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INITE Brain

de inite-aiversión 2.7.0i★ –iSe ejecuta en la nube
Nombre en el registroio.github.inite-ai/inite-brain-service

Instalar

Resumen

Descripción del creador (original en inglés)

Long-term memory for AI agents: a bitemporal knowledge graph with cited answers.

Los tres pilares de PopNx MCP

Estos tres nunca se combinan en una sola puntuación: las opiniones, las mediciones y la popularidad son cosas distintas.

Qué puedes hacer con él

Todavía no escribimos un resumen sencillo para este. Abajo están los datos que leímos del registro.

  • Conexión: Se ejecuta en la nube
  • Transporte: Streamable HTTP
  • Categoría: Búsqueda y memoria de IA

Instalación

Elige la app de IA que usas y copia la configuración.

Antes de instalar

Necesitas

¿Qué app usas?

Elige tu app arriba. La recordamos, así que la próxima vez el botón dirá “Instalar en …”.

Funciona en Claude para web, escritorio y móvil:

  1. Abre Settings → Connectors
  2. Presiona Add custom connector
  3. Pega la dirección de abajo y presiona Add
  4. Si te pide iniciar sesión, inicia sesión en el servicio del creador
SettingsConnectors1+ Add2https://…3
https://brain.inite.ai/mcp

Ejecuta esto en tu terminal

claude mcp add --transport http inite-brain-service https://brain.inite.ai/mcp

Abrir en Cursor

O copia la configuración tú

Pégalo en .cursor/mcp.json de tu proyecto, o en ~/.cursor/mcp.json

{
  "mcpServers": {
    "inite-brain-service": {
      "url": "https://brain.inite.ai/mcp"
    }
  }
}

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.

Abrir en VS Code

O copia la configuración tú

Pégalo en .vscode/mcp.json de tu proyecto

{
  "servers": {
    "inite-brain-service": {
      "type": "http",
      "url": "https://brain.inite.ai/mcp"
    }
  }
}

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.

ChatGPT solo se conecta a servidores en internet:

  1. Abre ChatGPT en la web → Settings → Connectors (Apps)
  2. Presiona Create o Add (en algunos planes primero hay que activar Developer mode en Advanced)
  3. Pega la dirección de abajo, ponle un nombre y guarda
  4. Si te pide iniciar sesión, inicia sesión en el servicio del creador
SettingsConnectors1+ Create2https://…3
https://brain.inite.ai/mcp

Los nombres de los menús pueden variar un poco según el plan y con el tiempo.

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.

Qué puede hacer

Nombres y descripciones de las herramientas tal como los informó el servidor el 6 nov 2026. Los escribió el creador, no nosotros; los mostramos como texto sin formato.

  • about_brain

    What this server is and what it can do, before you connect. Reads nothing and stores nothing.

  • how_to_connect

    The two ways to authenticate, and what each is for. Reads nothing and stores nothing.

  • search_knowledge

    Semantic search over the company knowledge graph: the facts the query is about, the links between their entities and what was left out. Apply asOf for historical "what was true on X" queries. Embedding model on this tenant: set per workspace.

  • search_multi_hop

    Planner-LLM decomposes the query into ≤ maxHops anchored sub-queries; later hops are anchored to the running entity set. Built for questions that combine evidence across turns / sessions, or that require reasoning over multiple entities ("tenants who complained in April AND upgra

  • graph_retrieve

    Resolves the named entities by canonical name, walks their 1-hop neighbourhood over knowledge_edge, and returns facts across seeds ∪ neighbours, optionally filtered by predicate hints. Works from entities named up front and returns what the graph holds around them ("who runs engi

  • synthesize

    Runs hybrid search then feeds the retrieved facts to a generator LLM that produces a citation-bearing answer (each claim ends with [factId]); a verifier LLM then judges whether every claim is supported. Three guardrail modes: strict (default) returns null on partial / unsupported

  • memory_diff

    Returns everything brain learned, unlearned, or replaced between two ISO 8601 cursors [from, to). createdFacts = new active facts; retractedFacts = facts marked retracted in-window with no successor; changedFacts = facts that were superseded by another (carries before+after); new

  • get_entity_profile

    One entity: canonical name, type and what brain holds about it now; detail "full" adds externalRefs, the ids of its records in the originating vertical.

  • get_entity_timeline

    Chronological audit of all facts brain has learned about this entity, including retracted ones. Useful for "what did we know when" investigations.

  • summarize_entity

    Returns a short one-line briefing about the entity — name, type, the most-confident active facts, external refs — suitable for dropping into an LLM context window. Caches in-process (per companyId / entityId / asOf / styleHint) so a hot entity touched across many turns doesn't re

  • get_competing_facts

    Returns facts in COMPETING status — those the conflict resolver couldn't auto-supersede because two same-predicate bitemporal facts overlap in valid-time and are too cosine-close within margin. Grouped by (entityId, predicate); 2-fact groups are pairs the resolver left for adjudi

  • detect_contradiction

    Dry-run preflight against fn::resolve_fact. Answers "if I were to record this fact right now, what would the resolver decide?" without writing to the database. wouldOutcome ∈ {INSERTED, SUPERSEDED, COMPETING, REJECTED}; reasoning explains which rule fired (semantics class, score

  • get_fact

    Read a single fact as stored — predicate (aspect), object (statement), confidence, validity window, source attribution (vertical / recorder / conversationId), and lifecycle state. Retracted facts still resolve (retracted: true) — "why did I stop remembering this" is part of the t

  • get_fact_provenance

    Return the grounding provenance behind one fact: the verbatim conversation turns it was derived from (episodes, chronological, with char-span quotes when the deriver stamped them). When the server runs with the recursive-closure flag the response additionally carries derivedFacts

  • find_related_entities

    Get entities connected to the given one via the knowledge graph.

  • match_procedure

    Cosine-matches procedural memory (curated 'how to' patterns the operator recorded) against a free-text context query. Returns top-K procedures sorted by similarity DESC then priority ASC. Surfaces the behaviour rules that apply to a context — e.g. "user asks about pricing" → "men

  • list_procedures

    Paginated listing of procedural memory for admin/review UIs. Sorted by priority ASC, createdAt DESC. Set includeRetired=true to see soft-deleted rows.

  • search_communities

    Cosine-matches the query against topic-community summaries — the coarse retrieval scope. A community is a cluster of related entities (label propagation over the entity graph) with one rolled-up summary. Suited to broad questions ("what do we know about X domain"): it returns an

  • list_communities

    Paginated listing of topic communities, largest first. Each row carries a label, a rolled-up summary, and the member count. Gives the knowledge map at a glance.

  • find_entity_communities

    Returns the topic communities a given entity is a member of. Places an entity in its broader context — which clusters / themes it participates in — and leads from one entity to the wider neighbourhood the cluster summarises.

  • why

    Return the design decisions, rationale, invariants and gotchas recorded against a code anchor — the non-derivable engineering "why" a parser cannot recover from source. The anchor is a SCIP-style symbol string ("pkg/namespace/symbol") or a file path ("repo/path/file.ts"). Pass as

  • recall_decisions

    Find recorded design decisions / rationale / invariants / gotchas by NL topic ("why do we resolve facts through one gateway?"), across ALL code anchors — a semantic search over code-memory, rather than one known anchor. Returns matches with their code anchor + kind, ranked by rel

  • get_source_reputation

    Return everything brain knows ABOUT a source (not its facts): the operator-declared identity (type, authLevel — authority in conflict resolution), the learned per-domain agreement rates (domain = predicate; null domain = the global blended rate; rates need >=8 samples before the

  • workspace_status

    The tenant this credential writes to (companyId, its display name, whether it is a personal workspace), what is already in its memory (entities, active facts, facts recorded in the last 7 days), how many Domain Packs are installed, for a user-bound connection who the user is in t

  • record_fact

    Records one fact about an entity directly; the memory settles it against the facts it holds (INSERTED / SUPERSEDED / COMPETING). Most facts arrive through ingestion.

  • link_entities

    Insert an edge between two entities. `kind` is the edge type — `identity_of` merges the `from` entity into `to` (cross-vertical identity reconciliation), other typed edges (`paid_for`, `mentioned_in`, `worked_with`, …) are surfaced in related-entity reads and contribute to PPR /

  • retract_fact

    Mark a fact as no longer believed. Cascades to facts derived from this one. Does not delete; the row remains for audit.

  • record_procedure

    Record a 'how to' pattern that is surfaced when a similar context appears later. trigger = the context phrase the rule should match against (e.g. "user asks about pricing"); action = the behaviour to apply (e.g. "mention they're on platinum tier; they get 20% off"). priority orde

  • retire_procedure

    Mark a procedural memory row as retired (sets retiredAt). A retired procedure is no longer matched or listed by default. For a rule that no longer applies — distinct from a hard delete because the row stays for audit.

  • record_decision

    Capture the non-derivable engineering "why" about a code location: a design decision, its rationale, an invariant that must hold, or a gotcha. Anchored to a SCIP-style symbol or file path (NOT line numbers — survives edits). Stored as a bitemporal typed fact: re-recording a `deci

Revisiones básicasi

Revisamos información que cualquiera puede ver; no hemos leído el código. Las revisiones que no aplican no se cuentan. · Revisado el 8 nov 2026

  • ✓ Superada

    El creador coincide con el repo

    El nombre en el registro se verificó con la cuenta de GitHub inite-ai, dueña del repo

  • … Aún sin revisar

    Sigue en mantenimiento

    Todavía no hemos leído este repo en GitHub

  • … Aún sin revisar

    Tiene licencia

    Todavía no hemos leído este repo en GitHub

  • ✓ Superada

    El endpoint responde

    Respondió en 589 ms (HTTP 200)

  • – No aplica

    Inicio de sesión con OAuth

    No hace falta iniciar sesión

  • ✓ Superada

    Sin instrucciones ocultas en las descripciones de herramientas

    Leímos 30 descripciones de herramientas y no encontramos instrucciones ocultas para la IA

Comparado con otros en Búsqueda y memoria de IAi

  • Más estrellas que el 0% de los otros 5670 servidores de esta categoría
  • Publicado en el registro más recientemente que el 89% de los otros 5670 servidores de esta categoría
  • Revisiones básicas iguales o mejores que el 100% de los otros 5670 servidores de esta categoría

Comparado con todos los servidores activos de esta categoría en el registro. Las estrellas solo cuentan cuando quien lo publica es dueño del repo.

Preguntas frecuentes

¿Es seguro INITE Brain?

Superó 3 de 3 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.

¿Qué claves o códigos necesita?

El registro no indica ninguna clave obligatoria, aunque algunos servidores te piden iniciar sesión la primera vez que los usas.

¿Se ejecuta en mi computadora o en la nube?

Se ejecuta en la nube del creador. Te conectas por internet sin instalar nada, pero lo que envías pasa por los servidores del creador.

¿Sigue en mantenimiento?

El repo de GitHub cambió por última vez –, y la última versión (2.7.0) llegó al registro hace 5 días.

¿Qué apps pueden usarlo?

Las apps compatibles con MCP, como Claude Desktop, Claude Code, Cursor y VS Code. Se conecta por URL, así que también funcionan las apps que aceptan servidores MCP remotos, como ChatGPT en modo desarrollador.

¿Qué puedo usar en su lugar?

En la misma categoría: Context7, Codebase Memory, Exa

Alternativas

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

Context7

de upstash

Up-to-date docs for AI agents

Computadora o nube★ 64.224MITActualizado ayer
Instalar

Codebase Memory

de DeusData

Codebase knowledge graph for AI agents — 162 languages, sub-ms queries, 99% fewer tokens.

Se ejecuta en tu computadora★ 48.148MITActualizado hace 21 días
Instalar

Exa

de exa-labs

Connect AI agents to Exa for web search, content fetching, and multi-step research.

Se ejecuta en la nube★ 5213MITActualizado hace 2 meses
Instalar

README del creador

Abrir el README

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.

INITE Brain

Open-source memory for AI agents: a bitemporal knowledge graph with provenance, over MCP and REST.

Website · Quick start · Architecture · Domain Packs · Getting started · Docs · Agent guide

Quick start

Needs Docker with Compose v2, openssl, and an OpenAI API key (the default model configuration). Runs the released image (linux/amd64 and linux/arm64) with SurrealDB; no clone, no build.

: "${OPENAI_API_KEY:?export OPENAI_API_KEY first}"

mkdir inite-brain && cd inite-brain curl -fsSLO https://raw.githubusercontent.com/inite-ai/inite-brain-service/main/docker-compose.quickstart.yml BRAIN_KEY="brain_$(openssl rand -hex 24)" KEY_HASH="$(printf %s "$BRAIN_KEY" | openssl dgst -sha256 -r | cut -d' ' -f1)" cat > .env <<EOF BRAIN_VERSION=2.4 OPENAI_API_KEY=$OPENAI_API_KEY BRAIN_API_KEYS='[{"keyHash":"sha256:$KEY_HASH","companyId":"co_demo","scopes":["brain:read","brain:write"]}]' SURREALDB_PASSWORD=$(openssl rand -hex 24) SURREALDB_SCOPED_PASS=$(openssl rand -hex 24) FORGET_HMAC_KEY=$(openssl rand -hex 32) EOF docker compose -f docker-compose.quickstart.yml up -d --wait export BRAIN_URL=http://localhost:3030 BRAIN_KEY curl "$BRAIN_URL/health" echo "API key (shown once, only its hash is stored): $BRAIN_KEY"

Then remember something and ask about it, or point an MCP client at http://localhost:3030/mcp with Authorization: Bearer $BRAIN_KEY. BRAIN_VERSION takes X.Y (newest patch of that release) or vX.Y.Z; :latest is overwritten by the main-branch deploy. The first start downloads the local ONNX models into a volume, so the first write can take a few minutes; later ones do not. Hosted service, running from source, and production: Getting started.

Why not Mem0, Zep or Letta?

  • Mem0 is quicker to adopt: an add/search API, a managed platform and many framework integrations. Brain asks for more up front (a tenant, scoped keys, userId on personal memory) and in exchange keeps both valid time and knowledge time on every fact. A conflicting write supersedes the earlier fact or is parked as COMPETING; it does not silently overwrite.
  • Zep (Graphiti) is the closest design: Graphiti is also a temporal knowledge graph, and you can embed it as a Python library over a graph database. Choose it if you want memory inside your own process. Brain is a service: a separate database per tenant, API keys scoped to read, write and admin, PII controls, and GDPR forget_entity with tombstones.
  • Letta is an agent runtime in which the agent edits its own memory. Use it when you want the agent loop and its memory in one server. Brain is memory only: the agent stays in Claude Code, Cursor, Codex or your own code and reaches Brain over MCP (Streamable HTTP, or the stdio shim @inite/brain-mcp).
  • Answers can abstain. synthesize ends each claim with the id of the fact it rests on, a verifier checks the support, and strict mode returns no answer rather than an unsupported one. Each fact keeps a pointer to the conversation or document it came from.
  • No borrowed benchmark numbers. We do not set our memory-accuracy results against other systems' self-reported scores, which mostly use lenient judges. How we measure: docs/eval-protocol.md.

Part of the INITE Triad: Brain verifies the tokens INITE Auth issues, as INITE Billing does; the contract between them is the Triad contract. Run all three with one compose file: inite-ai/inite-triad.

What Brain is

Brain gives an agent memory between conversations: what was said, what changed, which entities are involved, and what supports an answer. A bitemporal graph stores structured facts and relationships; source episodes preserve context; scenes and beliefs capture events and evolving state. Domain Packs adapt that memory to a product's vocabulary and lifecycles without forking the engine.

Run Brain on your infrastructure or connect to the hosted service at brain.inite.ai. The web app provides memory search and exploration; REST and MCP provide the integration surface.

Why Brain

  • Memory with its context. Retrieve structured facts, source passages and, when enabled, scenes and beliefs. Follow provenance back to the supporting conversation, document or evidence fragment.
  • Changes stay inspectable. Facts carry valid time and knowledge time. Updates can supersede earlier facts; unresolved disagreements remain COMPETING. Retraction preserves history; administrative forgetting deletes associated records and leaves tombstones.
  • A model of your domain. Packs define typed predicates, extraction guidance, scene schemas, state transitions, verification and retention hints, and optional media capabilities and MCP tools.
  • Scope is explicit. Each tenant has a separate database. Personal memory uses userId; omitting it selects tenant-global memory. Key scopes, ABAC policies and PII controls further constrain access.
  • Answers can be checked. Hybrid search, graph retrieval and multi-hop gather evidence. Synthesis cites it and runs verification; strict guardrails can abstain when support is insufficient.

Architecture

Brain separates source material, derived memory and the read path. This is a conceptual view; individual ingestion routes and retrieval profiles activate different parts of it.

flowchart LR

input["REST / MCP conversations · documents · facts"] raw[("Source records documents · episodes · evidence")] derive["Indexers and scene builders candidates · entity resolution · conflicts"] memory[("Connected memory on SurrealDB facts + graph · scenes · beliefs")] retrieve["Retrieval semantic + lexical · graph · multi-hop"] answer["Synthesis + verification answer with citations or abstention"] packs["Domain Packs vocabulary · memoryModel · tools"] media["Evidence registration / upload scan hook · trusted processors"] input --> raw input --> derive media --> raw raw --> derive derive --> memory memory --> retrieve raw --> retrieve retrieve --> answer packs -. "domain configuration" .-> derive packs -. "capabilities and consent" .-> media

Memory layerWhat it preserves
Facts and entitiesTyped claims, canonical entities, relationships, confidence, source attribution and temporal history.
EpisodesSource turns and conversation context that derived memory can point back to or be rebuilt from.
ScenesBounded events with gists, entities, state changes and source links. Optional gist embeddings and a scene retrieval lane make them searchable alongside other evidence.
BeliefsDerived state with revision history and supporting scenes. Pack-projected state deltas can feed belief promotion.
EvidenceAssets, located fragments, processing runs and derived representations, with links to the memories they support.

Rebuildable memory. Derived worlds can be rebuilt from retained episodes in a staging version. Readers stay pinned to the previous world until promotion. Scene maintenance can process dirty conversations on a schedule, with budgets, leases and metrics; it can compose scenes and promote supported beliefs.

Configurable retrieval. Retrieval profiles select mechanisms for the corpus and question. Semantic and lexical search, graph expansion, reranking, raw passages, scene retrieval and belief retrieval are distinct capabilities. A known entity can enter through graph_retrieve; a question spanning relationships can use search_multi_hop.

Separate serving and background work. The NestJS service uses a SurrealDB-backed job queue, leases and worker-thread offloads. Run the image as one process or split PROCESS_ROLE=api|worker; see the operations guide.

The code includes capabilities that are opt-in, not a promise that every installation runs every layer. Scene construction, scene serving, belief promotion, evidence upload/processing and pack projections have separate gates. Check the architecture, operations and flag definitions for scenes, evidence and pack projections when enabling them.

Domain Packs

A pack is a versioned JSON manifest installed per tenant. It tells the engine how to interpret a domain's material. Pack-derived observations remain candidates for the core pipeline to validate and resolve.

A pack can declarePurpose
predicatesTyped vocabulary, value constraints, conflict semantics, decay and PII classes.

| extractionProfile, `evalFi

Solo se muestra el principio; lee el resto en el repo

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