LLMKit

Know exactly what your AI agents cost.

Open-source API gateway that sits between your app and AI providers. Every request gets logged with token counts and dollar costs. Budget limits reject requests when exceeded, not after.

Why LLMKit

Most cost tracking tools give you "soft limits" that agents blow past in the first hour. LLMKit runs cost estimation before every request. If it would exceed the budget, the request gets rejected before reaching the provider. Per-key or per-session scope.

Tag requests with a session ID or end-user ID to track costs per agent, per conversation, per user. The dashboard and MCP server surface this data in real time. Cost anomaly detection alerts when a single request costs 3x the recent median.

11 providers through one interface: Anthropic, OpenAI, Google Gemini, Groq, Together, Fireworks, DeepSeek, Mistral, xAI, Ollama, OpenRouter. Fallback chains with one header (x-llmkit-fallback: anthropic,openai,gemini).

Runs on Cloudflare Workers at the edge. Cache-aware pricing for Anthropic, DeepSeek, and Fireworks prompt caching. 45+ models priced. Open source, MIT licensed.

How it works

flowchart TD
    A["Your app"] --> B["LLMKit Proxy"]
    B --> C["AI Provider"]
    C --> B
    B --> D["Supabase"]
    D --> E["Dashboard"]
    D --> F["MCP Server"]

Auth, budget check, route to provider (with fallback), log tokens and costs, update budget, fire alerts at 80%.

Get started

1. Create an account at llmkit-dashboard.vercel.app (free while in beta)
2. Create an API key in the Keys tab
3. Use it: pick any method below

CLI

Wrap any command. The CLI intercepts OpenAI and Anthropic API calls, forwards them through the proxy, and prints a cost summary when the process exits. No code changes.

npx @f3d1/llmkit-cli -- python my_agent.py

LLMKit Cost Summary
---
Total: $0.0215 (3 requests, 4.2s)

By model:
  claude-sonnet-4-20250514  1 req   $0.0156
  gpt-4o                    2 reqs  $0.0059

Works with Python, Ruby, Go, Rust, anything that calls the OpenAI or Anthropic API. Use -v for per-request costs as they happen, --json for machine-readable output.

Python

pip install llmkit-sdk

Two ways to track costs:

With the proxy (budget enforcement, logging, dashboard):

from openai import OpenAI

client = OpenAI(
    base_url="https://llmkit-proxy.smigolsmigol.workers.dev/v1",
    api_key="llmk_your_key_here",
)

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

Without the proxy (local cost estimation, zero setup):

from llmkit import tracked
from openai import OpenAI

client = OpenAI(http_client=tracked())

response = client.chat.completions.create(
    model="gpt-4o",
    messages=[{"role": "user", "content": "hello"}],
)
# costs estimated locally from bundled pricing table

tracked() wraps your HTTP client and estimates costs from token usage. No proxy needed. Works with any SDK that accepts http_client. See the SDK docs for all options.

TypeScript

npm install @f3d1/llmkit-sdk

import { LLMKit } from '@f3d1/llmkit-sdk'

const kit = new LLMKit({ apiKey: process.env.LLMKIT_KEY })
const agent = kit.session()

const res = await agent.chat({
  provider: 'anthropic',
  model: 'claude-sonnet-4-20250514',
  messages: [{ role: 'user', content: 'summarize this document' }],
})

console.log(res.content)
console.log(res.cost)   // { inputCost: 0.003, outputCost: 0.015, totalCost: 0.018, currency: 'USD' }

Streaming, CostTracker (local cost tracking without the proxy), and Vercel AI SDK provider also available. See the package README for deta