unifiedai-sdk 1.0.8

Unified SDK for multi-provider LLM comparison (Cerebras, AWS Bedrock) with OpenAI-compatible interface.

UnifiedAI SDK

OpenAI-compatible Python SDK unifying multiple providers (Cerebras, AWS Bedrock) with Solo and Comparison modes, strict models, and built‑in telemetry.

Highlights

• OpenAI-compatible API: UnifiedAI().chat.completions.create(...) (sync) and AsyncUnifiedAI (async)
• Multi-Provider Support: Cerebras and AWS Bedrock (extensible architecture)
• Dual Modes: Solo execution or side-by-side Comparison
• Rich Metrics: Duration, TTFB, tokens/sec, provider-specific timing
• Observability: Structured logs, Prometheus metrics, OpenTelemetry tracing hooks
• Flexible Credentials: Pass at client construction or use environment variables

Install

From PyPI (core):

pip install unifiedai-sdk

Optional extras:

# AWS Bedrock support (requires boto3)
pip install "unifiedai-sdk[bedrock]"

# HTTP/2 support for httpx
pip install "unifiedai-sdk[http2]"

From GitHub (optional):

pip install git+https://github.com/<your-org-or-user>/<your-repo>.git#subdirectory=cerebras

Usage

Sync (scripts/CLI)

Cerebras Example:

from unifiedai import UnifiedAI

client = UnifiedAI(
    provider="cerebras",
    model="llama3.1-8b",
    credentials={"api_key": "csk-..."},  # or set CEREBRAS_KEY in env
)
resp = client.chat.completions.create(
    messages=[{"role": "user", "content": "Hello"}]
)
print(resp.choices[0].message["content"])
print(f"Tokens: {resp.usage.total_tokens}")

Bedrock Example:

from unifiedai import UnifiedAI

client = UnifiedAI(
    provider="bedrock",
    model="qwen.qwen3-32b-v1:0",
    credentials={
        "aws_access_key_id": "...",
        "aws_secret_access_key": "...",
        "region_name": "us-east-1"
    }
)
resp = client.chat.completions.create(
    messages=[{"role": "user", "content": "Hello"}]
)
print(resp.choices[0].message["content"])

Async (web backends)

from unifiedai import AsyncUnifiedAI

async with AsyncUnifiedAI(provider="cerebras", model="llama3") as client:
    resp = await client.chat.completions.create(
        messages=[{"role": "user", "content": "Hello"}]
    )

Streaming (async)

async with AsyncUnifiedAI(provider="cerebras", model="llama3") as client:
    async for chunk in client.chat.completions.stream(
        messages=[{"role": "user", "content": "Stream this"}]
    ):
        print(chunk.delta.get("content", ""), end="")

Comparison (two providers)

from unifiedai import AsyncUnifiedAI

async with AsyncUnifiedAI(
    credentials_by_provider={
        "cerebras": {"api_key": "csk-..."},
        "bedrock": {
            "aws_access_key_id": "...",
            "aws_secret_access_key": "...",
            "region_name": "us-east-1"
        }
    }
) as client:
    result = await client.compare(
        providers=["cerebras", "bedrock"],
        models={"cerebras": "llama3.1-8b", "bedrock": "qwen.qwen3-32b-v1:0"},
        messages=[{"role": "user", "content": "Compare outputs"}],
    )
    print(f"Provider A: {result.provider_a.response.choices[0].message['content']}")
    print(f"Provider B: {result.provider_b.response.choices[0].message['content']}")
    print(f"Speed difference: {result.comparative_metrics.speed_difference_ms}ms")

Credentials

Cerebras

Set environment variable or pass credentials:

# Environment variable
export CEREBRAS_KEY="csk-..."

# Or pass directly
client = UnifiedAI(
    provider="cerebras",
    credentials={"api_key": "csk-..."}
)

AWS Bedrock

Set AWS credentials via environment or pass directly:

# Environment variables
export AWS_ACCESS_KEY_ID="..."
export AWS_SECRET_ACCESS_KEY="..."
export AWS_REGION="us-east-1"

# Or pass directly
client = AsyncUnifiedAI(
    provider="bedrock",
    credentials={
        "aws_access_key_id": "...",
        "aws_secret_access_key": "...",
        "region_name": "us-east-1"
    }
)

Multi-Provider (Comparison Mode)

client = AsyncUnifiedAI(
    credentials_by_provider={
        "cerebras": {"api_key": "csk-..."},
        "bedrock": {
            "aws_access_key_id": "...",
            "aws_secret_access_key": "...",
            "region_name": "us-east-1"
        }
    }
)

Precedence: Direct credentials > Environment variables > IAM roles (for Bedrock)

FastAPI demo (Swagger UI)

uvicorn apps.chat.backend:app --reload --port 8000
# Swagger UI: http://localhost:8000/docs

Supported Models

Cerebras

• llama3.1-8b - Llama 3.1 8B
• llama3.1-70b - Llama 3.1 70B
• qwen-3-32b - Qwen 3 32B

AWS Bedrock

• qwen.qwen3-32b-v1:0 - Qwen 3 32B
• anthropic.claude-3-haiku-20240307-v1:0 - Claude 3 Haiku (fastest)
• anthropic.claude-3-sonnet-20240229-v1:0 - Claude 3 Sonnet
• anthropic.claude-3-5-sonnet-20240620-v1:0 - Claude 3.5 Sonnet
• meta.llama3-70b-instruct-v1:0 - Llama 3 70B

Note: Some Bedrock models require requesting access through the AWS Bedrock console.

Response Metrics

All responses include comprehensive metrics:

• duration_ms: Total SDK round-trip time
• ttfb_ms: Time to first byte
• round_trip_time_s: Total time in seconds
• inference_time_s: Provider-reported inference time
• output_tokens_per_sec: Output generation speed
• total_tokens_per_sec: Overall token throughput

Project Structure

• src/unifiedai/: SDK implementation
  • _client.py, _async_client.py: Public API clients
  • adapters/: Provider implementations (Cerebras, Bedrock)
  • models/: Pydantic data models
  • core/: Comparison orchestration
  • metrics/: Prometheus metrics
• examples/: Usage examples (solo, streaming, comparison, Bedrock)
• apps/chat/: Demo FastAPI backend with comparison UI
• tests/: Unit and integration tests

License

MIT