Context7
Up-to-date docs for AI agents
作者提供的说明(英文原文)
Session memory for coding agents: local-first recall, drift detection, on-device inference.
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。
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。
选择你使用的 AI 应用,然后复制设置。
BRAVE_API_KEY, GEMINI_API_KEY, SUPABASE_URL, SUPABASE_KEY, PRISM_USER_ID, GCP_PROJECT_ID, VERTEX_LOCATION, VERTEX_DATA_STORE_ID标为机密的值是你账户的钥匙。千万不要分享给别人,也不要粘贴到聊天里。
你需要
你用的是哪个应用?
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": {
"prism-coder": {
"command": "npx",
"args": [
"-y",
"prism-mcp-server"
],
"env": {
"BRAVE_API_KEY": "your-value",
"GEMINI_API_KEY": "your-value",
"SUPABASE_URL": "your-value",
"SUPABASE_KEY": "your-value",
"PRISM_USER_ID": "your-value",
"GCP_PROJECT_ID": "your-value",
"VERTEX_LOCATION": "your-value",
"VERTEX_DATA_STORE_ID": "your-value"
}
}
}
}在终端中运行这条命令
claude mcp add prism-coder --env BRAVE_API_KEY=your-value --env GEMINI_API_KEY=your-value --env SUPABASE_URL=your-value --env SUPABASE_KEY=your-value --env PRISM_USER_ID=your-value --env GCP_PROJECT_ID=your-value --env VERTEX_LOCATION=your-value --env VERTEX_DATA_STORE_ID=your-value -- npx -y prism-mcp-server运行前请先填写空着的值。
粘贴到项目中的 .cursor/mcp.json,或 ~/.cursor/mcp.json
{
"mcpServers": {
"prism-coder": {
"command": "npx",
"args": [
"-y",
"prism-mcp-server"
],
"env": {
"BRAVE_API_KEY": "your-value",
"GEMINI_API_KEY": "your-value",
"SUPABASE_URL": "your-value",
"SUPABASE_KEY": "your-value",
"PRISM_USER_ID": "your-value",
"GCP_PROJECT_ID": "your-value",
"VERTEX_LOCATION": "your-value",
"VERTEX_DATA_STORE_ID": "your-value"
}
}
}
}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。
粘贴到项目中的 .vscode/mcp.json
{
"servers": {
"prism-coder": {
"type": "stdio",
"command": "npx",
"args": [
"-y",
"prism-mcp-server"
],
"env": {
"BRAVE_API_KEY": "your-value",
"GEMINI_API_KEY": "your-value",
"SUPABASE_URL": "your-value",
"SUPABASE_KEY": "your-value",
"PRISM_USER_ID": "your-value",
"GCP_PROJECT_ID": "your-value",
"VERTEX_LOCATION": "your-value",
"VERTEX_DATA_STORE_ID": "your-value"
}
}
}
}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。
SUPABASE_URL envPRISM_USER_ID envGCP_PROJECT_ID envVERTEX_LOCATION envVERTEX_DATA_STORE_ID env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。
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
注册名称已通过 GitHub 账户 dcostenco 验证,该账户拥有此仓库
我们还没有从 GitHub 读取这个仓库
我们还没有从 GitHub 读取这个仓库
它在你的电脑上运行,没有互联网端点
在你的电脑上运行,改用本地设置
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。
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。
你需要填写:BRAVE_API_KEY, GEMINI_API_KEY, SUPABASE_KEY
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)。
GitHub 仓库最后更新于–,最新版本(20.22.4)于14天前进入注册中心。
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。
同一分类中有:Context7, Codebase Memory, Exa
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
Up-to-date docs for AI agents
Codebase knowledge graph for AI agents — 162 languages, sub-ms queries, 99% fewer tokens.
Connect AI agents to Exa for web search, content fetching, and multi-step research.
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。
Give your AI agent memory that lasts — and see the cloud tokens it never had to spend. Persistent sessions, knowledge graphs, offline tool-routing, and an auditable savings meter. Fully local and free.
Current release: v20.22.5 — local oMLX images and memory accounting
Choose an independent image model from discovered local inventory. Compatible
oMLX servers support controlled PNG image requests with local screening and
generation. Image accuracy depends on the model; Ollama image execution remains
unavailable for the saved image choice. See the oMLX guide.
*Local AI dashboard development preview. Screenshots below use an isolated demo with illustrative inventory and responses; they do not show a live account or prove model accuracy. The npm release may show an earlier interface.*
Prism Coder is an MCP server that gives Claude, Cursor, and other AI tools long-term memory that survives across sessions. It ships with the open-weight prism-coder model fleet (2B–27B) for fast, offline tool-routing — no cloud required. And it keeps score: every call served locally is metered, so prism savings shows the token volume that never reached your cloud model — measured honestly, in tokens.
No account needed. No API keys. Runs on your machine. A paid subscription adds cloud sync, higher model tiers, and team features through the Synalux portal.
prism savings (or the local_savings tool from any host) reports the token volume local serving kept off your cloud model: headline, local share, per-model breakdown. It reports tokens, never an invented dollar figure, and prints its assumptions and known undercounts inline — a number you can check, not marketing.route_guard: "local" disables that correction only. cloud_fallback: false forbids cloud inference fallback and verify: false (with no evidence) disables the grounding verifier. With all three off, no request carries your prompt, draft or evidence; the per-call entitlement check and telemetry still contact the portal and carry neither.prism connect configures Claude Code, Claude Desktop, Cursor, Gemini CLI, and Codex while preserving unrelated settings.prism connect adds two small ones on top: mid-session prompt routing, and a post-compaction re-injection of the protected-floor digest.PRISM_DARK_FACTORY_ENABLED=false disables Factory after an MCP restart. Try the three-test filename fix.Run prism dashboard, open Account & Settings → Local AI, then work through Connection → Models → Test. These controls configure local inference; session memory continues to work without installing a model.
| Step | What you do | What to expect |
|---|---|---|
| 1. Connection | Choose an existing oMLX server or a Prism-managed installation, then Preview connection. | Review the effect on coder, reviewer and image assignments before Use this server switches the shared oMLX connection. Saved model IDs are preserved. |
| 2. Models | Choose coder and reviewer models for each backend and Save coding preference. Save the separate Image model form independently. | Saving records a choice; it does not download or load weights. Missing saved IDs stay visible instead of being silently replaced. |
| 3. Test | Select backend and purpose, then Test saved model. | A small request may load the saved model. The result reports execution or a blocking reason, with no cloud inference fallback. |
Coding and review. Auto prefers configured oMLX for eligible coding. You can choose Ollama instead; explicit task choices take precedence. oMLX coding and review need the integration's separate text safety classifier. Local drafts and reviews still need host inspection and tests.
Images have their own choice. Select an oMLX vision model under Image model, then Save image preference. It may be the same model used for coding, but the setting is independent. Refresh image models reads metadata without loading a model. Vision metadata and RAM estimates are advisory; each request repeats compatibility and resource checks. The saved Ollama image option currently refuses execution; leave the image backend Not selected to retain existing image routing.
Read image-test results in two parts. The built-in diagnostic sends a red square. Reference facts describe that known PNG; AI observations contain the model's answer. A wrong answer cannot change the reference. A passing test proves execution, not visual accuracy. User images do not receive catalog facts from this diagnostic. Starting another test or changing backend or purpose clears the previous image result.
Server setup is optional and separate from npm. On supported Apple Silicon Macs, expand Install or update a Prism-managed server to install the server without downloading the optional coding starter pack. Add models through Open oMLX model manager. Installing a server update does not restart it or switch your connection. Restart managed server interrupts local inference and is available only when Prism can establish managed ownership and idle activity. External servers use their own upgrade and restart procedure.
Updating the Prism npm package or restarting your AI client does not upgrade an external oMLX server. If image status says Server update required, follow the existing-server upgrade guide. Tested Prism-compatible source identifies the compatible revision; Latest upstream releases is for release notes and may lack Prism's routes. See the oMLX guide for requirements and troubleshooting.
Memory search automatically applies ACT-R ranking: relevance plus recency, frequency and memory associations. No upvote or prompt instruction is required. To opt out, use Account & Settings → Preferences → Automatic Memory Ranking (ACT-R), then restart your AI client. PRISM_ACTR_ENABLED=false also disables it. This ranking is separate from the neural graph's stored importance filter.
npm install -g prism-mcp-server
prism connect prism dashboard
Use prism connect --dry-run to preview changes, prism connect --all to configure every detected host, or prism connect --refresh to reconcile Prism-managed entries after an upgrade. Restart the host after connecting.
prism dashboard opens the current local Mind Palace without a Synalux login. Prism works locally without an account, API key, or cloud subscription. Add a Synalux subscription when you want cloud memory, paid-tier skills, or team features.
To check your plan, open ⚙ Account & Settings → Account in the dashboard. Free users see Start 14-day trial; paid users see Manage subscription. During a trial, the same panel shows the paid tier and exact end date. No card is required to start. Add payment details before the end date to continue; if you do not, the subscription cancels and local Prism Free remains available. You can also compare plans directly at synalux.ai/pricing#prism-plans.
After a few sessions, ask what it's been worth:
prism savings --period month
💾 Local serving — LAST 30 DAYS
~510K tokens kept off your cloud model 53 call(s) served locally of 58 routed (91%)
Your numbers will differ — that's the point: it reports what *your* machine actually served, not a projection. Full report anatomy and the honesty rules behind it are in the local_savings section.
Prism also ships as a plugin, which registers the MCP server and the startup skill for you.
Both hosts install straight from this repository. There is nothing to host and no server to run: the catalogue is the .claude-plugin/marketplace.json file committed here, and your client clones it from GitHub.
Claude Code:
claude plugin marketplace add dcostenco/prism-coder
claude plugin install prism-coder@prism
Codex:
codex plugin marketplace add dcostenco/prism-coder
codex plugin add prism-coder@prism
The plugin registers prism-mcp via npx -y prism-mcp-server. If you already configured Prism by hand — prism connect writes an mcp_servers.prism-mcp entry — you have that server twice under one key. Install the plugin or run prism connect, not both.
prism connect changes about host subagentsconnect steers bounded work to prism_infer on your machine rather than to host-spawned agents. What it writes differs per host, and it does not disable subagents everywhere — Claude Code keeps them and is pointed at an economy model instead. Prism's local workers stay available over MCP in every case.
| Host | Setting written | Effect |
|---|---|---|
| Claude Code | env.CLAUDE_CODE_SUBAGENT_MODEL = "sonnet" in ~/.claude/settings.json | Subagents stay enabled, pinned to an economy model. Fan-out is discouraged by policy text, not by config |
| Gemin
只显示了开头部分;其余内容请在仓库中阅读