AccInt
@maxbaluev
关于 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.
基本信息
配置
使用下面的配置,将此服务器添加到你的 MCP 客户端。
{
"mcpServers": {
"accint": {
"command": "acc",
"args": [
"mcp"
]
}
}
}工具
未检测到工具
工具是从 README 中自动提取的。维护者可以在 ## Tools 标题下列出工具,即可填充这部分内容。
概览
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?
—
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
—
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
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