Dreamd

Name: Dreamd
Author: botzrDev

作者：botzrDev版本 1.2.2i★ –i在你的电脑上运行

注册名称io.github.botzrDev/dreamd

安装

概览

作者提供的说明（英文原文）

Auditable agent memory: lessons cite their sources, and bad memories can be traced and removed.

PopNx MCP 的三大支柱

这三项永远不会合并成一个分数：意见、测量和人气是不同的东西。

用户评价i暂无评价评价功能将在后续阶段与账户功能一起开放
我们的基础检查i1 项中通过 1 项依据公开信息检查；尚未扫描代码
使用情况i尚未统计★ –

它能帮你做什么

我们还没有为它写通俗的介绍。下面是我们从注册中心读取到的事实。

连接方式: 在你的电脑上运行
传输方式: stdio
分类: 搜索与 AI 记忆

安装

选择你使用的 AI 应用，然后复制设置。

安装之前

基础检查：1 项中通过 1 项
在你的电脑上运行，拥有你的用户账户的权限（文件、网络、应用）。
注册中心没有列出需要填写的密钥。

你需要

npm已安装 Node.js（用于 npx 命令）

你用的是哪个应用？

ClaudeClaude CodeCursorVS CodeChatGPT

在上方选择你的应用。我们会记住它，下次按钮会显示“安装到 …”。

打开 Claude Desktop → Settings → Developer → Edit Config，把下面的内容粘贴到 claude_desktop_config.json

{
  "mcpServers": {
    "dreamd": {
      "command": "npx",
      "args": [
        "-y",
        "dreamd-mcp"
      ]
    }
  }
}

在终端中运行这条命令

claude mcp add dreamd -- npx -y dreamd-mcp

在 Cursor 中打开

或者自己复制设置

粘贴到项目中的 .cursor/mcp.json，或 ~/.cursor/mcp.json

{
  "mcpServers": {
    "dreamd": {
      "command": "npx",
      "args": [
        "-y",
        "dreamd-mcp"
      ]
    }
  }
}

浏览器会先询问是否打开应用，然后应用会显示设置并请你确认。密钥的值会留空，由你自己填写。

在 VS Code 中打开

或者自己复制设置

粘贴到项目中的 .vscode/mcp.json

{
  "servers": {
    "dreamd": {
      "type": "stdio",
      "command": "npx",
      "args": [
        "-y",
        "dreamd-mcp"
      ]
    }
  }
}

浏览器会先询问是否打开应用，然后应用会显示设置并请你确认。密钥的值会留空，由你自己填写。

这个服务器在你的电脑上运行，而 ChatGPT 只能连接互联网上的服务器。请选择其他应用。

你的选择通过 Cookie 保存在这台设备上，无需账户。

这些设置是根据作者在注册中心的条目生成的。如果 README 的写法不同，请以 README 为准。

它能做什么

它在你的电脑上运行；安装后，工具列表会显示在你的 AI 应用中。

基础检查i

我们检查的是任何人都能看到的信息；我们没有阅读代码。不适用的检查项不计入总数。 · 检查于 2026年11月6日

✓ 通过
作者与仓库一致
注册名称已通过 GitHub 账户 botzrDev 验证，该账户拥有此仓库
… 尚未检查
仍在维护
我们还没有从 GitHub 读取这个仓库
… 尚未检查
有许可证
我们还没有从 GitHub 读取这个仓库
– 不适用
端点有响应
它在你的电脑上运行，没有互联网端点
– 不适用
使用 OAuth 登录
在你的电脑上运行，改用本地设置
… 尚未检查
工具描述中没有隐藏指令
它在你的电脑上运行；我们只能读取互联网端点的工具列表

与“搜索与 AI 记忆”中的其他服务器比较i

星标数多于此分类中其他 7246 个服务器的 0%
发布到注册中心的时间比此分类中其他 7246 个服务器的 94% 更新
基础检查结果等于或优于此分类中其他 7246 个服务器的 100%

与注册中心里这个分类中所有活跃的服务器比较。只有当发布者拥有该仓库时，星标数才计入。

常见问题

Dreamd 安全吗？

它在 1 项适用的基础检查中通过了 1 项。我们没有阅读代码。在添加密钥或令牌之前，请先看看这个服务器要求哪些权限，并使用只具备所需权限的密钥。

它需要哪些密钥或代码？

注册中心没有列出必需的密钥，不过有些服务器会在首次使用时要求你登录。

它在我的电脑上运行还是在云端？

它在你的电脑上运行。你的 AI 应用会在需要时启动它，数据不会经过作者的服务器（除非它自己调用在线服务）。

它还在维护吗？

GitHub 仓库最后更新于–，最新版本（1.2.2）于前天进入注册中心。

哪些应用可以使用它？

支持 MCP 的应用，例如 Claude Desktop、Claude Code、Cursor 和 VS Code。你的电脑上还需要有 Node.js。

有什么可以替代它？

同一分类中有：Context7, Codebase Memory, Exa

替代选择

同一分类中其他作者的服务器，按星标数排序。

如何看卡片：★ GitHub 星标i三大支柱：评价 · 检查 · 使用情况i更新 = 最新版本发布到注册中心的时间i

Context7

作者：upstash

Up-to-date docs for AI agents

电脑或云端★ 64,224MIT今天更新

安装

Codebase Memory

作者：DeusData

Codebase knowledge graph for AI agents — 162 languages, sub-ms queries, 99% fewer tokens.

在你的电脑上运行★ 48,148MIT20天前更新

安装

Exa

作者：exa-labs

Connect AI agents to Exa for web search, content fetching, and multi-step research.

在云端运行★ 5,213MIT上个月更新

安装

作者的 README

打开 README

按原文从作者的仓库复制。PopNx MCP 没有审查或背书其内容。HTML、图片和脚本已被移除。链接会离开本站。

dreamd

Agent memory you can audit. When a memory goes bad, trace it to its source, see what it fed, and remove it.

dreamd is local-first, long-term memory for AI agents. Every memory is a line in a file in your repo, every lesson your agents learn cites the events it came from, and each dream cycle commits its lessons to git. When an agent acts on something wrong, you can find the memory that caused it, see which lessons it fed, and take it out, with a receipt.

npx -y dreamd-mcp setup   # scaffold .agent/ and wire your harness

npx -y dreamd-mcp # MCP server (stdio): what the harness spawns

Recorded live on dreamd 1.2.0, sped up 1.5x: blame finds the event behind a bad answer, forget removes it with a receipt, and the same query comes back without it. Tape: docs/assets/blame-forget.tape.

Works today with Claude Code, Cursor, Cline and other MCP-aware harnesses. Self-hosted, single binary, Apache-2.0 core.

If you want agent memory you can trace and undo, star the repo to follow releases. When a memory goes wrong, an issue with the blame output helps us fix it.

Why auditable memory

Memory is the layer of an agent stack you usually can't see into. A prompt can be read and a model can be swapped, but when memory goes wrong there is rarely a way to tell what the agent believed, where that belief came from, or what else it touched.

That matters more now that memory is an attack surface. The OWASP Top 10 for Agentic Applications lists memory and context poisoning (ASI06): an attacker, a bad tool result or a plain mistake plants a "fact" that the agent keeps acting on in later sessions. The defenses OWASP points to start with knowing the source of every memory and being able to remove one cleanly (OWASP agentic top 10).

dreamd is built for that response:

You need to	dreamd gives you
See what drove an answer	`dreamd blame` lists the stored memories behind a recall, with ids, timestamps, source and score. `dreamd recall --explain` prints the salience factors for each hit
Know where a lesson came from	Every lesson in `LESSONS.md` cites the episodic events it was promoted from, and the provenance ledger records which event fed which lesson and index entry
Check the record is consistent	`dreamd doctor --provenance` recomputes the ledger's Merkle root and lists orphan, missing and corrupt edges. Read-only
Remove a bad memory	`dreamd forget` removes the event and the lesson state that names it. `--dry-run` shows what would change; `--proof` writes a `forget-receipt/1.0` file
Review what agents learned	`.agent/` is committed with your code, and each dream cycle commits its output as `dreamd: cycle YYYY-MM-DD`. Review new lessons with `git diff` or in a pull request, and undo a cycle with `git revert`
Read everything yourself	Plain JSONL and Markdown. `cat`, `grep` and `git log` work on every byte

What dreamd does not do. It does not detect or block poisoned writes, and it does not filter injected lesson content (SECURITY.md). source_harness is asserted by the caller, not verified. The provenance ledger and the forget receipt are unsigned, and there are no trust labels yet. dreamd makes bad memory traceable and removable; it does not make it impossible. See Roadmap for what comes next.

The audit loop in four commands

Say an agent keeps repeating advice you know is wrong.

# 1. Find the memories behind it

npx -y dreamd-mcp blame "deploy without tests" --explain

2. Check the lesson it came from and what that lesson cites

cat .agent/semantic/LESSONS.md npx -y dreamd-mcp doctor --provenance

3. Preview, then remove the bad event (stop the daemon, `npx -y dreamd-mcp watch`, first; forget refuses while it runs)

npx -y dreamd-mcp forget evt_... --dry-run npx -y dreamd-mcp forget evt_... --proof forget-receipt.json

4. Review the change like code

git diff .agent/

After a forget, doctor --provenance reports the removed event's old ledger edges as orphans and exits 1. That is expected: the ledger is append-only, so it keeps the record that the event once existed.

To undo a whole dream cycle instead, git revert its dreamd: cycle commit with the daemon stopped, then run npx -y dreamd-mcp doctor --repair to rebuild the recall index.

Install

npm (recommended)

npx -y dreamd-mcp setup   # scaffold .agent/ and write the harness MCP config

npx -y dreamd-mcp # MCP server (stdio): your harness spawns this

stdio is the default transport. dreamd mcp --bind 127.0.0.1: is an opt-in Streamable HTTP server at /mcp. It is unauthenticated, and it is only in source builds with --features mcp-http. See docs/mcp-transports.md.

dreamd needs a project root sentinel (.git/, Cargo.toml, package.json or pyproject.toml).

setup prompts when it has a TTY. In scripts and non-interactive shells, pass --yes --harness claude|cursor|both (--harness none or --no-write-mcp scaffolds without touching any MCP config).

Cargo / from source

git clone https://github.com/botzrDev/dreamd.git

cd dreamd cargo install --path crates/dreamd-cli

See CONTRIBUTING.md for the full dev setup.

Quick start (under a minute)

If ~/your-project is a brand-new folder, run git init first.

cd ~/your-project

npx -y dreamd-mcp setup

Optional: shared daemon, recommended when several agents write

npx -y dreamd-mcp watch

Reload your harness. setup already wired the dreamd MCP server, so the harness spawns npx -y dreamd-mcp itself.

Ask the agent to search memory for something it learned earlier. It calls search_nodes and recalls prior context, with the source harness on every hit.

cat .agent/episodic/AGENT_LEARNINGS.jsonl

npx -y dreamd-mcp dream --dry # preview the lesson the next cycle would write npx -y dreamd-mcp doctor

The npm shim does not put dreamd on PATH. Use npx -y dreamd-mcp on the npm path, or cargo install --path crates/dreamd-cli for the dreamd binary. From 1.2.1 the shim forwards every dreamd command; the 1.2.0 shim did not forward recall, score, status, archive and migrate.

Adapters: Claude Code · Cursor · Cline

Recorded live, sped up 1.5x: Claude Code writes the lesson, OpenCode reads it back through the same .agent/ folder. Tape: docs/assets/demo.tape.

How agents learn

Agents write episodes (what happened, how painful it was, how important) through MCP append_node. The dream cycle clusters episodes by skill_action and promotes a recurring pattern into a lesson in LESSONS.md. With an API key it composes the lesson with an LLM under a $0.10 per-cycle spend cap; without one it writes a deterministic lesson and makes no network call. Either way the lesson cites the events it came from, and every output is staged and committed through one redo WAL, so a crash leaves the store as it was or rolls the cycle forward.

Recall ranks episodes and lessons with BM25 × salience (age decay, pain, importance, recurrence). Recall is deliberately lexical; vector recall is not shipped (docs/vectors.md). Formula: docs/salience.md.

What dreamd writes

Location	Contents	Commit?
`/.agent/`	Episodic JSONL, semantic lessons, personal prefs	Yes (this is the shared memory)
`/.agent/.dreamd/`	Local index, daemon state, config template, provenance ledger	No (gitignored by `init`)
`~/.agent/registry.toml`	Which projects have a store	No
`~/.agent/dreamd.sock`	Daemon API socket (while running)	No

setup scaffolds the store by calling init, then writes the harness MCP config. Both are idempotent. To uninstall from a machine, run npx -y dreamd-mcp uninstall. On a tty it waits for y; pass --yes in a script. Without a tty and without --yes it exits 2 and changes nothing. Project .agent/ stores are left in place. npx -y is npx's flag, not dreamd's --yes. npx -y dreamd-mcp init --uninstall-project drops only the registry entry; dreamd init puts it back and does not rewrite the store.

All inspection commands

The MCP surface is two tools (search_nodes, append_node). Everything else is CLI over the same files. dreamd --help is the reference for flags.

Command	What it does
`dreamd recall`	Ranked search as a markdown table. `--explain` prints the salience factors per hit; `--without` / `--without-cluster` drop hits before top-k, so you can see the answer without a suspect memory
`dreamd blame`	Which stored memories drove a query's ranking: id, timestamp, `skill_action`, bm25, salience, total, layer. `--json` for one compact array
`dreamd score`	Top memories by salience alone, no query
`dreamd salience-drift`	Salience distribution, top clusters, and drift against the snapshot from seven days earlier. See docs/observability.md
`dreamd dream --dry`	Print the `LESSONS.md` the next cycle would write, without writing
`dreamd forget`	Remove one episodic event and the lesson state that names it. `--dry-run` writes nothing; `--proof` writes a `forget-receipt/1.0` file (a receipt, not a signed proof). Refuses while the daemon is running
`dreamd doctor --provenance`	Recompute the provenance ledger's Merkle root and list orphan edges, missing edges and corrupt lines. Read-only. See docs/provenance.md
`dreamd memory branch\	checkout\	branches\	delete\	diff\	bisect`	Deprecated in 1.2.0, removed in 2.0. `.agent/` is plain files, so use git. See docs/branching-guide.md

Architecture (one paragraph)

Agents talk to dreamd over MCP. On Linux and macOS the MCP server proxies to a single-writer daemon (dreamd watch) over HTTP on a Unix domain socket, or runs in-process when no daemon is present. On Windows it proxies to loopback dreamd watch and exits 2 when that daemon is down. One writer lock covers every process that writes a project. The coordinator appends to AGENT_LEARNINGS.jsonl and feeds a Tantivy BM25 index; recall ranks hits with the salience formula, and each hit carries source_harness and skill_action. Details: ARCHITECTURE.md · SPEC.md · docs/http-api.md. Warm recall latency (local Criterion benches) is in PERF.md.

Roadmap

Direction, not dated commitments. Full view: ROADMAP.md.

Trust labels. A label on every memory saying where it came from and how far to trust it, joined along provenance edges, so a lesson from an untrusted source can't drive a privileged action. Needs its own spe

只显示了开头部分；其余内容请在仓库中阅读