AccInt MCP server

Accreted Intelligence

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Make your AI work compound. Offload the task. Never the learning.

AI agents are powerful but amnesiac: every run burns your tokens on real work, ships an output, then forgets. You rent capability — you never build it. acc changes the unit from a task that evaporates to an investment that compounds. Hand work to the agents you already run (Claude Code, Codex, OpenCode, Cursor), and two things compound into one owned asset — a Work Model of your business: your intellect (what you decide, what good looks like, what you'll never allow) and your agents' tokens (every verified path distilled into a runtime that replays instead of re-reasoning). It learns what actually worked, checked against your own results, and predicts the better path before the next run starts. It acts in your real accounts with a receipt for every step, holds anything that leaves your machine for your OK, and lets reality settle it. So the same job gets cheaper, faster, and genuinely better every time it runs. The learning is yours: swap the model, keep the company veteran. Your work turns into capital you own, on a machine you control.

predict the better path  →  act in your accounts, receipted  →  reality settles it  →  the Work Model sharpens

See it live: accint.xyz. The commitments ledger settles in real time there, alongside the full story and a measured readout that updates as the system runs. The engine source is private; the binary installs in one line (below) and the building blocks are open. We say what's proven and what's young.

Why this exists

A model that scores 90% on a benchmark today scores 90% tomorrow. It doesn't learn from deployment, doesn't track which of its outputs led to good outcomes, and doesn't remember last week's mistake. It generates intelligence and throws it away. You keep paying — in time and tokens — to rediscover what already worked.

Accreted Intelligence is a bet that this is temporary. The idea is to move learning out of model weights and into scored external state, where judgment compounds from contact with reality and the model becomes a replaceable processor rather than the place intelligence lives. The reasoning engine is the part you can swap; the judgment it earned in your world is the part you keep.

There's a gap in how the existing tools are positioned, and it's where acc sits. Memory remembers context. Observability shows traces. Automation runs playbooks. acc closes the learning loop: commitment, action, approval, outcome, reusable path — scored by results, audited on a ledger, and running fully on your hardware. And it does the one thing memory can't: it predicts the path most likely to work from everything that worked in your world before, then watches its own error.

Shortened. The full README is on GitHub.