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# ```Capsule```
**A secure, durable runtime for AI agents**
[](https://github.com/mavdol/capsule/actions/workflows/ci.yml)
[Getting Started](#getting-started) • [Documentation](#documentation-v063) • [Contributing](#contributing)
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---
## Overview
```Capsule``` is a runtime for coordinating AI agent tasks in isolated environments. It is designed to handle untrusted code execution, long-running workflows, large-scale processing, or even multi-agent systems.
Each task runs inside its own WebAssembly sandbox, providing:
- **Isolated execution**: Each task runs isolated from your host system
- **Resource limits**: Set CPU, memory, and timeout limits per task
- **Automatic retries**: Handle failures without manual intervention
- **Lifecycle tracking**: Monitor which tasks are running, completed, or failed
This enables safe task-level execution of untrusted code within AI agent systems.
## How It Works
### With Python
Simply annotate your Python functions with the `@task` decorator:
```python
from capsule import task
@task(name="analyze_data", compute="MEDIUM", ram="512MB", timeout="30s", max_retries=1)
def analyze_data(dataset: list) -> dict:
"""Process data in an isolated, resource-controlled environment."""
# Your code runs safely in a Wasm sandbox
return {"processed": len(dataset), "status": "complete"}
```
### With TypeScript / JavaScript
Use the `task()` wrapper function with full access to the npm ecosystem:
```typescript
import { task } from "@capsule-run/sdk";
export const analyzeData = task({
name: "analyze_data",
compute: "MEDIUM",
ram: "512MB",
timeout: "30s",
maxRetries: 1
}, (dataset: number[]): object => {
// Your code runs safely in a Wasm sandbox
return { processed: dataset.length, status: "complete" };
});
// The "main" task is required as the entrypoint
export const main = task({
name: "main",
compute: "HIGH"
}, () => {
return analyzeData([1, 2, 3, 4, 5]);
});
```
> [!NOTE]
> The runtime requires a task named `"main"` as the entry point. Python will create one automatically if none is defined, but it's recommended to set it explicitly.
When you run `capsule run main.py` (or `main.ts`), your code is compiled into a WebAssembly module and executed in a dedicated sandbox to isolate tasks.
Each task operates within its own sandbox with configurable resource limits, ensuring that failures are contained and don't cascade to other parts of your workflow. The host system controls every aspect of execution, from CPU allocation via Wasm fuel metering to memory constraints and timeout enforcement.
### Response Format
Every task returns a structured JSON envelope containing both the result and execution metadata:
```json
{
"success": true,
"result": "Hello from Capsule!",
"error": null,
"execution": {
"task_name": "data_processor",
"duration_ms": 1523,
"retries": 0,
"fuel_consumed": 45000
}
}
```
**Response fields:**
- `success` — Boolean indicating whether the task completed successfully
- `result` — The actual return value from your task (json, string, null on failure etc.)
- `error` — Error details if the task failed (`{ error_type: string, message: string }`)
- `execution` — Performance metrics:
- `task_name` — Name of the executed task
- `duration_ms` — Execution time in milliseconds
- `retries` — Number of retry attempts that occurred
- `fuel_consumed` — CPU resources used (see [Compute Levels](#compute-levels))
## Getting Started
### Python
```bash
pip install capsule-run
```
Create `hello.py`:
```python
from capsule import task
@task(name="main", compute="LOW", ram="64MB")
def main() -> str:
return "Hello from Capsule!"
```
Run it:
```bash
capsule run hello.py
```
### TypeScript / JavaScript
```bash
npm install -g @capsule-run/cli
npm install @capsule-run/sdk
```
Create `hello.ts`:
```typescript
import { task } from "@capsule-run/sdk";
export const main = task({
name: "main",
compute: "LOW",
ram: "64MB"
}, (): string => {
return "Hello from Capsule!";
});
```
Run it:
```bash
capsule run hello.ts
```
> [!TIP]
> Add `--verbose` to see real-time task execution details.
## Production
Running source code directly (like `.py` or `.ts`) evaluates and compiles your file at runtime. While great for development, this compilation step adds a few seconds of latency. For use cases where sub-second latency is critical, you should build your tasks ahead of time.
```bash
# Generates an optimized hello.wasm file
capsule build hello.py --export
# Execute the compiled artifact directly
capsule exec hello.wasm
```
> [!NOTE]
> Or from your existing code:
>
> ```python
> from capsule import run
>
> result = await run(
> file="./hello.wasm", # or `hello.py`
> args=[]
> )
>
> print(f"Task completed: {result['result']}")
> ```
>
> See [in-code usage documentation](#in-code-usage) for details on both Python and TypeScript integration.
Executing a `.wasm` file bypasses the compiler completely, reducing initialization time to milliseconds while using a natively optimized (`.cwasm`) format behind the scenes.
## Documentation (v0.6.3)
### Task Configuration Options
Configure your tasks with these parameters:
| `name` | Task identifier | `str` | function name (Python) / *required* (TS) | `"process_data"` |
| `compute` | CPU allocation level: `"LOW"`, `"MEDIUM"`, or `"HIGH"` | `str` | `"MEDIUM"` | `"HIGH"` |
| `ram` | Memory limit for the task | `str` | unlimited | `"512MB"`, `"2GB"` |
| `timeout` | Maximum execution time | `str` | unlimited | `"30s"`, `"5m"`, `"1h"` |
| `max_retries` / `maxRetries` | Number of retry attempts on failure | `int` | `0` | `3` |
| `allowed_files` / `allowedFiles` | Folders accessible in the sandbox | `list` | `[]` | `["./data", "./output"]` |
| `allowed_hosts` / `allowedHosts` | Domains accessible in the sandbox | `list` | `["*"]` | `["api.openai.com", "*.anthropic.com"]` |
| `env_variables` / `envVariables` | Environment variables accessible in the sandbox | `list` | `[]` | `["API_KEY"]` |
### Compute Levels
Capsule controls CPU usage through WebAssembly's **fuel mechanism**, which meters instruction execution. The compute level determines how much fuel your task receives.
- **LOW** provides minimal allocation for lightweight tasks
- **MEDIUM** offers balanced resources for typical workloads
- **HIGH** grants maximum fuel for compute-intensive operations
- **CUSTOM** to specify an exact fuel value (e.g., `compute="1000000"`) for precise control over execution limits.
### Project Configuration (Optional)
You can create a `capsule.toml` file in your project root to set default options for all tasks and define workflow metadata:
```toml
# capsule.toml
[workflow]
name = "My AI Workflow"
version = "1.0.0"
entrypoint = "src/main.py" # Default file when running `capsule run`
[tasks]
default_compute = "MEDIUM"
default_ram = "256MB"
default_timeout = "30s"
default_max_retries = 2
```
With an entrypoint defined, you can simply run:
```bash
capsule run
```
Task-level options always override these defaults when specified.
### HTTP Client API
#### Python
The standard Python `requests` library and socket-based networking aren't natively compatible with WebAssembly's sandboxed I/O model. Capsule provides its own HTTP client that works within the Wasm environment:
```python
from capsule import task
from capsule.http import get, post, put, delete
@task(name="http_example", compute="MEDIUM", timeout="30s")
def main() -> dict:
"""Example demonstrating HTTP client usage within a task."""
# GET request
response = get("https://api.example.com/data")
# POST with JSON body
response = post("https://api.example.com/submit", json={"key": "value"})
# Response methods
is_ok = response.ok() # Returns True if status code is 2xx
status = response.status_code # Get the HTTP status code
data = response.json() # Parse response as JSON
text = response.text() # Get response as text
return {"status": status, "success": is_ok}
```
#### TypeScript / JavaScript
Standard libraries like `fetch` are already compatible, so no custom HTTP client is needed for TypeScript/JavaScript.
```typescript
import { task } from "@capsule-run/sdk";
export const main = task({
name: "main",
compute: "MEDIUM"
}, async () => {
const response = await fetch("https://api.example.com/data");
return response.json();
});
```
### Network Access
Tasks can make HTTP requests to domains specified in `allowed_hosts`. By default, all outbound requests are allowed (`["*"]`). Restrict access by providing a whitelist of domains.
#### Python
```python
from capsule import task
from capsule.http import get
@task(name="main", allowed_hosts=["api.openai.com", "*.anthropic.com"])
def main() -> dict:
response = get("https://api.openai.com/v1/models")
return response.json()
```
#### TypeScript / JavaScript
```typescript
import { task } from "@capsule-run/sdk";
export const main = task({
name: "main",
allowedHosts: ["api.openai.com", "*.anthropic.com"]
}, async () => {
const response = await fetch("https://api.openai.com/v1/models");
return response.json();
});
```
### File Access
Tasks can read and write files within directories specified in `allowed_files`. Any attempt to access files outside these directories is not possible.
> [!NOTE]
> Currently, `allowed_files` supports directory paths, not individual files.
#### Python
Python's standard file operations work normally. Use `open()`, `os`, `pathlib`, or any file manipulation library.
```python
from capsule import task
@task(name="restricted_writer", allowed_files=["./output"])
def restricted_writer() -> None:
with open("./output/result.txt", "w") as f:
f.write("result")
@task(name="main")
def main() -> str:
restricted_writer()
```
#### TypeScript / JavaScript
Common Node.js built-ins are available. Use the standard `fs` module:
```typescript
import { task } from "@capsule-run/sdk";
import fs from "fs/promises";
export const restrictedWriter = task({
name: "restricted_writer",
allowedFiles: ["./output"]
}, async () => {
await fs.writeFile("./output/result.txt", "result");
});
export const main = task({ name: "main", allowedFiles: ["./data"] }, async () => {
await restrictedWriter();
return await fs.readFile("./data/input.txt", "utf8");
});
```
### Environment Variables
Tasks can access environment variables to read configuration, API keys, or other runtime settings.
#### Python
Use Python's standard `os.environ` to access environment variables:
```python
from capsule import task
import os
@task(name="main", env_variables=["API_KEY"])
def main() -> dict:
api_key = os.environ.get("API_KEY")
return {"api_key": api_key}
```
#### TypeScript / JavaScript
Use the standard `process.env` to access environment variables:
```typescript
import { task } from "@capsule-run/sdk";
export const main = task({
name: "main",
envVariables: ["API_KEY"]
}, () => {
const apiKey = process.env.API_KEY;
return { apiKeySet: apiKey !== undefined };
});
```
### In-Code Usage
The `run()` function lets you execute tasks programmatically from your code instead of using the CLI. The `args` are automatically forwarded as parameters to the `main` task.
#### Python
```python
from capsule import run
result = await run(
file="./sandbox.py", # or `sandbox.wasm`
args=["code to execute"]
)
```
Create `sandbox.py`:
```python
from capsule import task
@task(name="main", compute="LOW", ram="64MB")
def main(code: str) -> str:
return exec(code)
```
#### TypeScript / JavaScript
> [!IMPORTANT]
> You need `@capsule-run/cli` in your dependencies to use the runner functions in TypeScript.
```typescript
import { run } from '@capsule-run/sdk/runner';
const result = await run({
file: './sandbox.ts', // or `sandbox.wasm`
args: ['code to execute']
});
```
Create `sandbox.ts`:
```typescript
import { task } from "@capsule-run/sdk";
export const main = task({
name: "main",
compute: "LOW",
ram: "64MB"
}, (code: string): string => {
return eval(code);
});
```
### Cache Management
When you run your code, Capsule creates a `.capsule` folder in your project root. This is the build cache. It stores compiled artifacts so subsequent runs are fast (from seconds to few milliseconds).
> [!TIP]
> `.capsule` should be added to `.gitignore`. The cache is specific to your own environment and will be regenerated automatically.
```
.capsule/
├── wasm/
│ ├── main_a1b2c3d4.wasm # Compiled WebAssembly module
│ └── main_a1b2c3d4.cwasm # Native precompiled cache
├── wit/ # Interface definitions
└── trace.db # Execution logs
```
Use `capsule build` to precompile ahead of time and skip the compilation cost on the first run:
```bash
capsule build main.ts # or `main.py`
```
## Compatibility
> [!NOTE]
> TypeScript/JavaScript has broader compatibility than Python since it doesn't rely on native bindings.
**Python:** Only pure Python is supported in sandboxes (no C extensions like `numpy` or `pandas`). However, your host code using `run()` has access to the full Python ecosystem, any pip package and native extensions. (see [in-code usage](#in-code-usage))
**TypeScript/JavaScript:** npm packages and ES modules work. Common Node.js built-ins are available. If you have any trouble with a built-in, do not hesitate to open an issue.
## Contributing
Contributions are welcome!
### Development setup
**Prerequisites:** Rust (latest stable), Python 3.13+, Node.js 22+
```bash
git clone https://github.com/mavdol/capsule.git
cd capsule
# Build and install CLI
cargo install --path crates/capsule-cli
# Python SDK (editable install)
pip install -e crates/capsule-sdk/python
# TypeScript SDK (link for local dev)
cd crates/capsule-sdk/javascript
npm install && npm run build && npm link
# Then in your project: npm link @capsule-run/sdk
```
### How to contribute
1. **Fork** the repository
2. **Create** a feature branch: `git checkout -b feature/amazing-feature`
3. **Run tests**: `cargo test` (only needed if modifying `crates/capsule-cli` or `crates/capsule-core`)
4. **Open** a Pull Request
Need help? [Open an issue](https://github.com/mavdol/capsule/issues)
## Credits
Capsule builds on these open source projects:
- [componentize-py](https://github.com/bytecodealliance/componentize-py) – Python to WebAssembly Component compilation
- [jco](https://github.com/bytecodealliance/jco) – JavaScript toolchain for WebAssembly Components
- [wasmtime](https://github.com/bytecodealliance/wasmtime) – WebAssembly runtime
- [WASI](https://github.com/WebAssembly/WASI) – WebAssembly System Interface
## License
This project is licensed under the **Apache License 2.0** - see the [LICENSE](LICENSE) file for details.