Connect LLMs to E2B

# pip install openai e2b-code-interpreter
from openai import OpenAI
from e2b_code_interpreter import Sandbox

# Create OpenAI client
client = OpenAI()
prompt = "Calculate how many r's are in the word 'strawberry'"

# Define the tools
tools = [{
    "type": "function",
    "function": {
        "name": "execute_python",
        "description": "Execute python code in a Jupyter notebook cell and return result",
        "parameters": {
            "type": "object",
            "properties": {
                "code": {
                    "type": "string",
                    "description": "The python code to execute in a single cell"
                }
            },
            "required": ["code"]
        }
    }
}]

# Generate text with OpenAI
response = client.chat.completions.create(
    model="gpt-4o",
    messages=[{"role": "user", "content": prompt}],
    tools=tools,
)

# Execute the tool if it's called by the model
if response.choices[0].message.tool_calls:
    tool_call = response.choices[0].message.tool_calls[0]
    if tool_call.function.name == "execute_python":
        # Create a sandbox and execute the code
        with Sandbox() as sandbox:
            code = tool_call.function.arguments
            execution = sandbox.run_code(code)
            result = execution.text

        # Send the result back to the model
        response = client.chat.completions.create(
            model="gpt-4o",
            messages=[
                {"role": "user", "content": prompt},
                {"role": "function", "name": "execute_python", "content": result}
            ]
        )

print(response.choices[0].message.content)

# pip install anthropic e2b-code-interpreter
from anthropic import Anthropic
from e2b_code_interpreter import Sandbox

# Create Anthropic client
anthropic = Anthropic()
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
response = anthropic.messages.create(
    model="claude-3-5-sonnet-20240620",
    max_tokens=1024,
    messages=[
        {"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:
    execution = sandbox.run_code(code)
    result = execution.logs.stdout

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
client = Mistral(api_key=api_key)
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
response = client.chat.complete(
    model="codestral-latest",
    messages=[
        {"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:
    execution = sandbox.run_code(code)
    result = execution.text

print(result)

// npm install ai @ai-sdk/openai zod @e2b/code-interpreter
import { openai } from '@ai-sdk/openai'
import { generateText } from 'ai'
import z from 'zod'
import { Sandbox } from '@e2b/code-interpreter'

// Create OpenAI client
const model = openai('gpt-4o')

const prompt = "Calculate how many r's are in the word 'strawberry'"

// Generate text with OpenAI
const { text } = await generateText({
  model,
  prompt,
  tools: {
    // Define a tool that runs code in a sandbox
    codeInterpreter: {
      description: 'Execute python code in a Jupyter notebook cell and return result',
      parameters: z.object({
        code: z.string().describe('The python code to execute in a single cell'),
      }),
      execute: async ({ code }) => {
        // Create a sandbox, execute LLM-generated code, and return the result
        const sandbox = await Sandbox.create()
        const { text, results, logs, error } = await sandbox.runCode(code)
        return results
      },
    },
  },
  // This is required to feed the tool call result back to the LLM
  maxSteps: 2
})

console.log(text)

# pip install crewai crewai[tools] e2b-code-interpreter
from crewai_tools import tool
from crewai import Agent, Task, Crew, LLM
from e2b_code_interpreter import Sandbox

@tool("Python interpreter tool")
def execute_python(code: str):
    """
    Execute Python code and return the results.
    """
    with Sandbox() as sandbox:
        execution = sandbox.run_code(code)
        return execution.text

# Define the agent
python_executor = Agent(
    role='Python Executor',
    goal='Execute Python code and return the results',
    backstory='You are an expert Python programmer capable of executing code and returning results.',
    tools=[execute_python],
    llm=LLM(model="gpt-4o")
)

# Define the task
execute_task = Task(
    description="Calculate how many r's are in the word 'strawberry'",
    agent=python_executor,
    expected_output="The number of r's in the word 'strawberry'"
)

# Create the crew
code_execution_crew = Crew(
    agents=[python_executor],
    tasks=[execute_task],
    verbose=True,
)

# Run the crew
result = code_execution_crew.kickoff()
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")
prompt_template = ChatPromptTemplate.from_messages([
    ("system", system_prompt),
    ("human", "{input}")
])

output_parser = StrOutputParser()

# Create the chain
chain = prompt_template | llm | output_parser

# Run the chain
code = chain.invoke({"input": prompt})

# Execute code in E2B Sandbox
with Sandbox() as sandbox:
    execution = sandbox.run_code(code)
    result = execution.text

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:
        execution = sandbox.run_code(code)
        return execution.text

e2b_interpreter_tool = FunctionTool.from_defaults(
    name="execute_python",
    description="Execute python code in a Jupyter notebook cell and return result",
    fn=execute_python
)

# 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'")