---
title: "E2BToolset"
id: e2btoolset
slug: "/e2btoolset"
description: "A Toolset that gives Agents access to a live E2B cloud sandbox for executing bash commands and managing files."
---

# E2BToolset

A Toolset that gives Agents access to a live [E2B](https://e2b.dev/) cloud sandbox for executing bash commands and managing files.

<div className="key-value-table">

|  |  |
| --- | --- |
| **Mandatory init variables** | `api_key`: E2B API key. Can be set with `E2B_API_KEY` env var. |
| **API reference**            | [E2B](/reference/integrations-e2b) |
| **GitHub link**              | https://github.com/deepset-ai/haystack-core-integrations/tree/main/integrations/e2b |

</div>

## Overview

`E2BToolset` bundles four tools that operate inside the same [E2B](https://e2b.dev/) cloud sandbox, giving an Agent a secure, isolated Linux environment to execute code and manipulate files:

- **`run_bash_command`** (`RunBashCommandTool`): Runs a bash command and returns the combined `exit_code`, `stdout`, and `stderr`. Use it for shell scripts, package installation, code compilation, or any system-level operation.
- **`read_file`** (`ReadFileTool`): Reads the text content of a file from the sandbox filesystem.
- **`write_file`** (`WriteFileTool`): Writes text content to a file in the sandbox. Parent directories are created automatically and existing files are overwritten.
- **`list_directory`** (`ListDirectoryTool`): Lists files and subdirectories at a given path.

All four tools share a single `E2BSandbox` instance, so a file written by `write_file` is immediately available to `run_bash_command` and `read_file` in the same Agent run. The toolset owns the sandbox lifecycle: `warm_up()` starts the sandbox, `close()` shuts it down, and YAML serialization round-trips preserve the shared-sandbox relationship.

### Parameters

- `api_key` is _mandatory_ and must be an E2B API key. The default setting uses the environment variable `E2B_API_KEY`. Get a key at [e2b.dev](https://e2b.dev/).
- `sandbox_template` is _optional_ and defaults to `"base"`. Sets the E2B sandbox template to use.
- `timeout` is _optional_ and defaults to `120`. Sets the sandbox inactivity timeout in seconds.
- `environment_vars` is _optional_ and lets you inject environment variables into the sandbox process.

## Usage

Install the E2B integration to use `E2BToolset`:

```shell
pip install e2b-haystack
```

Set your E2B API key:

```shell
export E2B_API_KEY="your-e2b-api-key"
```

### With an Agent

You can use `E2BToolset` with the [Agent](../../pipeline-components/agents-1/agent.mdx) component. The Agent will automatically start the sandbox, invoke the tools to write, run, and inspect code, and let the LLM chain calls together inside the same sandbox process.

```python
from haystack.components.agents import Agent
from haystack.components.generators.chat import OpenAIChatGenerator
from haystack.dataclasses import ChatMessage

from haystack_integrations.tools.e2b import E2BToolset

agent = Agent(
    chat_generator=OpenAIChatGenerator(model="gpt-4o-mini"),
    tools=E2BToolset(),
    system_prompt=(
        "You are a helpful coding assistant with access to a live Linux sandbox. "
        "Use the available tools freely to explore, write files, and run commands. "
        "All tools operate inside the same sandbox environment, so files written "
        "with write_file are immediately available to run_bash_command and read_file."
    ),
    max_agent_steps=15,
)

response = agent.run(
    messages=[
        ChatMessage.from_user(
            "Write a Python script to /tmp/primes.py that prints all prime numbers "
            "up to 50, run it, and then read the file back so I can see both the "
            "script and its output.",
        ),
    ],
)

print(response["last_message"].text)
```

### Using individual tools

If you only need a subset of the tools, you can instantiate them directly and pass them a shared `E2BSandbox`:

```python
from haystack.components.agents import Agent
from haystack.components.generators.chat import OpenAIChatGenerator

from haystack_integrations.tools.e2b import (
    E2BSandbox,
    ListDirectoryTool,
    ReadFileTool,
    RunBashCommandTool,
    WriteFileTool,
)

sandbox = E2BSandbox(sandbox_template="base", timeout=300)

agent = Agent(
    chat_generator=OpenAIChatGenerator(model="gpt-4o-mini"),
    tools=[
        RunBashCommandTool(sandbox=sandbox),
        ReadFileTool(sandbox=sandbox),
        WriteFileTool(sandbox=sandbox),
        ListDirectoryTool(sandbox=sandbox),
    ],
)
```

When using the tools standalone (outside an Agent or Pipeline), call `sandbox.warm_up()` before the first invocation and `sandbox.close()` when you are done to release the cloud resources.

### In a Pipeline

`E2BToolset` is fully serializable, so you can wrap an Agent that uses it in a Pipeline and save the Pipeline to YAML:

```python
from haystack.components.agents import Agent
from haystack.components.generators.chat import OpenAIChatGenerator
from haystack.core.pipeline import Pipeline
from haystack.dataclasses import ChatMessage

from haystack_integrations.tools.e2b import E2BToolset

agent = Agent(
    chat_generator=OpenAIChatGenerator(model="gpt-4o-mini"),
    tools=E2BToolset(sandbox_template="base", timeout=120),
    system_prompt="You are a helpful coding assistant with access to a live Linux sandbox.",
    max_agent_steps=10,
)

pipeline = Pipeline()
pipeline.add_component("agent", agent)

# Serialize and restore - all four tools still share the same E2BSandbox after the round-trip.
yaml_str = pipeline.dumps()
restored = Pipeline.loads(yaml_str)

result = restored.run(
    data={
        "agent": {
            "messages": [
                ChatMessage.from_user(
                    "Write a Python one-liner to /tmp/hello.py that prints "
                    "'Hello from E2B!', run it, then show me the output.",
                ),
            ],
        },
    },
)
print(result["agent"]["last_message"].text)
```
