Agishub MCP vs Mnemosyne OS
A side-by-side look at the Agishub MCP and Mnemosyne OS MCP servers: transport, auth, tools, quality signals, and the config to install either one in Claude, Cursor, Windsurf or VS Code.
Verdict: choose Agishub MCP for knowledge & memory work as a local process; choose Mnemosyne OS for knowledge & memory as a local process. You can also run both side by side in one mcpServers block.
Which MCP server should you choose?
Choose Agishub MCP when
- You need dedicated capabilities in the Knowledge & Memory domain.
- You prefer a local stdio subprocess transport.
- You want tools like
ai.chat, ai.classify, ai.embed.
Choose Mnemosyne OS when
- You need dedicated capabilities in the Knowledge & Memory domain.
- You prefer a local stdio subprocess transport.
- You want tools like
mnemosyne_about, mnemosyne_memory_query, mnemosyne_memory_ask.
Feature & specification comparison
Rows where the two servers differ are marked.
| Spec | ||
|---|---|---|
| Category | Knowledge & Memory | Knowledge & Memory |
| Quality signal | 53/100 · Good | 64/100 · Good |
| Install command | Confirmed | Confirmed |
| Transport | Local subprocess (stdio) | Local subprocess (stdio) |
| Auth | Not declared | Not declared |
| Pricing | Not declared | Free |
| Required env vars | None listed | None listed |
| Verified owner | Community listing | Community listing |
| GitHub stars | 0 | 20 |
| Directory activity | 0 views 0 installs 0 upvotes | 1 views 0 installs 0 upvotes |
| Tools | 34 | 25 |
| Full listing | View Agishub MCP | View Mnemosyne OS |
Tools & capabilities
Agishub MCP tools 34
ai.chatAsk a general-purpose LLM a question or give it an instruction, with an optional system prompt. No external API key required.ai.classifyClassify a text into exactly one of the candidate labels you provide (e.g. sentiment, topic, intent).ai.embedTurn text into a numeric embedding vector for semantic search, RAG and similarity. Multilingual.ai.extractExtract named entities from text — people, organizations, locations, dates and miscellaneous — returned as structured JSON.ai.summarizeSummarize a block of text into a short abstract, with an optional target length. No external API key required.web.browserDrive a headless browser: open a URL and run an ordered list of steps — click, type, press keys, wait, extract text and screenshot. For flows the plain scraper can't reach (logins, forms, multi-step pages).web.crawlCrawl multiple pages of a site, respecting link depth and domain limits. Returns async job_id; results include markdown or HTML per page. Use it to fetch and process many pages of content at once.web.extractFetch any public web page and return its main content as clean, token-efficient Markdown (title, description, headings, links, lists). Set render:true to execute JavaScript first for single-page apps or JS-heavy pages that would otherwise come back empty. Built for RAG and for agents that need to r…web.extract_structuredAI-powered structured extraction: give a URL plus a natural-language prompt and/or a JSON Schema, and get back clean structured JSON (e.g. product name, price, rating). Renders the page in a headless browser first, so it works on SPAs.web.linksReturn every hyperlink on a JavaScript-rendered page as a list of absolute URLs, with options to keep only visible links or only same-site links. Backed by a headless browser. Use it to map a site or seed a crawler.web.mapDiscover all URLs reachable from a domain within a link depth limit. Returns a flat list of absolute URLs, respects robots.txt crawl delays. Use it to map a site's structure before crawling.web.scrapeExtract specific elements from a JavaScript-rendered page by CSS selector. Give a list of selectors (e.g. 'h1', '.price', 'a.product') and get back the text and attributes of every match. Backed by a headless browser, so it works on SPAs and JS-heavy pages.- +22 more on the listing page
Mnemosyne OS tools 25
mnemosyne_aboutRe-read the server instructions: the governance tenet, the vault protection model (NORMAL and MAXIMUM, mixableWith, isolated sandbox vaults), the spine model, and the rules for an agent working on someone else's memory. Most clients show this text on connect. Call it if yours did not, or to read it again.mnemosyne_memory_querySearch a vault and get the matching memories back word for word: notes, code, decisions, session summaries, commit history. Nothing is rewritten, so use this when you need the source text itself, to quote it or to write documentation from it. Ranking is vector similarity fused with a local BM25 channel, weighted by spine type. To FIND something rather than quote it, prefer mnemosyne_memory_ask, even when you only want its sources. It retrieves deeper and re-ranks, so it catches rare literal terms this tool misses: proper nouns, identifiers, product names. The score is not a confidence value. A hit and a miss come back with similar numbers, so judge the text, not the number beside it.mnemosyne_memory_askAsk a question and get a prose answer grounded in the vault, plus the memories it drew on. It runs the full local RAG pipeline: deeper retrieval, lexical fusion and a re-rank. That also makes it the better retriever, so reach for it whenever you need to find something, and read the Sources list even if you ignore the prose. It is at its best on why, who and how questions that span many memories. It takes up to about 30 seconds. The prose is a model rewording of the sources, so quote the sources instead, or fetch them with mnemosyne_memory_query. Check them before you trust the answer.mnemosyne_vault_listList the memory vaults this Mnemosyne OS exposes, each with its token, display name and memory count. Call it first when you are unsure which vault to work against, or when the user names a store you have not seen. Pass the bold token as the `vault` argument of the other tools. The `id` line is a local path and the other tools do not take it. Vaults this server was not configured for are flagged here and refused until the user adds them.mnemosyne_memory_ingestWrite a memory into a vault: a decision, an architecture note, a debug finding, a session summary. It is stored permanently and indexed for every future agent to retrieve. Use it at the end of a meaningful work session, or whenever a decision is reached that someone will want to recall later.mnemosyne_resonance_listList the resonances recorded in the default vault. A resonance is a workspace that tracks one ongoing project. Each entry carries its id, its last phase, how many minutes ago it moved, and the id of the memory behind it. Read-only. There is no status filter, so you get every resonance the scan matched in one vault, from at most 30 candidates. An empty result is written in words and means no resonance has been recorded yet. Read one in full with mnemosyne_position_get, or write a new one with mnemosyne_position_update.mnemosyne_position_getRead where one resonance was left: the phase, and the free-text note an agent or the cockpit wrote when it stopped. Read-only. It returns the resonance id, when it was saved, the spine type of the memory, and the whole note. An id nothing was ever saved under answers in plain words and points at mnemosyne_position_update, so a blank history and a failed call do not look alike. Call mnemosyne_resonance_list when you do not know the id.mnemosyne_position_updateRecord where you left off on a resonance: phase, current state, next steps. Call it at the end of a session. It is stored as a DECISION memory in the vault.mnemosyne_git_logRead recent commits from the git repository this Mnemosyne OS is configured to read. Read-only. Each commit carries an 8-character hash, the subject line, the author and the date, newest first. The path is set on the app side, so this cannot be pointed at another checkout, and it needs the monorepo:read scope. When either is missing the answer names what is missing, rather than returning an empty list that would read as "no commits". Use mnemosyne_memory_query for the reasoning behind a change.mnemosyne_dream_bridgesList the connections the Dream State engine found between memories while the machine was idle. Each bridge links two memories, sometimes from different vaults, with a Dream Bridge Score, a raw cosine similarity, and a short excerpt of both sides. Use it to surface associations the memory made on its own, to audit whether they are insightful or noise, or to seed creative exploration. An empty list is normal and means the engine has not produced bridges yet. It runs on idle time, when the user enables it in Settings.mnemosyne_spine_assignmentsSee how Mnemosyne classified its memories. It returns the memory-to-spine assignments for one vault, newest first, and the per-spine counts for the whole vault. Use it to check whether memories landed in the right spines, to read a vault's composition at a glance, or to find the taxon ids to pass as spine_type_filter in mnemosyne_memory_query. Set include_taxonomy to also get the global spine tree.mnemosyne_agent_listList the other coding-agent sessions on this machine, read from the transcript files their harnesses already write to disk. Metadata only: conversation name, project, git branch, model, last tool, how many files were touched, and when a line was last written. It reads every harness installed here, not just your own, so you can see a session from a different agent working in your repository. Use it before you touch shared state. It reports when a line was last seen and you draw the conclusion: a crashed session and an idle one fall equally silent, so nothing here can tell you an agent is working. Works with the Mnemosyne OS app closed.- +13 more on the listing page
Ready-to-paste client configs
Paste either block (or both) into your client config, e.g. claude_desktop_config.json or ~/.cursor/mcp.json. We only show a config when the install command is confirmed.
Agishub MCP
{
"mcpServers": {
"agent-memory": {
"command": "npx",
"args": [
"-y",
"@agishub/mcp"
]
}
}
}
Mnemosyne OS
{
"mcpServers": {
"mnemosyne-os": {
"command": "npx",
"args": [
"-y",
"@mnemosyne_os/mcp"
]
}
}
}
Frequently asked questions
Agishub MCP is categorized under Knowledge & Memory and uses a local stdio subprocess. Mnemosyne OS belongs to Knowledge & Memory and uses a local stdio subprocess. Pick Agishub MCP for knowledge & memory work and Mnemosyne OS for knowledge & memory.
Copy the mcpServers block from the config section on this page into your client's config (for example claude_desktop_config.json or ~/.cursor/mcp.json), then fully restart the client.
Agishub MCP has no declared pricing — check the project's own site. Mnemosyne OS is listed as free. If a server needs environment variables such as API keys, define them under the env key in your client's MCP config.
Yes. MCP clients support multiple servers: add agent-memory and mnemosyne-os as separate keys in the same mcpServers object, and your assistant will call tools from either one.
On AllMCPs, Agishub MCP has a quality signal of 53/100 (Good) with 0 views, 0 installs and 0 GitHub stars. Mnemosyne OS has 64/100 (Good) with 1 views, 0 installs and 20 GitHub stars.