GitHub
GitHub’s official server lets AI read and manage code, issues and pull requests in your account. Use GitHub’s hosted version or run it locally with Docker.
Description from the maker (original English)
AI cost tracking: 11 tools for spend, budgets, and Claude Code + Cursor + Cline costs
These three are never merged into one score: opinions, measurements and popularity are different things.
We have not written a plain-language summary for this one yet. Below are the facts we read from the registry.
Pick the AI app you use, then copy the settings.
LLMKIT_API_KEY, LLMKIT_PROXY_URLValues marked secret are keys to your account. Never share them or paste them into a chat.
You need
Which app do you use?
Pick your app above. We remember it, so next time the button says “Install in …”.
Open Claude Desktop → Settings → Developer → Edit Config, and paste this into claude_desktop_config.json
{
"mcpServers": {
"llmkit": {
"command": "npx",
"args": [
"-y",
"@f3d1/llmkit-mcp-server"
],
"env": {
"LLMKIT_API_KEY": "your-value",
"LLMKIT_PROXY_URL": "your-value"
}
}
}
}Run this in your terminal
claude mcp add llmkit --env LLMKIT_API_KEY=your-value --env LLMKIT_PROXY_URL=your-value -- npx -y @f3d1/llmkit-mcp-serverFill in the empty values before running it.
Paste into .cursor/mcp.json in your project, or ~/.cursor/mcp.json
{
"mcpServers": {
"llmkit": {
"command": "npx",
"args": [
"-y",
"@f3d1/llmkit-mcp-server"
],
"env": {
"LLMKIT_API_KEY": "your-value",
"LLMKIT_PROXY_URL": "your-value"
}
}
}
}Your browser asks to open the app, then the app shows the settings and asks you to confirm. Key values are left empty for you to fill in.
Paste into .vscode/mcp.json in your project
{
"servers": {
"llmkit": {
"type": "stdio",
"command": "npx",
"args": [
"-y",
"@f3d1/llmkit-mcp-server"
],
"env": {
"LLMKIT_API_KEY": "your-value",
"LLMKIT_PROXY_URL": "your-value"
}
}
}
}Your browser asks to open the app, then the app shows the settings and asks you to confirm. Key values are left empty for you to fill in.
This one runs on your computer, and ChatGPT can only connect to servers on the internet. Pick another app.
Your choice stays on this device in a cookie; no account needed.
LLMKIT_PROXY_URL envThese settings are generated from the maker’s registry entry. If the README says otherwise, follow the README.
Runs on your computer; the tool list appears in your AI app after you install it.
We check information anyone can see; we have not read the code. Checks that do not apply are left out of the count. · Checked Nov 8, 2026
The registry name was verified with the GitHub account smigolsmigol, which owns the repo
We have not read this repo from GitHub yet
We have not read this repo from GitHub yet
This one runs on your computer; there is no internet endpoint
Runs on your computer and uses local settings instead
Runs on your computer; we can only read tool lists from internet endpoints
Compared with every active server in this category in the registry. Stars count only when the publisher owns the repo.
It passed 1 of 1 applicable basic checks. We have not read the code. Before adding a key or token, see what access the server asks for and use a key with only the permissions you need.
You need to fill in: LLMKIT_API_KEY
It runs on your computer. Your AI app starts it when needed, and data does not pass through the maker’s servers (unless it calls an online service itself).
The GitHub repo last changed –, and the latest version (0.4.10) reached the registry 2 months ago.
Apps that support MCP, such as Claude Desktop, Claude Code, Cursor and VS Code. You also need Node.js on your computer.
In the same category: GitHub, Desktop Commander, Cloudflare MCP
Servers in the same category from other makers, sorted by stars.
Reading a card:↓ Downloads per weeki★ GitHub starsiThree pillars: reviews · checks · usageiUpdated = latest version published to the registryi
GitHub’s official server lets AI read and manage code, issues and pull requests in your account. Use GitHub’s hosted version or run it locally with Docker.
MCP server for terminal commands, file operations, and process management
Copied from the maker’s repo as written. PopNx MCP has not reviewed or endorsed it. HTML, images and scripts are removed. Links lead off this site.
Measure what your AI agents cost. Stop requests before they exceed a budget.
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.
| Surface | Use it when | Package |
|---|---|---|
| Python transport | You want local cost estimates around existing SDK calls | llmkit-sdk |
| CLI wrapper | Your OpenAI or Anthropic client honors its standard base-URL environment variable | @f3d1/llmkit-cli |
| TypeScript SDK | You have an existing key and want sessions, streaming, and gateway access from TypeScript | @f3d1/llmkit-sdk |
| MCP server | You want spend, budget, and local coding-session tools inside an MCP client | @f3d1/llmkit-mcp-server |
| AI SDK provider | You use Vercel AI SDK 6 | @f3d1/llmkit-ai-sdk-provider |
| Gateway and dashboard | You need shared budgets, provider routing, receipts, and analytics | packages/proxy, packages/dashboard |
pip install llmkit-sdk
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.
npx @f3d1/llmkit-cli -- python my_agent.py
Use -v for per-request output or --json for machine-readable results.
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.
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 control path is built around three boundaries:
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.
{
"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.
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:
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.
| Claim | Evidence in this repository | Boundary |
|---|---|---|
| Concurrent budget admission is serialized | Worker fixtures exercise competing reservations, retries, settlement, and recovery | Deterministic local Worker and database proof |
| Retry behavior avoids duplicate dispatch | Idempotency tests cover payload mismatch, pre-dispatch release, and post-dispatch indeterminate state | Provider behavior is simulated in CI |
| Large provider responses are bounded | Success, error, and unterminated SSE fixtures verify rejection and stream cancellation | Bound is per buffered response or SSE frame |
| Pricing artifacts are reproducible | One generator and CI --check path cover all published language tables | Catalog values still require source updates |
| Hosted recovery can be evaluated safely | Guarded staging deploy and proof runners bind an isolated Worker, database, revision, and cleanup journal | A 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.
| Document | What it owns |
|---|---|
| Governance | Decision authority, roles, disputes, and the current continuity gap |
| Roadmap | Intended and excluded work through August 2027 |
| Architecture | Components, request flows, identity, storage, deployment, and failure boundaries |
| Security | Security requirements, excluded guarantees, reporting, and supported versions |
| Security assurance | Threat model, trust boundaries, executable evidence, residual risks, and runtime HOLDs |
| Accessibility | Public-site controls, verification method, known gaps, and language scope |
| Contributing | Setup, quality gates, review expectations, and DCO sign-off |
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:
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.
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.
MIT