ai-agent — frameworks check

The ai-agent template ships Python 3.10 + Node.js 20 plus the major LLM and agent SDKs preinstalled in one resolved pip set:

LLM clients: openai, anthropic, litellm
Agent frameworks: langchain + langchain-openai + langchain-anthropic, crewai, autogen-agentchat, llama-index-core + llama-index-llms-openai, haystack-ai, pydantic-ai-slim
Helpers: instructor, tiktoken, tenacity
MCP: mcp, fastmcp
Storage / tracing: chromadb, arize-phoenix, opentelemetry

Pick it whenever your sandbox runs an LLM-driven agent — it removes a 30–60s pip install from every cold boot.

Heavy ML deps (torch, transformers, sentence-transformers) are intentionally not included to keep the image small. Use the OpenAI / Anthropic embedding APIs (already wired through llama-index-embeddings-openai) when you need embeddings, or build a custom template for local inference.

What you’ll learn

Picking template="ai-agent" to skip framework pip install steps
Verifying the agent-SDKs import cleanly inside the sandbox
Running a minimal LangChain expression (without an LLM call) to prove the framework is wired up

Prerequisites

export DECLAW_API_KEY="your-api-key"
export DECLAW_DOMAIN="your-declaw-instance.example.com:8080"

This example does not call any LLM — it only verifies the SDKs load. For a full LLM-in-sandbox example, see Cookbook → LLM Providers.

Code

Python
TypeScript

import textwrap

from declaw import Sandbox


CHECK = textwrap.dedent("""
    import importlib

    targets = [
        "openai", "anthropic", "litellm",
        "langchain", "langchain_openai", "langchain_anthropic",
        "crewai",
        "autogen_agentchat",
        "llama_index.core",
        "haystack",
        "pydantic_ai",
        "instructor", "tiktoken",
        "mcp", "fastmcp",
        "chromadb",
        "phoenix", "opentelemetry",
    ]

    for name in targets:
        try:
            mod = importlib.import_module(name)
            ver = getattr(mod, "__version__", "n/a")
        except Exception as e:
            ver = f"MISSING ({type(e).__name__})"
        print(f"  {name:32s} {ver}")
""")

LANGCHAIN_DEMO = textwrap.dedent("""
    # No LLM call — proves the prompt + parser pipeline is intact.
    from langchain_core.prompts import ChatPromptTemplate
    from langchain_core.output_parsers import StrOutputParser

    prompt = ChatPromptTemplate.from_messages([
        ("system", "Echo {role} pings."),
        ("human", "ping #{n}"),
    ])
    msg = prompt.format_messages(role="agent", n=42)
    print("rendered prompt:", msg[1].content)

    parser = StrOutputParser()
    print("parser ok:", parser.parse("hello world"))
""")


def main() -> None:
    sbx = Sandbox.create(template="ai-agent", timeout=180)
    try:
        print("=== framework SDK versions ===")
        r = sbx.commands.run('python3 -c "' + CHECK.replace('"', r'\"') + '"')
        print(r.stdout)
        if r.exit_code != 0:
            print("import failed:", r.stderr)
            return

        print("=== LangChain pipeline smoke test ===")
        sbx.files.write("/tmp/lc.py", LANGCHAIN_DEMO)
        r = sbx.commands.run("python3 /tmp/lc.py")
        print(r.stdout)
        if r.exit_code != 0:
            print("langchain demo failed:", r.stderr)
    finally:
        sbx.kill()


if __name__ == "__main__":
    main()

import "dotenv/config";
import { Sandbox } from "@declaw/sdk";

const CHECK = `
import importlib

targets = [
    "openai", "anthropic", "litellm",
    "langchain", "langchain_openai", "langchain_anthropic",
    "crewai",
    "autogen_agentchat",
    "llama_index.core",
    "haystack",
    "pydantic_ai",
    "instructor", "tiktoken",
    "mcp", "fastmcp",
    "chromadb",
    "phoenix", "opentelemetry",
]

for name in targets:
    try:
        mod = importlib.import_module(name)
        ver = getattr(mod, "__version__", "n/a")
    except Exception as e:
        ver = f"MISSING ({type(e).__name__})"
    print(f"  {name:32s} {ver}")
`;

const LANGCHAIN_DEMO = `
from langchain_core.prompts import ChatPromptTemplate
from langchain_core.output_parsers import StrOutputParser

prompt = ChatPromptTemplate.from_messages([
    ("system", "Echo {role} pings."),
    ("human", "ping #{n}"),
])
msg = prompt.format_messages(role="agent", n=42)
print("rendered prompt:", msg[1].content)

parser = StrOutputParser()
print("parser ok:", parser.parse("hello world"))
`;

async function main(): Promise<void> {
  const sbx = await Sandbox.create({ template: "ai-agent", timeout: 180 });
  try {
    console.log("=== framework SDK versions ===");
    await sbx.files.write("/tmp/check.py", CHECK);
    let r = await sbx.commands.run("python3 /tmp/check.py");
    console.log(r.stdout);
    if (r.exitCode !== 0) {
      console.log("import failed:", r.stderr);
      return;
    }

    console.log("=== LangChain pipeline smoke test ===");
    await sbx.files.write("/tmp/lc.py", LANGCHAIN_DEMO);
    r = await sbx.commands.run("python3 /tmp/lc.py");
    console.log(r.stdout);
    if (r.exitCode !== 0) {
      console.log("langchain demo failed:", r.stderr);
    }
  } finally {
    await sbx.kill();
  }
}

main().catch(console.error);

Expected output

=== framework SDK versions ===
  openai                           2.31.0
  anthropic                        ...
  litellm                          ...
  langchain                        1.2.15
  langchain_openai                 ...
  langchain_anthropic              ...
  crewai                           0.193.2
  autogen_agentchat                ...
  llama_index.core                 ...
  haystack                         ...
  pydantic_ai                      ...
  instructor                       ...
  tiktoken                         ...
  mcp                              ...
  fastmcp                          ...
  chromadb                         ...
  phoenix                          ...
  opentelemetry                    ...

=== LangChain pipeline smoke test ===
rendered prompt: ping #42
parser ok: hello world

When you actually run LLM calls from inside an ai-agent sandbox, attach a SecurityPolicy with PII redaction + a network allowlist scoped to your LLM provider. See Agent-in-Sandbox → Fully Secured for a worked example with all four guardrails enabled.

Getting Started

Built-in Templates

Commands

PTY

OpenAI Agents

Filesystem

Volumes

Network Policies

PII Protection

Security Features

LLM Providers

Framework Integrations

Agent-in-Sandbox

Real-World Patterns

Security Demos

ai-agent — frameworks check

What you’ll learn

Prerequisites

Code

Expected output

​What you’ll learn

​Prerequisites

​Code

​Expected output

What you’ll learn

Prerequisites

Code

Expected output