Pseudolife Memory
개요
제작자가 쓴 설명(영어 원문)
Persistent memory for MCP-compatible agents: memory bank, fact cortex, dreams, graph.
PopNx MCP의 세 기둥
이 세 가지는 절대 하나의 점수로 합치지 않아요. 의견, 측정, 인기는 서로 다른 것이니까요.
- 사용자 의견i아직 리뷰가 없어요리뷰는 다음 단계에서 계정 기능과 함께 열려요
- 우리의 기본 점검i1개 중 1개 통과공개된 정보로 점검했으며, 코드는 아직 스캔하지 않았어요
- 사용 정도i초기↓ 2천/주 · ★ –
할 수 있는 일
이 서버는 아직 쉬운 말로 된 요약을 쓰지 못했어요. 아래는 레지스트리에서 읽어 온 사실이에요.
- 연결 방식: 내 컴퓨터에서 실행
- 전송 방식: stdio
- 카테고리: 검색 및 AI 기억
설치
쓰고 있는 AI 앱을 고른 다음 설정을 복사하세요.
설치하기 전에
- 기본 점검: 1개 중 1개 통과
- 내 컴퓨터에서 내 사용자 계정의 권한(파일, 네트워크, 앱)으로 실행돼요.
- 레지스트리에 입력해야 할 키가 나와 있지 않아요.
필요한 것
- PyPIPython의 uv 설치(uvx 명령어용)
어떤 앱을 쓰세요?
위에서 앱을 고르세요. 기억해 두었다가 다음부터는 버튼에 “…에 설치”라고 표시해요.
Claude Desktop → Settings → Developer → Edit Config를 열고 claude_desktop_config.json에 이 내용을 붙여 넣으세요
{
"mcpServers": {
"pseudolife": {
"command": "uvx",
"args": [
"pseudolife-mcp"
]
}
}
}터미널에서 이 명령어를 실행하세요
claude mcp add pseudolife -- uvx pseudolife-mcp또는 설정을 직접 복사하기
프로젝트의 .cursor/mcp.json 또는 ~/.cursor/mcp.json에 붙여 넣으세요
{
"mcpServers": {
"pseudolife": {
"command": "uvx",
"args": [
"pseudolife-mcp"
]
}
}
}브라우저가 앱을 열지 물어본 다음, 앱이 설정을 보여 주고 확인을 요청해요. 키 값은 비워 두니 직접 입력하세요.
또는 설정을 직접 복사하기
프로젝트의 .vscode/mcp.json에 붙여 넣으세요
{
"servers": {
"pseudolife": {
"type": "stdio",
"command": "uvx",
"args": [
"pseudolife-mcp"
]
}
}
}브라우저가 앱을 열지 물어본 다음, 앱이 설정을 보여 주고 확인을 요청해요. 키 값은 비워 두니 직접 입력하세요.
이 서버는 내 컴퓨터에서 실행되는데, ChatGPT는 인터넷에 있는 서버에만 연결할 수 있어요. 다른 앱을 고르세요.
선택한 앱은 이 기기의 쿠키에만 저장돼요. 계정은 필요 없어요.
이 설정은 제작자의 레지스트리 정보로 만들었어요. README와 다르면 README를 따르세요.
할 수 있는 일
내 컴퓨터에서 실행돼요. 설치하면 AI 앱에 도구 목록이 나타나요.
기본 점검i
누구나 볼 수 있는 정보를 점검했고, 코드는 읽지 않았어요. 해당하지 않는 항목은 개수에서 뺐어요. · 2026년 11월 8일 점검
- ✓ 통과
제작자와 저장소 일치
레지스트리 이름이 GitHub 계정 Pseudogiant-xr 기준으로 확인되었고, 이 계정이 저장소를 소유하고 있어요
- … 아직 점검 안 함
계속 관리되고 있음
아직 GitHub에서 이 저장소를 읽어 오지 않았어요
- … 아직 점검 안 함
라이선스 있음
아직 GitHub에서 이 저장소를 읽어 오지 않았어요
- – 해당 없음
엔드포인트 응답
내 컴퓨터에서 실행되는 서버라 인터넷 엔드포인트가 없어요
- – 해당 없음
OAuth로 로그인
내 컴퓨터에서 실행되며, 대신 로컬 설정을 사용해요
- … 아직 점검 안 함
도구 설명에 숨겨진 지시 없음
내 컴퓨터에서 실행돼요. 도구 목록은 인터넷 엔드포인트가 있는 서버에서만 읽을 수 있어요
검색 및 AI 기억 카테고리의 다른 서버와 비교i
- 이 카테고리의 다른 서버 5722개 중 0%보다 스타가 많아요
- 이 카테고리의 다른 서버 5722개 중 94%보다 최근에 레지스트리에 공개됐어요
- 기본 점검 결과가 이 카테고리의 다른 서버 5722개 중 100%와 같거나 더 좋아요
레지스트리에서 이 카테고리의 활성 서버 전체와 비교했어요. 스타는 게시자가 저장소를 소유한 경우에만 세요.
자주 묻는 질문
Pseudolife Memory, 안전한가요?
해당하는 기본 점검 1개 중 1개를 통과했어요. 코드는 읽지 않았어요. 키나 토큰을 넣기 전에 서버가 어떤 권한을 요구하는지 확인하고, 꼭 필요한 권한만 있는 키를 쓰세요.
어떤 키나 코드가 필요한가요?
레지스트리에 필수 키가 나와 있지 않아요. 다만 처음 쓸 때 로그인을 요구하는 서버도 있어요.
내 컴퓨터에서 실행되나요, 클라우드에서 실행되나요?
내 컴퓨터에서 실행돼요. AI 앱이 필요할 때 실행하고, 데이터는 제작자의 서버를 거치지 않아요(서버 자체가 온라인 서비스를 호출하는 경우는 제외).
아직 관리되고 있나요?
GitHub 저장소는 마지막으로 – 변경됐고, 최신 버전(0.22.1)은 그저께 레지스트리에 올라왔어요.
어떤 앱에서 쓸 수 있나요?
Claude Desktop, Claude Code, Cursor, VS Code처럼 MCP를 지원하는 앱에서 쓸 수 있어요. 내 컴퓨터에 Python (uv)도 있어야 해요.
대신 쓸 수 있는 건 뭐가 있나요?
같은 카테고리에는: Context7, Codebase Memory, Exa
대안
같은 카테고리에서 다른 제작자가 만든 서버예요. 스타 순으로 정렬했어요.
카드 보는 법:↓ 주간 다운로드i★ GitHub 스타i세 기둥: 리뷰 · 점검 · 사용 정도i업데이트 = 최신 버전을 레지스트리에 공개한 날i
Codebase Memory
Codebase knowledge graph for AI agents — 162 languages, sub-ms queries, 99% fewer tokens.
Exa
Connect AI agents to Exa for web search, content fetching, and multi-step research.
제작자의 README
README 열기
제작자의 저장소에서 쓰인 그대로 가져왔어요. PopNx MCP가 검토하거나 보증한 내용이 아니에요. HTML, 이미지, 스크립트는 제거했어요. 링크를 누르면 이 사이트를 벗어나요.
Pseudolife-MCP
简体中文 · 日本語 · 한국어 · Português (BR) · Español
Persistent long-term memory for Claude Code, Codex, and other MCP clients.
An MCP server that gives coding agents a long-term memory that persists across sessions — surviving context compactions and fresh tasks. Your coding agent is the intelligence; this server is its memory on disk.
What you get:
- Associative memory with honest forgetting — a flat similarity store ranked by hybrid dense-plus-lexical retrieval, with conflict detection that admits potential updates while preserving earlier source notes; whole-note replacement is explicit. (The measured verdict: a preregistered ablation campaign found the previous 8-band continuum tied a flat store on every gate, so the simpler structure ships; the continuum remains one config line away.)
- Canonical facts, not vibes — one *current* value per
entity.attributeslot (or a member set, for slots that hold many concurrent values); corrections supersede rather than silently overwrite, and the full version history survives. - Dreams — a bundled local extractor, or any OpenAI-compatible endpoint (a Claude model on your Max plan, a GPT-5.6 model on a ChatGPT plan, LM Studio, Ollama, vLLM), consolidates the memory stream into facts and a knowledge graph while you're not looking.
- Lessons from its own work — successes, dead-ends, and your corrections become do/avoid guidance surfaced at the start of every session.
- A web console to watch it think — the Cortex Console above, plus cited world facts, session episodes, and document RAG.
Measured, with receipts — the full 500-question LongMemEval sweep, all six question types, and every number ships with its committed run artifact:
| LongMemEval oracle, 500 questions | naive RAG | commit-gated cascade |
|---|---|---|
| accuracy, all six question types | 0.688 | 0.690 |
| context tokens per question | ~1210 | ~883 |
| knowledge-update slice (78 of the 500) | 0.859 | ~~0.936~~ (retired — see below) |
Equal accuracy to naive RAG across the whole benchmark on ~73% of the context, and better calibrated about what it does not know: on BEAM-100K's abstention questions the fact spine scores 0.950 against naive RAG's 0.775, unchanged under two independent judges. Read that as calibration, not recall — in the budget-matched five-arm run of 2026-09-02 (rag 0.725 there; one replicate, local judge) an arm served no memory at all scores 1.000 on the same questions, because refusing is the right answer there and an empty context always refuses. The fact spine loses where an answer has to be aggregated across sessions. The second claim to survive a judge swap is a win rather than a wash: re-run on 2026-09-04 with the hybrid arm budget-matched to the control at 6 turns, the same 500 questions give hybrid 0.730 against naive RAG's 0.690 under the local judge and 0.736 against 0.694 under claude-opus-5 — paired +0.040 / +0.042, p 0.015 / 0.013 — bought with *more* context, ~1229 tokens against the control's ~1124, not less, and carried mostly by temporal-reasoning questions. Graded by a local, byte-reproducible judge (the cross-judge check names its second judge) — compare within rows, never against GPT-judged leaderboards.
Retired 2026-08-25 (#188): the 0.936 knowledge-update headline. It was
measured on the 2026-07-30 bench stack (Qwen3.6-27B answerer and judge).
Re-running the same 78 questions after the 2026-08-17 migration to
Qwen3.8-27B puts the cascade at 0.846, below the naive-RAG control —
which lands on 0.859 on both stacks. The cascade serves the fact-spine
answer unless that channel says "I don't know", so it measures the
*answerer's* abstention behaviour as much as the memory: 32/78 abstentions
at 46/46 commit precision on the old stack, 22/78 at 0.839 on the new one.
The 500-question table above is on the older judge and has not been
re-judged, so read its cascade row as an upper bound.
Full tables, the per-type breakdown, both stacks side by side, and every artifact: Benchmarks.
Quickstart
Install and register the lite tier. No Docker, no database to set up, no container runtime:
pip install "pseudolife-mcp[lite]"
claude mcp add --scope user pseudolife-memory -- pseudolife-mcp
Codex instead of Claude Code — same shape:
pip install "pseudolife-mcp[lite]"
codex mcp add pseudolife-memory --env PSEUDOLIFE_WRITER_ID=codex -- pseudolife-mcp
For Codex, finish setup before starting a fresh task. In the existing [mcp_servers.pseudolife-memory] table in ~/.codex/config.toml, add startup_timeout_sec = 240, tool_timeout_sec = 240, and required = true. The shim can wait up to 180 seconds for a cold daemon; Codex's default startup budget is 10 seconds. required makes missing memory visible at startup and waits for its initial catalog. These are starting budgets, not a promise that a first model download fits. The tool budget leaves time for the shim's 180-second deadline to report a failure before the host cancels it; prewarm with pseudolife-mcp serve in a terminal if needed.
Approve memory_message in that table's tool configuration ([mcp_servers.pseudolife-memory.tools.memory_message] approval_mode = "approve", or allow it once and keep the approval); Codex hook setup (python ops/setup-codex-hooks.py) sets it when you approve the hooks, and keeps any value you chose. Board mail can wake an idle Codex task by default, and the woken task reads its mail with that tool: without the approval it stalls on a prompt until someone answers it. Set PSEUDOLIFE_CODEX_DOORBELL = "0" in the same env table to keep the task from being woken (see Codex doorbell).
The agent board (peer awareness and addressed mail between sessions) is on by default, behind bearer authentication. Without coordination.allowed_principals in the daemon's config.yaml, only the singular PSEUDOLIFE_MCP_TOKEN principal is admitted. A Codex bearer from a PSEUDOLIFE_MCP_TOKENS map stays off the board until an operator lists it (allowed_principals: [default, codex]). Until then a shim on the default setting leaves coordination off without an error; one pinned with --enable shows an attach-unavailable hint instead. python ops/setup-codex-coordination.py --check reports ready (default-on) or names the cause. See Codex CLI and desktop.
The MCP handshake delivers compact recall/capture/reflection instructions. For the complete standing guidance, copy the bundled memory block into your project AGENTS.md or ~/.codex/AGENTS.md. For session briefings and per-turn reminders, follow Codex hooks and verification. Use one MCP registration and one hook source; an installed plugin may already provide either. After the daemon is running, execute pseudolife-mcp doctor from the same environment as the registered command. It checks the handshake and annotations without calling bank tools. When the shell has no PSEUDOLIFE_MCP_TOKEN or PSEUDOLIFE_MCP_TOKEN_FILE, doctor takes the token from the Claude Code registration (~/.claude.json, or under CLAUDE_CONFIG_DIR), else from the Codex one, and names where it came from in credential_source; with none and a daemon that requires a token, it reports BearerMissing.
Then in either coding agent: *"remember that my staging box is haze-02"* → the agent calls memory_store; next session, *"which box is staging?"* → memory_search finds it. Browse everything at the Cortex Console: http://127.0.0.1:8765/ui/.
The first session auto-starts the daemon, which provisions an embedded PostgreSQL 18 (pgvector included, via pg0-embedded) under a stable per-user data dir and downloads the embedding model (~1.2 GB, one-time). It is a real Postgres bank, not a cut-down one: pseudolife-mcp backup writes a standard owner-free pg_dump archive (plus a state archive, 7-day rotation) that restores into any PostgreSQL 18 target regardless of role — the Docker tier included — so outgrowing lite is a dump/restore, not a migration project (backups). For a tier- and Postgres-version-independent copy, pseudolife-mcp export / import move the whole bank as portable JSONL (logical export / import). Windows needs an ASCII-only data path (PSEUDOLIFE_MCP_DATA_DIR).
What lite gives you, and the one thing it doesn't
| lite (pip) | durable (Docker) | |
|---|---|---|
| Associative store, hybrid search, supersession, version history | yes | yes |
| Cortex facts, knowledge graph, lessons, world facts, episodes | yes | yes |
Cortex Console, document RAG, pseudolife-mcp backup | yes | yes |
| Dream consolidation filling the cortex on its own | no extractor ships | yes — bundled local CPU sidecar |
| External volumes, health-checked services, deploy/rollback tooling | no | yes |
The gap, stated plainly. Lite ships no extractor, so the dream pass still runs, prunes, and acknowledges its input batch, but writes no canonical facts: on this path memory_fact_set is the only cortex writer. Everything else above works. Nothing about this is silent — curl http://127.0.0.1:8765/health reports "extractor": "none", and the stdio shim says the same on stderr at session start.
Any OpenAI-compatible endpoint closes it. The daemon inherits the environment it starts from, so two variables are the whole fix — with a local Ollama:
export PSEUDOLIFE_DREAM_BASE_URL=http://localhost:11434/v1
export PSEUDOLIFE_DREAM_MODEL=qwen2.5:7b pseudolife-mcp serve
$env:PSEUDOLIFE_DREAM_BASE_URL = "http://localhost:11434/v1"
$env:PSEUDOLIFE_DREAM_MODEL = "qwen2.5:7b" pseudolife-mcp serve
/health then reports "extractor": "configured". One gotcha: a daemon that is already running keeps the environment it started with, and the shim reattaches to it rather than spawning a new one — stop the old daemon first. A hosted endpoint works too, and costs you the zero-egress property: memory text leaves the machine. Extractor tiers, quality, and the trade-offs: Dreaming.
Durable tier — Docker (recommended for a long-lived bank)
Everything above plus the bundled extractor, external volumes, health-checked services, and backup/rollback tooling. Requires Docker (with the compose v2 and buildx plugins) and at least one MCP-capable coding agent — Claude Code, Codex, and Gemini CLI are wired end-to-end; anything else gets paste-ready config (provider matrix). For Portainer, Dockge, Komodo or a plain compose folder, use the supported external-stack path; never hand-flatten the source compose. One command from clone to first memory:
git clon앞부분만 보여 드려요. 나머지는 저장소에서 읽어 보세요