AI cost tracking: 11 tools for spend, budgets, and Claude Code + Cursor + Cline costs
Config is the same across clients — only the file and path differ.
{
"mcpServers": {
"llmkit": {
"env": {
"LLMKIT_API_KEY": "llmk_your_key_here"
},
"args": [
"@f3d1/llmkit-mcp-server"
],
"command": "npx"
}
}
}Are you the author?
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Open-source API gateway for AI providers. Logs every request with token counts and dollar costs. Budget limits reject requests before they reach the provider, not after.
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npx -y '@f3d1/llmkit-cli' 2>&1 | head -1 && echo "✓ Server started successfully"
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Open-source API gateway for AI providers. Logs every request with token counts and dollar costs.
Budget limits reject requests before they reach the provider, not after.
$ npx @f3d1/llmkit-cli -- python my_agent.py
$0.0215 total 3 requests 4.2s ~$18.43/hr
claude-sonnet-4-20250514 1 req $0.0156 ████████████████████
gpt-4o 2 reqs $0.0059 ███████░░░░░░░░░░░░░
Works with Python, Ruby, Go, Rust - anything that calls the OpenAI or Anthropic API. One command, no code changes.
Wrap any command. The CLI intercepts API calls, forwards them through the proxy, and prints a cost summary when the process exits.
npx @f3d1/llmkit-cli -- python my_agent.py
Use -v for per-request costs as they happen, --json for machine-readable output.
pip install llmkit-sdk
With the proxy (budget enforcement, logging, dashboard):
from openai import OpenAI
client = OpenAI(
base_url="https://api.llmkit.sh/v1",
api_key="llmk_your_key_here",
)
response = client.chat.completions.create(
model="gpt-4o",
messages=[{"role": "user", "content": "hello"}],
)
Without the proxy (local cost estimation, zero setup):
from llmkit import tracked
from openai import OpenAI
client = OpenAI(http_client=tracked())
response = client.chat.completions.create(
model="gpt-4o",
messages=[{"role": "user", "content": "hello"}],
)
# costs estimated locally from bundled pricing table
tracked() wraps your HTTP client and estimates costs from token usage. No proxy needed. Works with any SDK that accepts http_client.
Framework integrations (LangChain, LlamaIndex, Pydantic AI):
from llmkit.integrations.langchain import LLMKitCallbackHandler
handler = LLMKitCallbackHandler()
chain.invoke("...", config={"callbacks": [handler]})
print(f"${handler.total_cost:.4f}")
npm install @f3d1/llmkit-sdk
import { LLMKit } from '@f3d1/llmkit-sdk'
const kit = new LLMKit({ apiK
... [View full README on GitHub](https://github.com/smigolsmigol/llmkit#readme)