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AccInt

A local memory that scores itself against real outcomes — it predicts the path most likely to work from what worked before, replays verified steps instead of re-reasoning, and holds anything that leaves your machine for your approval.

by maxbaluev ↗Agent MemoryCommunity

LABSCO SUMMARY

Self-confidence is deliberately worth almost nothing here.

The design decision that separates this from a memory server is that a commitment closed on the model's own say-so is kept as weak evidence and never promoted — full credit needs a test, a human, or the world replying. That is what stops the system compounding from its own mistakes, and it is why the approval gate on outward actions is structural rather than a setting. The engine source is private, so evaluate it on what it does on your machine rather than on the code.

What it is

A local kernel that sits behind the agents you already run and keeps what they learn. Instead of storing conversation, it records a commitment, retrieves from a scored memory, acts, and then closes the loop with a real-world verdict — so paths that actually worked accumulate weight and get replayed rather than re-derived.

What you get

  • Two tools, which are the entire interface: acc_retrieve reads memory by MaxSim over per-token multi-vectors and is natively multimodal, so it can look at an image
  • acc_act does the rest through four operations — solve to recurse on a sub-goal, exec to run sandboxed code, register to store a reusable runtime, outcome to close a commitment with a verdict
  • Decomposition as a side effect rather than an operation: when the reasoner cannot answer directly it solves a sub-question, and the tree of solves is the breakdown
  • A transitions ledger that ranks which action is most likely to improve things next, and watches its own prediction error so a shift in your world is visible
  • Verified paths saved as runtimes and scored like anything else — a flow that worked replays cheaply, one that broke loses its score
  • Credit that reality has to earn: closing a commitment defaults to self_graded, a deliberately weak prior; full weight needs the owner's confirmation, a passing test, a reply from the world, or a runtime that exited clean
  • A receipt written as the work happens, and CLI views over it — commitments with their status, and open deliberation frames waiting on you

Requirements

No cloud memory account. One installer line on macOS and Linux, or the PowerShell equivalent on Windows; it probes your hardware, picks an embedder tier it can honestly run, verifies the release binary, starts a local daemon and wires your agent's .mcp.json. The first run may download several GB of embedder model and take minutes. Published to the MCP Registry as io.github.maxbaluev/accint with MCPB packages for macOS, Linux and Windows, which is the route for an MCPB-aware client; the installer is the broader path because acc hosts-sync wires Claude Code, Codex, Cursor and OpenCode on the same machine. The substrate is a local SQLite file and retrieval and prediction run locally. Anything that leaves the machine — sends, publishes, deploys, transfers, deletes, credentialed actions — needs owner authority and leaves a receipt. The installer, host adapters and docs are open; the memory, appraisal and credit engine is not.

Setup effort

Follow the published steps

See installation options