BB AI SDK - Grand Central Documentation

What is the BB AI SDK?

The BB AI SDK is a Python package published on Backbase Artifactory (repo.backbase.com) that connects your agentic applications to platform services (AI Gateway, Observability) with minimal code. It enables you to build production-ready AI agents with enterprise-grade features while maintaining complete framework flexibility.

Current release: bb-ai-sdk 0.1.9 on Backbase Artifactory. Pin this version in pyproject.toml when installing from the index (see Installation). The SDK acts as a bridge between any agentic framework (Agno, LangChain, LangGraph, or custom) and Backbase platform services, requiring a few lines of code to get started.

from bb_ai_sdk.ai_gateway import AIGateway
from bb_ai_sdk.observability import configure_observability

# Scripts: configure once (FastAPI apps: pass fastapi_app=app — see Observability)
configure_observability(service_name="customer-support", framework="agno")

gateway = AIGateway.create(
    model_id="gpt-4o",
    agent_id="550e8400-e29b-41d4-a716-446655440000"
)

response = gateway.chat.completions.create(
    model="gpt-4o",
    messages=[{"role": "user", "content": "Hello!"}]
)

Key features

OpenAI-Compatible: Works with any framework that supports OpenAI SDK
Automatic Authentication: Handles API keys and agent ID validation
Built-in Observability: Automatic tracing via OpenTelemetry; export to Langfuse, Grafana, or an OTLP endpoint
Framework-independent: Native support for LangChain, LangGraph, and Agno
Zero Vendor Lock-in: Uses standard APIs - your code is fully portable

Core modules

AI gateway

Access AI models through a unified OpenAI-compatible interface

Observability

Trace and monitor your agents with OpenTelemetry; export to Langfuse, Grafana, or an OTLP endpoint

Logging and redaction

Secure logs with trace correlation; same redaction rules as span attributes

Framework adapters

Integrate with LangChain, LangGraph, and Agno

Why the BB AI SDK?

Framework flexibility

Use your preferred Agentic AI framework:

✅ LangChain: Full support with adapters
✅ LangGraph: Native async integration
✅ Agno: Direct client compatibility
✅ Custom: OpenAI SDK interface works anywhere

Enterprise features

Production-ready capabilities built-in:

🔐 Multi model AI Gateway with content safety filters and policies
📊 Observability with Langfuse, Grafana, or OTLP export via OpenTelemetry
🏢 Multi-tenant context tracking
💰 Cost tracking per organization
🔍 Distributed tracing across services

Zero vendor lock-in

Your code remains portable and future-proof:

Uses standard OpenAI SDK interface
OpenTelemetry for observability (switch backends anytime)
Framework-native objects - no custom abstractions
Works with or without the platform

Quick examples

Basic usage
With LangChain
With LangGraph
With observability

from bb_ai_sdk.ai_gateway import AIGateway

gateway = AIGateway.create(
    model_id="gpt-4o",
    agent_id="550e8400-e29b-41d4-a716-446655440000"
)

response = gateway.chat.completions.create(
    model="gpt-4o",
    messages=[
        {"role": "system", "content": "You are a helpful assistant"},
        {"role": "user", "content": "What is AI?"}
    ]
)

print(response.choices[0].message.content)

from bb_ai_sdk.ai_gateway import AIGateway
from bb_ai_sdk.ai_gateway.adapters.langchain import to_langchain

gateway = AIGateway.create(
    model_id="gpt-4o",
    agent_id="550e8400-e29b-41d4-a716-446655440000"
)

# Convert to LangChain model
model = to_langchain(gateway)

# Use with LangChain components
from langchain.schema.output_parser import StrOutputParser

chain = model | StrOutputParser()
response = chain.invoke("Tell me a joke")
print(response)

from langgraph.graph import StateGraph, END
from bb_ai_sdk.ai_gateway import AsyncAIGateway
from bb_ai_sdk.ai_gateway.adapters.langchain import to_langchain_async

gateway = AsyncAIGateway.create(
    model_id="gpt-4o",
    agent_id="550e8400-e29b-41d4-a716-446655440000"
)

model = to_langchain_async(gateway)

async def generate(state):
    response = await model.ainvoke(state["messages"])
    return {"messages": [response]}

graph = StateGraph(AgentState)
graph.add_node("generate", generate)
graph.add_edge("generate", END)
graph.set_entry_point("generate")

app = graph.compile()

import logging
from bb_ai_sdk.logging import DEFAULT_DATEFMT, STANDARD_FORMAT, init as init_logging
from bb_ai_sdk.observability import configure_observability
from bb_ai_sdk.ai_gateway import AIGateway

logging.basicConfig(
    level=logging.INFO,
    format=STANDARD_FORMAT,
    datefmt=DEFAULT_DATEFMT,
)
init_logging(capture_warnings=True)

configure_observability(
    service_name="my-agent",
)

# Gateway calls are automatically traced
gateway = AIGateway.create(
    model_id="gpt-4o",
    agent_id="550e8400-e29b-41d4-a716-446655440000"
)

response = gateway.chat.completions.create(
    model="gpt-4o",
    messages=[{"role": "user", "content": "Hello"}]
)

# View traces in Langfuse dashboard

Next steps

Get started

Install the SDK and build your first agent

AI gateway

Learn about model access and authentication

Observability

Set up tracing and monitoring

Examples

View complete working examples

​What is the BB AI SDK?

​Key features

​Core modules

AI gateway

Observability

Logging and redaction

Framework adapters

​Why the BB AI SDK?

​Framework flexibility

​Enterprise features

​Zero vendor lock-in

​Quick examples

​Next steps

Get started

AI gateway

Observability

Examples

What is the BB AI SDK?

Key features

Core modules

Why the BB AI SDK?

Framework flexibility

Enterprise features

Zero vendor lock-in

Quick examples

Next steps