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Recall

von PolignVersion 0.24.0i★ –i–
Name in der Registryio.github.Polign/recall

Installieren

Überblick

Beschreibung vom Entwickler (englisches Original)

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

Die drei Säulen von PopNx MCP

Diese drei werden nie zu einer Punktzahl zusammengefasst: Meinungen, Messungen und Beliebtheit sind verschiedene Dinge.

Was du damit machen kannst

Für diesen Server haben wir noch keine Zusammenfassung in einfacher Sprache geschrieben. Unten stehen die Fakten, die wir aus der Registry gelesen haben.

  • Verbindung: –
  • Transport:
  • Kategorie: Suche & KI-Gedächtnis

Installation

Wähle die KI-App, die du nutzt, und kopiere die Einstellungen.

Bevor du installierst

  • Basischecks: 1 von 1 bestanden
  • Läuft auf deinem Computer mit den Rechten deines Benutzerkontos (Dateien, Netzwerk, Apps).
  • Die Registry nennt keinen Schlüssel zum Eintragen.

Der Entwickler hat in der Registry keine Installationsangaben gemacht. Sieh dir unten die README des Entwicklers an.

Was er kann

Läuft auf deinem Computer; die Tool-Liste erscheint nach der Installation in deiner KI-App.

Basischecksi

Wir prüfen Informationen, die jeder sehen kann; den Code haben wir nicht gelesen. Checks, die nicht zutreffen, werden nicht mitgezählt. · Geprüft am 6. Nov. 2026

  • ✓ Bestanden

    Entwickler passt zum Repo

    Der Registry-Name wurde mit dem GitHub-Konto Polign bestätigt, dem das Repo gehört

  • … Noch nicht geprüft

    Wird noch gepflegt

    Dieses Repo haben wir noch nicht von GitHub gelesen

  • … Noch nicht geprüft

    Hat eine Lizenz

    Dieses Repo haben wir noch nicht von GitHub gelesen

  • – Nicht zutreffend

    Endpunkt antwortet

    Dieser Server läuft auf deinem Computer; es gibt keinen Endpunkt im Internet

  • – Nicht zutreffend

    Anmeldung mit OAuth

    Läuft auf deinem Computer und nutzt stattdessen lokale Einstellungen

  • … Noch nicht geprüft

    Keine versteckten Anweisungen in Tool-Beschreibungen

    Läuft auf deinem Computer; Tool-Listen können wir nur von Endpunkten im Internet lesen

Im Vergleich mit anderen in Suche & KI-Gedächtnisi

  • Mehr Sterne als 0 % der anderen 7231 Server in dieser Kategorie
  • Neuer in der Registry veröffentlicht als 83 % der anderen 7231 Server in dieser Kategorie
  • Basischecks gleich gut wie oder besser als bei 100 % der anderen 7231 Server in dieser Kategorie

Verglichen mit allen aktiven Servern dieser Kategorie in der Registry. Sterne zählen nur, wenn dem Herausgeber das Repo gehört.

Häufige Fragen

Ist Recall sicher?

Er hat 1 von 1 zutreffenden Basischecks bestanden. Den Code haben wir nicht gelesen. Bevor du einen Schlüssel oder Token hinzufügst, sieh nach, welche Zugriffe der Server verlangt, und nutze einen Schlüssel, der nur die Berechtigungen hat, die du brauchst.

Welche Schlüssel oder Codes braucht er?

Die Registry nennt keinen erforderlichen Schlüssel, manche Server bitten dich aber bei der ersten Nutzung, dich anzumelden.

Läuft er auf meinem Computer oder in der Cloud?

Er läuft auf deinem Computer. Deine KI-App startet ihn bei Bedarf, und die Daten laufen nicht über die Server des Entwicklers (außer der Server ruft selbst einen Onlinedienst auf).

Wird er noch gepflegt?

Das GitHub-Repo wurde zuletzt – geändert, und die neueste Version (0.24.0) kam vor 7 Tagen in die Registry.

Welche Apps können ihn nutzen?

Apps, die MCP unterstützen, etwa Claude Desktop, Claude Code, Cursor und VS Code.

Was kann ich stattdessen nutzen?

In derselben Kategorie: Context7, Codebase Memory, Exa

Alternativen

Server derselben Kategorie von anderen Entwicklern, sortiert nach Sternen.

So liest du eine Karte:★ GitHub-SterneiDrei Säulen: Bewertungen · Checks · NutzungiAktualisiert = neueste Version in der Registry veröffentlichti

Context7

von upstash

Up-to-date docs for AI agents

Computer oder Cloud★ 64.224MITAktualisiert vor 3 Tagen
Installieren

Codebase Memory

von DeusData

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

Läuft auf deinem Computer★ 48.148MITAktualisiert vor 20 Tagen
Installieren

Exa

von exa-labs

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

Läuft in der Cloud★ 5.213MITAktualisiert vor 44 Tagen
Installieren

README vom Entwickler

README öffnen

Unverändert aus dem Repo des Entwicklers übernommen. PopNx MCP hat den Inhalt weder geprüft noch empfiehlt es ihn. HTML, Bilder und Skripte wurden entfernt. Links führen von dieser Website weg.

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

Nur der Anfang wird gezeigt; den Rest findest du im Repo

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