llmkit-sdk 0.1.10

Local LLM cost estimates for existing Python SDK calls

llmkit-sdk wraps supported HTTP clients, reads token usage from provider responses, and estimates cost from a bundled pricing catalog. Local tracking does not require an LLMKit account or proxy.

pip install llmkit-sdk

Track an existing client

from llmkit import tracked
from openai import OpenAI

costs = []
client = OpenAI(http_client=tracked(on_cost=costs.append))

client.chat.completions.create(
    model="gpt-4.1",
    messages=[{"role": "user", "content": "Explain CQRS."}],
)

print(f"${sum(item.total_cost or 0 for item in costs):.6f}")

The same transport can wrap an Anthropic client:

from anthropic import Anthropic
from llmkit import tracked

costs = []
client = Anthropic(http_client=tracked(on_cost=costs.append))

client.messages.create(
    model="claude-sonnet-4-20250514",
    max_tokens=512,
    messages=[{"role": "user", "content": "Explain event sourcing."}],
)

Estimate a completed response

from llmkit import estimate_cost

cost = estimate_cost(response)
print(f"~${cost.total_cost:.6f}")

LangChain callback

from llmkit.integrations.langchain import LLMKitCallbackHandler

handler = LLMKitCallbackHandler()
chain.invoke("Summarize this report", config={"callbacks": [handler]})
print(f"${handler.total_cost:.4f}")

Framework integrations are optional. Install the framework you use separately.

Sessions and gateway mode

Use the hosted or self-hosted LLMKit gateway when you need shared budgets, request receipts, provider routing, or dashboard analytics:

from llmkit import LLMKit

client = LLMKit(api_key="llmk_your_key_here")
session = client.session()

completion, cost = session.chat(
    model="gpt-4.1",
    messages=[{"role": "user", "content": "Draft a release note."}],
)

print(f"${cost.total_cost:.4f} via {cost.provider}")

For an OpenAI-compatible client:

from openai import OpenAI

client = OpenAI(
    base_url="https://api.llmkit.sh/v1",
    api_key="llmk_your_key_here",
)

Async client

from llmkit import AsyncLLMKit

client = AsyncLLMKit(api_key="llmk_your_key_here")
completion, cost = await client.chat(
    model="gpt-4.1",
    messages=[{"role": "user", "content": "Summarize this incident."}],
)

Accuracy boundary

• Local values are estimates derived from response usage metadata and the bundled pricing table.
• Provider invoice adjustments, account-specific discounts, and pricing changes may differ.
• Local tracking observes cost; budget rejection requires gateway mode.
• Streaming cost is final only after the stream completes and usage metadata is available.

LLMKit repository

The LLMKit monorepo also contains the Cloudflare Worker gateway, dashboard, TypeScript SDK, CLI, Vercel AI SDK provider, MCP server, database migrations, and deterministic budget-control fixtures.

License

MIT