Agishub MCP vs Agent Recall
A side-by-side look at the Agishub MCP and Agent Recall 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 Agent Recall 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 Agent Recall when
- You need dedicated capabilities in the Knowledge & Memory domain.
- You prefer a local stdio subprocess transport.
- Its auth model fits you: No auth required.
- You want tools like
session_start, session_end, remember.
Feature & specification comparison
Rows where the two servers differ are marked.
| Spec | ||
|---|---|---|
| Category | Knowledge & Memory | Knowledge & Memory |
| Quality signal | 53/100 · Good | 69/100 · Great |
| Install command | Confirmed | Confirmed |
| Transport | Local subprocess (stdio) | Local subprocess (stdio) |
| Auth | Not declared | No auth required |
| Pricing | Not declared | Free |
| Required env vars | None listed | None listed |
| Verified owner | Community listing | Community listing |
| GitHub stars | 0 | 371 |
| Directory activity | 0 views 0 installs 0 upvotes | 3 views 0 installs 0 upvotes |
| Tools | 34 | 5 |
| Full listing | View Agishub MCP | View Agent Recall |
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
Agent Recall tools 5
session_start[ENTRY — call FIRST, before acting] Use when the user asks to start, load, continue, resume, or open memory for a project. Set mode='lite' for a ≤500-token briefing (good for fresh conversations where the agent will pull memory on demand via recall()).session_end[ON SAVE/EXIT — YOU must call this; nothing auto-saves] Use when the user asks to save, checkpoint, summarize, end, retain, or persist the current session. Optionally pass close_phase / open_phase to update the project pipeline narrative spine in the same call.remember[MID-SESSION WRITE — single fact/decision; saying it is not saving it] Use when the user asks to remember, store, note, or save a specific decision, fact, or insight.recall[RETRIEVE — use freely, any time] Use when the user asks to recall, search, find, or look up previous memory, context, or decisions.check[MID-SESSION — safe any time; for alignment, before risky decisions] Use when the user asks to validate understanding, verify alignment, or check if their interpretation matches the human's intent. Also call BEFORE a high-risk action — publish, deploy, delete, credential exposure, external send/message, or any other irreversible write — passing `action_description` (one sentence, what you're about to do). Returns matching corrections/rules/insights plus a `verdict`: `blocked` means an authoritative correction OVERRIDES the plan — read it before proceeding. To RECORD a durable human correction, pass `human_correction` as the STRUCTURED object {rule, why, applies_when} — a plain string is only STAGED for later review, never activated.
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"
]
}
}
}
Agent Recall
{
"mcpServers": {
"agent-recall": {
"command": "npx",
"args": [
"-y",
"agent-recall-mcp"
]
}
}
}
Frequently asked questions
Agishub MCP is categorized under Knowledge & Memory and uses a local stdio subprocess. Agent Recall belongs to Knowledge & Memory and uses a local stdio subprocess. Pick Agishub MCP for knowledge & memory work and Agent Recall 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. Agent Recall is listed as free. Auth: no auth required. 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 agent-recall 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. Agent Recall has 69/100 (Great) with 3 views, 0 installs and 371 GitHub stars.