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seanmarkwei/agentram-mcp

seanmarkwei/agentram-mcp

Key-value agent memory over an HTTP A
在 GitHub 查看 ↗直链 · 无中转页
配置模板(请按你的客户端调整)
claude mcp add agentram-mcp -- npx -y agentram-mcp
这是根据仓库名生成的可粘贴骨架,命令行参数与包名请以该项目 README 为准。
★ 数取值
0 ★ · seanmarkwei/agentram-mcp · 取自该仓库的 GitHub 页面 · (2026-10-16 20:00:20 核对)
来源明细
收录自 punkpeye/awesome-mcp-servers 榜单(我们从中收集的条目之一) · 条目类型:MCP 服务
收录与活跃
收录于 2026-10-09 · 最近见到 2026-10-16 · 共见到 32 次 · 被 7 个来源列出
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综合分 703 · 7 个来源 · mcp
常见问题(答案都来自我们的记录)
怎么安装?
我们【没有】从该仓库取到安装命令(我们的 install 覆盖率只有约 4%)。请看它的 README 或 releases 页。
它适合谁 / 用来做什么?
我们把它归在这些标签下: MCP 服务。标签来自条目自身,不是我们强加的分类。
谁维护它、还在更新吗?
仓库 seanmarkwei/agentram-mcp;我们收录于 2026-10-09;最近见到 2026-10-16。这里没有"官方认证"或"质量评级"——我们只记录来源与时间。
第三方质量标记

agentram-mcp

以下摘录自该仓库的 README(片段) · 在 GitHub 读完整 README →

MCP server for AgentRAM. It gives your AI agents persistent memory through a set of tools, with no vector database and no embedding pipeline to set up. Get a key, paste the config below, and you're running in about a minute.
Works with any MCP client: Claude Desktop, Claude Code, Cline, Cursor, Windsurf, and others.
Why
Every agent session starts with amnesia. AgentRAM is a plain HTTP memory store built for agents: one call to save something, one call to read it back. No infra to stand up, no vectors to babysit. This package wraps that API as MCP tools so your agent can just use it.
Setup
You need an API key first. Register at agentram.dev and it gets emailed to you. New accounts start with 1,000 credits and no card is required.
Then add this to your client's MCP config. For Claude Desktop that file is claude_desktop_config.json (on macOS it lives at ~/Library/Application Support/Claude/claude_desktop_config.json):
Restart the client and the tools show up. Most clients let you mark them "always allow" so you're not clicking approve on every call.
One gotcha on macOS: Claude Desktop doesn't ship with Node, so install Node 18 or newer first or the server won't start.
Tools
Fifteen tools. Ten for flat memory, five for temporal memory (facts that change over time).
Flat memory:
- store_memory: save a value under a key for an agent
- retrieve_memory: read one back by key
- list_memories: list everything stored for an agent
- search_memories: text search across keys and values, no embeddings needed
- delete_memory: remove one
- check_credits: current balance, free to call
Shared memory, for when more than one agent needs the same pool:
- create_namespace: make a shared space, free to call
- store_shared_memory: write into it
- retrieve_shared_memory: read from it
- list_shared_memories: list what's in it
Temporal memory (assertions):
- store_assertion: record a fact that can change, like a plan, a status, or a preference
- retire_assertion: mark a fact as no longer true while keeping the history
- get_assertion: what's currently true for a key
- list_assertions: all the current facts for an agent
- get_assertion_history: the full chain of what a key has been over time
About temporal memory
Flat memory overwrites. Temporal memory keeps the trail. When a fact changes you supersede the old value, and the previous values stick around so you can see how you got to the current one.
The part worth knowing: if two different values land on the same key and neither one supersedes the other, get_assertion will not quietly pick a winner. It tells you the key is contested and returns both values, so your agent can decide instead of running on a guess. list_assertions flags which keys are in that state.
来源
awesome · https://github.com/seanmarkwei/agentram-mcp
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