update pyodide sandbox example (#30)

README.md

@@ -100,6 +100,7 @@ You can use any code sandbox you want, pass it in as a function which accepts tw
 
 > [!Warning]
 > Use a sandboxed environment in production! The `eval` function below is just for demonstration purposes, not safe!
+> See example of using a secure [LangChain Sandbox](https://github.com/langchain-ai/langchain-sandbox) [here](examples/pyodide_sandbox_example.py)
 
 ```py
 import builtins

examples/pyodide_sandbox_example.py

@@ -0,0 +1,177 @@
+# pip install langgraph-codeact "langchain[anthropic]"
+import asyncio
+import inspect
+from typing import Any
+
+from langchain.chat_models import init_chat_model
+from langchain_sandbox import PyodideSandbox
+from langgraph.checkpoint.memory import MemorySaver
+
+from langgraph_codeact import EvalCoroutine, create_codeact
+
+
+def create_pyodide_eval_fn(sandbox: PyodideSandbox) -> EvalCoroutine:
+    """Create an eval_fn that uses PyodideSandbox.
+    """
+
+    async def async_eval_fn(
+        code: str, _locals: dict[str, Any]
+    ) -> tuple[str, dict[str, Any]]:
+        # Create a wrapper function that will execute the code and return locals
+        wrapper_code = f"""
+def execute():
+    try:
+        # Execute the provided code
+{chr(10).join("        " + line for line in code.strip().split(chr(10)))}
+        return locals()
+    except Exception as e:
+        return {{"error": str(e)}}
+
+execute()
+"""
+        # Convert functions in _locals to their string representation
+        context_setup = ""
+        for key, value in _locals.items():
+            if callable(value):
+                # Get the function's source code
+                src = inspect.getsource(value)
+                context_setup += f"\n{src}"
+            else:
+                context_setup += f"\n{key} = {repr(value)}"
+
+        try:
+            # Execute the code and get the result
+            response = await sandbox.execute(
+                code=context_setup + "\n\n" + wrapper_code,
+            )
+
+            # Check if execution was successful
+            if response.stderr:
+                return f"Error during execution: {response.stderr}", {}
+
+            # Get the output from stdout
+            output = (
+                response.stdout
+                if response.stdout
+                else "<Code ran, no output printed to stdout>"
+            )
+            result = response.result
+
+            # If there was an error in the result, return it
+            if isinstance(result, dict) and "error" in result:
+                return f"Error during execution: {result['error']}", {}
+
+            # Get the new variables by comparing with original locals
+            new_vars = {
+                k: v
+                for k, v in result.items()
+                if k not in _locals and not k.startswith("_")
+            }
+            return output, new_vars
+
+        except Exception as e:
+            return f"Error during PyodideSandbox execution: {repr(e)}", {}
+
+    return async_eval_fn
+
+
+def add(a: float, b: float) -> float:
+    """Add two numbers together."""
+    return a + b
+
+
+def multiply(a: float, b: float) -> float:
+    """Multiply two numbers together."""
+    return a * b
+
+
+def divide(a: float, b: float) -> float:
+    """Divide two numbers."""
+    return a / b
+
+
+def subtract(a: float, b: float) -> float:
+    """Subtract two numbers."""
+    return a - b
+
+
+def sin(a: float) -> float:
+    """Take the sine of a number."""
+    import math
+
+    return math.sin(a)
+
+
+def cos(a: float) -> float:
+    """Take the cosine of a number."""
+    import math
+
+    return math.cos(a)
+
+
+def radians(a: float) -> float:
+    """Convert degrees to radians."""
+    import math
+
+    return math.radians(a)
+
+
+def exponentiation(a: float, b: float) -> float:
+    """Raise one number to the power of another."""
+    return a**b
+
+
+def sqrt(a: float) -> float:
+    """Take the square root of a number."""
+    import math
+
+    return math.sqrt(a)
+
+
+def ceil(a: float) -> float:
+    """Round a number up to the nearest integer."""
+    import math
+
+    return math.ceil(a)
+
+
+tools = [
+    add,
+    multiply,
+    divide,
+    subtract,
+    sin,
+    cos,
+    radians,
+    exponentiation,
+    sqrt,
+    ceil,
+]
+
+model = init_chat_model("claude-3-7-sonnet-latest", model_provider="anthropic")
+
+sandbox = PyodideSandbox(allow_net=True)
+eval_fn = create_pyodide_eval_fn(sandbox)
+code_act = create_codeact(model, tools, eval_fn)
+agent = code_act.compile(checkpointer=MemorySaver())
+
+query = """A batter hits a baseball at 45.847 m/s at an angle of 23.474° above the horizontal. The outfielder, who starts facing the batter, picks up the baseball as it lands, then throws it back towards the batter at 24.12 m/s at an angle of 39.12 degrees. How far is the baseball from where the batter originally hit it? Assume zero air resistance."""
+
+
+async def run_agent(query: str, thread_id: str):
+    config = {"configurable": {"thread_id": thread_id}}
+    # Stream agent outputs
+    async for typ, chunk in agent.astream(
+        {"messages": query},
+        stream_mode=["values", "messages"],
+        config=config,
+    ):
+        if typ == "messages":
+            print(chunk[0].content, end="")
+        elif typ == "values":
+            print("\n\n---answer---\n\n", chunk)
+
+
+if __name__ == "__main__":
+    # Run the agent
+    asyncio.run(run_agent(query, "1"))

langgraph_codeact/__init__.py

@@ -1,5 +1,5 @@
 import inspect
-from typing import Any, Callable, Optional, Sequence, Union
+from typing import Any, Awaitable, Callable, Optional, Sequence, Type, TypeVar, Union
 
 from langchain_core.language_models import BaseChatModel
 from langchain_core.tools import StructuredTool
@@ -9,6 +9,9 @@ from langgraph.types import Command
 
 from langgraph_codeact.utils import extract_and_combine_codeblocks
 
+EvalFunction = Callable[[str, dict[str, Any]], tuple[str, dict[str, Any]]]
+EvalCoroutine = Callable[[str, dict[str, Any]], Awaitable[tuple[str, dict[str, Any]]]]
+
 
 class CodeActState(MessagesState):
     """State for CodeAct agent."""
@@ -19,6 +22,10 @@ class CodeActState(MessagesState):
     """Dictionary containing the execution context with available tools and variables."""
 
 
+StateSchema = TypeVar("StateSchema", bound=CodeActState)
+StateSchemaType = Type[StateSchema]
+
+
 def create_default_prompt(tools: list[StructuredTool], base_prompt: Optional[str] = None):
     """Create default prompt for the CodeAct agent."""
     tools = [t if isinstance(t, StructuredTool) else create_tool(t) for t in tools]
@@ -48,20 +55,22 @@ Reminder: use Python code snippets to call tools"""
 def create_codeact(
     model: BaseChatModel,
     tools: Sequence[Union[StructuredTool, Callable]],
-    eval_fn: Callable[[str, dict[str, Any]], tuple[str, dict[str, Any]]],
+    eval_fn: Union[EvalFunction, EvalCoroutine],
     *,
     prompt: Optional[str] = None,
+    state_schema: StateSchemaType = CodeActState,
 ) -> StateGraph:
     """Create a CodeAct agent.
 
     Args:
         model: The language model to use for generating code
         tools: List of tools available to the agent. Can be passed as python functions or StructuredTool instances.
-        eval_fn: Function that executes code in a sandbox. Takes code string and locals dict,
+        eval_fn: Function or coroutine that executes code in a sandbox. Takes code string and locals dict,
             returns a tuple of (stdout output, new variables dict)
         prompt: Optional custom system prompt. If None, uses default prompt.
             To customize default prompt you can use `create_default_prompt` helper:
             `create_default_prompt(tools, "You are a helpful assistant.")`
+        state_schema: The state schema to use for the agent.
 
     Returns:
         A StateGraph implementing the CodeAct architecture
@@ -74,7 +83,7 @@ def create_codeact(
     # Make tools available to the code sandbox
     tools_context = {tool.name: tool.func for tool in tools}
 
-    def call_model(state: CodeActState) -> Command:
+    def call_model(state: StateSchema) -> Command:
         messages = [{"role": "system", "content": prompt}] + state["messages"]
         response = model.invoke(messages)
         # Extract and combine all code blocks
@@ -85,19 +94,33 @@ def create_codeact(
             # no code block, end the loop and respond to the user
             return Command(update={"messages": [response], "script": None})
 
-    def sandbox(state: CodeActState):
-        script = state["script"]
-        existing_context = state.get("context", {})
-        context = {**existing_context, **tools_context}
-        # Execute the script in the sandbox
-        output, new_vars = eval_fn(script, context)
-        new_context = {**existing_context, **new_vars}
-        return {
-            "messages": [{"role": "user", "content": output}],
-            "context": new_context,
-        }
+    # If eval_fn is a async, we define async node function.
+    if inspect.iscoroutinefunction(eval_fn):
 
-    agent = StateGraph(CodeActState)
+        async def sandbox(state: StateSchema):
+            existing_context = state.get("context", {})
+            context = {**existing_context, **tools_context}
+            # Execute the script in the sandbox
+            output, new_vars = await eval_fn(state["script"], context)
+            new_context = {**existing_context, **new_vars}
+            return {
+                "messages": [{"role": "user", "content": output}],
+                "context": new_context,
+            }
+    else:
+
+        def sandbox(state: StateSchema):
+            existing_context = state.get("context", {})
+            context = {**existing_context, **tools_context}
+            # Execute the script in the sandbox
+            output, new_vars = eval_fn(state["script"], context)
+            new_context = {**existing_context, **new_vars}
+            return {
+                "messages": [{"role": "user", "content": output}],
+                "context": new_context,
+            }
+
+    agent = StateGraph(state_schema)
     agent.add_node(call_model, destinations=(END, "sandbox"))
     agent.add_node(sandbox)
     agent.add_edge(START, "call_model")

pyproject.toml

@@ -1,15 +1,15 @@
 [project]
 name = "langgraph-codeact"
-version = "0.1.1"
+version = "0.1.3"
 description = "LangGraph implementation of CodeAct agent that generates and executes code instead of tool calling."
 authors = [
     {name = "Nuno Campos",email = "nuno@langchain.dev"}
 ]
 license = {text = "MIT"}
 readme = "README.md"
-requires-python = ">=3.9,<4.0"
+requires-python = ">=3.10,<4.0"
 dependencies = [
-    "langgraph (>=0.3.5,<0.4.0)"
+    "langgraph>=0.4.5"
 ]
 
 [build-system]
@@ -19,7 +19,8 @@ build-backend = "pdm.backend"
 [dependency-groups]
 dev = [
     "langchain-anthropic>=0.3.9,<0.4.0",
-    "langchain>=0.3.20,<0.4.0"
+    "langchain>=0.3.20,<0.4.0",
+    "langchain-sandbox>=0.0.5,<0.1.0"
 ]
 test = [
     "pytest>=8.0.0",

uv.lock

@@ -1,6 +1,6 @@
 version = 1
 revision = 1
-requires-python = ">=3.9, <4.0"
+requires-python = ">=3.10, <4.0"
 resolution-markers = [
     "python_full_version >= '3.12.4'",
     "python_full_version < '3.12.4'",
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