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AgentOps integration #34

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AgentOps integration #34

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30 changes: 27 additions & 3 deletions examples/langchain-python/langchain_e2b_python/code_interpreter_tool.py
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Original file line number Diff line number Diff line change
@@ -1,5 +1,7 @@
import os
import json
import agentops
from dotenv import load_dotenv

from typing import Any, List
from langchain_core.tools import Tool
Expand All @@ -10,11 +12,15 @@
ToolAgentAction,
)

# Load environment variables
load_dotenv()

# Initialize AgentOps
agentops.init(os.getenv('AGENTOPS_API_KEY'))

class LangchainCodeInterpreterToolInput(BaseModel):
code: str = Field(description="Python code to execute.")


class CodeInterpreterFunctionTool:
"""
This class calls arbitrary code against a Python Jupyter notebook.
Expand All @@ -23,6 +29,7 @@ class CodeInterpreterFunctionTool:

tool_name: str = "code_interpreter"

@agentops.record_function('CodeInterpreterFunctionTool_init')
def __init__(self):
# Instantiate the E2B sandbox - this is a long lived object
# that's pinging E2B cloud to keep the sandbox alive.
Expand All @@ -32,24 +39,34 @@ def __init__(self):
)
self.code_interpreter = CodeInterpreter()

@agentops.record_function('CodeInterpreterFunctionTool_close')
def close(self):
self.code_interpreter.close()

@agentops.record_function('CodeInterpreterFunctionTool_call')
def call(self, parameters: dict, **kwargs: Any):
code = parameters.get("code", "")
print(f"***Code Interpreting...\n{code}\n====")
execution = self.code_interpreter.notebook.exec_cell(code)

with agentops.record_span('code_execution'):
execution = self.code_interpreter.notebook.exec_cell(code)

# Record metrics
agentops.record_metric('code_execution_success', 1 if not execution.error else 0)
agentops.record_metric('code_execution_time', execution.duration)

return {
"results": execution.results,
"stdout": execution.logs.stdout,
"stderr": execution.logs.stderr,
"error": execution.error,
}

# langchain does not return a dict as a parameter, only a code string
@agentops.record_function('CodeInterpreterFunctionTool_langchain_call')
def langchain_call(self, code: str):
return self.call({"code": code})

@agentops.record_function('CodeInterpreterFunctionTool_to_langchain_tool')
def to_langchain_tool(self) -> Tool:
tool = Tool(
name=self.tool_name,
Expand All @@ -60,6 +77,7 @@ def to_langchain_tool(self) -> Tool:
return tool

@staticmethod
@agentops.record_function('CodeInterpreterFunctionTool_format_to_tool_message')
def format_to_tool_message(
agent_action: ToolAgentAction,
observation: dict,
Expand All @@ -77,4 +95,10 @@ def format_to_tool_message(
ToolMessage(content=content, tool_call_id=agent_action.tool_call_id)
)

# Record metric for message count
agentops.record_metric('tool_message_count', len(new_messages))

return new_messages

# End of program
agentops.end_session('Success')