Context7
Up-to-date docs for AI agents
Descripción del creador (original en inglés)
Session memory for coding agents: local-first recall, drift detection, on-device inference.
Estos tres nunca se combinan en una sola puntuación: las opiniones, las mediciones y la popularidad son cosas distintas.
Todavía no escribimos un resumen sencillo para este. Abajo están los datos que leímos del registro.
Elige la app de IA que usas y copia la configuración.
BRAVE_API_KEY, GEMINI_API_KEY, SUPABASE_URL, SUPABASE_KEY, PRISM_USER_ID, GCP_PROJECT_ID, VERTEX_LOCATION, VERTEX_DATA_STORE_IDLos valores marcados como secretos son llaves de tu cuenta. Nunca los compartas ni los pegues en un chat.
Necesitas
¿Qué app usas?
Elige tu app arriba. La recordamos, así que la próxima vez el botón dirá “Instalar en …”.
Abre Claude Desktop → Settings → Developer → Edit Config y pega esto en 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"
}
}
}
}Ejecuta esto en tu terminal
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-serverCompleta los valores vacíos antes de ejecutarlo.
Pégalo en .cursor/mcp.json de tu proyecto, o en ~/.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"
}
}
}
}Tu navegador te pide permiso para abrir la app; luego la app muestra la configuración y te pide confirmar. Los valores de las claves quedan vacíos para que los completes tú.
Pégalo en .vscode/mcp.json de tu proyecto
{
"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"
}
}
}
}Tu navegador te pide permiso para abrir la app; luego la app muestra la configuración y te pide confirmar. Los valores de las claves quedan vacíos para que los completes tú.
Este se ejecuta en tu computadora, y ChatGPT solo puede conectarse a servidores en internet. Elige otra app.
Tu elección se guarda en una cookie en este dispositivo; no necesitas cuenta.
SUPABASE_URL envPRISM_USER_ID envGCP_PROJECT_ID envVERTEX_LOCATION envVERTEX_DATA_STORE_ID envEsta configuración se genera a partir de la ficha del creador en el registro. Si el README dice otra cosa, sigue el README.
Se ejecuta en tu computadora; la lista de herramientas aparece en tu app de IA después de instalarlo.
Revisamos información que cualquiera puede ver; no hemos leído el código. Las revisiones que no aplican no se cuentan. · Revisado el 6 nov 2026
El nombre en el registro se verificó con la cuenta de GitHub dcostenco, dueña del repo
Todavía no hemos leído este repo en GitHub
Todavía no hemos leído este repo en GitHub
Este se ejecuta en tu computadora; no hay endpoint en internet
Se ejecuta en tu computadora y usa ajustes locales en su lugar
Se ejecuta en tu computadora; solo podemos leer listas de herramientas de endpoints en internet
Comparado con todos los servidores activos de esta categoría en el registro. Las estrellas solo cuentan cuando quien lo publica es dueño del repo.
Superó 1 de 1 revisiones básicas aplicables. No hemos leído el código. Antes de agregar una clave o un token, revisa qué acceso pide el servidor y usa una clave que tenga solo los permisos que necesitas.
Debes completar: BRAVE_API_KEY, GEMINI_API_KEY, SUPABASE_KEY
Se ejecuta en tu computadora. Tu app de IA lo inicia cuando hace falta, y los datos no pasan por los servidores del creador (a menos que el propio servidor llame a un servicio en línea).
El repo de GitHub cambió por última vez –, y la última versión (20.22.4) llegó al registro hace 14 días.
Las apps compatibles con MCP, como Claude Desktop, Claude Code, Cursor y VS Code. También necesitas Node.js en tu computadora.
En la misma categoría: Context7, Codebase Memory, Exa
Servidores de la misma categoría hechos por otros creadores, ordenados por estrellas.
Cómo leer una tarjeta:★ Estrellas de GitHubiTres pilares: reseñas · revisiones · usoiActualizado = fecha en que se publicó la última versión en el registroi
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.
Copiado tal cual del repo del creador. PopNx MCP no lo ha revisado ni lo respalda. Se quitaron HTML, imágenes y scripts. Los enlaces llevan fuera de este sitio.
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
Solo se muestra el principio; lee el resto en el repo