Perenna
Perenna
AIエージェント向けの、Gitを基盤とする軽量な永続メモリ。Claude Code、Codex、ChatGPT、Cursor、その他のMCPクライアントは、ベンダーアカウントや会話履歴を共有しなくても、永続的なメモリを共有できます。
メモリの読み取り、書き込み、削除のための独立したMCPツール
ローカルstdioと、単一ユーザーのOAuthで保護されたStreamable HTTPトランスポート
独立したGitリポジトリに保存された、人間が読めるMarkdown
Gitからいつでも再構築できるローカルVexor検索インデックス
複数のローカルエージェントプロセスを支えるプロセス間ロック
なぜPerennaなのか?
あなたのメモリは、たまたま使っているエージェントではなく、あなた自身に従うべきです。
Claude Code、Codex、Cursor、ChatGPTは、メモリをそれぞれ別々のサイロに保存します。エージェントを切り替えるとメモリは消え、マシンを切り替えるとローカルメモリはそこに取り残されます。
Perennaは、それらに共有された、Git基盤のメモリを提供します。ローカルエージェントとChatGPTは同じセルフホストのPerennaサービスに接続でき、その間、すべての永続メモリは、あなた自身が検査・編集・バージョン管理・バックアップできる普通のMarkdownとして残ります。
Mem0のセルフホスト型スタックははるかに重く、一方、ホスト型のFree Planでは、現在、顧客コンテンツをモデルのトレーニングや製品改善に使用することが許可されています。
Perennaは設計上異なります:アカウント不要、専有のメモリクラウドなし、ロックインなし。 あなたが管理するインフラ上のGitリポジトリの中に、あなたのメモリだけがあるだけです。
Related MCP server: Hypermnesic
クイックスタート
AIエージェントでインストールする
これを、ターミナルとローカルMCP設定のアクセス権を持つClaude Code、Codex、ChatGPT Desktop、Cursor、またはその他のコーディングエージェントに貼り付けます:
Install Perenna and connect it to this AI agent as a local stdio MCP server.
Work through the complete setup autonomously.
Use these as the source of truth:
- https://github.com/scarletkc/Perenna/blob/main/docs/getting-started.md
- https://github.com/scarletkc/Perenna/blob/main/docs/guides/client-setup.md
1. Detect the operating system, shell, and current MCP client.
2. Check for Python 3.12 or newer, Git, and uv. Install uv in user scope if it
is missing. If Python or Git needs administrator approval, give me the exact
command and stop there.
3. Install Perenna with `uv tool install perenna`. If Perenna is already
installed, upgrade it with `uv tool upgrade perenna`.
For Codex, also run `perenna skill install --agent codex`. For Claude Code,
run `perenna skill install --agent claude-code`. Do not replace an existing
modified copy or remove unrelated installed skills.
4. Check the effective Vexor embedding provider configuration. Reuse a working
`~/.vexor/config.json` or inherited environment configuration. If none is
available, ask me to choose between a remote provider and local embeddings.
Explain that a remote provider receives memory text and search queries. For
a remote provider, keep the provider and model in Vexor configuration and
supply its secret through `VEXOR_API_KEY` or the provider-specific environment
variable. For local embeddings, install `perenna[local]` and configure the
local model according to the Perenna configuration reference. Verify the
selected provider with `uvx vexor doctor` using the same environment that
the Perenna process will inherit.
5. Ask whether I want to synchronize Perenna with a private Git repository. If
I do, ask me to provide or approve its URL, run
`perenna sync setup <repository-url>`, and verify it with
`perenna sync status`. Treat repository creation, remote replacement, and
reconciling diverged history as separate choices that require my explicit
approval.
6. Register `perenna mcp --source <stable-client-name>` using the client-specific
method in the setup guide. Preserve unrelated MCP servers and settings. Use
a stable source such as `claude-code`, `codex`, or `cursor` for this client.
For another client, use its official instructions for adding a local stdio
MCP server. Make sure the Perenna process inherits `VEXOR_CONFIG_JSON`,
`VEXOR_API_KEY`, or any provider-specific key used in step 4. Report only
whether a secret is present.
7. Verify `perenna --help` and the saved MCP configuration. Reload MCP servers
and call `memory_read` with `action: "list"` when the client supports it. If
a restart is required, tell me the single restart step.
8. Report the commands run, files changed, and verification results. Keep API
keys out of tracked configuration files.公開リリースのインストール
Perennaには、Python 3.12以上、Git、そしてuvが必要です。
uv tool install perennaローカルクライアント用のオプションのメモリ動作スキルをインストールします:
perenna skill install --agent codex
# or
perenna skill install --agent claude-code両方のクライアントにスキルを配布する場合は、1つのコマンド内で--agentを繰り返します。設定リファレンスには、ユーザーとプロジェクトのスコープ、配布先、置換を保護する手段が説明されています。
また、CodexとClaude Codeは、PerennaのリポジトリのMarketplaceから、スキルとMCP接続を組み合わせてインストールすることもできます。プラグイン設定ガイドに従って、クライアントごとに一つの設定パスを選択してください。
Perennaには、動作するVexor埋め込みプロバイダーが必要です。対話的にプロバイダーを選択・設定するには、以下を実行します。
uvx vexor initPerennaは自動的に~/.vexor/config.jsonを再利用します。プロセス・レベルの設定を使用する場合、MCPサーバーがホスト環境からVEXOR_CONFIG_JSONに加え、VEXOR_API_KEYまたは選択したプロバイダーのキーを受け取ることを確認してください。リモートプロバイダーには、メモリテキストと検索クエリが送信されます。
ローカル埋め込みを選択した場合は、Perennaのローカルエクストラもインストールしてください:
uv tool install "perenna[local]"Vexorプロバイダーの設定では、ローカルリモートの設定を網羅しています。MCPクライアントを起動する環境から、選択したプロバイダーを以下のもので確認します:
uvx vexor doctorMCPクライアントが実行するように設定します:
perenna mcp --source <client-name>Perennaは、別のホームが設定されていない限り、~/.peronna/の下にローカルデータを作成します。
互換性のある履歴をプライベートGitリポジトリ経由でインポート、公開、またはfast-forwardする場合、次を実行します:
perenna sync setup <repository-url>ソースからの開発インストール
git clone https://github.com/scarletkc/Perenna.git
cd Perenna
uv tool install .ドキュメント
最初にドキュメントインデックスに目を通し、それからご自分のタスクに合った道を進みます:
ライセンス
Available Tools
3 toolsmemory_deleteADestructive
Delete exactly one committed memory by ID, expected title, and revision. The deletion removes it from current recall but remains recoverable from Git history.
| Name | Required | Description | Default |
|---|---|---|---|
| memory_id | Yes | ||
| base_revision | Yes | ||
| expected_title | Yes |
Output Schema
| Name | Required | Description |
|---|---|---|
| action | Yes | |
| commit | Yes | |
| memory | Yes | |
| changed | Yes | |
| sync_status | Yes | |
| index_status | Yes | |
| recoverable_via_git | Yes |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare destructiveHint=true and idempotentHint=false. The description adds valuable non-obvious context: the deletion removes from recall but remains recoverable via Git history, and it deletes 'exactly one' memory. This goes beyond the annotations without contradicting them. It does not mention side effects like concurrency checks, but the added recoverability and scoping are meaningful.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is one compact sentence that leads with the action and core constraints, then adds the recoverability detail. There is no fluff or repetition; every word earns its place.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a destructive operation with three required parameters and an output schema, the description is thin. It omits behavior on mismatch (e.g., if expected_title or base_revision does not match), error semantics, or any guidance on when this tool should be chosen over siblings. The recoverability note is helpful (something the output schema won't reveal), but other important context is missing.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, so the description must compensate. However, it only mentions parameters as 'ID, expected title, and revision' without explaining their purpose, format, or how they are used (e.g., what an expected title is for, what a base revision means, or how they act as safety checks). No examples or constraints are provided, leaving agents to infer semantics from names alone.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description states a specific verb ('Delete'), resource ('one committed memory'), and identifies the parameters by role (ID, expected title, revision). It clearly distinguishes from siblings memory_read and memory_write by the action it performs.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description does not provide explicit guidance on when to use this tool versus alternatives. It only implies deletion use case without mentioning conditions, prerequisites, or exclusions (e.g., 'use memory_read to view' or 'use memory_write to create'). No context is given for when a deletion is appropriate.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
memory_readARead-onlyIdempotent
Read permanent memory. List returns stable memory IDs and titles, search returns bounded ranked candidate passages, and get returns one complete committed memory with its revision.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Output Schema
| Name | Required | Description |
|---|---|---|
No output parameters | ||
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already cover read-only, non-destructive, idempotent, and open-world behavior. The description adds useful context beyond annotations: search results are 'bounded ranked candidate passages,' list returns only IDs/titles, and get returns a 'complete committed memory with its revision.' No contradiction with annotations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single sentence that front-loads the core purpose and uses parallel clauses to describe each action without redundancy or filler. It is compact yet informative.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's three modes and the existence of an output schema, the description is complete: it explains the behavior of each action and what kind of result to expect. Annotations cover safety and side-effect concerns, and sibling names make the read/write/delete division clear.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema fully documents the action variants, required fields, and limit bounds through its oneOf structure and const values. The description does not add parameter-level detail such as query format or memory_id semantics, but with schema coverage effectively complete, the baseline 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description states a specific verb and resource ('Read permanent memory') and enumerates three distinct modes—list, search, get—with their concrete outcomes. This distinguishes the tool from memory_write and memory_delete without relying on the name alone.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description tells an agent what each mode returns, which is clear guidance for choosing list vs search vs get. It does not explicitly say 'use this instead of memory_write or memory_delete,' but 'Read' and the sibling names make the intended usage obvious.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
memory_writeADestructive
Create or modify permanent memory. Summary is a stable one-line description of what a memory covers. Patch applies exact all-or-nothing edits; replace overwrites the complete summary and body. Existing memories require a current base revision.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Output Schema
| Name | Required | Description |
|---|---|---|
| action | Yes | |
| commit | Yes | |
| memory | Yes | |
| changed | Yes | |
| sync_status | Yes | |
| index_status | Yes |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Beyond the annotations (destructiveHint=true, readOnlyHint=false), the description discloses valuable behavioral details: patch is all-or-nothing, replace overwrites the complete summary/body, and existing memories require a current base revision, implying optimistic concurrency. This does not contradict any annotation.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is three dense sentences with no filler. The purpose is front-loaded, and each sentence addresses a distinct aspect: overall behavior, summary semantics, and modifications semantics.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The description covers the key operational facts an agent needs: the three modes, the summary convention, patch atomicity, and the base-revision requirement. Since an output schema exists, return-value details are not needed here. A minor gap is not pointing agents to memory_read to retrieve the current base revision.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The description adds meaning beyond the schema by defining summary as a 'stable one-line description', explaining the exact semantics of patch and replace, and mentioning the base revision requirement. This is useful context that the bare schema types do not convey, though title and project are left to the schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description states a specific action ('Create or modify') on a clear resource ('permanent memory') and further differentiates the three operational modes: create, patch, and replace. This makes the tool unmistakably the write counterpart to memory_read and memory_delete.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides internal guidance on when to use patch vs replace and notes the base-revision prerequisite for modifying existing memories. However, it does not explicitly mention memory_read or memory_delete or state when to prefer this tool over those siblings.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Tool Schema Changelog
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
2 tool updates
v0.1.1- Changed
memory_read5 fields changed- removed
Input schema / additionalPropertiesRemoved value: -false - added
Input schema / oneOfAdded value: +[ + { + "additionalProperties": false, + "properties": { + "action": { + "const": "list" + }, + "project": { + "type": "string" + } + }, + "required": [ + "action" + ], + "type": "object" + }, + { + "additionalProperties": false, + "properties": { + "action": { + "const": "search" + }, + "limit": { + "maximum": 5, + "minimum": 1, + "type": "integer" + }, + "project": { + "type": "string" + }, + "query": { + "type": "string" + } + }, + "required": [ + "action", + "query" + ], + "type": "object" + }, + { + "additionalProperties": false, + "properties": { + "action": { + "const": "get" + }, + "memory_id": { + "type": "string" + } + }, + "required": [ + "action", + "memory_id" + ], + "type": "object" + } +] - removed
Input schema / propertiesRemoved value: -{ - "action": { - "enum": [ - "list", - "search", - "get" - ], - "type": "string" - }, - "limit": { - "maximum": 5, - "minimum": 1, - "type": "integer" - }, - "memory_id": { - "type": "string" - }, - "project": { - "type": "string" - }, - "query": { - "type": "string" - } -} - removed
Input schema / requiredRemoved value: -[ - "action" -] - changed
Output schema / oneOfPrevious value: -[ - { - "additionalProperties": false, - "properties": { - "action": { - "const": "list" - }, - "memories": { - "items": { - "additionalProperties": false, - "properties": { - "memory_id": { - "type": "string" - }, - "scope": { - "type": "string" - }, - "summary": { - "type": "string" - }, - "title": { - "type": "string" - } - }, - "required": [ - "memory_id", - "title", - "scope", - "summary" - ], - "type": "object" - }, - "type": "array" - }, - "project": { - "type": [ - "string", - "null" - ] - }, - "projects": { - "items": { - "type": "string" - }, - "type": "array" - } - }, - "required": [ - "action", - "project", - "memories", - "projects" - ], - "type": "object" - }, - { - "additionalProperties": false, - "properties": { - "action": { - "const": "search" - }, - "limit": { - "maximum": 5, - "minimum": 1, - "type": "integer" - }, - "matches": { - "items": { - "additionalProperties": false, - "properties": { - "memory_id": { - "type": "string" - }, - "passages": { - "items": { - "additionalProperties": false, - "properties": { - "end_char": { - "minimum": 1, - "type": "integer" - }, - "start_char": { - "minimum": 0, - "type": "integer" - }, - "text": { - "type": "string" - } - }, - "required": [ - "text", - "start_char", - "end_char" - ], - "type": "object" - }, - "minItems": 1, - "type": "array" - }, - "rank": { - "minimum": 1, - "type": "integer" - }, - "revision": { - "type": "string" - }, - "scope": { - "type": "string" - }, - "summary": { - "type": "string" - }, - "title": { - "type": "string" - } - }, - "required": [ - "memory_id", - "title", - "scope", - "summary", - "revision", - "rank", - "passages" - ], - "type": "object" - }, - "type": "array" - }, - "project": { - "type": [ - "string", - "null" - ] - }, - "truncated": { - "type": "boolean" - } - }, - "required": [ - "action", - "project", - "limit", - "matches", - "truncated" - ], - "type": "object" - }, - { - "additionalProperties": false, - "properties": { - "action": { - "const": "get" - }, - "memory": { - "additionalProperties": false, - "properties": { - "body": { - "type": "string" - }, - "created_at": { - "type": "string" - }, - "memory_id": { - "type": "string" - }, - "revision": { - "type": "string" - }, - "scope": { - "type": "string" - }, - "source": { - "type": "string" - }, - "summary": { - "type": "string" - }, - "title": { - "type": "string" - }, - "updated_at": { - "type": "string" - } - }, - "required": [ - "memory_id", - "title", - "scope", - "summary", - "source", - "created_at", - "updated_at", - "revision", - "body" - ], - "type": "object" - } - }, - "required": [ - "action", - "memory" - ], - "type": "object" - } -]New value: +[ + { + "additionalProperties": false, + "properties": { + "action": { + "const": "list" + }, + "memories": { + "items": { + "additionalProperties": false, + "properties": { + "memory_id": { + "type": "string" + }, + "scope": { + "type": "string" + }, + "summary": { + "type": "string" + }, + "title": { + "type": "string" + } + }, + "required": [ + "memory_id", + "title", + "scope", + "summary" + ], + "type": "object" + }, + "type": "array" + }, + "project": { + "type": [ + "string", + "null" + ] + }, + "projects": { + "items": { + "type": "string" + }, + "type": "array" + } + }, + "required": [ + "action", + "project", + "memories", + "projects" + ], + "type": "object" + }, + { + "additionalProperties": false, + "properties": { + "action": { + "const": "search" + }, + "limit": { + "maximum": 5, + "minimum": 1, + "type": "integer" + }, + "matches": { + "items": { + "additionalProperties": false, + "properties": { + "memory_id": { + "type": "string" + }, + "passages": { + "items": { + "additionalProperties": false, + "properties": { + "end_char": { + "minimum": 1, + "type": "integer" + }, + "start_char": { + "minimum": 0, + "type": "integer" + }, + "text": { + "type": "string" + } + }, + "required": [ + "text", + "start_char", + "end_char" + ], + "type": "object" + }, + "minItems": 1, + "type": "array" + }, + "rank": { + "minimum": 1, + "type": "integer" + }, + "revision": { + "type": "string" + }, + "scope": { + "type": "string" + }, + "summary": { + "type": "string" + }, + "title": { + "type": "string" + } + }, + "required": [ + "memory_id", + "title", + "scope", + "summary", + "revision", + "rank", + "passages" + ], + "type": "object" + }, + "type": "array" + }, + "project": { + "type": [ + "string", + "null" + ] + }, + "truncated": { + "type": "boolean" + } + }, + "required": [ + "action", + "project", + "limit", + "matches", + "truncated" + ], + "type": "object" + }, + { + "additionalProperties": false, + "properties": { + "action": { + "const": "get" + }, + "memory": { + "additionalProperties": false, + "properties": { + "body": { + "type": "string" + }, + "created_at": { + "type": "string" + }, + "memory_id": { + "type": "string" + }, + "revision": { + "type": "string" + }, + "scope": { + "type": "string" + }, + "summary": { + "type": "string" + }, + "title": { + "type": "string" + }, + "updated_at": { + "type": "string" + } + }, + "required": [ + "memory_id", + "title", + "scope", + "summary", + "created_at", + "updated_at", + "revision", + "body" + ], + "type": "object" + } + }, + "required": [ + "action", + "memory" + ], + "type": "object" + } +]
- Changed
memory_write4 fields changed- removed
Input schema / additionalPropertiesRemoved value: -false - added
Input schema / oneOfAdded value: +[ + { + "additionalProperties": false, + "properties": { + "action": { + "const": "create" + }, + "body": { + "type": "string" + }, + "project": { + "type": "string" + }, + "summary": { + "type": "string" + }, + "title": { + "type": "string" + } + }, + "required": [ + "action", + "title", + "summary", + "body" + ], + "type": "object" + }, + { + "additionalProperties": false, + "properties": { + "action": { + "const": "patch" + }, + "base_revision": { + "type": "string" + }, + "edits": { + "items": { + "additionalProperties": false, + "properties": { + "new_text": { + "type": "string" + }, + "old_text": { + "type": "string" + } + }, + "required": [ + "old_text", + "new_text" + ], + "type": "object" + }, + "minItems": 1, + "type": "array" + }, + "memory_id": { + "type": "string" + }, + "summary": { + "type": "string" + } + }, + "required": [ + "action", + "memory_id", + "base_revision", + "edits" + ], + "type": "object" + }, + { + "additionalProperties": false, + "properties": { + "action": { + "const": "replace" + }, + "base_revision": { + "type": "string" + }, + "body": { + "type": "string" + }, + "memory_id": { + "type": "string" + }, + "summary": { + "type": "string" + } + }, + "required": [ + "action", + "memory_id", + "base_revision", + "summary", + "body" + ], + "type": "object" + } +] - removed
Input schema / propertiesRemoved value: -{ - "action": { - "enum": [ - "create", - "patch", - "replace" - ], - "type": "string" - }, - "base_revision": { - "type": "string" - }, - "body": { - "type": "string" - }, - "edits": { - "items": { - "additionalProperties": false, - "properties": { - "new_text": { - "type": "string" - }, - "old_text": { - "type": "string" - } - }, - "required": [ - "old_text", - "new_text" - ], - "type": "object" - }, - "minItems": 1, - "type": "array" - }, - "memory_id": { - "type": "string" - }, - "project": { - "type": "string" - }, - "summary": { - "type": "string" - }, - "title": { - "type": "string" - } -} - removed
Input schema / requiredRemoved value: -[ - "action" -]
3 tool updates
v0.1.0- First observed
memory_delete - First observed
memory_read - First observed
memory_write
TDQS
Scored across 3 tools
Each tool handles a distinct lifecycle operation: reading/querying, writing/updating, and deleting memories. There is no overlap between the primary actions, and the sub-modes within memory_read are explicitly separated (list, search, get).
All tool names follow the same memory_verb pattern using clear, lowercase snake_case verbs. The naming makes the operation type immediately obvious and perfectly consistent across the set.
Three tools is a compact but complete surface for a permanent memory store. Each tool earns its place, and no redundant or extraneous operations exist.
The set covers the full lifecycle: create/modify via memory_write, read via memory_read (including list/search/get), and delete via memory_delete. Revision-handling and Git-history recovery details further round out the functionality, leaving no critical gaps.
Maintenance
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Persistent memory for AI agents. Search, store, and recall across sessions.
Persistent memory and knowledge graphs for AI agents. Hybrid search, context checkpoints, and more.
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