Budget Governor
Budget & cost control for AI agents: hard per-agent spend caps, rate limits, idempotency, and human-in-the-loop approval — enforced before each LLM call, not after the invoice. One hosted MCP endpoint (no proxy or self-hosting), settled via x402 (USDC on Base).
README
Gvnr
Spend caps, rate coordination, idempotency, and a human-in-the-loop gate for AI agents — enforced before the call, not after the invoice. One MCP endpoint, settled via x402 (USDC on Base). No proxy, no self-hosting, no infrastructure to deploy.
Listed on the Official MCP Registry as dev.gvnr/gvnr.
The problem
Agents cost 10–12× more than estimated in production. System prompts, retry loops, and tool calls multiply fast — a runaway agent can generate a $47,000 bill in 11 days. The usual fix, self-hosting a gateway like LiteLLM, means running infrastructure most developers won't set up.
Gvnr is the hosted alternative: an external authority your agent checks before it spends. Your agent asks "am I clear to make this call?" and acts on the answer.
How it works
Gvnr is not an LLM proxy — your tokens never pass through it, and you pay your model provider directly. Gvnr governs the decision to spend, in two independent meters:
1. The governance-operation quota — what you buy from Gvnr.
Your account holds a balance of governance operations (operations_remaining). One budget_clear burns one op. You top this up with USDC; it's decoupled from your LLM spend. get_balance reports it.
2. The spend envelope — a USD cap you set, per agent.
set_envelope(agent_id, limit_usd, window) gives an agent a daily or per-session USD ceiling. Gvnr tracks estimated spend against it and denies once it's exceeded — this is the runaway guardrail. No dollars move; it's an accounting limit you control.
A budget_clear is approved only when both hold: the account has ops left in the quota, and the agent is under its envelope. The loop:
- Your agent calls
budget_clear(agent_id, model, estimated_tokens)before each LLM request. - Gvnr checks the op quota and the agent's envelope, and returns
{ approved: true, ... }or{ approved: false, reason }. - If denied, your agent skips (or escalates via
request_approval). - After the LLM responds, call
reconcile(...)with the real token counts so the envelope tracks actual cost, not the estimate.
Quick start
1. Provision an account
curl -X POST https://gvnr.dev/v1/account
# { "api_key": "bg_...", "account_id": "...", "operations_remaining": 25 }
New accounts include 25 free trial ops — enough to run the full loop (set an envelope, budget_clear, reconcile) before you fund anything. Your api_key is the credential for every call; account_id is just an internal reference for support. Account creation is credential-only by design (agent-native) — email is optional, set separately via POST /v1/account/notification-email, and used only for human approval notices.
2. Top up your governance-op quota
Pay-as-you-go: 1,000 ops per $1, any amount (try the whole rail for $1), USDC on Base. Open the pay page, name your amount, and pass your API key:
https://gvnr.dev/pay?usd=1&api_key=bg_YOUR_KEY
Send USDC to the address shown and paste your tx hash — ops are credited proportional to the amount received, after on-chain verification. Programmatic clients can submit the hash directly:
curl -X POST \
-H "Authorization: Bearer bg_YOUR_KEY" \
-H "Content-Type: application/json" \
-d '{"tx_hash":"0x..."}' \
https://gvnr.dev/v1/account/topup-verify
# { "operations_remaining": 1000, "credited_ops": 1000, "credited_usd": 1 }
Or settle in one round-trip with an x402 client (Base MCP, AgentKit, x402-fetch) — name your own amount:
POST https://gvnr.dev/v1/account/topup?usd=5 # → 402 challenge → pay → 5,000 ops credited
3. Set a spend envelope for your agent
curl -X PUT \
-H "Authorization: Bearer bg_YOUR_KEY" \
-H "Content-Type: application/json" \
-d '{"agent_id":"my-agent","limit_usd":5,"window":"daily"}' \
https://gvnr.dev/v1/budget/envelope
# { "success": true, "agent_id": "my-agent", "limit_usd": 5, "window": "daily" }
4. (optional) Set a rate envelope per (agent, provider, model)
curl -X PUT \
-H "Authorization: Bearer bg_YOUR_KEY" \
-H "Content-Type: application/json" \
-d '{"agent_id":"my-agent","provider":"anthropic","model":"claude-sonnet-4-6","requests_per_minute":30}' \
https://gvnr.dev/v1/rate/envelope
5. Before each LLM request: budget_clear, then rate_check
curl -X POST \
-H "Authorization: Bearer bg_YOUR_KEY" \
-H "Content-Type: application/json" \
-d '{"agent_id":"my-agent","model":"claude-sonnet-4-6","estimated_tokens":2000}' \
https://gvnr.dev/v1/budget/clear
# { "approved": true, "remaining_usd": 4.994, "operations_remaining": 18999 }
curl -X POST \
-H "Authorization: Bearer bg_YOUR_KEY" \
-H "Content-Type: application/json" \
-d '{"agent_id":"my-agent","provider":"anthropic","model":"claude-sonnet-4-6"}' \
https://gvnr.dev/v1/rate/check
# { "allowed": true, "requests_remaining_this_minute": 29 }
6. (optional) Dedupe retries with idempotency_check
curl -X POST \
-H "Authorization: Bearer bg_YOUR_KEY" \
-H "Content-Type: application/json" \
-d '{"key":"job-abc-123","ttl_seconds":3600}' \
https://gvnr.dev/v1/idempotency/check
# First call: { "is_first_call": true, "ttl_remaining_seconds": 3600 }
# Replay: { "is_first_call": false, "ttl_remaining_seconds": 3598 }
7. After the LLM responds, reconcile against actual usage
curl -X POST \
-H "Authorization: Bearer bg_YOUR_KEY" \
-H "Content-Type: application/json" \
-d '{"agent_id":"my-agent","actual_input_tokens":1800,"actual_output_tokens":2400}' \
https://gvnr.dev/v1/budget/reconcile
# { "ok": true, "drift_usd": 0.003, "remaining_usd": 4.991, "operations_remaining": 18999 }
reconcile adjusts the spend envelope by the drift between your estimate and actual cost (the op quota is untouched). You don't pass the model again — reconcile reuses the one from your prior budget_clear. Anthropic, OpenAI, and Gemini all return usage fields with real token counts — pass those in to keep the envelope honest.
Human-in-the-loop approvals
When an agent hits a denial or a sensitive action, pause for a human instead of failing:
# 1. Open a request — returns an approval_id and a mobile-friendly approval_url
curl -X POST -H "Authorization: Bearer bg_YOUR_KEY" -H "Content-Type: application/json" \
-d '{"agent_id":"my-agent","action_summary":"Spend $40 on a research run","ttl_seconds":3600}' \
https://gvnr.dev/v1/approval/request
# { "approval_id": "...", "approval_url": "https://gvnr.dev/approve/...", "expires_at": ... }
# 2. The human opens approval_url and taps approve / deny (emailed if notification-email is set).
# 3. Your agent polls until the decision lands:
curl -H "Authorization: Bearer bg_YOUR_KEY" \
https://gvnr.dev/v1/approval/check/APPROVAL_ID
# { "decision": "pending" } → "approved" | "denied" | "timeout"
The agent proceeds on approved, skips on denied, and handles timeout (no decision before expires_at) however it likes.
MCP setup
Add to Claude Desktop or any MCP-compatible client:
https://gvnr.dev/mcp?api_key=bg_YOUR_KEY
Claude Code
claude mcp add gvnr --transport http \
"https://gvnr.dev/mcp?api_key=bg_YOUR_KEY"
MCP tools
| Tool | Description |
|---|---|
budget_clear(agent_id, model, estimated_tokens) |
Check clearance against the op quota + spend envelope; burns one op |
set_envelope(agent_id, limit_usd, window?) |
Create or update an agent's USD spend cap |
get_balance() |
Remaining governance-operation quota (operations_remaining) |
reconcile(agent_id, actual_input_tokens, actual_output_tokens) |
Apply estimate-vs-actual drift to the spend envelope after the LLM responds |
set_rate_envelope(agent_id, provider, model, requests_per_minute) |
Allocate a per-(agent, provider, model) rate share |
rate_check(agent_id, provider, model) |
Approve or deny against the rate envelope; returns retry_after_ms on denial |
idempotency_check(key, ttl_seconds?) |
Dedupe retries on a caller-supplied key; returns is_first_call |
request_approval(agent_id, action_summary, ttl_seconds?) |
Open a human-in-the-loop approval request; returns an approval_id |
check_approval(approval_id) |
Poll an approval: pending / approved / denied / timeout |
REST API
All endpoints except POST /v1/account require Authorization: Bearer bg_YOUR_KEY.
TypeScript users: generate full types from the live OpenAPI spec — npx openapi-typescript@latest https://gvnr.dev/openapi.json -o types/gvnr.d.ts. See TYPESCRIPT.md for the integration pattern (typed fetch, x402 topups, discriminated response unions).
Account & top-up
| Method | Path | Description |
|---|---|---|
POST |
/v1/account |
Provision account — returns api_key |
GET |
/v1/account/balance |
Remaining governance-op quota (operations_remaining) |
GET |
/v1/packs/:pack/info |
Public — preset details, USDC address, raw amount |
POST |
/v1/account/topup-verify/:pack |
Submit tx hash → verify on-chain → credit (proportional to amount received) |
POST |
/v1/account/topup?usd=<amount> |
x402-gated pay-as-you-go top-up — name your amount (min $1, max $100) |
POST |
/v1/account/topup/:pack |
x402-gated preset top-up (machine clients) |
Budget
| Method | Path | Description |
|---|---|---|
POST |
/v1/budget/clear |
Clearance call — approve or deny |
POST |
/v1/budget/reconcile |
Apply estimate-vs-actual drift to the envelope |
PUT |
/v1/budget/envelope |
Create or update agent spend cap |
GET |
/v1/budget/envelope/:agent_id |
Read envelope state |
DELETE |
/v1/budget/envelope/:agent_id |
Delete an agent's envelope |
Rate · Idempotency · Approval
| Method | Path | Description |
|---|---|---|
PUT |
/v1/rate/envelope |
Set a per-(agent, provider, model) RPM share |
POST |
/v1/rate/check |
Runtime rate check — allowed flag, retry_after_ms on denial |
POST |
/v1/idempotency/check |
Dedupe on a caller-supplied key — is_first_call |
POST |
/v1/approval/request |
Open a human approval request — returns approval_id |
GET |
/v1/approval/check/:approval_id |
Poll decision: pending / approved / denied / timeout |
Clearance response
{ "approved": true, "remaining_usd": 4.994, "operations_remaining": 18999 }
{ "approved": false, "remaining_usd": 0, "reason": "envelope_exceeded" }
Denial reasons: no_credits (op quota exhausted) · no_envelope (agent has no envelope) · envelope_exceeded
Status codes
A denial is not an HTTP error — it's a 200 with a flag, so check the body, not the status:
| Situation | HTTP | Body |
|---|---|---|
budget_clear / rate_check allow or deny |
200 |
{ approved/allowed: true | false, ... } |
| Top-up requires payment | 402 |
x402 challenge (in the payment-required header; see Billing) |
| Missing / invalid API key | 401 |
{ error } |
| Per-IP account-creation throttle | 429 |
{ error: "rate_limited", retry_after_ms } |
| Bad request body | 400 |
{ error, hint } |
Billing — governance ops, not your tokens
Gvnr charges for governance operations, not LLM usage. Your model tokens are billed by your provider; Gvnr never sees them.
Top-ups are pay-as-you-go at 1,000 ops/$1 in USDC on Base mainnet — name any amount on /pay and ops are credited proportionally after on-chain verification. No minimum, no subscription; the amounts below are just one-tap presets:
| Amount | Governance ops | Link |
|---|---|---|
| $1 (trial) | 1,000 | /pay?usd=1 |
| $19 | 19,000 | /pay?usd=19 |
| $39 | 39,000 | /pay?usd=39 |
| $79 | 79,000 | /pay?usd=79 |
Works with Base MCP, AgentKit, and any x402 client. The 402 challenge follows x402 v2 — payment requirements (network, USDC asset, amount, payTo) are returned in the payment-required response header (the body is empty), so an x402 client settles it automatically; you only hand-parse it if you're rolling your own.
Envelope windows
daily— resets at UTC midnight each daysession— never resets (use for one-shot tasks; caller-managed)
Supported models
Model pricing is a static lookup on the hot path — no external calls. The estimate deducted from the envelope is rate(model) × estimated_tokens ÷ 1,000,000 (output rate for chat models, input rate for embeddings), reconciled to actual afterward. These are the per-million-token rates (USD):
| Model | Input | Output |
|---|---|---|
claude-opus-4-8 / 4-7 / 4-6 |
$5 | $25 |
claude-sonnet-4-6 |
$3 | $15 |
claude-haiku-4-5 |
$1 | $5 |
gpt-4o |
$2.50 | $10 |
gpt-4o-mini |
$0.15 | $0.60 |
gpt-4-turbo |
$10 | $30 |
text-embedding-3-small / -large |
$0.02 / $0.13 | input-only |
gemini-embedding-001 / -2 |
$0.15 / $0.20 | input-only |
Embedding / input-only models are billed on input tokens — pass input tokens as estimated_tokens to budget_clear. Unknown models fall back to a conservative $15 / $75 default.
Network
Runs on Base mainnet (X402_NETWORK=eip155:8453), settling real USDC. There's no minimum to try it: top up as little as $1 (1,000 governance ops) to exercise the full live rail end-to-end — no testnet needed.
License
MIT — see LICENSE.
The canonical hosted service is https://gvnr.dev. Self-hosted instances are unaffiliated.
Acknowledgments
gvnr was designed and built by mightbesaad in close partnership with Claude Code (Anthropic) — chiefly Claude Opus 4.8. From the substrate architecture and the billing model to the code in this repository, it was a genuine collaboration. Thank you.
Recommended Servers
playwright-mcp
A Model Context Protocol server that enables LLMs to interact with web pages through structured accessibility snapshots without requiring vision models or screenshots.
Magic Component Platform (MCP)
An AI-powered tool that generates modern UI components from natural language descriptions, integrating with popular IDEs to streamline UI development workflow.
Audiense Insights MCP Server
Enables interaction with Audiense Insights accounts via the Model Context Protocol, facilitating the extraction and analysis of marketing insights and audience data including demographics, behavior, and influencer engagement.
VeyraX MCP
Single MCP tool to connect all your favorite tools: Gmail, Calendar and 40 more.
graphlit-mcp-server
The Model Context Protocol (MCP) Server enables integration between MCP clients and the Graphlit service. Ingest anything from Slack to Gmail to podcast feeds, in addition to web crawling, into a Graphlit project - and then retrieve relevant contents from the MCP client.
Kagi MCP Server
An MCP server that integrates Kagi search capabilities with Claude AI, enabling Claude to perform real-time web searches when answering questions that require up-to-date information.
E2B
Using MCP to run code via e2b.
Neon Database
MCP server for interacting with Neon Management API and databases
Exa Search
A Model Context Protocol (MCP) server lets AI assistants like Claude use the Exa AI Search API for web searches. This setup allows AI models to get real-time web information in a safe and controlled way.
Qdrant Server
This repository is an example of how to create a MCP server for Qdrant, a vector search engine.