Context7
Up-to-date docs for AI agents
Descripción del creador (original en inglés)
Knowledge Graphs to enable memory persistence, knowledge evolution, explainable interaction.
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.
KIP_BACKEND_URL, KIP_API_KEYLos 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": {
"kip": {
"command": "npx",
"args": [
"-y",
"@ldclabs/kip-mcp-server"
],
"env": {
"KIP_BACKEND_URL": "your-value",
"KIP_API_KEY": "your-value"
}
}
}
}Ejecuta esto en tu terminal
claude mcp add kip --env KIP_BACKEND_URL=your-value --env KIP_API_KEY=your-value -- npx -y @ldclabs/kip-mcp-serverCompleta los valores vacíos antes de ejecutarlo.
Pégalo en .cursor/mcp.json de tu proyecto, o en ~/.cursor/mcp.json
{
"mcpServers": {
"kip": {
"command": "npx",
"args": [
"-y",
"@ldclabs/kip-mcp-server"
],
"env": {
"KIP_BACKEND_URL": "your-value",
"KIP_API_KEY": "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": {
"kip": {
"type": "stdio",
"command": "npx",
"args": [
"-y",
"@ldclabs/kip-mcp-server"
],
"env": {
"KIP_BACKEND_URL": "your-value",
"KIP_API_KEY": "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.
Esta 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 ldclabs, 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: KIP_BACKEND_URL, KIP_API_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 (1.0.1) llegó al registro hace 10 meses.
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.
[English](./README.md) | [中文](./README_CN.md)
Default development version: KIP 2.0 — normative draft. The repository root contains the current protocol and tooling. KIP v1.0-RC11 is frozen for historical reference and migration; changing the default does not upgrade existing deployments or declare the 2.0 protocol stable.
kip://profiles/cognitive-memory@2.0.0, one general domain package, kip://domains/general@1.0.0, one standard projection policy, kip:memory-default, one standard strength policy, kip:strength-half-life-30d, and schema IDs urn:kip:2.0:schema:*. A draft revision is identified by its content digest; earlier draft packages are not retained.not_run until a Brain evaluation measures it.What the latest revision changed, and how it was validated, is in the revision record.
@ldclabs/kip-lang) and VS Code extension, both targeting KIP 2.0.The archived MCP server and Skill implement v1 interfaces; native v2 integration starts with the current Interface and wire contracts above.
An interactive version — pan, zoom, relationship tracing, five guided views — is a self-contained file: download it and open it in a browser.
A memory that cannot separate *what was said* from *what is true* will eventually lie to its owner with total confidence. KIP 2.0 refuses to collapse three questions:
Meaning what can be represented at all
Belief what the Brain currently accepts Authority who may read, write, project, or elevate
So a stored Proposition is truth-neutral. An Assertion carries one actor's stance toward it, with mode, confidence, validity and evidence. Belief is not a stored field but an Epistemic Projection computed at read time over the eligible assertions:
Proposition exists ≠ Proposition is true ≠ Brain accepts Proposition
Change never rewrites the past. Three kinds of change keep three different histories:
Time is stated as honestly as it is known: asserted_at is when a claim was made, not when it was recorded; a claim with no stated start began no later than that; and a coarse date is a time bound, not an invented instant. A preference is an ordinary claim that changes within its kind — there is no Preference type. Where claims of one slot still conflict, the standard memory policy kip:memory-default resolves them the same way on every engine: context specificity first, then first-person testimony, then recency.
Evidence, provenance and schema get the same treatment: Evidence and Activity are first-class elements rather than metadata bags, and schema lives in versioned, digest-pinned packages. A Brain that meets a relation the Profile lacks drafts it into the Space's draft vocabulary with DEFINE, which only adds; promoting a draft onto an installed symbol is a Schema migration that only a Principal holding manage_schema performs. Retrieval composes with belief in one query through the Search Pattern: search finds candidates, and BELIEF decides what is accepted.
A memory that only answers when asked is half a memory. The other half is what the Brain does when nobody is talking to it — and it must not be a cron job. KIP 2.0 gives that half four mechanisms, none of which decides policy on the Brain's behalf:
| Mechanism | What it is |
|---|---|
Watch | Durable attention state. A declared condition under which a change — or the absence of one — deserves attention. Armed watches are evaluated against committed Change Envelopes: a delta watch fires on a matching committed change; a *silence* watch fires once due_at has passed and the evaluator has consumed the change stream through that deadline without a match — the clock alone proves nothing. Proactivity becomes a state differential rather than a blind schedule. A fired Watch grants nothing: it creates attention, never an action, and reaches the business Agent through the Memory Interface's attention recall. |
action_gate | An Activity class recording what the gate decided — act, ask, defer, or silence — with the inputs it weighed. Restraint is the hardest thing to justify after the fact, so deliberate silence is written down like any other outcome. |
LIST DEPENDENTS | Bounded reverse traversal of provenance. Revise a root and the cognition compiled from it — insights, preference summaries, skills, the self-model — becomes discoverable in one operation instead of quietly stale. §57.5 makes the rule explicit: a revised root must not auto-retract its dependents, and must leave them reviewable. Whether one survives is a review decision, not a protocol rule. |
PURGE PAYLOAD | Destroy an Evidence element's observed bytes while keeping the record: its digest, class, observation time, source, and the citations that depend on it. Data minimization that costs no provenance — corroboration and independence counting keep working on the surviving digest. Distinct from element purge, which destroys the record itself. |
Supporting state lives in the Cognitive Memory Profile: WorkingState is the consolidated resume digest stamped with its basis_seq, so a Brain wakes from compiled state plus a delta rather than re-reading scrollback; computed dependency validity makes a revised root visible at the next read; and MnemonicState.utility holds the admission bet — how useful this memory is expected to be — kept deliberately separate from salience, from memory_strength, and from epistemic confidence. Strength itself is computed at read time from a base, an anchor and a pinned decay policy; nothing sweeps it, and reading never reinforces it.
Everything above makes the system watch the world better. The consequence channel is how the world watches back.
Outcome Evidence records what actually happened after a decision, action, or trialed procedure — written by instrumentation (telemetry, verifiers, test harnesses, human review), never by the actor whose action it grades. An actor's own account is agent_statement, citable as context only; the separation is a conformance invariant, enforced as auditability — engine origin always records who wrote what, and Governance can restrict who may write outcomes — because an open protocol can make self-grading visible even where it cannot make it impossible.
Each outcome carries a task family that selects candidate consequences. Sharing the family establishes neither attribution nor baseline membership: under the optional Validated Learning companion, TrialRecord explicitly freezes comparable baseline attempts and outcomes. Treatment observations link through the instrument's outcome_observation Activity to an attempt and the action_gate decision that applied the exact Skill revision. Its DecisionRecord distinguishes retrieval from actual use, and grading counts independent attempts assigned before execution. A SkillRevision must name its task family before trial; a pattern that nothing could prove wrong is not procedural memory. On the channel sits the Skill lifecycle:
proposed → trialed → adopted → revoked
Lifecycle changes commit with a validated immutable EvaluationRecord on a lifecycle_verdict Activity and a guarded update (Validated Learning §7). Auditors replay exact inputs against the immutable TrialRecord; a Skill's current_trial only selects it, and its GradingState is a computed view. Without the companion, Skills stay unproven candidates — useful, recallable, never promoted. Only trialed → adopted promotes through a comparative verdict, and revocation is never harder than adoption. Same-state monitoring may retain standing under authorized policy without claiming new improvement; policy withdrawal may have zero outcom
Solo se muestra el principio; lee el resto en el repo