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Only respond with the code to be executed and nothing else. Strip backticks in code blocks." prompt = "Calculate how many r's are in the word 'strawberry'" # Send messages to OpenAI API model= "gpt-4o" , { "role" : "system" , "content" : system}, { "role" : "user" , "content" : prompt} # Extract the code from the response code = response.choices[ 0 ].message.content # Execute code in E2B Sandbox if code: with Sandbox() as sandbox: print (result) ‍ ‍~~~~~~~ # pip install anthropic e2b-code-interpreter from anthropic import Anthropic from e2b_code_interpreter import Sandbox # Create Anthropic client system_prompt = "You are a helpful assistant that can execute python code in a Jupyter notebook. Only respond with the code to be executed and nothing else. Strip backticks in code blocks." prompt = "Calculate how many r's are in the word 'strawberry'" # Send messages to Anthropic API model= "claude-3-5-sonnet-20240620" , max_tokens= 1024 , { "role" : "assistant" , "content" : system_prompt}, { "role" : "user" , "content" : prompt} # Extract code from response code = response.content[ 0 ].text # Execute code in E2B Sandbox with Sandbox() as sandbox: print (result) ‍ ‍~~~~~ # pip install mistralai e2b-code-interpreter import os from mistralai import Mistral from e2b_code_interpreter import Sandbox api_key = os.environ[ "MISTRAL_API_KEY" ] # Create Mistral client system_prompt = "You are a helpful assistant that can execute python code in a Jupyter notebook. Only respond with the code to be executed and nothing else. Strip backticks in code blocks." prompt = "Calculate how many r's are in the word 'strawberry'" # Send the prompt to the model model= "codestral-latest" , { "role" : "system" , "content" : system_prompt}, { "role" : "user" , "content" : prompt} # Extract the code from the response code = response.choices[ 0 ].message.content # Execute code in E2B Sandbox with Sandbox() as sandbox: print (result) ‍ ‍~~~~~~~~~~ # pip install ollama import ollama from e2b_code_interpreter import Sandbox # Send the prompt to the model response = ollama.chat(model= "llama3.2" , messages=[ "role" : "system" , "content" : "You are a helpful assistant that can execute python code in a Jupyter notebook. Only respond with the code to be executed and nothing else. Strip backticks in code blocks." "role" : "user" , "content" : "Calculate how many r's are in the word 'strawberry'" # Extract the code from the response code = response[ 'message' ][ 'content' ] # Execute code in E2B Sandbox with Sandbox() as sandbox: print (result) ‍ ‍~~~~~ # pip install langchain langchain-openai e2b-code-interpreter from langchain_openai import ChatOpenAI from langchain_core.prompts import ChatPromptTemplate from langchain_core.output_parsers import StrOutputParser from e2b_code_interpreter import Sandbox system_prompt = "You are a helpful assistant that can execute python code in a Jupyter notebook. Only respond with the code to be executed and nothing else. Strip backticks in code blocks." prompt = "Calculate how many r's are in the word 'strawberry'" # Create LangChain components llm = ChatOpenAI(model= "gpt-4o" ) ( "system" , system_prompt), ( "human" , "{input}" ) # Create the chain # Run the chain code = chain.invoke({ "input" : prompt}) # Execute code in E2B Sandbox with Sandbox() as sandbox: print (result) ~ ~~~~~~~~~~~ from llama_index.core.tools import FunctionTool from llama_index.llms.openai import OpenAI from llama_index.core.agent import ReActAgent from e2b_code_interpreter import Sandbox # Define the tool def execute_python ( code: str ): with Sandbox() as sandbox: return execution.text name= "execute_python" , description= "Execute python code in a Jupyter notebook cell and return result" , # Initialize LLM llm = OpenAI(model= "gpt-4o" ) # Initialize ReAct agent agent = ReActAgent.from_tools([e2b_interpreter_tool], llm=llm, verbose= True ) agent.chat( "Calculate how many r's are in the word 'strawberry'" ) All JS Python Next.js LangChain LangGraph Meta OpenAI Anthropic Mistral Fireworks AI Together AI great product one hour revolutionized It just works. gain enterprises’ trust