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io.github.labham-llc/agentcents

Official registry Productivity

Local LLM cost proxy: forecasts each call and recommends the cheapest model that clears the task.

Source code Documentation

Install

Add this to your client's MCP configuration (Claude Desktop: claude_desktop_config.json; Cursor: .cursor/mcp.json; VS Code and others use the same "mcpServers" shape).

PYPI package agentcents
Claude Code 
claude mcp add agentcents -- uvx agentcents==1.1.3
JSON configuration (Claude Desktop, Cursor, VS Code) 
{
    "mcpServers": {
        "agentcents": {
            "command": "uvx",
            "args": [
                "agentcents==1.1.3"
            ]
        }
    }
}

Documentation

From the project's README on GitHub, refreshed weekly. Links open on GitHub.

Contents
  1. agentcents — LLM cost consulting skill for Claude
  2. Install
  3. What's in the box
  4. The product behind it

agentcents — LLM cost consulting skill for Claude

A Claude Skill that turns Claude into a neutral LLM cost advisor: estimate what any call or agent workflow will cost across candidate models, get the cheapest model that clears the task's capability bar, and see the comparison as a Mermaid diagram — with honest ranges, never false point estimates.

Works two ways:

  • Consult mode (no install): answers from a bundled, hand-verified snapshot of current model prices and Intelligence Index scores.
  • Live mode: if the agentcents proxy is running locally, the skill uses your real ledger — calibrated forecasts, spend, and accuracy instead of priors.

Install

Claude.ai / Desktop: Settings → Capabilities → Skills → Add → upload this folder (or the packaged .skill).

Claude Code: /plugin marketplace add labham-llc/agentcents-skill then /plugin install agentcents@labham-skills

Then just ask things like:

what would it cost to classify ~500 support emails a day, about 200 tokens each?

I'm building a 5-step research agent — which model should each step use?

What's in the box

SKILL.md                 — the consulting method (measure, range, bar, pick, flag instability)
assets/pricing.json      — price + capability snapshot (see _meta.verified_utc)
references/local-api.md  — live-mode reference for a locally running agentcents

Snapshot verified: 2026-07-15 (see _meta.verified_utc) — refreshed with each agentcents release.

The product behind it

agentcents is a local proxy that meters, forecasts, and budget-enforces LLM API calls — and grades its own forecasting accuracy on a calibration page. pip install agentcents. The skill is free; so is the agentcents free tier.

© Labham LLC · skill instructions and snapshot data may be redistributed with attribution; the agentcents software itself is proprietary (see its LICENSE).

Frequently asked questions

What is io.github.labham-llc/agentcents?

Local LLM cost proxy: forecasts each call and recommends the cheapest model that clears the task.

How do I install io.github.labham-llc/agentcents?

It runs on your machine. In Claude Code: claude mcp add agentcents -- uvx agentcents==1.1.3. For other clients, paste the JSON configuration from this page into their MCP settings.

Does io.github.labham-llc/agentcents need an API key?

Its registry entry lists no required key or secret. Some features may still need an account with the service it connects to.

Is io.github.labham-llc/agentcents open source?

Its code is public on GitHub, but the repository does not declare a standard license.

Who maintains io.github.labham-llc/agentcents?

It is published by labham-llc. AI.tn lists it but does not maintain or endorse it.

From the official MCP Registry. Listing is not an endorsement: review a server's code and permissions before giving it access to your data.