MetricAI Python SDK

AI billing and metering proxy for India — route LLM calls, track costs, attribute spend, and manage budgets in INR/UPI.

Modes

MetricAI SDK supports two modes of operation:

Local Mode is ideal for:

• Enterprise deployments
• Air-gapped environments
• Compliance requirements
• Cost-sensitive projects

Get started with Local Mode: Quick Start Guide

Features

Installation

pip install metricai

With Optional Providers

# Just OpenAI
pip install "metricai[openai]"

# Just Anthropic
pip install "metricai[anthropic]"

# All providers at once
pip install "metricai[all]"

Available extras: openai, anthropic, gemini, groq, grok, perplexity, bedrock, tavily, langchain, langgraph, crewai, llamaindex, fastapi, django, deepgram, elevenlabs, sarvam

Quickstart

1. Set your API key

export METRICAI_API_KEY="your_metricai_api_key"

For BYOK mode (bring your own provider keys):

export METRICAI_API_KEY="your_metricai_api_key"
export OPENAI_API_KEY="sk-proj-..."        # Optional — for OpenAI routes
export ANTHROPIC_API_KEY="sk-ant-..."      # Optional — for Anthropic routes
export GEMINI_API_KEY="AI..."              # Optional — for Gemini routes

2. Use native SDKs via proxy

from metricai import MetricAI

client = MetricAI(api_key="your_metricai_api_key")

# OpenAI via proxy
openai_client = client.openai_sdk(agent_id="support-agent", user_id="user_42")
response = openai_client.chat.completions.create(
    model="gpt-4o",
    messages=[{"role": "user", "content": "What's the weather in Mumbai?"}]
)
print(response.choices[0].message.content)

3. Track usage and costs

from metricai import MetricAI, MetricAISession, timed_call

client = MetricAI(api_key="your_metricai_api_key")

# Session-based tracking with budget cap
with MetricAISession(client, agent_id="booking-agent", user_id="user_123", max_budget_inr=50.0) as session:
    response = client.openai_sdk().chat.completions.create(
        model="gpt-4o-mini",
        messages=[{"role": "user", "content": "Book a flight to Delhi"}]
    )
    session.track(provider="openai", model="gpt-4o-mini", input_tokens=1500, output_tokens=300)

4. Outcome billing

# Simulate first to estimate cost
client.track(
    provider="openai",
    model="gpt-4o-mini",
    outcome={"success": True, "outcome_type": "booking_confirmed", "charge_on_success_inr": 2.5},
    simulate=True,
)

# Live outcome billing
client.track(
    provider="openai",
    model="gpt-4o-mini",
    outcome={"success": True, "charge_on_success_inr": 2.5},
    latency_ms=850,
)

5. Standalone agent with tools

from metricai import MetricAI
from metricai.workflow import MetricAIAgent

client = MetricAI(api_key="your_metricai_api_key", llm_keys={"gemini": "your_gemini_key"})

agent = MetricAIAgent(
    client,
    agent_id="research-agent",
    user_id="user_456",
    model="gemini-2.0-flash",
    system_prompt="You are a helpful research assistant.",
    budget_cap_inr=25.0,
)

response = agent.run("What are the top 5 AI news stories today?")
print(response.content)

Core Concepts

Client Initialization

from metricai import MetricAI, MetricAIConfig

# Simple initialization
client = MetricAI(api_key="your_metricai_api_key")

# With configuration object
config = MetricAIConfig(
    api_key="your_metricai_api_key",
    mode="byok",                        # "byok" or "managed"
    llm_keys={"openai": "sk-..."},     # Provider keys for BYOK mode
    proxy_base_url="https://proxy.metricai.co.in",
    default_billing_mode="hybrid",      # "usage", "outcome", or "hybrid"
    default_budget_cap_inr=100.0,       # Daily INR cap
    fail_open=True,                     # Forward to provider if proxy unreachable
)

client = MetricAI(config=config)

Headers and Attribution

# Get headers for any SDK call
headers = client.headers(
    agent_id="my-agent",
    user_id="user_123",
    session_id="session_abc",
    billing_mode="hybrid",
    budget_cap_inr=50.0,
)

# Pass headers to any HTTP client
response = openai_client.chat.completions.create(
    model="gpt-4o",
    messages=[...],
    extra_headers=headers,
)

Routing

from metricai import MetricAI, RoutingConfig, RoutingManager

client = MetricAI(api_key="your_metricai_api_key")

# Configure routing policy server-side
router = RoutingManager(client)
router.configure(
    agent_id="routing-agent",
    strategy="cost-optimized",
    config={"max_model": "gpt-4o-mini", "fallback_model": "gpt-3.5-turbo"}
)

# Or via config
client = MetricAI(
    api_key="your_metricai_api_key",
    routing=RoutingConfig(strategy="latency-optimized")
)

Global Instrumentation

Instrument all provider SDK clients globally — no need to change existing code:

from metricai import init, instrument

# Initialize once at app startup
init(api_key="your_metricai_api_key", auto_instrument=True)

# All subsequent OpenAI/Anthropic calls are automatically routed through MetricAI
from openai import OpenAI
client = OpenAI()  # Already patched — routes through MetricAI proxy

Framework Integrations

LangChain

from langchain_openai import ChatOpenAI
from metricai.integrations.langchain import MetricAIChatCallbackHandler

handler = MetricAIChatCallbackHandler(agent_id="lc-agent", user_id="user_1")
llm = ChatOpenAI(callbacks=[handler])
response = llm.invoke("Hello!")

LangGraph

from langgraph.prebuilt import create_react_agent
from metricai.integrations.langgraph import MetricAIGraphTracer

tracer = MetricAIGraphTracer(agent_id="graph-agent", user_id="user_2")
agent = create_react_agent(model, tools, tracer=tracer)

CrewAI

from crewai import Agent
from metricai.integrations.crewai import MetricAICrewAIWrapper

wrapper = MetricAICrewAIWrapper(api_key="your_key")
agent = wrapper.wrap_agent(
    Agent(role="Researcher", goal="Find AI news", backstory="You are a researcher"),
    agent_id="crewai-researcher",
)

FastAPI Middleware

from fastapi import FastAPI
from metricai.integrations.web import MetricAIFastAPIMiddleware

app = FastAPI()
app.add_middleware(
    MetricAIFastAPIMiddleware,
    api_key="your_metricai_api_key",
    default_agent_id="fastapi-agent",
)

Environment Variables

Supported Providers

Error Handling

from metricai import (
    MetricAI,
    MetricAIError,
    MetricAIAuthError,
    MetricAIQuotaExhaustedError,
    MetricAIRateLimitError,
    MetricAIProxyError,
)

client = MetricAI(api_key="your_metricai_api_key")

try:
    response = client.openai_sdk().chat.completions.create(
        model="gpt-4o",
        messages=[{"role": "user", "content": "Hello"}]
    )
except MetricAIAuthError:
    print("Invalid API key")
except MetricAIQuotaExhaustedError:
    print("Budget cap reached — upgrade your plan")
except MetricAIRateLimitError:
    print("Rate limited — retry after a moment")
except MetricAIProxyError as e:
    print(f"Proxy error: {e}")
except MetricAIError as e:
    print(f"Unexpected error: {e}")

API Reference

MetricAI

Main client class for all operations.

MetricAI(
    api_key: str,                          # Required: your MetricAI API key
    base_url: str | None = None,           # Override proxy URL
    timeout: int = 60,                     # Request timeout in seconds
    agent_id: str | None = None,           # Default agent ID
    user_id: str | None = None,            # Default user ID
    billing_mode: str | None = None,       # "usage", "outcome", or "hybrid"
    budget_cap_inr: float | None = None,   # Daily budget cap in INR
    mode: str = "byok",                    # "byok" or "managed"
    llm_keys: dict | None = None,          # Provider API keys
    config: MetricAIConfig | None = None,  # Configuration object
)

Methods:

• openai_sdk(...) → Returns OpenAI-compatible client routed through proxy
• anthropic_sdk(...) → Returns Anthropic client routed through proxy
• gemini_sdk(...) → Returns Gemini client routed through proxy
• grok_sdk(...) → Returns Grok client routed through proxy
• headers(...) → Returns headers dict for any SDK call
• track(...) → Track a billing event
• track_stream(...) → Track streaming responses
• session(...) → Context manager for session tracking
• get_quota_status() → Get current billing usage

MetricAISession

Session context manager for grouped tracking:

with MetricAISession(
    client,
    agent_id="my-agent",
    user_id="my-user",
    max_budget_inr=50.0,
) as session:
    # Your LLM calls here
    session.track(provider="openai", model="gpt-4o", input_tokens=100, output_tokens=50)

MetricAIAgent

Tool-capable agent with built-in governance:

agent = MetricAIAgent(
    client,
    agent_id="my-agent",
    user_id="my-user",
    provider="openai",          # or "anthropic", "gemini", "grok"
    model="gpt-4o",
    system_prompt="You are a helpful assistant.",
    tools=[...],                # Optional: list of tool definitions
    max_iterations=10,
    budget_cap_inr=25.0,
    billing_mode="hybrid",
)

response = agent.run("User's request here")
print(response.content)
print(f"Tokens: {response.input_tokens} + {response.output_tokens}")

Examples

More examples are available in the examples/ directory:

• byok_openai_sdk.py — Using official SDKs via proxy
• standalone_workflow.py — Standalone agent with tools
• langchain_metricai_sample.py — LangChain integration
• langgraph_tracer.py — LangGraph tracing
• crewai_crew.py — CrewAI integration

License

MIT License — see LICENSE for details.

Support

• Documentation: https://docs.metricai.co.in
• Issues: GitHub Issues
• Email: support@metricai.co.in