AccInt

@maxbaluev

About AccInt

AccInt is a local Work Model and MCP server for persistent scored agent memory, retrieval, commitments, browser/runtime observations, and outcome-based learning across coding agents.

#memory #mcp #agent-memory #developer-tools #ai-agents #retrieval

Config

Add this server to your MCP-compatible client using the configuration below.

{
  "mcpServers": {
    "accint": {
      "command": "acc",
      "args": [
        "mcp"
      ]
    }
  }
}

Tools

No tools detected

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Overview

What is AccInt?

AccInt is a local work model for coding agents that integrates scored memory, recursive retrieval, commitments, runtime/browser observations, and outcome feedback in one substrate. It exposes an MCP interface so agents can retrieve prior work, record decisions, and close the loop when reality validates or rejects an action.

How to use AccInt?

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Key features of AccInt

Scored memory for prioritizing agent experiences
Recursive retrieval of relevant prior work
Commitments to track intended actions
Runtime and browser observations
Outcome feedback to validate or reject actions
MCP interface for integration with coding agents

Use cases of AccInt

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FAQ from AccInt

What does AccInt do?

AccInt is a local work model for coding agents that provides scored memory, recursive retrieval, commitments, runtime/browser observations, and outcome feedback via an MCP interface.

Where can I find the repository and website?

Repository: https://github.com/maxbaluev/accreted-intelligence ; Website: https://accint.xyz

Frequently asked questions

What does AccInt do?

AccInt is a local work model for coding agents that provides scored memory, recursive retrieval, commitments, runtime/browser observations, and outcome feedback via an MCP interface.