neolata-mem

name	neolata-mem
version	0.8.4
description	Graph-native memory engine for AI agents — hybrid vector+keyword search, biological decay, Zettelkasten linking, trust-gated conflict resolution, explainability, episodes, compression & consolidation. Zero dependencies. npm install and go.
metadata	{"openclaw":{"requires":{"bins":"[Truncated]"},"optionalEnv":["OPENAI_API_KEY","OPENCLAW_GATEWAY_TOKEN","NVIDIA_API_KEY","AZURE_API_KEY","SUPABASE_URL","SUPABASE_KEY"],"dataFlow":{"local":"[Truncated]","remote":"[Truncated]","note":"No data leaves the host unless you explicitly configure a remote backend, embedding provider, LLM, or webhook. Default config is fully local (JSON storage + no embeddings)."},"securityNotes":["Prefer Supabase anon key + RLS over service key — service key bypasses row-level security","Webhook URLs are an explicit exfiltration surface — only configure trusted endpoints","Test with storage.type='memory' first to evaluate without persisting any data","All env vars except OPENAI_API_KEY and OPENCLAW_GATEWAY_TOKEN are passed via config objects, not read from env directly"],"license":"Elastic-2.0","homepage":"https://github.com/Jeremiaheth/neolata-mem","repository":"https://github.com/Jeremiaheth/neolata-mem"}}

neolata-mem — Agent Memory Engine

Graph-native memory for AI agents with hybrid search, biological decay, and zero infrastructure.

npm package: @jeremiaheth/neolata-mem Repository: github.com/Jeremiaheth/neolata-mem License: Elastic-2.0 | Tests: 367/367 passing (34 files) | Node: ≥18

When to Use This Skill

Use neolata-mem when you need:

• Persistent memory across sessions that survives context compaction
• Semantic search over stored facts, decisions, and findings
• Memory decay so stale information naturally fades
• Multi-agent memory with cross-agent search and graph linking
• Conflict resolution — detect and evolve contradictory memories

Do NOT use if:

• You only need OpenClaw's built-in memorySearch (keyword + vector on workspace files)
• You want cloud-hosted memory (use Mem0 instead)
• You need a full knowledge graph database (use Graphiti + Neo4j)

Install

npm install @jeremiaheth/neolata-mem

No Docker. No Python. No Neo4j. No cloud API required.

Supply-chain verification: This package has zero runtime dependencies and no install scripts. Verify before installing:

# Check for install scripts (should show only "test"):
npm view @jeremiaheth/neolata-mem scripts
# Check for runtime deps (should be empty):
npm view @jeremiaheth/neolata-mem dependencies
# Audit the tarball contents (15 files, ~40 kB):
npm pack @jeremiaheth/neolata-mem --dry-run

Source is fully auditable at github.com/Jeremiaheth/neolata-mem.

Security & Data Flow

Default configuration is fully local — JSON files on disk, no network calls, no embeddings, no external services.

Data only leaves the host if you explicitly configure one of these:

Feature	What leaves	Where it goes	How to avoid
Embeddings (OpenAI/NVIDIA/Azure)	Memory text	Embedding API endpoint	Use noop embeddings or Ollama (local)
LLM (OpenAI/OpenClaw/Ollama)	Memory text for extraction/compression	LLM API endpoint	Don't configure llm option, or use Ollama
Supabase storage	All memory data	Your Supabase project	Use json or memory storage (default)
Webhook writethrough	Store/decay event payloads	Your webhook URL	Don't configure webhookWritethrough

Key security properties:

• Only 2 env vars are read directly by code: OPENAI_API_KEY and OPENCLAW_GATEWAY_TOKEN. All others (Supabase, NVIDIA, Azure) are passed via explicit config objects.
• All provider URLs are validated against SSRF (private IPs blocked, cloud metadata blocked).
• Supabase: prefer anon key + RLS over service key. Service key bypasses row-level security.
• JSON storage uses atomic writes (temp file + rename) to prevent corruption.
• All user content sent to LLMs is XML-fenced with injection guards.
• Test safely with storage: { type: 'memory' } — nothing touches disk or network.

See docs/guide.md § Security for the full security model.

Quick Start (Zero Config)

import { createMemory } from '@jeremiaheth/neolata-mem';

const mem = createMemory();
await mem.store('agent-1', 'User prefers dark mode');
const results = await mem.search('agent-1', 'UI preferences');

Works immediately with local JSON storage and keyword search. No API keys needed.

With Semantic Search

const mem = createMemory({
  embeddings: {
    type: 'openai',
    apiKey: process.env.OPENAI_API_KEY,
    model: 'text-embedding-3-small',
  },
});

// Agent IDs like 'kuro' and 'maki' are just examples — use any string.
await mem.store('kuro', 'Found XSS in login form', { category: 'finding', importance: 0.9 });
const results = await mem.search('kuro', 'security vulnerabilities');

Supports 5+ embedding providers: OpenAI, NVIDIA NIM, Ollama, Azure, Together, or any OpenAI-compatible endpoint.

Key Features

Hybrid Search (Vector + Keyword Fallback)

Uses semantic similarity when embeddings are configured; falls back to tokenized keyword matching when they're not:

// With embeddings → vector cosine similarity search
// Without embeddings → normalized keyword matching (stop word removal, lowercase, dedup)
const results = await mem.search('agent', 'security vulnerabilities');

Keyword search uses an inverted token index for O(1) lookups. When >500 memories exist, vector search pre-filters candidates using token overlap before cosine similarity (candidate narrowing).

Biological Decay

Memories fade over time unless reinforced. Old, unaccessed memories naturally lose relevance:

await mem.decay();        // Run maintenance — archive/delete stale memories
await mem.reinforce(id);  // Boost a memory to resist decay

Memory Graph (Zettelkasten Linking)

Every memory is automatically linked to related memories by semantic similarity:

const links = await mem.links(memoryId);     // Direct connections
const path = await mem.path(idA, idB);       // Shortest path between memories
const clusters = await mem.clusters();        // Detect topic clusters

Conflict Resolution & Quarantine

Detect contradictions before storing — with claim-based structural detection or LLM-based semantic detection:

// Structural (no LLM needed): claim-based conflict detection
await mem.store('agent', 'Server uses port 443', {
  claim: { subject: 'server', predicate: 'port', value: '443' },
  provenance: { source: 'user_explicit', trust: 1.0 },
  onConflict: 'quarantine',  // low-trust conflicts quarantined for review
});

// Semantic (requires LLM): LLM classifies as conflict/update/novel
await mem.evolve('agent', 'Server now uses port 8080');

// Review quarantined memories
const quarantined = await mem.listQuarantined();
await mem.reviewQuarantine(quarantined[0].id, { action: 'activate' });

Predicate Schema Registry

Define per-predicate rules for conflict handling, normalization, and deduplication:

const mem = createMemory({
  predicateSchemas: {
    'preferred_language': { cardinality: 'single', conflictPolicy: 'supersede', normalize: 'lowercase_trim' },
    'spoken_languages':   { cardinality: 'multi', dedupPolicy: 'corroborate' },
    'salary':             { cardinality: 'single', conflictPolicy: 'require_review', normalize: 'currency' },
  },
});

Options: cardinality (single/multi), conflictPolicy (supersede/require_review/keep_both), normalize (none/trim/lowercase/lowercase_trim/currency), dedupPolicy (corroborate/store).

Explainability API

Understand why search returned or filtered specific memories:

const results = await mem.search('agent', 'query', { explain: true });
console.log(results.meta);        // query options, result count
console.log(results[0].explain);  // retrieved, rerank, statusFilter details

const detail = await mem.explainMemory(memoryId);
// { id, status, trust, confidence, provenance, claimSummary }

Multi-Agent Support

await mem.store('kuro', 'Vuln found in API gateway');
await mem.store('maki', 'API gateway deployed to prod');
const all = await mem.searchAll('API gateway');  // Cross-agent search

Episodes (Temporal Grouping)

Group related memories into named episodes:

const ep = await mem.createEpisode('Deploy v2.0', [id1, id2, id3], { tags: ['deploy'] });
const ep2 = await mem.captureEpisode('kuro', 'Standup', { start: '...', end: '...' });
const results = await mem.searchEpisode(ep.id, 'database migration');
const { summary } = await mem.summarizeEpisode(ep.id);  // requires LLM

Memory Compression & Consolidation

Consolidate redundant memories into digests:

await mem.compress([id1, id2, id3], { method: 'llm', archiveOriginals: true });
await mem.compressEpisode(episodeId);
await mem.autoCompress({ minClusterSize: 3, maxDigests: 5 });

// Full maintenance: dedup → contradictions → corroborate → compress → prune
await mem.consolidate({ dedupThreshold: 0.95, compressAge: 30, pruneAge: 90 });

Labeled Clusters

Persistent named groups:

await mem.createCluster('Security findings', [id1, id2]);
await mem.autoLabelClusters();  // LLM labels unlabeled clusters

Event Emitter

Hook into the memory lifecycle:

mem.on('store', ({ agent, content, id }) => { /* ... */ });
mem.on('search', ({ agent, query, results }) => { /* ... */ });
mem.on('decay', ({ archived, deleted, dryRun }) => { /* counts, not arrays */ });

Batch APIs

Amortize embedding calls and I/O with bulk operations:

// Store many memories in one call (single embed batch + single persist)
const result = await mem.storeMany('agent', [
  { text: 'Fact one', category: 'fact', importance: 0.8 },
  { text: 'Fact two', tags: ['infra'] },
  'Plain string also works',
]);
// { total: 3, stored: 3, results: [{ id, links }, ...] }

// Search multiple queries in one call (single embed batch)
const results = await mem.searchMany('agent', ['query one', 'query two']);
// [{ query: 'query one', results: [...] }, { query: 'query two', results: [...] }]

Batch operations include:

• Atomic rollback on persist failure (memories, indexes, backlinks all reverted)
• Cross-linking within the same batch
• Configurable caps: maxBatchSize (default 1000), maxQueryBatchSize (default 100)

Bulk Ingestion with Fact Extraction

Extract atomic facts from text using an LLM, then store each with A-MEM linking:

const mem = createMemory({
  embeddings: { type: 'openai', apiKey: process.env.OPENAI_API_KEY },
  extraction: { type: 'llm', apiKey: process.env.OPENAI_API_KEY },
});

const result = await mem.ingest('agent', longText);
// { total: 12, stored: 10, results: [...] }

CLI

npx neolata-mem store myagent "Important fact here"
npx neolata-mem search myagent "query"
npx neolata-mem decay --dry-run
npx neolata-mem health
npx neolata-mem clusters

OpenClaw Integration

neolata-mem complements OpenClaw's built-in memorySearch:

• memorySearch = searches your workspace .md files (BM25 + vector)
• neolata-mem = structured memory store with graph, decay, evolution, multi-agent

Use both together: memorySearch for workspace file recall, neolata-mem for agent-managed knowledge.

Recommended Setup

In your agent's daily cron or heartbeat:

// Store important facts from today's session
await mem.store(agentId, 'Key decision: migrated to Postgres', {
  category: 'decision',
  importance: 0.8,
  tags: ['infrastructure'],
});

// Run decay maintenance
await mem.decay();

Comparison

Feature	neolata-mem	Mem0	OpenClaw memorySearch
Local-first (data stays on machine)	✅ (default)	❌	✅
Hybrid search (vector + keyword)	✅	❌	✅
Memory decay	✅	❌	❌
Memory graph / linking	✅	❌	❌
Conflict resolution	✅	Partial	❌
Quarantine lane	✅	❌	❌
Predicate schemas	✅	❌	❌
Explainability API	✅	❌	❌
Episodes & compression	✅	❌	❌
Labeled clusters	✅	❌	❌
Multi-agent	✅	✅	Per-agent
Zero infrastructure	✅	❌	✅
Event emitter	✅	❌	❌
Batch APIs (storeMany/searchMany)	✅	❌	❌
npm package	✅	✅	Built-in

Security

neolata-mem includes hardening against common agent memory attack vectors:

• Prompt injection mitigation: XML-fenced user content in all LLM prompts + structural output validation
• Input validation: Agent names (alphanumeric, max 64), text length caps (10KB), bounded memory count (50K), batch size caps (1000 store / 100 query)
• Batch atomicity: storeMany rolls back all memories, indexes, and backlinks on persist failure
• SSRF protection: All provider URLs validated via validateBaseUrl() — blocks cloud metadata endpoints (169.254.169.254), private IP ranges, non-HTTP protocols
• Supabase hardening: UUID validation on query params, error text sanitized (strips tokens/keys), upsert-based save (crash-safe), 429 retry with backoff
• Atomic writes: Write-to-temp + rename prevents file corruption
• Path traversal guards: Storage directories and write-through paths validated with resolve() + prefix checks
• Cryptographic IDs: crypto.randomUUID() — no predictable memory references
• Retry bounds: Exponential backoff with max 3 retries on 429s
• Error surfacing: Failed conflict detection returns { error } instead of silent fallthrough

Supabase key guidance: Prefer the anon key with Row Level Security (RLS) policies over the service role key. The service key bypasses RLS and grants full access to all stored memories. Only use it for admin/migration tasks.

See the full security section for details.

Data Residency & External API Usage

Local-only mode (default): Memories are stored as JSON at ./neolata-mem-data/graph.json (relative to CWD). No data leaves your machine. Keyword search works without any API keys.

With embeddings/extraction/LLM: When you configure an external provider (OpenAI, NIM, Ollama, etc.), your memory text is sent to that provider's API for embedding or extraction. This is opt-in — you must explicitly provide an API key and base URL.

Mode	Data sent externally?	Storage location
Default (no config)	❌ No	./neolata-mem-data/graph.json
Ollama embeddings	❌ No (local)	./neolata-mem-data/graph.json
OpenAI/NIM embeddings	⚠️ Memory text → provider	./neolata-mem-data/graph.json
Supabase storage	⚠️ All data → Supabase	Supabase PostgreSQL
LLM conflict resolution	⚠️ Memory text → provider	Storage unchanged

To keep all data local: Use Ollama for embeddings and JSON storage. No API keys needed for keyword-only search.

Links

• npm: @jeremiaheth/neolata-mem
• GitHub: Jeremiaheth/neolata-mem
• Full docs: See docs/guide.md in the package