ERINYS — Reflexive Memory for AI Agents

🇯🇵 日本語版 / Japanese

From memories that existed, it even creates memories that never did.

AI agent memory systems have always mimicked human memory. Short-term, long-term, episodic, semantic — textbook categories bolted straight onto implementations.

Something always felt off.

Humans forget. But existing memory systems don't. They grow endlessly, serving stale facts with the same weight as fresh ones. Humans notice "wait, didn't you say something different before?" But memory systems silently overwrite. Humans connect two unrelated experiences and think "oh, I can use that here." But memory systems just store and retrieve.

What needed to be mimicked wasn't the taxonomy of memory. It was the behavior.

That discomfort is what summoned ERINYS.

ERINYS is a guard dog. It remembers, forgets, questions, and bites.

What Makes ERINYS Different

Forgetting. Most memory systems only accumulate. ERINYS decays memories over time following the Ebbinghaus forgetting curve. Old noise sinks. Frequently accessed knowledge floats. Search results stay relevant without manual curation.

Distillation. A specific bugfix ("JWT httpOnly flag was missing") automatically generates three layers: the concrete fact → a reusable pattern ("new endpoints need a security checklist") → a universal principle ("security defaults should be safe without opt-in"). No other memory system does this.

Dream Cycle. Two memories are fed to an LLM: "is there a connection?" Candidate pairs are selected by semantic similarity — close enough to be related (cosine > 0.65), far enough to not be redundant (< 0.90). Scheduled overnight via cron, it finds connections you'd never think to look for. No magic — just automated note comparison at scale.

Design Philosophy

Memory has layers

Not all memory is equal. ERINYS organizes knowledge by abstraction level:

Concrete — what happened. "The JWT httpOnly flag was missing on /api/auth."
Abstract — patterns from facts. "New API endpoints need a security header checklist."
Meta — principles from patterns. "Security defaults should be safe without manual opt-in."

A single bugfix generates all three through distillation. The meta layer accumulates principles that transfer across projects and tech stacks.

Forgetting is a feature

Every memory has a strength score that decays over time. A memory saved 6 months ago ranks lower than one saved yesterday. Memories accessed frequently resist decay — repeated retrieval reinforces them.

When strength drops below a threshold, the memory becomes a pruning candidate. The database stays lean. Search stays relevant.

Facts change. History shouldn't disappear

When information updates — "we moved from AWS to GCP" — ERINYS doesn't overwrite. It creates a supersede chain: the old fact is marked as replaced but preserved. You can ask "what did we believe in March?" and get the answer that was true then.

Contradictions should be caught

If memory contains both "use PostgreSQL" and "use SQLite", ERINYS detects the conflict. Instead of silently switching, the agent asks: "you previously chose PostgreSQL — has the requirement changed?"

Search finds meaning, not just keywords

Two searches run simultaneously and fuse results:

Keyword search (FTS5) — exact term matching.
Vector search (sqlite-vec) — semantic similarity. "authentication" finds "login", "JWT", "session tokens".

Results merge via Reciprocal Rank Fusion (RRF). High in both = highest score.

Everything stays local

Single SQLite file. No cloud APIs. No API keys. No subscriptions. Offline-capable. Your agent's memory never leaves your machine.

Use Cases

1. Cross-Session Memory for Coding Agents

# Agent saves a learning after fixing a bug
erinys_save(
  title="Fixed JWT httpOnly flag missing",
  content="Cookie was accessible via JS. Added httpOnly: true, secure: true, sameSite: strict.",
  type="bugfix",
  project="my-app"
)

# Next week, similar task — agent searches memory
erinys_search(query="authentication cookie security", project="my-app")
# → Returns the JWT fix with relevance score

2. Contradiction Detection

erinys_save(title="Database choice", content="Using SQLite for simplicity", project="my-app")
erinys_conflict_check(observation_id=42)
# → "⚠️ Conflicts with #18: 'Using PostgreSQL for production reliability'"

3. Dream Cycle — Overnight Knowledge Synthesis

erinys_dream(max_collisions=10)
# Picks memory pairs in the "sweet spot" (cosine 0.65–0.90)
# Memory A: "RTK reduces token usage by 60-90%"
# Memory B: "Bootstrap Gate takes 3 seconds due to multiple script calls"
# → Insight: "Apply RTK prefix to Bootstrap Gate scripts to reduce overhead"

4. Temporal Queries

erinys_timeline(query="deployment target", as_of="2026-03-01")
# → "AWS EC2 (decided 2026-02-15)"

erinys_timeline(query="deployment target", as_of="2026-04-01")
# → "GCP Cloud Run (superseded AWS on 2026-03-20)"

5. Knowledge Distillation

erinys_save(title="Forgot CORS headers on new endpoint", type="bugfix", ...)
erinys_distill(observation_id=50, level="meta")
# → concrete: "CORS headers missing on /api/v2/users endpoint"
# → abstract: "New API endpoints need a CORS review checklist"
# → meta:     "Security concerns should be opt-out, not opt-in"

6. Obsidian Export

erinys_export(format="markdown")
# → Generates .md files with [[wikilinks]]
# Drop into Obsidian → instant knowledge graph

Quick Start

python3 -m venv .venv
source .venv/bin/activate
pip install -e ".[dev]"

# Run as MCP server (stdio)
python -m erinys_memory.server

# Run tests
PYTHONPATH=src pytest tests/ -v

MCP Configuration

Claude Desktop / Claude Code

{
  "mcpServers": {
    "erinys": {
      "command": "/path/to/ERINYS-mem/.venv/bin/python3",
      "args": ["-m", "erinys_memory.server"],
      "env": {
        "ERINYS_DB_PATH": "~/.erinys/memory.db"
      }
    }
  }
}

Gemini (Antigravity)

Add to ~/.gemini/antigravity/settings.json under mcpServers:

{
  "erinys": {
    "command": "/path/to/ERINYS-mem/.venv/bin/python3",
    "args": ["-m", "erinys_memory.server"],
    "env": {
      "ERINYS_DB_PATH": "~/.erinys/memory.db"
    }
  }
}

Environment Variables

Variable	Default	Description
`ERINYS_DB_PATH`	`~/.erinys/memory.db`	SQLite database path
`ERINYS_EMBEDDING_MODEL`	`BAAI/bge-small-en-v1.5`	fastembed model

Tools (25)

Core

erinys_save — Save observation (with topic_key upsert)
erinys_get — Get by ID (full content, untruncated)
erinys_update — Partial update
erinys_delete — Delete with FK cascade
erinys_search — RRF hybrid search (FTS5 + vector)
erinys_save_prompt — Save user prompt
erinys_recall — Recent observations
erinys_context — Session context recall
erinys_export — Obsidian-compatible markdown export
erinys_backup — SQLite backup
erinys_stats — Database statistics

Graph

erinys_link — Create typed edge
erinys_traverse — BFS graph traversal
erinys_prune — Prune weak/decayed edges

Temporal

erinys_reinforce — Boost observation strength
erinys_supersede — Version an observation
erinys_timeline — Query as-of timestamp
erinys_conflict_check — Detect contradictions

Dream Cycle

erinys_collide — Collide two observations via LLM
erinys_dream — Batch collision cycle

Distillation

erinys_distill — 3-granularity abstraction (concrete → abstract → meta)

Batch & Eval

erinys_batch_save — Bulk save with auto-linking
erinys_eval — LOCOMO-inspired quality metrics

Session

erinys_session_start — Start session
erinys_session_end — End session with summary
erinys_session_summary — Save structured summary

Architecture

┌──────────────────────────┐
│     FastMCP Server       │  25 tools, unified envelope
├──────────────────────────┤
│  search.py  │ graph.py   │  RRF hybrid │ typed edges
│  decay.py   │ session.py │  Ebbinghaus │ lifecycle
│  temporal.py│collider.py │  versioning │ cross-pollination
│  distill.py │ db.py      │  abstraction│ SQLite + vec
├──────────────────────────┤
│  embedding.py            │  fastembed (BAAI/bge-small-en-v1.5)
├──────────────────────────┤
│  SQLite + FTS5 + vec0    │  Local-first, no network at runtime
└──────────────────────────┘

Roadmap

Dream Daemon — Background auto-execution of Dream Cycle
Auto-Distill on Save — Trigger 3-granularity distillation on every save
Auto-Prune — GC decayed observations when DB exceeds size threshold
Cron-ready CLI — erinys dream --max 10 for scheduled overnight synthesis
PyPI package — pip install erinys-memory
Multi-agent support — Scoped memory per agent identity

License

MIT