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  1. Home
  2. ๐Ÿ“Š Monitoring
  3. Llmkit
Llmkit logo
Health: ActiveRecent health check succeeded.Last checked 9/11/2026, 7:16:02 PM

Llmkit

User RatingsBe the first to rate and review this MCP server!
View Repository16 GitHub StarsTotal stargazers on GitHub for the source repository (16 stars).Visit Website
monitoringcost-trackingbudget-enforcementllmproxy

Open-source API gateway for AI cost tracking and budget enforcement across 11 LLM providers with CLI, SDKs, and MCP server.

Quick Install

Automated & IDE Setup

Copy the AI prompt to install this server into Claude Code, Cursor, or another agent โ€” or use 1-click editor setup below.

Add to CursorAdd to VS Code
Not yet automatically verified

We haven't yet run this listing's install command through our automated sandbox check. This isn't a red flag โ€” we're steadily working through the catalog.

Manual Client & Custom JSON ConfigExpand JSON โ–พ

Client Config & Setup

Choose your client or environment
Target File:~/Library/Application Support/Claude/claude_desktop_config.json
claude_desktop_config.json
{
  "mcpServers": {
    "smigolsmigol-llmkit": {
      "command": "npx",
      "args": [
        "-y",
        "@f3d1/llmkit-cli"
      ]
    }
  }
}

๐Ÿ’ก Paste the JSON block into your client's configuration file under mcpServers, then restart the application.

Install Directory Badge Claim listing Alternatives๐Ÿ“Š More in Monitoring

Overview

This server provides detailed cost tracking, budget enforcement, and spend analytics for AI API usage across multiple LLM providers. It logs every request with token counts and dollar costs, rejecting requests that exceed budget limits before reaching the provider. It supports multiple client languages and frameworks, offers a CLI wrapper, SDKs for Python and TypeScript, and an MCP server for querying AI costs from compatible clients. Use it to monitor and control AI API spending in development, testing, or production environments.

Use cases

โ€ขTrack AI API costs in real time
โ€ขEnforce budget limits on AI API usage
โ€ขAnalyze spending across multiple LLM providers
โ€ขIntegrate cost tracking into Python or TypeScript projects
โ€ขQuery AI cost data from MCP-compatible clients

Key features

โ€ขProxy API gateway with request logging and cost calculation
โ€ขBudget enforcement rejecting requests exceeding limits
โ€ขCLI wrapper for any AI API command with cost summary
โ€ขSDKs for Python and TypeScript with proxy and local modes
โ€ขMCP server exposing cost and budget query tools
โ€ขFramework integrations including LangChain and LlamaIndex

Capabilities & Tool Schemas

Inspect callable tools, capabilities, and parameters exposed to AI agents by Llmkit.

Extracted Tool Capabilities
Proxy API gateway with request logging and cost calculation
Budget enforcement rejecting requests exceeding limits
CLI wrapper for any AI API command with cost summary
SDKs for Python and TypeScript with proxy and local modes
MCP server exposing cost and budget query tools
Framework integrations including LangChain and LlamaIndex

Documentation Overview

LLMKit

Measure what your AI agents cost. Stop requests before they exceed a budget.

CI PyPI npm OpenSSF Scorecard OpenSSF Best Practices MIT license

Website | Docs | Pricing API | Architecture | Security | Assurance case

LLMKit is an open-source AI gateway and SDK suite for cost attribution, budget admission, and request evidence. The gateway reserves estimated spend before provider dispatch. It rejects requests that cannot fit the active budget, then settles admitted reservations to actual usage when the response completes.

The repository also ships local tracking surfaces that do not require an LLMKit account or proxy.

Choose a surface

SurfaceUse it whenPackage
Python transportYou want local cost estimates around existing SDK callsllmkit-sdk
CLI wrapperYour OpenAI or Anthropic client honors its standard base-URL environment variable@f3d1/llmkit-cli
TypeScript SDKYou have an existing key and want sessions, streaming, and gateway access from TypeScript@f3d1/llmkit-sdk
MCP serverYou want spend, budget, and local coding-session tools inside an MCP client@f3d1/llmkit-mcp-server
AI SDK providerYou use Vercel AI SDK 6@f3d1/llmkit-ai-sdk-provider
Gateway and dashboardYou need shared budgets, provider routing, receipts, and analyticspackages/proxy, packages/dashboard

Quick start

Local Python tracking

Terminal
pip install llmkit-sdk
server.ts
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": "Summarize this incident."}],
)

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

The transport reads provider usage metadata and estimates cost from the bundled pricing catalog. It does not send tracking data to LLMKit.

Zero-code CLI tracking

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

Use -v for per-request output or --json for machine-readable results.

Gateway mode (existing key)

Gateway examples require an existing LLMKit API key. Account creation and key management are temporarily unavailable while the authenticated service is restored. If you do not already have a key, use one of the local tracking paths above.

server.ts
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-4.1",
    messages=[{"role": "user", "content": "Draft a release note."}],
)

The budget path

LLMKit authenticates each request, reserves its estimated cost, rejects requests over budget before provider dispatch, and settles admitted requests to actual usage.

The control path is built around three boundaries:

  • Atomic admission: a Durable Object owns reservation state for each budget scope. Concurrent requests cannot spend the same remaining balance.
  • Dispatch-aware idempotency: deterministic failures before dispatch release the key. After provider dispatch may have occurred, failures remain terminal to avoid duplicate spend.
  • Bounded responses: non-streaming bodies and individual SSE frames have explicit byte limits. LLMKit cancels upstream reads when a limit is exceeded.

Request receipts bind the admission decision, provider attempt, settlement, and analytics handoff with stable identifiers. Database writes use an outbox, so an analytics outage does not silently erase budget evidence.

MCP server

config.json
{
  "mcpServers": {
    "llmkit": {
      "command": "npx",
      "args": ["-y", "@f3d1/llmkit-mcp-server"]
    }
  }
}

Five local tools inspect supported Claude Code sessions and Cline task data without an LLMKit key. Six gateway tools query spend, budgets, keys, sessions, and service health when LLMKIT_API_KEY contains an existing key. Together they expose 11 tools.

Pricing data

The pinned catalog is a bundled reference snapshot, not a live quote. One source file, packages/shared/pricing.json, records the snapshot date and generates the TypeScript, Python, and MCP tables. CI rejects drift between the source and generated files. The public site renders only populated provider tables and displays the source date.

The public comparison endpoint requires no account:

text
https://api.llmkit.sh/v1/pricing/compare?mode=text-token&models=anthropic%2Fclaude-sonnet-4-6%2Copenai%2Fgpt-4o&input=1000&output=1000&cacheRead=0&cacheWrite=0

The endpoint prices only the exact model keys supplied by the caller. It does not search for or recommend the cheapest model. Pricing is an estimate, not a provider invoice. Provider billing rules, model modality, and catalog freshness remain part of the error boundary.

Evidence and current boundary

ClaimEvidence in this repositoryBoundary
Concurrent budget admission is serializedWorker fixtures exercise competing reservations, retries, settlement, and recoveryDeterministic local Worker and database proof
Retry behavior avoids duplicate dispatchIdempotency tests cover payload mismatch, pre-dispatch release, and post-dispatch indeterminate stateProvider behavior is simulated in CI
Large provider responses are boundedSuccess, error, and unterminated SSE fixtures verify rejection and stream cancellationBound is per buffered response or SSE frame
Pricing artifacts are reproducibleOne generator and CI --check path cover all published language tablesCatalog values still require source updates
Hosted recovery can be evaluated safelyGuarded staging deploy and proof runners bind an isolated Worker, database, revision, and cleanup journalA completed hosted concurrency and outage-recovery receipt is not claimed here

See STAGING_PROOF.md for the isolated hosted proof contract. It deliberately refuses production targets and dirty worktrees.

Project policy and design

DocumentWhat it owns
GovernanceDecision authority, roles, disputes, and the current continuity gap
RoadmapIntended and excluded work through August 2027
ArchitectureComponents, request flows, identity, storage, deployment, and failure boundaries
SecuritySecurity requirements, excluded guarantees, reporting, and supported versions
Security assuranceThreat model, trust boundaries, executable evidence, residual risks, and runtime HOLDs
AccessibilityPublic-site controls, verification method, known gaps, and language scope
ContributingSetup, quality gates, review expectations, and DCO sign-off

Development

bash
git clone https://github.com/smigolsmigol/llmkit
cd llmkit
corepack pnpm@9.15.4 install --frozen-lockfile
corepack pnpm@9.15.4 build
corepack pnpm@9.15.4 quality:pr

Run the Worker locally with development-only bindings:

bash
corepack pnpm@9.15.4 --filter @f3d1/llmkit-proxy dev

Generic deploy commands are intentionally omitted. Staging and production use separate guarded scripts with explicit target confirmation.

Security

Provider credentials are encrypted with AES-256-GCM using a random IV and owner/provider-bound additional authenticated data. LLMKit API keys are hashed before storage. CI includes secret scanning, static analysis, dependency review, CodeQL, and package provenance checks.

Read the security policy and architecture and the machine-readable Security Insights snapshot. Please report vulnerabilities through GitHub private vulnerability reporting or email security@llmkit.sh.

License

MIT

Read the full README โ†’View source on GitHub โ†’

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Adoption & maintenance

Factual signals from GitHub, npm, and our automated checks โ€” not a rating.

GitHub stars
16
Stargazers on the source repository.
npm downloads
65
Package downloads in the last 30 days.
Last commit
2d ago
Most recent push to the default branch.
Directory activity
2 views
Config copies, upvotes, and views on AllMCPs.

Reviews

No reviews yet โ€” be the first to share how this listing worked for you.

Frequently Asked Questions about Llmkit

It supports cost tracking and budget enforcement across 11 LLM providers, including OpenAI and Anthropic.

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Technical Specs & Signals

Category๐Ÿ“ŠMonitoring
PricingFreemium
More technical detailsExpand โ–พ
TransportSTDIO
RuntimeNode.js
AuthAPI key
LicenseMIT
ClientsClaude Desktop, Cline / VS Code, Cursor
Last updatedSep 9, 2026
9/10 checks healthy over the last 32d
Views2
Unique ViewsTotal visits recorded for this listing page on AllMCPs.
Installs0
Installs & Copy ActionsTotal times users copied install commands or configuration snippets for this server.
GitHub stars16
GitHub Star CountTotal stargazers on GitHub representing community popularity (16 stars).
Last commit2d ago
Last Repository CommitThe most recent commit or push recorded for this server's GitHub repository.Last commit on Sep 9, 2026
npm downloads65/mo
Monthly npm DownloadsAverage monthly package installs recorded from npm registry statistics.
55Quality signal: Good ยท 55/100How this signal is calculated โ–พ
Server availabilityNot measured

Not scored for repo-hosted servers โ€” we can't reach the running server, only its GitHub page. Hosted MCP endpoints are health-checked live.

Verified ownership10/20
Documentation & tools23/30
Adoption & activity8/15
Community engagement0/10

A guidance signal from public completeness & health data โ€” not a user rating. New listings start lower and rise as they add docs, get verified, and grow adoption. Signals we can't observe for a listing are skipped, not counted against it.

Supply-chain signal

No high-severity advisories surfaced by our automated scan.

Critical 0High 0Medium 0Low 0

Scanned 22d ago via OSV.dev ยท @f3d1/llmkit-cli (npm)

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