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Recall

de Polignversión 0.24.0i★ –i–
Nombre en el registroio.github.Polign/recall

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Resumen

Descripción del creador (original en inglés)

Agent memory that keeps every correction, with history and as-of reads. Runs on your database.

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: –
  • Transporte:
  • Categoría: Búsqueda y memoria de IA

Instalación

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

Antes de instalar

El creador no incluyó detalles de instalación en el registro. Consulta el README del creador más abajo.

Qué puede hacer

Se ejecuta en tu computadora; la lista de herramientas aparece en tu app de IA después de instalarlo.

Revisiones básicasi

Revisamos información que cualquiera puede ver; no hemos leído el código. Las revisiones que no aplican no se cuentan. · Revisado el 6 nov 2026

  • ✓ Superada

    El creador coincide con el repo

    El nombre en el registro se verificó con la cuenta de GitHub Polign, 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

  • – No aplica

    El endpoint responde

    Este se ejecuta en tu computadora; no hay endpoint en internet

  • – No aplica

    Inicio de sesión con OAuth

    Se ejecuta en tu computadora y usa ajustes locales en su lugar

  • … Aún sin revisar

    Sin instrucciones ocultas en las descripciones de herramientas

    Se ejecuta en tu computadora; solo podemos leer listas de herramientas de endpoints en internet

Comparado con otros en Búsqueda y memoria de IAi

  • Más estrellas que el 0% de los otros 7240 servidores de esta categoría
  • Publicado en el registro más recientemente que el 83% de los otros 7240 servidores de esta categoría
  • Revisiones básicas iguales o mejores que el 100% de los otros 7240 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 Recall?

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.

¿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 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).

¿Sigue en mantenimiento?

El repo de GitHub cambió por última vez –, y la última versión (0.24.0) llegó al registro hace 7 días.

¿Qué apps pueden usarlo?

Las apps compatibles con MCP, como Claude Desktop, Claude Code, Cursor y VS Code.

¿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 hace 3 días
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 20 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 44 días
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.

Polign Recall

Recall gives an agent memory and state that survive the conversation and the process. Memory is what the agent knows: facts a user stated, with every correction kept, so the agent can say what it believes now, what it believed before, and why. State is where the agent is: its progress, its notes to its next instance, and the tool calls it had in flight, so a run that crashes or moves machines resumes from its own records.

Recall is a Go library, a recall binary that serves the same operations as MCP tools and as HTTP, and a Python client. It stores everything in a database you run: polign_db, PostgreSQL, MongoDB or Qdrant. Apache 2.0.

One user over nine months, three homes, three employers, three work emails, six questions, Claude Opus 5.5 extracting, answering and judging for all three systems: Recall 18/18, mem0 18/18, Zep 12/18. Tokens to save one message: Recall 3.0k, mem0 12.7k, Zep 14.7k.

The code to the Recall repository.

Install

pip install polign-recall          # the Python client, the recall binary, and the database it runs

brew install polign/tap/recall # the same for Homebrew go install github.com/Polign/recall/cmd/recall@latest # the binary alone; it fetches the database on first use

Then give it to the agent you use:

claude mcp add recall -- recall mcp -write      # Claude Code

Add to Cursor · Add to VS Code · any other MCP host: command recall, arguments mcp -write (details).

With nothing configured, memory lives in a local database in ~/.local/share/recall, started on first use and shared by every agent on the machine. No model and no API key are needed to start. To keep memory in a database you already run, see Backends.

What a correction looks like

Every change is kept. Three commands read the record:

$ recall history "which editor do I use"

user preferred editor believed now: user preferred editor: zed 2026-09-01 10:00 stated, user_stated

  • user preferred editor: vim 2026-09-20 10:00 replaced, user_stated
  • user preferred editor: vim
  • user preferred editor: emacs 2026-10-05 10:00 replaced, user_stated
  • user preferred editor: emacs
  • user preferred editor: zed

$ recall ask -as-of 2026-09-25 "which editor do I use" Believed as of 2026-09-25 00:00: user preferred editor: emacs (since 2026-09-20) replaced: vim (since 2026-09-01)

$ recall diff -from 2026-09-15 -to 2026-10-31 Changed between 2026-09-15 00:00 and 2026-10-31 00:00:

  • user home city: Lisbon
  • user home city: Porto
  • user preferred editor: vim
  • user preferred editor: zed

Why Recall

  • Corrections are records, not overwrites. A new value replaces the old one by a rule you can read, and every answer arrives with what it replaced.
  • Any moment is readable. as_of, history and diff say what the agent believed then and when that changed.
  • Every answer has a source. recall explain shows the message a fact came from, the rule that decided it, and its history.
  • Agents resume from records. A crashed or moved agent gets its working state, recent turns and unfinished tool calls back, not a snapshot.
  • Your storage, your rules. Memory lives in a database you already run, and the vocabulary of what gets remembered is yours to define or leave open.

Quickstart

Python 3.10 or later. Save a preference, then correct it:

pip install polign-recall

python - <<'PY' from polign_recall import Client

with Client(local_dir="./recall-data", typed=True) as memory: memory.remember("user", "prefers_editor", "vim") memory.remember("user", "prefers_editor", "neovim") PY

A second process reads the current value, the history, and what was believed before the correction:

python - <<'PY'

from polign_recall import Client

with Client(local_dir="./recall-data", typed=True) as memory: print(memory.recall("user", "prefers_editor")[0].value) # neovim

events = memory.history("user", "prefers_editor") print([e.value for e in events]) # ['vim', 'neovim']

before = memory.recall("user", "prefers_editor", as_of=events[0].observed_at) print(before[0].value) # vim PY

The first client to open ./recall-data starts a local database for it and later clients share it; the memories stay there after the program exits. A bare Client() uses ~/.local/share/recall, the memory every agent on the machine shares. The Python client guide covers the rest: plain-text memory with a model, forgetting, history, and resuming an agent.

How memory works

A memory is a subject, a predicate and a typed value: user, prefers_editor, neovim. Predicates live in a registry that says what type a value has, whether the predicate holds one value or many, and which predicates it contradicts: dislikes excludes likes, gave_away excludes owns. A single-valued predicate replaces its value on a new statement; a multi-valued one accumulates. Forgetting records a withdrawal; nothing is deleted. Recall ships starter registries for coding, personal assistant, support, sales and voice agents, and you can change a registry later without touching what was already remembered.

You do not have to define predicates. With an open vocabulary the first use of a predicate defines it, and with a model configured the MCP tools take plain text and no predicates appear anywhere:

recall setup -extract-model anthropic:claude-haiku-4-5-20251001
remember("I switched from helix to zed last week")

recall("which editor do I use?") -> "user prefers editor: zed" (before: helix) forget("my editor")

The model decides how each statement is filed. Once filed, replacement follows the predicate's rule, every filing is recorded, and a wrong filing is corrected with one event that every later answer follows. No model reconciles memory on its own: Recall applies the registry's rules, shows what may conflict, and lets the agent confirm the rest, so every correction reads back to the rule or statement that made it. recall mcp -typed serves the typed tools (remember(subject, predicate, value)) instead. The developer reference has the details and the Memory API is the contract.

State

The second half of Recall, with its own contract, docs/state-api-v1.md. An agent writes down where it is as it works, and a new process resumes from those records instead of from a snapshot:

  • Working state: goal, plan, progress, focus, decisions, open questions.
  • Turns, verbatim, and large tool outputs stored by reference.
  • Pointers to where the work lives: a branch, an object key, a process.
  • Actions: each tool call that touches the world, recorded before it runs and closed with its result, so a crash between the two is visible and the next resume lists the call as unconfirmed instead of repeating it.

Resume takes a lease on the agent id, so one process acts for an agent at a time, rebuilds the context within a token budget, and renders it as one briefing for the model:

agent, err := client.Resume(ctx, recall.ResumeRequest{AgentID: "coder-1", TokenBudget: 8000})

if err != nil { return err } defer agent.Release(context.Background())

briefing := agent.Resumed().Briefing // hand this to the model

In Python, an agent remembers a fact and records where it is, the process is killed, and a new process resumes from the records:

import os, signal

from polign_recall import Client

with Client(local_dir="./recall-data", agent=True, typed=True) as client: client.remember("user", "project_constraint", "deploy only on weekdays after 18:00 UTC") with client.resume("coder-1") as agent: agent.update_working_state(goal="Move billing to the v2 API", plan=["find call sites", "migrate them", "run the tests"]) agent.record_turn("user", "Start with the charge endpoint.") agent.milestone("call sites found", progress="12 call sites listed") os.kill(os.getpid(), signal.SIGKILL) # dies here, lease unreleased

import time

from polign_recall import Client

with Client(local_dir="./recall-data", agent=True, typed=True) as client: agent = client.resume("coder-1", token_budget=8000, defer_lease=True) print(agent.context.briefing) # at once: the goal, plan, milestone, turn, and constraint while not agent.acquire(): # False until the dead process's lease expires time.sleep(1) agent.record_turn("assistant", "Picking up at the charge endpoint.")

Without defer_lease=True, resume raises lease_held until the crashed process's lease expires (Python client guide).

The same operations are MCP tools (agent_resume, update_working_state, record_turn, begin_action, ...) and, on recall serve, HTTP routes at POST /v1/agent/ . Over HTTP the server holds the lease between requests: resume returns an epoch and every write carries it, so a stale process cannot write over its successor. The State API is beta; the records it writes are stable (STABILITY.md). The resume example kills an agent mid-task and continues it in a new process.

Serving many agents

recall mcp is one agent, one process. recall serve is the same memory and state for many agents over HTTP, each inside the namespace its key names. The server holds the database credentials; agents hold Recall keys.

recall serve -backend polign -url http://polign:23000 -bootstrap-key-file admin.key

recall key create -backend polign -url http://polign:23000 -namespace acme:agent-7 -expires 168h

Each namespace gets its own collection, so nothing a caller sends can reach another namespace's records. POST /mcp is MCP over streamable HTTP; POST /v1/ takes a memory tool's arguments as the body, and POST /v1/agent/ a state tool's. Any stdio MCP client reaches the server through recall connect with RECALL_URL and RECALL_API_KEY set.

On Kubernetes a pod needs no key at all. With recall serve -kubernetes, it posts its projected service account token and gets a short-lived key fixed to its own agent id, which recall connect and the Python client obtain and renew by themselves. The Helm chart is charts/recall in Polign/polign; the agent-sandbox demo runs it on kind.

Backends

local (default)polign_dbPostgreSQL + pgvectorMongoDBQdrant
RECALL_BACKENDlocalpolignpgvectormongodbqdrant
RECALL_BACKEND_URLa directoryhttp://host:23000postgres://user:pw@host/dbmongodb://user:pw@host/dbhttp://host:6333
Memory, history, as-ofyesyesyesyesyes
Keyword searchyesyesyesyesno
Cached readsyesyesyesyesno
State API (leases)yesyesyesyesno
export RECALL_BACKEND=pgvector RECALL_BACKEND_URL=postgres://user:password@localhost:5432/mydb

recall mcp -write

Three settings name the database for every command: -backend, -url and `-ke

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

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