shoes,Clothing,3,129.99,South"""
+
+sandbox_tool.attach_file("sales.csv", sales_data)
+
+# Create an agent with the sandbox tool
+agent = create_react_agent(
+    "anthropic:claude-3-7-sonnet-latest", [sandbox_tool]
+)
+
+query = """Please analyze the sales data and tell me:
+1. What is the total revenue by category?
+2. Which region has the highest sales?
+3. What are the top 3 best-selling products by revenue?
+
+Use pandas to read the CSV file and perform the analysis."""
+
+async def run_agent(query: str):
+    # Stream agent outputs
+    async for chunk in agent.astream({"messages": query}):
+        print(chunk)
+        print("\n")
+
+if __name__ == "__main__":
+    # Run the agent
+    asyncio.run(run_agent(query))