hipocampus
Advanced tools
Drop-in memory harness for AI agents — 3-tier memory, compaction tree, hybrid search via qmd
Drop-in memory harness for AI agents. Zero infrastructure — just files.
3-tier memory architecture with a 5-level compaction tree, auto-loaded ROOT.md topic index, and optional hybrid search via qmd. One command to set up, works immediately with Claude Code and OpenClaw bots.
npx hipocampus init
This creates the full memory structure in your project:
MEMORY.md # Long-term memory (OpenClaw only — Claude Code uses platform auto memory)
USER.md # User profile (OpenClaw only — Claude Code uses platform auto memory)
SCRATCHPAD.md # Active working state
WORKING.md # Current tasks in progress
TASK-QUEUE.md # Task backlog (queued items only)
memory/ # ROOT.md + daily logs + 5-level compaction tree
knowledge/ # Searchable knowledge base
plans/ # Task plans
hipocampus.config.json # Configuration
.claude/skills/ # Agent skills (hipocampus-core, hipocampus-compaction, hipocampus-search)
It also:
--no-search).gitignore# Disable vector search (BM25 only, saves ~2GB disk)
npx hipocampus init --no-vector
# Skip qmd entirely (compaction tree + manual file reads only)
npx hipocampus init --no-search
# Override platform detection (auto-detects by default)
npx hipocampus init --platform claude-code
npx hipocampus init --platform openclaw
Install hipocampus on a Claude Code or OpenClaw project, and your agent gains persistent memory across sessions. It remembers what you worked on, what decisions were made, what lessons were learned — and it knows what it knows without loading everything into context.
The effect is similar to injecting your entire conversation history into every API call, but at a fraction of the token cost (~3K tokens instead of 100K+).
Modern models support 200K–1M token context windows. You could theoretically dump all past history into context — 500K tokens for conversation, 500K for past memory. But this creates two problems:
Hipocampus gives the agent the same awareness at ~3K tokens: ROOT.md tells it what it knows, and the agent loads specific details on demand.
Without hipocampus, when an agent doesn't know what it knows, it explores. It reads file after file trying to find relevant context — and every file it reads stays in the session context until the session ends. Ten files read and discarded is 30K+ tokens of waste, paid on every subsequent API call for the rest of the session.
Worse, when the agent doesn't know it already has the answer, it researches from scratch. You discussed database migration strategies two weeks ago, reached a decision, documented the rationale — but the agent doesn't know that knowledge exists, so it spends 20 minutes and thousands of tokens re-investigating the same question.
ROOT.md eliminates both problems. At ~3K tokens, the agent knows exactly what it has and what it doesn't — so it either retrieves the specific file it needs, or skips memory and researches only what's genuinely new. The real savings isn't "hipocampus vs full history dump" — it's targeted retrieval vs blind exploration that pollutes your context, and instant recall vs redundant research on problems you already solved.
AI agents forget everything between sessions. Existing solutions each solve part of the problem:
Working state files (SCRATCHPAD.md, WORKING.md) give the agent awareness of what's currently happening — active tasks, pending decisions, recent context. But they're limited to the present. They can't tell the agent about a decision made three weeks ago or a pattern that emerged over months.
Long-term memory (MEMORY.md / platform auto memory) persists facts and lessons across sessions. But system prompt space is finite — a 50-line memory works for the first week. After a month, hundreds of decisions and insights simply can't fit. You're forced to choose what to keep and what to lose. Worse, the agent doesn't know what it has forgotten.
RAG (vector search, BM25) solves the storage problem — index thousands of files and search them. But search requires knowing what to search for. When a user asks "how should we handle session timeouts?", the agent doesn't know whether it discussed this exact problem three weeks ago. Without awareness that the knowledge exists, it defaults to external search or guessing. You can't search for something you don't know you know.
| Capability | Working state | Long-term memory | RAG | Compaction tree |
|---|---|---|---|---|
| Current task awareness | Yes | No | No | No |
| Persist facts across sessions | No | Yes (until overflow) | Yes (if indexed) | Yes (ROOT.md) |
| Scale over months | No | No (overflows) | Yes | Yes (self-compresses) |
| Know what you know | Current only | Only what fits | No — requires query | Yes — ROOT.md index |
| Retrieve specific past detail | No | No (if pruned) | Yes — semantic search | Yes (tree traversal) |
| Find things you didn't know to search for | No | No | No | Yes — browse tree |
No single mechanism is sufficient. Working state handles the present. Long-term memory handles key facts. RAG handles retrieval when you know what to search for. The compaction tree handles awareness and browsing. Hipocampus combines all four.
Hipocampus organizes memory into three tiers, like a CPU cache hierarchy:
Injected into every API call. The agent's "working memory" — what it needs to know right now.
| File | Purpose | Why it's here |
|---|---|---|
| SCRATCHPAD.md | Active work state — current findings, pending decisions, cross-task lessons | Without this, the agent loses track of what it's doing mid-session |
| WORKING.md | Tasks in progress — status, blockers, next steps | Without this, the agent doesn't know what tasks are active |
| TASK-QUEUE.md | Backlog of pending tasks | Without this, follow-up tasks from prior sessions are lost |
| memory/ROOT.md | Compaction tree root — compressed index of ALL accumulated history (~100 lines) | The key innovation. This is what gives the agent awareness of its entire past at ~3K tokens. Like injecting all history, but 50x cheaper. |
| MEMORY.md | Long-term facts, rules, lessons (OpenClaw only — Claude Code uses platform auto memory) | Core facts that apply to every interaction |
| USER.md | User profile, preferences (OpenClaw only) | Personalization across sessions |
ROOT.md deserves special attention. It's a ~100-line functional index that compresses ALL past conversations and work into four sections:
## Active Context (recent ~7 days)
- hipocampus open-source: finalizing spec, ROOT.md format refactor
- legal research: Civil Act §750 brief → knowledge/legal-750.md
## Recent Patterns
- compaction design: functional sections outperform chronological for O(1) lookup
## Historical Summary
- 2026-01~02: initial 3-tier design, clawy.pro K8s launch
- 2026-03: hipocampus open-source, qmd integration
## Topics Index
- hipocampus: compaction tree, ROOT.md, skills → spec/
- legal: Civil Act §750, tort liability → knowledge/legal-750.md
- clawy.pro: K8s infra, provisioning, 80-bot deployment
The agent checks the Topics Index to decide in one glance: search memory, search externally, or answer from general knowledge. O(1) lookup — no file reads needed. This solves the "you can't search for what you don't know you know" problem.
Detailed records the agent reads when it needs specifics. Not loaded by default — accessed when Layer 1 indicates relevant knowledge exists.
| Path | Purpose | Why it's here |
|---|---|---|
memory/YYYY-MM-DD.md | Raw daily logs — structured session records | Permanent source of truth. Every decision, analysis, and outcome is recorded here. The compaction tree is built from these. |
knowledge/*.md | Curated knowledge base | Deep-dive documents too large for Layer 1 but too important to only exist in daily logs |
plans/*.md | Task plans and execution records | Multi-step work that spans sessions |
Two retrieval mechanisms for finding information across months of history:
RAG (qmd) — Best for: specific retrieval when you know what you're looking for. "What was the DB migration decision?" → semantic search finds it. RAG excels at precision recall from large corpora.
Compaction tree — Best for: browsing and discovery when you're not sure what exists. The tree provides hierarchical drill-down: ROOT.md → monthly → weekly → daily → raw. This works even when RAG misses because you can browse time periods rather than searching by keyword.
Compaction chain: Raw → Daily → Weekly → Monthly → Root
memory/
├── ROOT.md # Root node — Layer 1, auto-loaded
├── 2026-03-15.md # Raw daily log — permanent
├── daily/2026-03-15.md # Daily compaction node
├── weekly/2026-W11.md # Weekly index node
└── monthly/2026-03.md # Monthly index node
| What RAG does that the tree can't | What the tree does that RAG can't |
|---|---|
| Semantic similarity search ("find things related to X") | Awareness without query (ROOT.md knows what topics exist) |
| Cross-topic retrieval (find connections between unrelated logs) | Time-based browsing (what happened in January?) |
| Fast lookup in large corpora (thousands of files) | Hierarchical drill-down (month → week → day → raw) |
| Works offline (no embedding models needed) |
Together: ROOT.md tells the agent what it knows → agent decides to search → RAG finds the specific document → or tree traversal browses the time period.
Below threshold, source files are copied/concatenated verbatim — no information loss. Above threshold, LLM generates keyword-dense summaries.
| Level | Threshold | Below | Above |
|---|---|---|---|
| Raw → Daily | ~200 lines | Copy verbatim | LLM keyword-dense summary |
| Daily → Weekly | ~300 lines combined | Concat dailies | LLM keyword-dense summary |
| Weekly → Monthly | ~500 lines combined | Concat weeklies | LLM keyword-dense summary |
| Monthly → Root | Always | Recursive recompaction | — |
| Ad-hoc MEMORY.md | OpenViking | Hipocampus | |
|---|---|---|---|
| Setup | Manual | Python server + embedding model + config | npx hipocampus init |
| Infrastructure | None | Server + DB | None — just files |
| Search | None | Vector + directory recursive | BM25 + vector hybrid (via qmd) |
| Memory structure | Unstructured | Filesystem paradigm | 3-tier (hot/warm/cold) |
| Agent integration | DIY | Plugin API | Drop-in skills |
| Cost optimization | None | L0/L1/L2 tiered loading | Prompt cache friendly |
| Knows what it knows | Only what fits (~50 lines) | No (search required) | ROOT.md (~3K tokens) |
| Scales over months | No — overflows | Yes | Yes — self-compressing tree |
Hipocampus has four execution mechanisms — all set up automatically by npx hipocampus init. Nothing requires manual intervention after install.
Key principle: all memory write operations are dispatched to subagents. This keeps the main session context clean — memory management never pollutes the conversation the user is having with the agent.
The hipocampus-core skill instructs the agent what to do at session start and after every task. This is injected into CLAUDE.md (Claude Code) or AGENTS.md (OpenClaw) during init, so the agent follows it automatically.
Session Start (FIRST RESPONSE RULE — runs before anything else on first user message):
1. Read hipocampus.config.json → determine platform
2. OpenClaw only: Read MEMORY.md (long-term memory)
3. OpenClaw only: Read USER.md (user profile)
4. Claude Code legacy: Read MEMORY.md if it exists (migration support)
5. Read SCRATCHPAD.md — current work state
6. Read WORKING.md — active tasks
7. Read TASK-QUEUE.md — pending items
8. Read most recent memory/daily/*.md (prior session context)
9. Compaction maintenance (subagent): dispatch subagent to scan for needs-summarization
files → LLM summaries → hipocampus compact → qmd reindex
ROOT.md is auto-loaded by the platform — no manual read needed.
End-of-Task Checkpoint (via subagent):
After completing any task, the agent composes a task summary and dispatches a subagent to:
1. Update SCRATCHPAD — findings, decisions, lessons
2. OpenClaw: Append to MEMORY.md — APPEND ONLY, never modify Core section
Claude Code: Save facts/lessons to platform memory (auto memory handles this natively)
3. OpenClaw only: Update USER.md — newly learned user info
4. Append structured log to memory/YYYY-MM-DD.md (see below)
5. Update WORKING — remove completed tasks
6. Update TASK-QUEUE — remove completed tasks, add follow-ups
7. Run qmd update
The agent provides the task summary to the subagent since the subagent has no access to the conversation. Completed tasks are removed from WORKING and TASK-QUEUE — the daily log is the permanent completion record.
Step 4 of the checkpoint is the most important. The agent writes a structured session dump — not a raw transcript, but a curated record for each topic discussed:
## Investment Portfolio Construction
- request: user asked for mid/long-term portfolio suggestion
- analysis: researched 16 stocks, Attention Economy theme
- decision: 50% Core (AAPL, MSFT, ...) + 25% Growth + 20% Korea + 5% Cash
- user feedback: wants higher Korea allocation → adjust next session
- references: knowledge/investment-research.md created
- tool calls: alpha-vantage 16 calls, fmp 4 calls
## Auth Middleware Refactor
- request: review session token storage for compliance
- work done: audited current middleware, identified 3 non-compliant patterns
- decision: migrate to httpOnly cookies with SameSite=Strict
- pending: migration script needed, blocked on DB schema change
This format includes enough detail for the daily compaction node to extract keywords, decisions, and patterns — the raw material that feeds the entire compaction tree.
Both Claude Code and OpenClaw automatically compress conversation context when it gets too long. If the agent hasn't written to the daily log before compression, those details are lost forever.
The hipocampus-core skill instructs the agent to flush proactively by dispatching a subagent with a summary of recent work:
Session in progress
→ Task A completed → subagent: checkpoint → daily log append
→ Task B completed → subagent: checkpoint → daily log append
→ Task C in progress, long conversation...
→ ~20 messages → subagent: proactive flush → daily log append
→ significant decision → subagent: proactive flush → daily log append
→ Context window fills up → pre-compaction hook fires (see below)
The daily log is append-only, so multiple flushes in the same session are safe. All writes go through subagents to keep the main session clean.
PreCompact hooks only support type: "command" (no agent hooks). Mechanical compaction runs automatically; LLM processing is deferred to session start, heartbeat, or manual /hipocampus-flush.
Both platforms — PreCompact hook (mechanical only):
Context fills up
→ PreCompact hook fires
→ hipocampus compact --stdin (command hook):
1. Back up session transcript to memory/.session-transcript-YYYY-MM-DD.jsonl
2. Mechanical compaction (verbatim/concat, needs-summarization marking)
3. Update ROOT.md timestamp + sync to MEMORY.md (OpenClaw)
4. qmd update + qmd embed
→ Context compression proceeds
LLM compaction (needs-summarization processing):
Claude Code:
→ Session Start step 9: check needs-summarization → hipocampus-compaction skill
→ Manual: /hipocampus-flush (flush + full compaction + qmd reindex)
OpenClaw:
→ Every heartbeat (~30 min): HEARTBEAT.md checks needs-summarization
→ Session Start step 9: same check as Claude Code
→ Manual: /hipocampus-flush
| Platform | Mechanical Compaction | LLM Compaction | Manual |
|---|---|---|---|
| Claude Code | PreCompact command hook | Session Start + /hipocampus-flush | /hipocampus-flush |
| OpenClaw | PreCompact command hook | HEARTBEAT.md + Session Start | /hipocampus-flush |
ROOT.md must be in the agent's context at every session start. Each platform has its own mechanism:
| Platform | Mechanism | Registered by init |
|---|---|---|
| Claude Code | @memory/ROOT.md import in CLAUDE.md | Automatic |
| OpenClaw | Embedded as ## Compaction Root section in MEMORY.md (auto-synced by hipocampus compact) | Automatic |
OpenClaw bootstraps a fixed set of files (AGENTS.md, MEMORY.md, etc.) — ROOT.md can't be added to that list. Instead, hipocampus embeds the ROOT content as a section inside MEMORY.md, which is always bootstrapped. The hipocampus compact command keeps this section in sync with memory/ROOT.md.
| Mechanism | What it does | When | Subagent | Cost |
|---|---|---|---|---|
| Session Start (reads) | Load SCRATCHPAD, WORKING, TASK-QUEUE, recent daily | First user message | No (main session) | Read only |
| Session Start (compaction) | Process needs-summarization files | First user message | Yes | LLM (if files exist) |
| End-of-Task Checkpoint | Update all memory files + daily log | Every task completion | Yes | LLM |
| Proactive flush | Dump context to daily log | Every ~20 messages | Yes | LLM |
| Pre-compaction hook | Mechanical compaction + qmd reindex | Before context compression | No (command hook) | Zero LLM |
| TaskCompleted hook (CC) | Mechanical compaction | After each task | No (command hook) | Zero LLM |
| Heartbeat (OpenClaw) | Process needs-summarization | Every ~30 min | Isolated session | LLM (if files exist) |
/hipocampus-flush | Manual: session → daily raw + compact | On demand | Yes | LLM |
| ROOT.md auto-load | Topic index in system prompt | Every session start | No (platform) | ~3K tokens |
Everything is set up by npx hipocampus init. The user never has to think about memory management.
project/
├── MEMORY.md (OpenClaw only)
├── USER.md (OpenClaw only)
├── SCRATCHPAD.md
├── WORKING.md
├── TASK-QUEUE.md
├── HEARTBEAT.md (OpenClaw only — heartbeat compaction checklist)
├── memory/
│ ├── ROOT.md # Full memory topic index (Layer 1, auto-loaded)
│ ├── (raw logs: YYYY-MM-DD.md) # Permanent structured session records
│ ├── daily/ # Daily compaction nodes
│ ├── weekly/ # Weekly index nodes
│ └── monthly/ # Monthly index nodes
├── knowledge/
├── plans/
├── .claude/
│ ├── skills/
│ │ ├── hipocampus-core/SKILL.md
│ │ ├── hipocampus-compaction/SKILL.md
│ │ └── hipocampus-search/SKILL.md
│ └── settings.json # PreCompact hook (Claude Code)
└── hipocampus.config.json
hipocampus.config.json (generated by npx hipocampus init):
{
"platform": "claude-code",
"search": {
"vector": true,
"embedModel": "auto"
},
"compaction": {
"rootMaxTokens": 3000
}
}
| Field | Type | Default | Description |
|---|---|---|---|
platform | string | auto-detected | "claude-code" or "openclaw" — determines memory file behavior |
search.vector | boolean | true | Enable vector embeddings (~2GB disk) |
search.embedModel | string | "auto" | "auto" for embeddinggemma-300M, "qwen3" for CJK-optimized |
compaction.rootMaxTokens | number | 3000 | Max token budget for ROOT.md (~100 lines) |
qmd is optional. Use --no-search during init to skip it entirely. Without qmd, the compaction tree still works via direct file reads (ROOT.md → monthly/ → weekly/ → daily/ → raw).
| Setting | Default | Description |
|---|---|---|
vector | true | Vector search via local GGUF models (~2GB). Set false for BM25-only |
embedModel | "auto" | "auto" for embeddinggemma-300M, "qwen3" for CJK-optimized |
Hipocampus installs four agent skills into .claude/skills/:
needs-summarization nodes left by mechanical compaction./hipocampus-flush) — Manual memory flush via subagent: dump current session context to daily raw log + mechanical compact. Use when you want to persist session state on demand. For full LLM compaction afterwards, run hipocampus-compaction.TASK-QUEUE (backlog) → pick up task
↓
WORKING (in progress) → actively working
↓
Task completed → subagent checkpoint:
├── daily log (permanent) ← detailed structured record
├── WORKING ← task removed
├── TASK-QUEUE ← task removed, follow-ups added
├── SCRATCHPAD ← lessons, decisions updated
└── MEMORY.md ← key facts appended (OpenClaw) / platform memory (Claude Code)
TASK-QUEUE is a backlog only — completed tasks are removed, not archived. The daily log (memory/YYYY-MM-DD.md) is the permanent record of all completed work. This keeps TASK-QUEUE small and focused on what's ahead.
The memory system is formally specified in spec/:
npx hipocampus init auto-appends memory files to .gitignore — personal memory should not be committed.
What to commit: hipocampus.config.json and .claude/skills/ — these define the shared project memory structure. All team members get the same skill documents.
What not to commit: Everything else (MEMORY.md, USER.md if present, SCRATCHPAD, WORKING, TASK-QUEUE, memory/, knowledge/, plans/) is personal context. Each developer runs npx hipocampus init to set up their own memory.
MIT
FAQs
Drop-in memory harness for AI agents — 3-tier memory, compaction tree, hybrid search via qmd
The npm package hipocampus receives a total of 17 weekly downloads. As such, hipocampus popularity was classified as not popular.
We found that hipocampus demonstrated a healthy version release cadence and project activity because the last version was released less than a year ago. It has 1 open source maintainer collaborating on the project.
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