Overview
Capsule is a runtime for coordinating AI agent tasks in isolated environments. It is designed to handle, long-running workflows, large-scale processing, autonomous decision-making securely, 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:
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
For TypeScript and JavaScript, use the task() wrapper function with full access to the npm ecosystem:
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 can define the main task itself, but it's recommended to set it manually.
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:
{
"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 successfullyresult— 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 taskduration_ms— Execution time in millisecondsretries— Number of retry attempts that occurredfuel_consumed— CPU resources used (see Compute Levels)
Quick Start
Python
Create hello.py:
from capsule import task @task(name="main", compute="LOW", ram="64MB") def main() -> str: return "Hello from Capsule!"
Run it:
TypeScript / JavaScript
npm install -g @capsule-run/cli npm install @capsule-run/sdk
Create hello.ts:
import { task } from "@capsule-run/sdk"; export const main = task({ name: "main", compute: "LOW", ram: "64MB" }, (): string => { return "Hello from Capsule!"; });
Run it:
Tip
Use --verbose to display real-time task execution details.
Documentation (v0.5.1)
Task Configuration Options
Configure your tasks with these parameters:
| Parameter | Description | Type | Default | Example |
|---|---|---|---|---|
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"] |
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:
# 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:
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:
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.
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(); });
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.
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:
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:
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:
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 }; });
Compatibility
Note
TypeScript/JavaScript has broader compatibility than Python since it doesn't rely on native bindings.
Python: Pure Python packages and standard library modules work. Packages with C extensions (numpy, pandas) are not yet supported.
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+
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
- Fork the repository
- Create a feature branch:
git checkout -b feature/amazing-feature - Run tests:
cargo test - Open a Pull Request
Need help? Open an issue
Credits
Capsule builds on these open source projects:
- componentize-py – Python to WebAssembly Component compilation
- jco – JavaScript toolchain for WebAssembly Components
- wasmtime – WebAssembly runtime
- WASI – WebAssembly System Interface
License
This project is licensed under the Apache License 2.0 - see the LICENSE file for details.