# What is Agent Tool?
Instead of writing custom tools based on Acontext low-level SDKs,
Acontext provides ready-to-use tools that integrate seamlessly with popular LLM providers.
## Available Tool Sets [#available-tool-sets]
| Tool Set | Description |
| --------------------------------- | ------------------------------------------ |
| [Sandbox Tools](/tool/bash_tools) | Execute code, edit files, export results |
| [Disk Tools](/tool/disk_tools) | Read and write files to persistent storage |
| [Skill Tools](/tool/skill_tools) | Access agent skills and knowledge |
## How It Works [#how-it-works]
## Quick Example [#quick-example]
```python title="Python"
from acontext import AcontextClient
from acontext.agent.sandbox import SANDBOX_TOOLS
client = AcontextClient()
sandbox = client.sandboxes.create()
disk = client.disks.create()
```
```typescript title="TypeScript"
import { AcontextClient, SANDBOX_TOOLS } from '@acontext/acontext';
const client = new AcontextClient();
const sandbox = await client.sandboxes.create();
const disk = await client.disks.create();
```
```python title="Python"
ctx = SANDBOX_TOOLS.format_context(client, sandbox.sandbox_id, disk.id)
```
```typescript title="TypeScript"
const ctx = await SANDBOX_TOOLS.formatContext(client, sandbox.sandbox_id, disk.id);
```
```python title="Python"
context_prompt = ctx.get_context_prompt()
messages = [
{"role": "system", "content": f"You have sandbox access.\n\n{context_prompt}"},
{"role": "user", "content": "Run a hello world script"}
]
```
```typescript title="TypeScript"
const contextPrompt = ctx.getContextPrompt();
const messages = [
{ role: "system", content: `You have sandbox access.\n\n${contextPrompt}` },
{ role: "user", content: "Run a hello world script" }
];
```
```python title="Python"
tools = SANDBOX_TOOLS.to_openai_tool_schema()
response = openai_client.chat.completions.create(
model="gpt-4.1", messages=messages, tools=tools
)
```
```typescript title="TypeScript"
const tools = SANDBOX_TOOLS.toOpenAIToolSchema();
const response = await openai.chat.completions.create({
model: "gpt-4.1", messages, tools
});
```
```python title="Python"
for tc in response.choices[0].message.tool_calls:
result = SANDBOX_TOOLS.execute_tool(ctx, tc.function.name, json.loads(tc.function.arguments))
```
```typescript title="TypeScript"
for (const tc of response.choices[0].message.tool_calls) {
const result = await SANDBOX_TOOLS.executeTool(ctx, tc.function.name, JSON.parse(tc.function.arguments));
}
```
## Next Steps [#next-steps]
Execute code in isolated containers
Persistent file storage for agents
Access reusable agent skills
Create and manage skills