🦛 Hippo

The secret to good memory isn't remembering more. It's knowing what to forget.

A memory layer for AI agents. Modeled on the hippocampus. Decay by default, strength through use, provenance on every memory. SQLite under the hood, zero runtime deps, works with every CLI agent you have.

npm install -g hippo-memory && hippo init --scan ~

One command. Every git repo on your machine gets memory.

Works with:    Claude Code, Codex, Cursor, OpenClaw, OpenCode, Pi, any MCP client
Imports from:  ChatGPT, Claude (CLAUDE.md), Cursor (.cursorrules), Slack, markdown
Storage:       SQLite backbone with markdown mirrors. Git-trackable, human-readable.
Dependencies:  Zero runtime deps. Node.js 22.5+. Optional embeddings via @xenova/transformers.

---

Why this exists

Most "AI memory" systems save everything and search later. That's storage with semantic search bolted on. It's why your agent kept hitting the same deploy bug last week. And the week before. The system saw the failure four times. It had no way to know it should remember.

Hippo applies the thing brains have been getting right for 500 million years. Memories decay over time. Retrieval makes them stronger. Three biological layers (buffer, episodic, semantic) consolidate during sleep. Hard lessons stick because you used them. Trivia fades because you didn't.

It also fixes the portability problem. Your ChatGPT memories don't travel to Claude. Your .cursorrules don't travel to Codex. Hippo is one process behind every agent. CLAUDE.md, Cursor rules, ChatGPT exports, Slack history, all in one SQLite store, all queryable from any tool that speaks MCP or HTTP.

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Receipts

Numbers, not adjectives. Every claim links to the benchmark or the test that proves it.

• 78% → 14% trap rate. Sequential Learning Benchmark. 50 tasks, 10 buried traps. Measures whether agents actually learn from past mistakes, not just retrieve text.
• R@5 = 74.0% on LongMemEval. 500-question industry retrieval benchmark, BM25 only, no embeddings.
• 10 of 10 incident scenarios beat transcript replay on a staged Slack corpus (benchmarks/e1.3/). Recall surfaces the cause faster than scrolling the last N messages.
• 0 outbound HTTP on the 1000-event ingestion smoke. Proven by a globalThis.fetch spy that throws on call, not a hardcoded zero.
• 886 tests, real DB, zero mocks. Project rule. The one mocks-vs-prod divergence that bit us early is now the constraint that kept the next ten releases honest.

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What it does for your agent

• Stops repeating mistakes. Tag a failure with --tag error once, the lesson surfaces every time the agent walks back into that part of the code. Errors decay slower than ordinary observations.
• Survives tool switches. Use Claude Code on Monday, Cursor on Tuesday, Codex on Wednesday. Same .hippo/ store. Same memories. Pick up exactly where you left off.
• Ingests systems of record. Slack today (POST /v1/connectors/slack/events). GitHub, Jira, Notion next. Webhooks land as kind='raw' memories with full provenance and GDPR-correct deletion.
• Knows where every memory came from. Every row carries kind, scope, owner, and artifact_ref. Right-to-be-forgotten is a single API call, not an audit nightmare.
• Plays nice with multi-tenant. API keys, scrypt-hashed. Audit log on every mutation. Tenant A literally cannot see tenant B's memories. Proven by negative test.

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Quick start

npm install -g hippo-memory

# Single project
hippo init

# All your projects at once (recommended)
hippo init --scan ~

--scan finds every git repo under your home directory, creates a .hippo/ store in each one, and seeds it with lessons from the last 30 days of commit history. One command, instant memory across all your projects.

After setup, hippo sleep runs at session end (via auto-installed agent hooks) and does five things:

1. Learns from today's git commits
2. Imports new entries from Claude Code MEMORY.md files
3. Consolidates memories (decay, merge, prune)
4. Deduplicates near-identical memories, keeping the stronger copy
5. Shares high-value lessons to a global store so they surface in every project

# Manual usage
hippo remember "FRED cache silently dropped the tips_10y series" --tag error
hippo recall "data pipeline issues" --budget 2000

---

What's new in v0.37.0

• Slack ingestion (E1.3). First end-to-end ingestion connector. POST /v1/connectors/slack/events accepts HMAC-signed Events API webhooks; messages land as kind='raw' memories with slack://team/channel/ts provenance and a slack:public:* or slack:private:* scope. Source deletions route through archiveRawMemory (GDPR). Backfill via hippo slack backfill --channel <id>; malformed events to hippo slack dlq list.
• Schema v17. New tables: slack_event_log (idempotency), slack_cursors (backfill resume), slack_dlq (parse failures), slack_workspaces (team_id to tenant_id routing).
• PUBLIC_ROUTES allow-list + HIPPO_REQUIRE_AUTH knob. The Slack webhook is the first explicit public /v1/* route (HMAC-signed, no Bearer). Every other /v1/* route returns 401 without auth when HIPPO_REQUIRE_AUTH=1.
• Recall default-deny on private scopes. No-scope queries cannot see slack:private:* memories. Frontend callers passing undefined scope no longer leak private content.
• api.remember.afterWrite hook. Connectors stamp idempotency rows atomically with the memory row via a SAVEPOINT-scoped callback.

For everything since v0.8.0, see CHANGELOG.md.

Zero-config agent integration

hippo init auto-detects your agent framework and wires itself in:

cd my-project
hippo init

# Initialized Hippo at /my-project
#    Directories: buffer/ episodic/ semantic/ conflicts/
#    Auto-installed claude-code hook in CLAUDE.md

If you have a CLAUDE.md, it patches it. AGENTS.md for Codex/OpenClaw/OpenCode. .cursorrules for Cursor. For Codex, Hippo also wraps the detected launcher in place so /exit can consolidate memory without a manual PATH step. No manual hook install needed. Your agent starts using Hippo on its next session.

It also registers the current project in Hippo's workspace registry and installs one machine-level daily runner (6:15am). That runner sweeps every registered workspace, runs hippo learn --git --days 1, then hippo sleep. You get strict daily consolidation without creating one OS task per project.

To skip: hippo init --no-hooks --no-schedule

---

Cross-Tool Import

Your memories shouldn't be locked inside one tool. Hippo pulls them in from anywhere.

# ChatGPT memory export
hippo import --chatgpt memories.json

# Claude's CLAUDE.md (skips existing hippo hook blocks)
hippo import --claude CLAUDE.md

# Cursor rules
hippo import --cursor .cursorrules

# Any markdown file (headings become tags)
hippo import --markdown MEMORY.md

# Any text file
hippo import --file notes.txt

All import commands support --dry-run (preview without writing), --global (write to ~/.hippo/), and --tag (add extra tags). Duplicates are detected and skipped automatically.

Conversation Capture

Extract memories from raw conversation text. No LLM needed: pattern-based heuristics find decisions, rules, errors, and preferences.

# Pipe a conversation in
cat session.log | hippo capture --stdin

# Or point at a file
hippo capture --file conversation.md

# Preview first
hippo capture --file conversation.md --dry-run

Slack ingestion (E1.3)

Hippo accepts Slack Events API webhooks at POST /v1/connectors/slack/events. Configure SLACK_SIGNING_SECRET (validated on every request) and point Slack at https://<your-host>/v1/connectors/slack/events. Messages land as kind='raw' memories with slack://team/channel/ts provenance and a slack:public:Cxxx or slack:private:Cxxx scope. Source deletions are honored (GDPR).

Backfill an existing channel: SLACK_BOT_TOKEN=xoxb-... hippo slack backfill --channel C0000. Inspect malformed events: hippo slack dlq list.

Multi-workspace deployments populate slack_workspaces (team_id, tenant_id) to route events per tenant; single-workspace falls back to HIPPO_TENANT.

Active task snapshots

Long-running work needs short-term continuity, not just long-term memory. Hippo can persist the current in-flight task so a later continue has something concrete to recover.

hippo snapshot save \
  --task "Ship SQLite backbone" \
  --summary "Tests/build/smoke are green, next slice is active-session recovery" \
  --next-step "Implement active snapshot retrieval in context output"

hippo snapshot show
hippo context --auto --budget 1500
hippo snapshot clear

hippo context --auto includes the active task snapshot before long-term memories, so agents get both the immediate thread and the deeper lessons.

Session event trails

Manual snapshots are useful, but real work also needs a breadcrumb trail. Hippo can now store short session events and link them to the active snapshot so context output shows the latest steps, not just the last summary.

hippo session log \
  --id sess_20260326 \
  --task "Ship continuity" \
  --type progress \
  --content "Schema migration is done, next step is CLI wiring"

hippo snapshot save \
  --task "Ship continuity" \
  --summary "Structured session events are flowing" \
  --next-step "Surface them in framework hooks" \
  --session sess_20260326

hippo session show --id sess_20260326
hippo context --auto --budget 1500

Hippo mirrors the latest trail to .hippo/buffer/recent-session.md so you can inspect the short-term thread without opening SQLite.

Session handoffs

When you're done for the day (or switching to another agent), create a handoff so the next session knows exactly where to pick up:

hippo handoff create \
  --summary "Finished schema migration, tests green" \
  --next "Wire handoff injection into context output" \
  --session sess_20260403 \
  --artifact src/db.ts

hippo handoff latest              # show the most recent handoff
hippo handoff show 3              # show a specific handoff by ID
hippo session resume              # re-inject latest handoff as context

Working memory

Working memory is a bounded scratchpad for current-state notes. It's separate from long-term memory and gets cleared between sessions.

hippo wm push --scope repo \
  --content "Investigating flaky test in store.test.ts, line 42" \
  --importance 0.9

hippo wm read --scope repo        # show current working notes
hippo wm clear --scope repo       # wipe the scratchpad
hippo wm flush --scope repo       # flush on session end

The buffer holds a maximum of 20 entries per scope. When full, the lowest-importance entry is evicted.

Explainable recall

See why a memory was returned:

hippo recall "data pipeline" --why --limit 5

# --- mem_a1b2c3 [episodic] [observed] [local] score=0.847
#     BM25: matched [data, pipeline]; cosine: 0.82
#     ...memory content...

---

How It Works

Input enters the buffer. Important things get encoded into episodic memory. During "sleep," repeated episodes compress into semantic patterns. Weak memories decay and disappear.

New information
      |
      v
+-----------+
|  Buffer   |  Working memory. Current session only. No decay.
| (session) |
+-----+-----+
      |  encoded (tags, strength, half-life assigned)
      v
+-----------+
|  Episodic |  Timestamped memories. Decay by default.
|   Store   |  Retrieval strengthens. Errors stick longer.
+-----+-----+
      |  consolidation (hippo sleep)
      v
+-----------+
|  Semantic |  Compressed patterns. Stable. Schema-aware.
|   Store   |  Extracted from repeated episodes.
+-----------+

         hippo sleep: decay + replay + merge

---

Key Features

Decay by default

Every memory has a half-life. 7 days by default. Persistence is earned.

hippo remember "always check cache contents after refresh"
# stored with half_life: 7d, strength: 1.0

# 14 days later with no retrieval:
hippo inspect mem_a1b2c3
# strength: 0.25  (decayed by 2 half-lives)
# at risk of removal on next sleep

---

Retrieval strengthens

Use it or lose it. Each recall boosts the half-life by 2 days.

hippo recall "cache issues"
# finds mem_a1b2c3, retrieval_count: 1 -> 2
# half_life extended: 7d -> 9d
# strength recalculated from retrieval timestamp

hippo recall "cache issues"   # again next week
# retrieval_count: 2 -> 3
# half_life: 9d -> 11d
# this memory is learning to survive

---

Active invalidation

When you migrate from one tool to another, old memories about the replaced tool should die immediately. Hippo detects migration and breaking-change commits during hippo learn --git and actively weakens matching memories.

hippo learn --git
# feat: migrate from webpack to vite
#    Invalidated 3 memories referencing "webpack"
#    Learned: migrate from webpack to vite

You can also invalidate manually:

hippo invalidate "REST API" --reason "migrated to GraphQL"
# Invalidated 5 memories referencing "REST API".

---

Architectural decisions

One-off decisions don't repeat, so they can't earn their keep through retrieval alone. hippo decide stores them with a 90-day half-life and verified confidence so they survive long enough to matter.

hippo decide "Use PostgreSQL for all new services" --context "JSONB support"
# Decision recorded: mem_a1b2c3

# Later, when the decision changes:
hippo decide "Use CockroachDB for global services" \
  --context "Need multi-region" \
  --supersedes mem_a1b2c3
# Superseded mem_a1b2c3 (half-life halved, marked stale)
# Decision recorded: mem_d4e5f6

---

Error memories stick

Tag a memory as an error and it gets 2x the half-life automatically.

hippo remember "deployment failed: forgot to run migrations" --error
# half_life: 14d instead of 7d
# emotional_valence: negative
# strength formula applies 1.5x multiplier

# production incidents don't fade quietly

---

Confidence tiers

Every memory carries a confidence level: verified, observed, inferred, or stale. This tells agents how much to trust what they're reading.

hippo remember "API rate limit is 100/min" --verified
hippo remember "deploy usually takes ~3 min" --observed
hippo remember "the flaky test might be a race condition" --inferred

When context is generated, confidence is shown inline:

[verified] API rate limit is 100/min per the docs
[observed] Deploy usually takes ~3 min
[inferred] The flaky test might be a race condition

Agents can see at a glance what's established fact vs. a pattern worth questioning.

Memories unretrieved for 30+ days are automatically marked stale during the next hippo sleep. If one gets recalled again, Hippo wakes it back up to observed so it can earn trust again instead of staying permanently stale.

Conflict tracking

Hippo detects obvious contradictions between overlapping memories and keeps them visible instead of silently letting both masquerade as truth. Shared tags alone do not count; the statements themselves need to overlap in content.

hippo sleep       # refreshes open conflicts
hippo conflicts   # inspect them

Open conflicts are stored in SQLite, mirrored under .hippo/conflicts/, and linked back into each memory's conflicts_with field.

---

Observation framing

Memories aren't presented as bare assertions. By default, Hippo frames them as observations with dates, so agents treat them as context rather than commands.

hippo context --framing observe   # default
# Output: "Previously observed (2026-03-10): deploy takes ~3 min"

hippo context --framing suggest
# Output: "Consider: deploy takes ~3 min"

hippo context --framing assert
# Output: "Deploy takes ~3 min"

Three modes: observe (default), suggest, assert. Choose based on how directive you want the memory to be.

---

Sleep consolidation

Run hippo sleep and episodes compress into patterns.

hippo sleep

# Running consolidation...
#
# Results:
#    Active memories:    23
#    Removed (decayed):   4
#    Merged episodic:     6
#    New semantic:        2

Three or more related episodes get merged into a single semantic memory. The originals decay. The pattern survives.

---

Outcome feedback

Did the recalled memories actually help? Tell Hippo. It tightens the feedback loop.

hippo recall "why is the gold model broken"
# ... you read the memories and fix the bug ...

hippo outcome --good
# Applied positive outcome to 3 memories
# reward factor increases, decay slows

hippo outcome --bad
# Applied negative outcome to 3 memories
# reward factor decreases, decay accelerates

Outcomes are cumulative. A memory with 5 positive outcomes and 0 negative has a reward factor of ~1.42, making its effective half-life 42% longer. A memory with 0 positive and 3 negative has a factor of ~0.63, decaying nearly twice as fast. Mixed outcomes converge toward neutral (1.0).

---

Token budgets

Recall only what fits. No context stuffing.

# fits within Claude's 2K token window for task context
hippo recall "deployment checklist" --budget 2000

# need more for a big task
hippo recall "full project history" --budget 8000

# machine-readable for programmatic use
hippo recall "api errors" --budget 1000 --json

Results are ranked by relevance * strength * recency. The highest-signal memories fill the budget first.

---

Auto-learn from git

Hippo can scan your commit history and extract lessons from fix/revert/bug commits automatically.

# Learn from the last 7 days of commits
hippo learn --git

# Learn from the last 30 days
hippo learn --git --days 30

# Scan multiple repos in one pass
hippo learn --git --repos "~/project-a,~/project-b,~/project-c"

The --repos flag accepts comma-separated paths. Hippo scans each repo's git log, extracts fix/revert/bug lessons, deduplicates against existing memories, and stores new ones. Pair with hippo sleep afterwards to consolidate.

Ideal for a weekly cron:

hippo learn --git --repos "~/repo1,~/repo2" --days 7
hippo sleep

---

Watch mode

Wrap any command with hippo watch to auto-learn from failures:

hippo watch "npm run build"
# if it fails, Hippo captures the error automatically
# next time an agent asks about build issues, the memory is there

---

CLI Reference

Command	What it does
hippo init	Create .hippo/ + auto-install agent hooks
hippo init --global	Create global store at ~/.hippo/
hippo init --no-hooks	Create .hippo/ without auto-installing hooks
hippo remember "<text>"	Store a memory
hippo remember "<text>" --tag <t>	Store with tag (repeatable)
hippo remember "<text>" --error	Store as error (2x half-life)
hippo remember "<text>" --pin	Store with no decay
hippo remember "<text>" --verified	Set confidence: verified (default)
hippo remember "<text>" --observed	Set confidence: observed
hippo remember "<text>" --inferred	Set confidence: inferred
hippo remember "<text>" --global	Store in global ~/.hippo/ store
hippo recall "<query>"	Retrieve relevant memories (local + global)
hippo recall "<query>" --budget <n>	Recall within token limit (default: 4000)
hippo recall "<query>" --limit <n>	Cap result count
hippo recall "<query>" --why	Show match reasons and source buckets
hippo recall "<query>" --json	Output as JSON
hippo context --auto	Smart context injection (auto-detects task from git)
hippo context "<query>" --budget <n>	Context injection with explicit query (default: 1500)
hippo context --limit <n>	Cap memory count in context
hippo context --budget 0	Skip entirely (zero token cost)
hippo context --framing <mode>	Framing: observe (default), suggest, assert
hippo context --format <fmt>	Output format: markdown (default) or json
hippo import --chatgpt <path>	Import from ChatGPT memory export (JSON or txt)
hippo import --claude <path>	Import from CLAUDE.md or Claude memory.json
hippo import --cursor <path>	Import from .cursorrules or .cursor/rules
hippo import --markdown <path>	Import from structured markdown (headings -> tags)
hippo import --file <path>	Import from any text file
hippo import --dry-run	Preview import without writing
hippo import --global	Write imported memories to ~/.hippo/
hippo capture --stdin	Extract memories from piped conversation text
hippo capture --file <path>	Extract memories from a file
hippo capture --dry-run	Preview extraction without writing
hippo sleep	Run consolidation (decay + merge + compress)
hippo sleep --dry-run	Preview consolidation without writing
hippo status	Memory health: counts, strengths, last sleep
hippo outcome --good	Strengthen last recalled memories
hippo outcome --bad	Weaken last recalled memories
hippo outcome --id <id> --good	Target a specific memory
hippo inspect <id>	Full detail on one memory
hippo forget <id>	Force remove a memory
hippo embed	Embed all memories for semantic search
hippo embed --status	Show embedding coverage
hippo watch "<command>"	Run command, auto-learn from failures
hippo learn --git	Scan recent git commits for lessons
hippo learn --git --days <n>	Scan N days back (default: 7)
hippo learn --git --repos <paths>	Scan multiple repos (comma-separated)
hippo daily-runner	Sweep registered workspaces and run daily learn+sleep
hippo conflicts	List detected open memory conflicts
hippo conflicts --json	Output conflicts as JSON
hippo resolve <id>	Show both conflicting memories for comparison
hippo resolve <id> --keep <mem_id>	Resolve: keep winner, weaken loser
hippo resolve <id> --keep <mem_id> --forget	Resolve: keep winner, delete loser
hippo promote <id>	Copy a local memory to the global store
hippo share <id>	Share with attribution + transfer scoring
hippo share <id> --force	Share even if transfer score is low
hippo share --auto	Auto-share all high-scoring memories
hippo share --auto --dry-run	Preview what would be shared
hippo peers	List projects contributing to global store
hippo sync	Pull global memories into local project
hippo invalidate "<pattern>"	Actively weaken memories matching an old pattern
hippo invalidate "<pattern>" --reason "<why>"	Include what replaced it
hippo decide "<decision>"	Record architectural decision (90-day half-life)
hippo decide "<decision>" --context "<why>"	Include reasoning
hippo decide "<decision>" --supersedes <id>	Supersede a previous decision
hippo hook list	Show available framework hooks
hippo hook install <target>	Install hook (claude-code also adds Stop hook for auto-sleep)
hippo hook uninstall <target>	Remove hook
hippo handoff create --summary "..."	Create a session handoff
hippo handoff latest	Show the most recent handoff
hippo handoff show <id>	Show a specific handoff by ID
hippo session latest	Show latest task snapshot + events
hippo session resume	Re-inject latest handoff as context
hippo current show	Compact current state (task + session events)
hippo wm push --scope <s> --content "..."	Push to working memory
hippo wm read --scope <s>	Read working memory entries
hippo wm clear --scope <s>	Clear working memory
hippo wm flush --scope <s>	Flush working memory (session end)
hippo dashboard	Open web dashboard at localhost:3333
hippo dashboard --port <n>	Use custom port
hippo mcp	Start MCP server (stdio transport)

---

Framework Integrations

Auto-install (recommended)

hippo init detects your agent framework and patches the right config file automatically:

Framework	Detected by	Patches
Claude Code	CLAUDE.md or .claude/settings.json	CLAUDE.md + SessionStart/SessionEnd hooks in settings.json
Codex	AGENTS.md or .codex	AGENTS.md + automatic in-place Codex launcher wrapper
Cursor	.cursorrules or .cursor/rules	.cursorrules
OpenClaw	.openclaw or AGENTS.md	native OpenClaw plugin or AGENTS.md
OpenCode	.opencode/ or opencode.json	AGENTS.md

No extra commands needed. Just hippo init and your agent knows about Hippo.

Manual install

If you prefer explicit control:

hippo hook install claude-code   # patches CLAUDE.md + adds SessionStart/SessionEnd + UserPromptSubmit hooks
hippo hook install codex         # optional repair/manual run: patches AGENTS.md + wraps the detected Codex launcher
hippo hook install cursor        # patches .cursorrules
hippo hook install openclaw      # patches AGENTS.md
hippo hook install opencode      # patches AGENTS.md

This adds a <!-- hippo:start --> ... <!-- hippo:end --> block that tells the agent to:

1. Run hippo context --auto --budget 1500 at session start
2. Run hippo remember "<lesson>" --error on errors
3. Run hippo outcome --good on completion

For Claude Code, it also adds:

• a SessionEnd hook so hippo sleep runs automatically when the session exits
• a SessionStart hook that prints the previous session's consolidation output
• a UserPromptSubmit hook that re-injects pinned memories (hippo remember <text> --pin) into every turn's context — so invariants survive long sessions where Opus 4.7 might otherwise "forget" them. Budget: 500 tokens per turn, skipped entirely when no pinned memories exist. Opt out with {"pinnedInject":{"enabled":false}} in .hippo/config.json.

To remove: hippo hook uninstall claude-code

What the hook adds (Claude Code example)

## Project Memory (Hippo)

Before starting work, load relevant context:
hippo context --auto --budget 1500

When you hit an error or discover a gotcha:
hippo remember "<what went wrong and why>" --error

After completing work successfully:
hippo outcome --good

MCP Server

For any MCP-compatible client (Cursor, Windsurf, Cline, Claude Desktop):

hippo mcp   # starts MCP server over stdio

Add to your MCP config (e.g. .cursor/mcp.json or claude_desktop_config.json):

{
  "mcpServers": {
    "hippo-memory": {
      "command": "hippo",
      "args": ["mcp"]
    }
  }
}

Exposes tools: hippo_recall, hippo_remember, hippo_outcome, hippo_context, hippo_status, hippo_learn, hippo_wm_push.

OpenClaw Plugin

Native plugin with auto-context injection, workspace-aware memory lookup, and tool hooks for auto-learn / auto-sleep. When autoSleep is enabled, the OpenClaw plugin now launches hippo sleep in a detached background worker at session end so the live session can exit immediately.

Query-time retrieval still uses the active workspace store plus the shared global store. Daily consolidation comes from the machine-level runner that hippo init / hippo setup installs.

openclaw plugins install hippo-memory
openclaw plugins enable hippo-memory

Plugin docs: extensions/openclaw-plugin/. Integration guide: integrations/openclaw.md.

Claude Code Plugin

Plugin with SessionStart/Stop hooks and error auto-capture. See extensions/claude-code-plugin/.

Full integration details: integrations/

---

The Neuroscience

Hippo is modeled on seven properties of the human hippocampus. Not metaphorically. Literally.

Why two stores? The brain uses a fast hippocampal buffer + a slow neocortical store (Complementary Learning Systems theory, McClelland et al. 1995). If the neocortex learned fast, new information would overwrite old knowledge. The buffer absorbs new episodes; the neocortex extracts patterns over time.

Why does decay help? New neurons born in the dentate gyrus actively disrupt old memory traces (Frankland et al. 2013). This is adaptive: it reduces interference from outdated information. Forgetting isn't failure. It's maintenance.

Why do errors stick? The amygdala modulates hippocampal consolidation based on emotional significance. Fear and error signals boost encoding. Your first production incident is burned into memory. Your 200th uneventful deploy isn't.

Why does retrieval strengthen? Recalled memories undergo "reconsolidation" (Nader et al. 2000). The act of retrieval destabilizes the trace, then re-encodes it stronger. This is the testing effect. Hippo implements it mechanically via the half-life extension on recall.

Why does sleep consolidate? During sleep, the hippocampus replays compressed versions of recent episodes and "teaches" the neocortex by repeatedly activating the same patterns. Hippo's sleep command runs this as a deliberate consolidation pass.

The 7 mechanisms in full: PLAN.md#core-principles

For how these mechanisms connect to LLM training, continual learning, and open research problems: RESEARCH.md

Why does reward modulate decay? In spiking neural networks, reward-modulated STDP strengthens synapses that contribute to positive outcomes and weakens those that don't. Hippo's reward-proportional decay (v0.11.0) implements this: memories with consistent positive outcomes decay slower, negatives decay faster, with no fixed deltas. Inspired by MH-FLOCKE's R-STDP architecture for quadruped locomotion, where the same mechanism produces stable learning with 11.6x lower variance than PPO.

Prior art in agent memory simulation. The idea that human-like memory produces human-like behavior as an emergent property was explored in IEEE research from 2010-2011 (5952114, 5548405, 5953964). Walking between rooms and forgetting why you went there doesn't need direct simulation; it emerges naturally from a memory system with capacity limits and decay. Hippo's design follows the same principle: implement the mechanisms, and the behavior follows.

Related work: HippoRAG (Gutierrez et al., 2024) applies hippocampal indexing to RAG via knowledge graphs. MemPalace (Sigman & Jovovich, 2026) organizes memory spatially (wings/halls/rooms) with AAAK compression, achieving 100% on LongMemEval. MH-FLOCKE (Hesse, 2026) uses spiking neurons with R-STDP for embodied cognition. Each system tackles a different facet: HippoRAG optimizes retrieval quality, MemPalace optimizes retrieval organization, MH-FLOCKE optimizes embodied learning, and Hippo optimizes memory lifecycle.

---

Comparison

Feature	Hippo	MemPalace	Mem0	Basic Memory
Decay by default	Yes	No	No	No
Retrieval strengthening	Yes	No	No	No
Reward-proportional decay	Yes	No	No	No
Hybrid search (BM25 + embeddings)	Yes	Embeddings + spatial	Embeddings only	No
Schema acceleration	Yes	No	No	No
Conflict detection + resolution	Yes	No	No	No
Multi-agent shared memory	Yes	No	No	No
Transfer scoring	Yes	No	No	No
Outcome tracking	Yes	No	No	No
Confidence tiers	Yes	No	No	No
Spatial organization	No	Yes (wings/halls/rooms)	No	No
Lossless compression	No	Yes (AAAK, 30x)	No	No
Cross-tool import	Yes	No	No	No
Auto-hook install	Yes	No	No	No
MCP server	Yes	Yes	No	No
Zero dependencies	Yes	No (ChromaDB)	No	No
LongMemEval R@5 (retrieval)	73.8% (hybrid, v0.28)	96.6% (raw) / 100% (reranked)	~49-85%	N/A
Git-friendly	Yes	No	No	Yes
Framework agnostic	Yes	Yes	Partial	Yes

Different tools answer different questions. Mem0 and Basic Memory implement "save everything, search later." MemPalace implements "store everything, organize spatially for retrieval." Hippo implements "forget by default, earn persistence through use." These are complementary approaches: MemPalace's retrieval precision + Hippo's lifecycle management would be stronger than either alone.

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Benchmarks

Two benchmarks testing two different things. Full details in benchmarks/.

LongMemEval (retrieval accuracy)

LongMemEval (ICLR 2025) is the industry-standard benchmark: 500 questions across 5 memory abilities, embedded in 115k+ token chat histories.

Hippo v0.28.0 results (hybrid BM25 + cosine, full 500 questions):

Metric	v0.28	v0.11 (BM25 only)
Recall@1	46.6%	50.4%
Recall@3	67.0%	66.6%
Recall@5	73.8%	74.0%
Recall@10	81.0%	82.6%
Answer in content@5	49.6%	46.6%

Question Type	Count	R@5	R@10
single-session-assistant	56	100.0%	100.0%
knowledge-update	78	89.7%	96.2%
multi-session	133	72.2%	82.0%
temporal-reasoning	133	72.9%	78.9%
single-session-user	70	62.9%	71.4%
single-session-preference	30	20.0%	33.3%

For context: MemPalace scores 96.6% (raw) using ChromaDB embeddings + spatial indexing. Hippo v0.28 achieves 73.8% R@5 with hybrid BM25 + cosine. Hybrid scoring trades a little R@1 accuracy for better top-5 content relevance (answer_in_content@5 +3pp vs v0.11).

Hippo's strongest categories (single-session-assistant 100% R@5, knowledge-update 89.7%) are where keyword overlap between question and stored content is highest. The weakest (preference 20%) involves indirect references that need deeper semantic understanding.

Note: v0.28 R@10 is 1.6pp below v0.11's BM25-only result. The earlier v0.27 benchmark showed an apparent 35pp regression — that was a methodology bug (budget-limited retrieval vs unlimited), fixed in v0.28 with the minResults option. See evals/README.md for the full investigation and per-type breakdown.

cd benchmarks/longmemeval
python ingest_direct.py --data data/longmemeval_oracle.json --store-dir ./store
python retrieve_fast.py --data data/longmemeval_oracle.json --store-dir ./store --output results/retrieval.jsonl
python evaluate_retrieval.py --retrieval results/retrieval.jsonl --data data/longmemeval_oracle.json

Sequential Learning Benchmark (agent improvement over time)

No other public benchmark tests whether memory systems produce learning curves. LongMemEval tests retrieval on a fixed corpus. This benchmark tests whether an agent with memory performs better on task 40 than task 5.

50 tasks, 10 trap categories, each appearing 2-3 times across the sequence.

Hippo v0.11.0 results:

Condition	Overall	Early	Mid	Late	Learns?
No memory	100%	100%	100%	100%	No
Static memory	20%	33%	11%	14%	No
Hippo	40%	78%	22%	14%	Yes

The hippo agent's trap-hit rate drops from 78% to 14% as it accumulates error memories with 2x half-life. Static pre-loaded memory helps from the start but doesn't improve. Any memory system can run this benchmark by implementing the adapter interface.

cd benchmarks/sequential-learning
node run.mjs --adapter all

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Contributing

Issues and PRs welcome. Before contributing, run hippo status in the repo root to see the project's own memory.

The interesting problems:

• Improve LongMemEval score. Current R@5 is 73.8% with hybrid BM25 + cosine (v0.28). Gap to MemPalace's 96.6% likely needs better chunking, reranking, or semantic compression — not just more of the same retrieval.
• Better consolidation heuristics (LLM-powered merge vs current text overlap)
• Web UI / dashboard for visualizing decay curves and memory health
• Optimal decay parameter tuning from real usage data
• Cross-agent transfer learning evaluation
• MemPalace-style spatial organization. Could spatial structure (wings/halls/rooms) improve hippo's semantic layer?
• AAAK-style compression for semantic memories. Lossless token compression for context injection.

License

MIT