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PopNx MCPAIを使う人のためのMCPサーバー · やさしい言葉で解説

Recall

作者:Polignバージョン 0.24.0i★ –i–
レジストリ名io.github.Polign/recall

インストール

概要

作者による説明(英語の原文)

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

PopNx MCPの3つの柱

この3つを1つのスコアにまとめることはしません。意見、測定、人気はそれぞれ別のものだからです。

できること

このサーバーのやさしい解説はまだ書いていません。以下はレジストリから読み取った事実です。

  • 接続方法: –
  • トランスポート:
  • カテゴリ: 検索・AIの記憶

インストール

使っているAIアプリを選んで、設定をコピーしてください。

インストールの前に

  • 基本チェック:1項目中1項目に合格
  • あなたのパソコンで、あなたのユーザーと同じ権限(ファイル、ネットワーク、アプリ)で動きます。
  • レジストリには、入力が必要なキーは載っていません。

作者はレジストリにインストール情報を載せていません。下にある作者のREADMEをご覧ください。

できること

パソコンで動くサーバーです。インストールすると、ツール一覧がAIアプリに表示されます。

基本チェックi

誰でも見られる情報をチェックしており、コードは読んでいません。当てはまらない項目は数に入れていません。 · 2026年11月6日にチェック

  • ✓ 合格

    作者とリポジトリが一致

    レジストリ名は、リポジトリを所有するGitHubアカウント Polign で確認済みです

  • … 未チェック

    今もメンテナンスされている

    このリポジトリはまだGitHubから読み取っていません

  • … 未チェック

    ライセンスがある

    このリポジトリはまだGitHubから読み取っていません

  • – 対象外

    エンドポイントが応答する

    これはパソコンで動くため、インターネット上のエンドポイントはありません

  • – 対象外

    OAuthでサインイン

    パソコンで動き、代わりにローカルの設定を使います

  • … 未チェック

    ツールの説明に隠れた指示がない

    パソコンで動くサーバーです。ツール一覧を読み取れるのは、インターネット上のエンドポイントがある場合だけです

検索・AIの記憶のほかのサーバーとの比較i

  • このカテゴリのほかの7233件のうち0%よりスターが多い
  • このカテゴリのほかの7233件のうち83%より最近レジストリに公開
  • 基本チェックの結果が、このカテゴリのほかの7233件のうち100%と同じかそれ以上

レジストリでこのカテゴリにある有効なすべてのサーバーと比較しています。スターは、公開者がリポジトリの所有者である場合だけ数えます。

よくある質問

Recallは安全ですか?

当てはまる基本チェック1項目のうち1項目に合格しました。コードは読んでいません。キーやトークンを追加する前に、サーバーがどんなアクセスを求めているかを確認し、必要な権限だけを持つキーを使ってください。

どんなキーやコードが必要ですか?

レジストリには必須のキーは載っていません。ただし、最初に使うときにサインインを求めるサーバーもあります。

パソコンで動きますか、それともクラウドですか?

あなたのパソコンで動きます。必要なときにAIアプリが起動し、データは作者のサーバーを通りません(サーバー自体がオンラインサービスを呼び出す場合を除きます)。

今もメンテナンスされていますか?

GitHubリポジトリの最終更新は–、最新バージョン(0.24.0)がレジストリに届いたのは7 日前です。

どのアプリで使えますか?

Claude Desktop、Claude Code、Cursor、VS CodeなどのMCP対応アプリで使えます。

代わりに使えるものは?

同じカテゴリには:Context7, Codebase Memory, Exa

代わりの候補

同じカテゴリで、ほかの作者が作ったサーバー。スターの多い順。

カードの見方:★ GitHubスターi3つの柱:レビュー · チェック · 利用状況i更新=最新バージョンをレジストリに公開した日i

Context7

作者:upstash

Up-to-date docs for AI agents

パソコンでもクラウドでも★ 64,224MIT3 日前更新
インストール

Codebase Memory

作者:DeusData

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

パソコンで動く★ 48,148MIT20 日前更新
インストール

Exa

作者:exa-labs

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

クラウドで動く★ 5,213MIT44 日前更新
インストール

作者のREADME

READMEを開く

作者のリポジトリから書かれたとおりにコピーしたものです。PopNx MCPは内容を確認・推奨していません。HTML、画像、スクリプトは取り除いています。リンクはこのサイトの外へ移動します。

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

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