Tencent SkillHub · Developer Tools

Neolata Memory Engine

Graph-native memory engine for AI agents — hybrid vector+keyword search, biological decay, Zettelkasten linking, trust-gated conflict resolution, explainabil...

skill openclawclawhub Free

0 Downloads

0 Stars

0 Installs

0 Score

High Signal

Graph-native memory engine for AI agents — hybrid vector+keyword search, biological decay, Zettelkasten linking, trust-gated conflict resolution, explainabil...

⬇ 0 downloads ★ 0 stars Unverified but indexed

Install for OpenClaw

Quick setup

Download the package from Yavira.
Extract the archive and review SKILL.md first.
Import or place the package into your OpenClaw setup.

Requirements

Target platform: OpenClaw
Install method: Manual import
Extraction: Extract archive
Prerequisites: OpenClaw
Primary doc: SKILL.md

Package facts

Download mode: Yavira redirect
Package format: ZIP package
Source platform: Tencent SkillHub
What's included: SKILL.md, references/guide.md, references/implementation-notes.md, references/runtime-helpers.md

Validation

Use the Yavira download entry.
Review SKILL.md after the package is downloaded.
Confirm the extracted package contains the expected setup assets.

Install with your agent

Agent handoff

Hand the extracted package to your coding agent with a concrete install brief instead of figuring it out manually.

Download the package from Yavira.
Extract it into a folder your agent can access.
Paste one of the prompts below and point your agent at the extracted folder.

New install

I downloaded a skill package from Yavira. Read SKILL.md from the extracted folder and install it by following the included instructions. Tell me what you changed and call out any manual steps you could not complete.

Upgrade existing

I downloaded an updated skill package from Yavira. Read SKILL.md from the extracted folder, compare it with my current installation, and upgrade it while preserving any custom configuration unless the package docs explicitly say otherwise. Summarize what changed and any follow-up checks I should run.

Open Send to Agent page Open JSON manifest Open Markdown brief

Trust & source

Release facts

Source: Tencent SkillHub
Verification: Indexed source record
Version: 0.8.5

Provenance

Publisher: Jeremiaheth
Source page: View original listing
Canonical URL: Open canonical page

Documentation

ClawHub primary doc Primary doc: SKILL.md 26 sections Open source page

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: FeatureWhat leavesWhere it goesHow to avoidEmbeddings (OpenAI/NVIDIA/Azure)Memory textEmbedding API endpointUse noop embeddings or Ollama (local)LLM (OpenAI/OpenClaw/Ollama)Memory text for extraction/compressionLLM API endpointDon't configure llm option, or use OllamaSupabase storageAll memory dataYour Supabase projectUse json or memory storage (default)Webhook writethroughStore/decay event payloadsYour webhook URLDon'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

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

Featureneolata-memMem0OpenClaw memorySearchLocal-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-agentZero 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. ModeData sent externally?Storage locationDefault (no config)❌ No./neolata-mem-data/graph.jsonOllama embeddings❌ No (local)./neolata-mem-data/graph.jsonOpenAI/NIM embeddings⚠️ Memory text → provider./neolata-mem-data/graph.jsonSupabase storage⚠️ All data → SupabaseSupabase PostgreSQLLLM conflict resolution⚠️ Memory text → providerStorage 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

Category context

Code helpers, APIs, CLIs, browser automation, testing, and developer operations.

Source: Tencent SkillHub

Largest current source with strong distribution and engagement signals.

Package contents

Included in package

4 Docs

SKILL.md Primary doc
references/guide.md Docs
references/implementation-notes.md Docs
references/runtime-helpers.md Docs