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Sang Hoon","name":"TripleA Lab","email":"isaac@triplealab.com"},"license":"MIT","homepage":"https://github.com/a3lab01create-bit/mcp-agents-memory#readme","keywords":["mcp","ai","memory","agents","postgresql","pgvector","claude","gemini"],"repository":{"url":"git+https://github.com/a3lab01create-bit/mcp-agents-memory.git","type":"git"},"description":"Long-term memory for AI agents with provenance tracking","maintainers":[{"name":"isaacyoon","email":"isaac@triplealab.com"}],"readme":"# mcp-agents-memory\n\n> Long-term, time-ordered memory for AI agents — a shared memory pool that persists across sessions and machines, modeled on human memory.\n\nMultiple agents (Claude Code, Codex, Gemini CLI, Grok, Antigravity, …) share **one** memory pool, accumulating and recalling memories in chronological order. Each platform/model gets its own view automatically, while project tags let collaborators see each other's relevant context.\n\n[![npm](https://img.shields.io/npm/v/mcp-agents-memory.svg)](https://www.npmjs.com/package/mcp-agents-memory)\n\n> 🇰🇷 한국어 README → [`README.ko.md`](./README.ko.md)\n> Design rationale and decisions → [`RESPEC.md`](./RESPEC.md)\n\n---\n\n## Motivation\n\n- [**supermemory**](https://supermemory.io) — a universal, semantic-graph memory layer\n- [**Hermes Agent**](https://github.com/NousResearch/hermes-agent) — `MEMORY.md`-style self-updating memory, skills & rules\n\nThis project aims to combine the strengths of both: supermemory's recall and Hermes' self-curation, without their weaknesses (machine-locked storage, opaque mutation).\n\n---\n\n## Core design\n\n1. **Time-ordered, like human memory** — every turn is stored raw in chronological order. No `fact_type` taxonomy. Older memories are summarized by tag and archived (soft-delete, never destroyed).\n2. **Automatic per-model separation** — the `agent_platform` / `agent_model` columns alone separate memories per model. No manual categories.\n3. **Two asynchronous tracks** — a **Hot Path** (instant raw INSERT, fast response) and a **Cold Path** (a background \"librarian\" that tags, embeds, clusters, and curates on a 1-minute / 5-message cadence).\n4. **Tag-centric recall** — recent days as raw text, older history as tag-centric summaries. Anything older is retrievable from the archive by date / tag / keyword.\n\n---\n\n## Architecture\n\n### Two asynchronous tracks\n\n```\n┌─────────────────┐      ┌──────────────────────────┐\n│  Agent          │      │ MCP Server               │\n│  (Claude Code,  │ ───▶ │ ▶ Hot Path (instant save)│ ──▶ memory table\n│   Codex, ...)   │      └──────────────────────────┘     (raw + role + platform/model)\n└─────────────────┘                  │\n                                     │  rows with NULL p_tag/d_tag/embedding accumulate\n                                     ▼\n                        ┌──────────────────────────────┐\n                        │ Cold Path  (1 min / 5 msgs)   │\n                        │  ├─ Tagger    → p_tag, d_tag   │\n                        │  ├─ Embedder  → embedding      │\n                        │  ├─ Librarian → user profile   │\n                        │  ├─ Clusterer → tag summaries  │\n                        │  └─ AliasPromoter → tag merges │\n                        └──────────────────────────────┘\n```\n\nThe Cold Path's LLM roles (tagger / librarian / clusterer / project-alias judge) all run on a **single shared backend** — local `Qwen3-14B` via llama.cpp, or a cloud fallback (see [Cold Path LLM backend](#cold-path-llm-backend)).\n\n### Data model\n\n**`memory`** — raw, time-ordered conversation log (single table, soft-delete archive)\n\n| Column | Notes |\n|---|---|\n| `user_id` | user identity |\n| `agent_platform` | claude-code / codex / gemini-cli / grok / antigravity / hermes / opencode … |\n| `agent_model` | e.g. opus-4-8 / gemini-3-pro / gpt-5.5 |\n| `subagent` | yes / no (1 level tracked) |\n| `subagent_model` / `subagent_role` | filled for subagents; role is free-form (lowercase-normalized) |\n| `role` | `user` / `assistant` |\n| `message` | raw body |\n| `p_tag` | predefined project tag (→ `project_tags`) |\n| `d_tag` | dynamic context tags |\n| `embedding` | `vector(3072)` — text-embedding-3-large |\n| `is_active` / `archived_at` | soft delete (lossless) |\n| `is_pinned` | force-remembered via `manage_knowledge`; exempt from archival |\n| `created_at` / `updated_at` | |\n\n**`users`** — core facts the Librarian promotes out of `memory`\n\n| Column | Notes |\n|---|---|\n| `user_id` / `user_name` | |\n| `core_profile` | the most important, durable facts about the user |\n| `sub_profile` | other facts worth remembering |\n\n**`project_tags`** — project tags, grown dynamically by the Cold Path\n\n| Column | Notes |\n|---|---|\n| `id` / `name` / `description` | |\n| `alias_of` | post-hoc merge of synonyms (e.g. \"centragens\" ↔ \"Centrazen project\") |\n\n---\n\n## Multi-machine — server / client\n\nWhen several machines share **one** database, the Cold Path (tagging / profiling / clustering / alias judging) must run on **exactly one** of them — otherwise machines double-process the same rows and double the cloud cost. The same package splits roles purely by **config**:\n\n| | Client | Server (processing) |\n|---|---|---|\n| **DB** | remote (e.g. SSH tunnel) | DB host / direct |\n| **Cold Path** | `COLD_PATH_ENABLED=false` | standalone daemon, always on |\n| **Does** | `search` / `manage_knowledge` only | tagging · profiling · clustering · alias judging |\n| **Setup** | a few `.env` lines | config + local LLM infra |\n\n- **Client**: the MCP server your editor spawns is the terminal. Just add `COLD_PATH_ENABLED=false`.\n- **Server**: run the Cold Path decoupled from the editor's lifetime, as a standalone daemon (processes even when no editor is open):\n  ```bash\n  mcp-agents-memory coldpath    # Cold Path worker only, no MCP server (systemd recommended)\n  ```\n  The daemon is a **singleton** via a PostgreSQL advisory lock — no matter how many instances exist, only the one holding the lock processes (dedup + automatic failover).\n- **Buzz (optional)**: `mcp-agents-memory buzz-ingest` copies chat messages from the Buzz rooms a given identity is a member of into memory, one row per message, through the official `buzz` CLI. It also strips the quoted history from captured Buzz turns once every quoted message exists as a row (the original stays in `raw_message`; `--no-reclean` turns this off). Run it from a timer; see CHANGELOG 0.9.20–0.9.21 for the settings.\n\n### Cold Path LLM backend\n\nPoint `LOCAL_LLM_BASE_URL` at any OpenAI-compatible endpoint to use local / self-hosted inference (llama.cpp, ollama, …). If unset, a cloud model is used; `LOCAL_GROK_FALLBACK=true` falls back to Grok when local inference fails.\n\n> e.g. serve `Qwen3-14B` with llama.cpp `llama-server` on an AMD/NVIDIA GPU and set `LOCAL_LLM_BASE_URL=http://localhost:8080/v1` → Cold Path cloud cost ≈ $0. (A `json_schema` grammar with thinking disabled guarantees valid JSON.)\n\n---\n\n## Memory load rules\n\n- **Short-term**: recent 2–3 days raw, or ~8000 tokens (whichever comes first). Token count is char-approximate (`chars / 1.7`) to protect Hot Path latency. Window is env-tunable.\n- **Model separation**: by default, only memories with the same `agent_platform` / `agent_model`. A `p_tag` match (same project) pulls in collaborating agents' memories too.\n- **Archive search**: on a user cue (\"a few days ago…\") or when older context is needed, retrieve from the archive by date / tag / keyword.\n- **Search fallback**: when semantic (cosine) results fall below threshold, fall back to `ILIKE` (env-tunable, starts at 0.3).\n\n---\n\n## Tools\n\nThe server exposes **4 tools**. Most clients only ever need the first three; `save_message` is a fallback.\n\n### `memory_startup` — session boot brief\nReturns a markdown brief (recent conversations, active projects, user profile) so a new session picks up where the last left off. On supported clients it is injected automatically at connect; call it explicitly to refresh mid-session.\n\n### `search_memory` — unified read / search\n```ts\nsearch_memory({\n  query?: string,        // semantic search (vector + ILIKE fallback)\n  p_tag?: string,        // restrict to a project\n  date_range?: string,   // e.g. \"2026-04-29..\", \"last_week\"\n  role?: 'user' | 'assistant',\n  agent_platform?: string,      // restrict to a platform; omit or '*' = all\n  device_scope?: 'local' | 'global',  // 'global' (default) = all machines; 'local' = this one\n  limit?: number,        // default 10, max 50\n  include_archived?: boolean,\n})\n```\n> \"When you don't remember, reach for this one.\" Agents just vary the parameters.\n\n### `manage_knowledge` — unified write / edit\n```ts\nmanage_knowledge({\n  action: 'add' | 'update' | 'remove',\n  target: 'sub_profile' | 'memory',\n  content: string,\n})\n```\n> Use when the user explicitly says \"remember this\" / \"forget that\".\n> `target='memory'` = force-remember (`is_pinned=true`, importance bump, archive-exempt).\n> `manage_knowledge` skips the Cold Path and **syncs tag + embedding immediately**, so the memory is searchable the instant you say \"got it\".\n\n### `save_message` — transcript fallback\nFor platforms that **don't** auto-capture transcripts, the agent calls this each turn to persist the message. On auto-capturing clients (see below) it must **not** be called — that would duplicate rows.\n\n### Automatic capture\n\n| Platform | Auto-capture |\n|---|---|\n| Claude Code · Codex CLI · Gemini CLI · Grok Build · Antigravity CLI | ✅ transcript captured automatically — do **not** call `save_message` |\n| Hermes · OpenCode (v2+) | ✅ same, read from their local SQLite store (needs `node:sqlite`, unflagged from Node 22.13; otherwise falls back to `save_message`) |\n| Everything else | call `save_message(role=…)` each turn |\n\n> Auto-injection of the startup brief / auto-capture depends on the **client**, not the transport — some clients (e.g. desktop/web) don't expose those hooks, so they fall back to explicit tool calls.\n\n---\n\n## Tech stack\n\n| Role | Tech |\n|---|---|\n| **Embedding** | OpenAI `text-embedding-3-large` (3072-dim) |\n| **Cold Path LLM** (tagger / librarian / clusterer / project-alias judge) | local `Qwen3-14B` (llama.cpp; `json_schema` grammar + thinking off → valid JSON), **or** cloud `grok-4-1-fast-non-reasoning` fallback — selected via `LOCAL_LLM_BASE_URL` |\n| **Search fallback** | PostgreSQL `ILIKE` (below cosine threshold) |\n| **DB** | PostgreSQL + pgvector |\n| **Librarian** (memory → users) | shares the Cold Path backend — recency-bias-resistant curation (core identity ↔ sub work split, null-preserve), gated by env-tunable thresholds |\n\n---\n\n## Environment variables\n\nSee [`.env.example`](./.env.example) for the full, annotated list. The essentials:\n\n```bash\n# DB\nDB_HOST=...      DB_PORT=5432   DB_USER=...   DB_PASS=...   DB_NAME=...\n\n# Keys\nOPENAI_API_KEY=...                 # embedding (required)\nXAI_API_KEY=...                    # Grok (Cold Path cloud + local fallback)\n\n# Cold Path LLM backend — OpenAI-compatible endpoint for local inference (omit → cloud)\nLOCAL_LLM_BASE_URL=http://localhost:8080/v1\nLOCAL_GROK_FALLBACK=true\nTAGGER_PROVIDER=local              TAGGER_MODEL=qwen3-14b\nLIBRARIAN_PROVIDER=local           LIBRARIAN_MODEL=qwen3-14b\nLIBRARIAN_ENABLED=true\n\n# Hot/Cold path control\nCOLD_PATH_ENABLED=true             # false = client terminal (no Cold Path); only the server is true\nCOLD_PATH_INTERVAL_SEC=60\nCOLD_PATH_BATCH_SIZE=5\n\n# Memory load tunables\nSHORT_TERM_DAYS=3\nSHORT_TERM_TOKEN_LIMIT=8000\nSEARCH_FALLBACK_THRESHOLD=0.3\n\n# Agent identity (caller self-reports)\nAGENT_PLATFORM=claude-code\nAGENT_MODEL=opus-4-8\n```\n\n---\n\n## Client setup\n\n### Claude Code / Codex\n```toml\n# ~/.codex/config.toml\n[mcp_servers.mcp-agents-memory]\ncommand = \"mcp-agents-memory\"\nargs = []\n```\n\n### Gemini CLI\n```json\n// ~/.gemini/settings.json\n{\n  \"mcpServers\": {\n    \"mcp-agents-memory\": {\n      \"type\": \"stdio\",\n      \"command\": \"mcp-agents-memory\",\n      \"args\": [],\n      \"env\": {},\n      \"trust\": true\n    }\n  }\n}\n```\n\n> Install globally with `npm i -g mcp-agents-memory`, or point `command` at a local `build/index.js`.\n\n---\n\n## Guiding principle\n\n> Solving the problem in front of you must not break the whole structure.\n\nEvery change is checked against the `RESPEC.md` vision — \"does this fix fit the big picture?\" — before proceeding. \"Just make it run\" is a stop signal.\n\n---\n\n## Reference docs\n\n- [`RESPEC.md`](./RESPEC.md) — current vision, decisions, implementation detail (single source of truth)\n- [`DEVLOG.md`](./DEVLOG.md) — operational issues, observations, ideas\n- [`README.ko.md`](./README.ko.md) — Korean README\n","readmeFilename":"README.md"}