Question 1

Which frameworks does MACAW support?

Accepted Answer

MACAW provides drop-in secure adapters for popular AI frameworks:

| Framework | Adapter | Status |
|-----------|---------|--------|
| OpenAI | SecureOpenAI | Production-ready |
| Anthropic/Claude | SecureAnthropic | Production-ready |
| LangChain | Full suite (LLMs, memory, agents, tools) | Production-ready |
| MCP (Model Context Protocol) | SecureMCP | Production-ready |

Install with:
bash
pip install macaw-adapters[all]          Everything

Open source: All adapters are Apache 2.0 licensed

Question 2

What is a MACAW adapter?

Accepted Answer

A MACAW adapter is a thin wrapper that extends the MACAWClient SDK to provide drop-in replacements for popular AI libraries. The architecture:

┌─────────────────────────────────────────────────────────┐
│                    Your Application                      │
│   from macaw_adapters.openai import SecureOpenAI        │
└─────────────────────────────────────────────────────────┘
                          │
                          ▼
┌─────────────────────────────────────────────────────────

Question 3

What is SecureOpenAI?

Accepted Answer

SecureOpenAI is a drop-in replacement for the OpenAI Python client. Same API, same methods - but with MACAW protection.

python
 Before
from openai import OpenAI
client = OpenAI()

After (one import change)
from macaw_adapters.openai import SecureOpenAI
client = SecureOpenAI(app_name="customer-support-bot")

Same API works exactly the same
response = client.chat.completions.create(
    model="gpt-4",
    messages=[{"role": "user", "content": "Hello"}]
)

Question 4

What is SecureAnthropic?

Accepted Answer

SecureAnthropic is a drop-in replacement for the Anthropic Python client:

python
 Before
from anthropic import Anthropic
client = Anthropic()

After
from macaw_adapters.anthropic import SecureAnthropic
client = SecureAnthropic(app_name="research-assistant")

Same API
response = client.messages.create(
    model="claude-sonnet-4-20250514",
    max_tokens=100,
    messages=[{"role": "user", "content": "Hello"}]
)

Question 5

Does MACAW work with LangChain?

Accepted Answer

Yes - comprehensive support. MACAW provides drop-in replacements for LangChain's core components:

| Category | MACAW Components |
|----------|-----------------|
| LLM Classes | ChatOpenAI, ChatAnthropic (or explicit SecureChatOpenAI, SecureChatAnthropic) |
| Memory | ConversationBufferMemory, ConversationBufferWindowMemory, ConversationSummaryMemory |
| Agents | create_react_agent, create_openai_functions_agent, AgentExecutor |
| Tools | SecureToolWrapper, wrap_tools, @secure_tool decorator |
|

Question 6

What is SecureMCP?

Accepted Answer

SecureMCP wraps MCP (Model Context Protocol) servers with MACAW policy enforcement. Every tool invocation is authenticated, authorized, and logged.

python
from macaw_adapters.mcp import SecureMCP, Context

server = SecureMCP(app_name="calculator-service")

@server.tool()
def calculate(expression: str, ctx: Context) -> str:
     This tool is now policy-protected
     ctx carries the authenticated caller identity
    return eval_safe(expression)

Question 7

What is the `app_name` parameter?

Accepted Answer

app_name identifies your application in MACAW. It's used for: - Registration: Your app registers as app: - Tool namespacing: Tools become tool:/generate, tool:/complete, etc. - Policy targeting: Policies can allow/deny specific apps (e.g., tool:customer-support-bot/) - Audit logs: All operations are tagged with your app name Choose something descriptive and consistent - it appears throughout the Console.

Question 8

How do I bind user identity to adapter calls?

Accepted Answer

Pass the user's JWT token to carry their identity:

python
client = SecureOpenAI(
    app_name="my-app",
    jwt_token=user_jwt,   From your IdP
    user_name="alice"
)
 All calls now carry Alice's identity
 Policies apply based on her claims

For multi-user SaaS apps, use bind_to_user() to efficiently share one service across many users. See [Identity Integration](/docs/identity) for details.

Question 9

Can I build my own adapter?

Accepted Answer

Yes. All adapters extend MACAWClient from the SDK. To build your own: 1. Import MACAWClient from macaw_client 2. Create tools that wrap your target API 3. Register tools with MACAW-compliant names (tool:/) 4. Route calls through invoke_tool() for policy enforcement python from macaw_client import MACAWClient class SecureMyAPI: def __init__(self, app_name: str): self.client = MACAWClient( app_name=app_name, tools={ f"tool:{app

Question 10

Can I use async/await with MACAW adapters?

Accepted Answer

Yes. All adapters support both sync and async:

python
 Sync
response = client.chat.completions.create(...)

Async
response = await client.chat.completions.acreate(...)

The async versions work exactly like the underlying OpenAI/Anthropic async APIs.

Question 11

Do adapters support streaming?

Accepted Answer

Yes. Streaming works exactly like the underlying APIs:

python
stream = client.chat.completions.create(
    model="gpt-4",
    messages=[{"role": "user", "content": "Hello"}],
    stream=True
)

for chunk in stream:
    print(chunk.choices[0].delta.content, end="")

Policy enforcement happens at the start of the stream. Audit logging captures the complete interaction.

Question 12

Do I need to change my existing code?

Accepted Answer

Minimal changes. For standard-compliant OpenAI, Anthropic, LangChain, or MCP:

1. Change the import
2. Add app_name parameter

That's it. Your existing API calls, parameters, and logic stay exactly the same.

Adapters & Frameworks