Templates

A template is the rootfs image used when a sandbox boots. Declaw ships a small set of curated built-in templates for the most common workloads, and you can build your own when you need a different runtime, custom packages, or project files baked in. If you don’t pass template, sandboxes use base — a minimal Ubuntu 22.04 image with shell utilities only.

Built-in templates

Template	What’s preinstalled	Use it for
`base`	`Ubuntu 22.04`, `git`, `curl`, `wget`, `jq`, `build-essential`	Shell-only workflows, custom toolchains
`python`	`base` + `Python 3.10`, `pip`, `numpy`, `pandas`, `requests`, `httpx`, `pydantic`	Python scripts, data processing, REST clients
`node`	`base` + `Node.js 20 LTS`, `npm`, `typescript`, `yarn`	TypeScript / Node scripts, npm packages
`ai-agent`	`base` + `Python 3.10` + `Node.js 20` + LLM/agent SDKs (full list)	Agent workloads — LangChain, CrewAI, AutoGen, LlamaIndex, MCP

Built-in templates are versioned with the platform — you don’t need to build them. They are referenced by name (template="python", etc.) and pull a pre-published rootfs image from the Declaw blob store.

How to choose

Need to run a one-off shell command or your own statically-linked binary? → base
Running Python code, especially with requests / pandas / numpy? → python
Running a TypeScript or Node.js script? → node
Running an LLM-driven agent (LangChain, CrewAI, AutoGen, LlamaIndex, OpenAI Agents, MCP)? → ai-agent

If your workload needs something not in the list above (a different language runtime, a heavy ML framework like torch, project files baked in, etc.) — build a custom template. See Build a custom template below.

Worked examples

Each built-in template has a runnable cookbook example you can copy:

base — shell tools

Run git, curl, and jq end-to-end inside a fresh sandbox.

python — pandas analysis

Pipe a CSV through pandas and read back JSON results.

node — TypeScript script

Compile a .ts file with tsc and run the output with Node.js 20.

ai-agent — frameworks check

Boot an agent sandbox and verify the major LLM-framework SDKs import.

ai-agent — KYC with CrewAI

Fintech: four-agent CrewAI KYC pipeline with PII + injection defense.

ai-agent — prior auth with LangGraph

Health-tech: LangGraph workflow with PHI redact + rehydrate around GPT-4.1.

Build a custom template

Use Template.build() when none of the built-ins fit — most commonly when you need additional Python packages, project files copied in, or a different base image.

Python
TypeScript

from declaw import Template

template = Template.build(
    template="""
FROM ubuntu:22.04
RUN apt-get update && apt-get install -y python3 python3-pip
RUN pip3 install numpy pandas scikit-learn matplotlib
WORKDIR /workspace
""",
    alias="data-science",
)
print(template.template_id)  # tpl-abc123

import { Template } from '@declaw/sdk';

const template = await Template.build({
  template: `
FROM ubuntu:22.04
RUN apt-get update && apt-get install -y python3 python3-pip
RUN pip3 install numpy pandas scikit-learn matplotlib
`,
  alias: 'data-science',
});
console.log(template.templateId);

Build with options

Python
TypeScript

template = Template.build(
    template="""
FROM ubuntu:22.04
RUN pip3 install anthropic openai langchain
COPY ./prompts /workspace/prompts
""",
    alias="llm-agent",
    cpu_count=2,
    memory_mb=1024,
    on_build_logs=lambda line: print(line),  # stream build output
)

const template = await Template.build({
  template: `
FROM ubuntu:22.04
RUN pip3 install anthropic openai langchain
`,
  alias: 'llm-agent',
  cpuCount: 2,
  memoryMb: 1024,
  onBuildLogs: (line) => console.log(line),
});

TemplateBase model

TemplateBase describes a template configuration before it is built.

Field	Type	Description
`template`	`str`	Dockerfile content for the template image
`alias`	`str \| None`	Human-readable name for the template
`cpu_count`	`int`	Default vCPUs for sandboxes from this template (1–8)
`memory_mb`	`int`	Default RAM for sandboxes from this template (128–8192)
`copy_files`	`list[CopyItem]`	Files to copy into the template at build time

Copy files into a template

Use CopyItem to embed files from the host into the template image at build time.

from declaw import Template, CopyItem

template = Template.build(
    template="""
FROM ubuntu:22.04
RUN pip3 install -r /workspace/requirements.txt
""",
    copy_files=[
        CopyItem(
            source_path="./requirements.txt",
            dest_path="/workspace/requirements.txt",
        ),
        CopyItem(
            source_path="./scripts/",
            dest_path="/workspace/scripts/",
        ),
    ],
    alias="my-project",
)

CopyItem model

Field	Type	Description
`source_path`	`str`	Path on the host machine
`dest_path`	`str`	Destination path inside the template image

Start background build

Use build_in_background() to start a build without waiting, then poll the status.

Python

build_id = Template.build_in_background(
    template="""
FROM ubuntu:22.04
RUN apt-get install -y heavy-dependency
""",
    alias="slow-build",
)
print(f"Build started: {build_id}")

Check build status

Python

from declaw import TemplateBuildStatus
import time

while True:
    status = Template.get_build_status(build_id)
    print(status.status, status.progress)

    if status.status == TemplateBuildStatus.done:
        print(f"Template ready: {status.template_id}")
        break
    elif status.status == TemplateBuildStatus.error:
        print(f"Build failed: {status.error}")
        break

    time.sleep(2)

BuildInfo model

Field	Type	Description
`build_id`	`str`	Unique build identifier
`template_id`	`str \| None`	Set once build completes successfully
`status`	`TemplateBuildStatus`	`pending`, `building`, `done`, or `error`
`progress`	`float`	Build progress 0.0–1.0
`error`	`str \| None`	Error message if status is `error`
`logs`	`list[str]`	Build log lines streamed during build

TemplateBuildStatus enum

Value	Description
`pending`	Build queued, not yet started
`building`	Build is in progress
`done`	Build succeeded — `template_id` is set
`error`	Build failed — `error` contains the reason

Use a custom template

from declaw import Sandbox

sbx = Sandbox.create(template="data-science")
result = sbx.commands.run("python3 -c 'import pandas; print(pandas.__version__)'")
print(result.stdout)

Or by template ID:

sbx = Sandbox.create(template="tpl-abc123")

Async template operations

from declaw import AsyncTemplate

template = await AsyncTemplate.build(
    template="""
FROM ubuntu:22.04
RUN pip3 install torch transformers
""",
    alias="ml-inference",
)

Template builds run on the server. Build time depends on the packages being installed and typically takes 1–5 minutes. Use build_in_background() for large builds to avoid blocking your process.

Getting Started

Core Features

CLI

Security

Platform

Deployment

Architecture

Built-in templates

How to choose

Worked examples

base — shell tools

python — pandas analysis

node — TypeScript script

ai-agent — frameworks check

ai-agent — KYC with CrewAI

ai-agent — prior auth with LangGraph

Build a custom template

Build with options

TemplateBase model

Copy files into a template

CopyItem model

Start background build

Check build status

BuildInfo model

TemplateBuildStatus enum

Use a custom template

Async template operations

​Built-in templates

​How to choose

​Worked examples

base — shell tools

python — pandas analysis

node — TypeScript script

ai-agent — frameworks check

ai-agent — KYC with CrewAI

ai-agent — prior auth with LangGraph

​Build a custom template

​Build with options

​TemplateBase model

​Copy files into a template

​CopyItem model

​Start background build

​Check build status

​BuildInfo model

​TemplateBuildStatus enum

​Use a custom template

​Async template operations

Built-in templates

How to choose

Worked examples

Build a custom template

Build with options

TemplateBase model

Copy files into a template

CopyItem model

Start background build

Check build status

BuildInfo model

TemplateBuildStatus enum

Use a custom template

Async template operations