CASE STUDY Perplexity shipped advanced data analysis in 1 week LEARN MORE → CASE STUDY How Manus Uses E2B to Provide Agents With Virtual Computers LEARN MORE → * * * * * * We raised $21M Series A Learn what’s next → * * * * * * Download logo (SVG/PNG) NEW JOIN STARTUPS PROGRAM Deep Research Agents Computer Use Agents Automations Agents Background Agents Reinforcement Learning Secure MCPs Deep Research Agents AI Sandboxes for Open-source, secure environment with real-world tools for enterprise-grade agents. START FOR FREE VIEW DOCS TRUSTED BY Rogo Delivers AI for Financial Institutions with Claude Managed Agents & E2B CASE STUDY How Hugging Face Is Using E2B to Replicate DeepSeek-R1 CASE STUDY Rogo Delivers AI for Financial Institutions with Claude Managed Agents & E2B CASE STUDY How Anything Is Building a Vibe-Coding Empire with 1M+ Users CASE STUDY How Manus Uses E2B to Provide Agents With Virtual Computers CASE STUDY Groq's Compound AI Systems Are Powered by E2B CASE STUDY Lindy Powers AI Workflows With E2B Code Action CASE STUDY Gumloop Has Run on E2B for Over Two Years — Serving Enterprises Like Shopify and Instacart CASE STUDY Rogo Delivers AI for Financial Institutions with Claude Managed Agents & E2B CASE STUDY How Hugging Face Is Using E2B to Replicate DeepSeek-R1 CASE STUDY Rogo Delivers AI for Financial Institutions with Claude Managed Agents & E2B CASE STUDY How Anything Is Building a Vibe-Coding Empire with 1M+ Users CASE STUDY How Manus Uses E2B to Provide Agents With Virtual Computers CASE STUDY Groq's Compound AI Systems Are Powered by E2B CASE STUDY Lindy Powers AI Workflows With E2B Code Action CASE STUDY Gumloop Has Run on E2B for Over Two Years — Serving Enterprises Like Shopify and Instacart CASE STUDY LLM [.500] [.873] [.542] [.704] [.285] [.717] [.598] [.557] [.232] [.746] [.211] [.013] [.510] [.718] [.621] [.223] [.124] [.801] [.798] [.117]️ [.817] [.070] [.353] ‍ [.833] [.477] [.620] [.829] [.195] [.245] [.891] [.454] [.145] [.984] [.634] [.342] [.746] [.330] [.103] [.742] [.004] [.165] [.459] [.597] [.910] [.072] [.336] [.788] [.400] [.410] [.273] [.477] [.087] [.707] [.212] [.642] [.829] [.616] [.805] [.206] [.505] [.265] [.043] [.829] [.195] [.245] [.891] [.505] [.265] [.043] ‍ [.195] [.245] [.891] [.410] [.273] [.505] [.765] [.143] [.095] [.335] [.891] [.287] [.921] [.206] [.813] [.104] [.665] [.083] [.900] [.040] [.784] [.087] [.171] [.616] [.805] [.206] [.505] [.265] [.043] [.829] [.195] [.245] [.891] [.921] [.820] [.061]️ [.679] [.034] [.810] [.322] [.061] [.381]️ [.285] [.679] [.034] [.810] ‍ [.061] [.381]️ [.285] [.179] [.034] [.810] [.061] [.001]️ [.275] [.551] [.707] [.212] [.642] [.660] [.102] [.790] [.041] [.081]️ [.445] [.021] [.517] [.019] [.311] [.921] [.820] [.061]️ [.679] [.034] [.810] [.322] [.061] [.381]️ [.285] [.817] [.070] [.353] [.744] [.663] [.844] [.452] [.045] [.305] [.027] [.744] [.663] [.844] ‍ [.452] [.045] [.027] [.733] [.463] [.824] [.452] [.145] [.677] [.505] [.265] [.043] [.829] [.733] [.463] [.824] [.452] [.145] [.677] [.505] [.265] [.043] [.829] [.817] [.070] [.353] [.744] [.663] [.844] [.452] [.045] [.305] [.027] [.820] [.061]️ [.679] [.034] [.810] [.322] [.070] [.353] [.744] [.663] [.034] [.810] [.322] ‍ [.070] [.353] [.663] [.114] [.077] [.722] [.084] [.253] [.665] [.452] [.045] [.305] [.027] [.874] [.104] [.022] [.604] [.310] [.103] [.502] [.178] [.285] [.006] ]·········[ ]·········[ ]·········[ ]· * ·······[ ]·········[ ]·· * ······[ ]··· * ·····[ ]· * ·······[ ]···· * ····[ ]····· * ···[ ]··· * ·····[ ]······· * ·[ ]······· * ·[ ]····· * ···[ ]·········[ ]·········[ ]······· * ·[ ]·········[ E2B SANDBOX RUNNING CODE… ]·····[ ]·····[ ]·····[ ]· * ···[ ]·····[ ]·· * ··[ ]··· * ·[ ]· * ···[ ]·····[ ]·····[ ]·· * ··[ ]·····[ ]·····[ ]··· * ·[ ]·····[ ]·····[ ]·····[ ]·····[ ✶✶ ✶✶ ✶✶ ✶✶ ✶✶ ✶✶ ✶✶ ✶✶ ✶✶ ✶✶ ✶✶ ✶✶ ✶✶ ✶✶ ✶✶ ✶✶ ✶✶ ✶✶ ✶✶ ✶✶ ✶✶ ✶✶ ✶✶ ✶✶ ✶✶ ✶✶ ✶✶ ✶✶ ✶✶ ✶✶ ✶✶ ✶✶ ]·····[ ]·····[ ]·····[ ]· * ···[ ]·····[ ]·· * ··[ ]··· * ·[ ]· * ···[ ]·····[ ]·····[ ]·· * ··[ ]·····[ ]·····[ ]··· * ·[ ]·····[ ]·····[ ]·····[ ]·····[ [_______________] [%%%%%__________] [%%%%%%%%%%_____] [%%%%%%%%%%%%%%%] CPU: 8 × ▤ / RAM: 4 GB ]·········[ ]·········[ ]·········[ ]· * ·······[ ]·········[ ]·· * ······[ ]··· * ·····[ ]· * ·······[ ]···· * ····[ ]····· * ···[ ]··· * ·····[ ]······· * ·[ ]······· * ·[ ]····· * ···[ ]·········[ ]·········[ ]······· * ·[ ]·········[ OUTPUT 8 – ––––– ––– ––––– ––– ––––– ––– 7 – ––––– ––– @@@@@ ––– ––––– ––– 6 – ––––– ––– @@@@@ ––– ––––– ––– 5 – @@@@@ ––– @@@@@ ––– ––––– ––– 4 – @@@@@ ––– @@@@@ ––– ––––– ––– 3 – @@@@@ ––– @@@@@ ––– @@@@@ ––– 2 – @@@@@ ––– @@@@@ ––– @@@@@ ––– 1 – @@@@@ ––– @@@@@ ––– @@@@@ ––– ––––––––––––––––––––––––––––––––– A B C ✓ CHART-1 OUTPUT ______ ______ ______ ❘ ❘_\ ❘ ❘_\ ❘ ❘_\ ╔═══════╗ ╔═══════╗ ╔═══════╗ ║ CSV ║ ║ TXT ║ ║ .JS ║ ╚═══════╝ ╚═══════╝ ╚═══════╝ ❘______❘ ❘______❘ ❘______❘ ✓ File OUTPUT ╔ Email ══════════════╗ ║ your@email.com ║ ╚═════════════════════╝ ╔ Pw ═════════════════╗ ║ ******** ║ ╚═════════════════════╝ ╔═════════════════════╗ ║ Sign In ║ ╚═════════════════════╝ ✓ UI OUTPUT NVDA @ $120.91 @ +32% @ @@@ @ @@@ @ @ @ @@ @ @ @ @@@@ @ @ @@@@ @@@ @ @ ✓ CHART-2 OUTPUT 1999 @@@@@@@@@@ │ │ 1998 @@@ │ │ 1997 @@@@@@@@@@@@@ │ 1996 @@@@@@@ │ │ 1995 @@@@@@@@@@@@@@@@ │ 1994 @@@@@@@@@@@@@@@@@@@@@@@@@ 1993 @@@@@ │ │ 1992 @@@@@@@@@@ │ │ ––––––––––––––––––––––––––––––––– 2 4 6 ✓ CHART-3 OUTPUT /!\ ‍ Error: [$rootScope:inprog] $apply already in progress http://errors.angular.js.org/1.3 .15/$rootScope/inprog?p0= %24apply at angular.js:63 ☓ Error OUTPUT 8 –––– │ ––––––––– │ ––––––––– │ ––––– @ 7 –––– │ ––––––––– │ ––– @ ––––– │ –––– @ – 6 –––– │ ––––––––– │ –– @ – @ – @@@@ ––– @ –– 5 –––– │ ––––– @@@ – │ – @ ––– @ ––– │ @ – @ – @@ 4 @@@@│ ––– @@ ––– @ │@ ––– @ – @ –– │ – @ – @ –– 3 –––– @@@@ –– @@@@ @ ––– @ ––– @@│@@@ ––– 2 ––– @ │@@@@@ –––– │ @@@ –––––– │ –––––– 1 – @@ – │ ––––––––– │ ––––––––– │ –––––– ––––––––––––––––––––––––––––––––– A B C ✓ CHART-4 94% of Fortune 100 COMPANIES 7M+ MONTHLY DOWNLOADS 1b+ STARTED SANDBOXES [ USE CASES ] AI AGENTS A1 AG3-TS –I 4GEN7S A* AG3NT5 4I #GEN7S Built for AI Agents , LLM Training, and MCPs > HOVER (↓↓) /EXPLORE Deep research agents Enable your agent to conduct time-consuming research on large datasets. LEARN MORE @@@ @@@ @@@ @@@ @@@ @@@ /\_/ /\/ _/ X% %%%%%% %%%%%%%%% %%% @ @ @ @ @ @ @ @ @ @ @ AI data analysis & visualization Connect your data to an isolated sandbox to securely explore data and generate charts. LEARN MORE ====== ======== === === < ====== ======== === === ====< ======== === === ==== < ======== === === ==== ‍ ===== =< === === ==== ===== = < === === ==== ===== = = ==< ==== ===== = = == < ==== ===== = = == == =====< ===== = = == == ===== < ===== = = == == ===== ======< = == == ===== ====== < = == == ===== ====== ========< == ===== ====== ======== < == ===== ====== ======== === ===< Coding agents Securely execute code, use I/O, access the internet, or start terminal commands. LEARN MORE ╔ ═ ╗ ╣ ╚ ═ ╔ ═ ═ ╗ ═ ═══ ╣ ╚ ═ ═ ╝ ╔ ═ ═ ═ ╗ ╠═ ═══ ═╣ ╚ ═ ═ ══╝ ╔═══════╗ ╠═══════╣ ║ ║ ╚═══════╝ ╔═══════╗ ╠═══════╣ ║ ✓ ║ ╚═══════╝ Vibe coding Use a sandbox as a code runtime for AI-generated apps. Supports any language and framework. LEARN MORE ==╔═══╗= = ==║ ✓ ║ = = ==╚═══╝== ==╔═══╗ = = ==║ ✓ ║== ==╚═══╝= = ==╔═══╗== ==║ ✓ ║== ==╚═══╝ = = ==╔═══╗== = = ║ ✓ ║== ==╚═══╝== = = ╔═══╗== = =║ ✓ ║== ==╚═══╝== = =╔═══╗== ==║ × ║== = = ╚═══╝== ==╔═══╗== ==║ × ║= = = =╚═══╝== Reinforcement learning Use tens of thousands of concurrent sandboxes to run and evaluate reward functions. LEARN MORE Computer use Use Desktop Sandbox to provide secure virtual computers in cloud for your LLM. LEARN MORE Read about how companies and developers use E2B SEE CUSTOMER CASE STUDIES JOIN DISCORD COMMUNITY [ GET STARTED ] A FEW LINES A F3W L1NES 4 FE# L!NES A FEW 7INE5 – FEW ILNES IN YOUR CODE WITH A FEW LINES Need help? Join Discord , check docs, or email us . + + + + + + NODE.JS PYTHON VERCEL OPEN AI ANTHROPIC Mistral Llama LangChain LlamaIndex More 1 2 3 4 5 6 7 8 9 10 11 12 ~ ~ ~ ~ ~ ~ ‍ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ // npm install @e2b/code-interpreter import { Sandbox } from '@e2b/code-interpreter' // Create a E2B Code Interpreter with JavaScript kernel const sandbox = await Sandbox.create() // Execute JavaScript cells await sandbox.runCode( 'x = 1' ) const execution = await sandbox.runCode( 'x+=1; x' ) // Outputs 2 console .log(execution.text) “~/index.ts” 1 2 3 4 5 6 7 8 9 10 ~ ~ ~ ~ ~ ~ ‍ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ # pip install e2b-code-interpreter from e2b_code_interpreter import Sandbox # Create a E2B Sandbox with Sandbox() as sandbox: # Run code sandbox.run_code( "x = 1" ) execution = sandbox.run_code( "x+=1; x" ) print (execution.text) # outputs 2 “~/index.py” 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 ‍ ‍ // 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) “~/aisdk_tools.ts” 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 ‍ ‍ ~ ~ ~ ~ ~ ~ ~ # pip install openai e2b-code-interpreter from openai import OpenAI from e2b_code_interpreter import Sandbox # Create OpenAI client client = OpenAI() system = "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 OpenAI API response = client.chat.completions.create( model= "gpt-4o" , messages=[ { "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: execution = sandbox.run_code(code) result = execution.text print (result) “~/oai.py” 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 ‍ ‍ ~ ~ ~ ~ ~ ~ ~ # 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) “~/anth.py” 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 ‍ ‍ ~ ~ ~ ~ ~ # 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) “~/mistral.py” 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 ‍ ‍ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ # 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: execution = sandbox.run_code(code) result = execution.logs.stdout print (result) “~/llama.py” 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 ‍ ‍ ~ ~ ~ ~ ~ # 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) “~/lchain.py” 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ 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'" ) “~/llindex.py” [ FEATURES ] FEATURES F#4TUR3S *EA–U?E5 FE^TURES FEA+UR3S Features your agents will love AI agents need real-world tools to complete superhuman level tasks. > MADE FOR AI > DSCVR ALL (↓↓) Works with any LLM Use OpenAI, Llama, Anthropic, Mistral, or your own custom models. E2B is LLM-agnostic and compatible with any model. Quick start The E2B Sandboxes in the same region as the client start in less than 200 ms. NO COLD STARTS Run ... or just any other AI-generated code. AI-generated Python, JavaScript, Ruby, or C++? Popular framework or custom library? If you can run it on a Linux box, you can run it in the E2B sandbox. Secure quick start The E2B Sandboxes in the same region as the client start in 80 ms. NO COLD STARTS install packages USE TERMINAL USE BROWSERS inspect errors SAVE & UPLOAD FILES Features made for agents The full-stack of secure tools for any agentic workflow. COMPUTERS FOR AGENTS ^ ^ ^ ^^^^^ ^^ ^^ ^^^ ^ ^ ^ ^ ^^ ^^^^ ^^^ ^ ^ ^^^^^ ^^ ^ ^^^ ^ ^ ^ ^ ^^^ ^^^^^ ^^^ ^ ^^ ^ ^^^ ^^^^ ^^^ ^ ^ ^ ^ ^ ^ ^^^ ^^^ ^ ^ ^^ ^^^^ ^^^ ^ ^^^ ^ ^ ^ ^ ^ ^ ^^^ ^^^ Secure & isolated Each sandbox is powered by Firecracker, a microVM made to run untrusted workflows. 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