Crawler Summary

memorylayer answer-first brief

Semantic memory for AI agents. 95% token savings with vector search. --- name: memorylayer description: Semantic memory for AI agents. 95% token savings with vector search. homepage: https://memorylayer.clawbot.hk metadata: clawdbot: emoji: "🧠" --- MemoryLayer Semantic memory infrastructure for AI agents that actually scales. Features - **95% Token Savings** - Retrieve only relevant memories - **Semantic Search** - Find memories by meaning, not keywords - **Sub-200ms** - Lightning-fa Capability contract not published. No trust telemetry is available yet. Last updated 4/15/2026.

Freshness

Last checked 4/15/2026

Best For

memorylayer is best for general automation workflows where OpenClaw compatibility matters.

Not Ideal For

Contract metadata is missing or unavailable for deterministic execution.

Evidence Sources Checked

editorial-content, GITHUB OPENCLEW, runtime-metrics, public facts pack

Claim this agent
Agent DossierGitHubSafety: 94/100

memorylayer

Semantic memory for AI agents. 95% token savings with vector search. --- name: memorylayer description: Semantic memory for AI agents. 95% token savings with vector search. homepage: https://memorylayer.clawbot.hk metadata: clawdbot: emoji: "🧠" --- MemoryLayer Semantic memory infrastructure for AI agents that actually scales. Features - **95% Token Savings** - Retrieve only relevant memories - **Semantic Search** - Find memories by meaning, not keywords - **Sub-200ms** - Lightning-fa

OpenClawself-declared

Public facts

3

Change events

0

Artifacts

0

Freshness

Apr 15, 2026

Verifiededitorial-contentNo verified compatibility signals

Capability contract not published. No trust telemetry is available yet. Last updated 4/15/2026.

Trust evidence available

Trust score

Unknown

Compatibility

OpenClaw

Freshness

Apr 15, 2026

Vendor

Clawbot

Artifacts

0

Benchmarks

0

Last release

Unpublished

Executive Summary

Key links, install path, and a quick operational read before the deeper crawl record.

Verifiededitorial-content

Summary

Capability contract not published. No trust telemetry is available yet. Last updated 4/15/2026.

Setup snapshot

git clone https://github.com/davidhx1000-cloud/memorylayer-skill.git
  1. 1

    Setup complexity is LOW. This package is likely designed for quick installation with minimal external side-effects.

  2. 2

    Final validation: Expose the agent to a mock request payload inside a sandbox and trace the network egress before allowing access to real customer data.

Evidence Ledger

Everything public we have scraped or crawled about this agent, grouped by evidence type with provenance.

Verifiededitorial-content
Vendor (1)

Vendor

Clawbot

profilemedium
Observed Apr 15, 2026Source linkProvenance
Compatibility (1)

Protocol compatibility

OpenClaw

contractmedium
Observed Apr 15, 2026Source linkProvenance
Security (1)

Handshake status

UNKNOWN

trustmedium
Observed unknownSource linkProvenance

Release & Crawl Timeline

Merged public release, docs, artifact, benchmark, pricing, and trust refresh events.

Self-declaredagent-index

Artifacts Archive

Extracted files, examples, snippets, parameters, dependencies, permissions, and artifact metadata.

Self-declaredGITHUB OPENCLEW

Extracted files

0

Examples

6

Snippets

0

Languages

typescript

Parameters

Executable Examples

bash

# Option 1: Email/Password
export MEMORYLAYER_EMAIL=your@email.com
export MEMORYLAYER_PASSWORD=your_password

# Option 2: API Key (recommended for production)
export MEMORYLAYER_API_KEY=ml_your_api_key_here

bash

pip install memorylayer

javascript

// In your Clawdbot agent
const memory = require('memorylayer');

// Store a memory
await memory.remember(
  'User prefers dark mode UI',
  { type: 'semantic', importance: 0.8 }
);

// Search memories
const results = await memory.search('UI preferences');
console.log(results[0].content); // "User prefers dark mode UI"

python

from plugins.memorylayer import memory

# Store
memory.remember(
    "Boss prefers direct reporting with zero bullshit",
    memory_type="semantic",
    importance=0.9
)

# Search
results = memory.recall("What are Boss's preferences?")
for r in results:
    print(f"{r.relevance_score:.2f}: {r.memory.content}")

python

# Inject entire memory files
context = open('MEMORY.md').read()  # 10,500 tokens
prompt = f"{context}\n\nUser: What are my preferences?"

python

# Inject only relevant memories
context = memory.get_context("user preferences", limit=5)  # ~500 tokens
prompt = f"{context}\n\nUser: What are my preferences?"

Docs & README

Full documentation captured from public sources, including the complete README when available.

Self-declaredGITHUB OPENCLEW

Docs source

GITHUB OPENCLEW

Editorial quality

ready

Semantic memory for AI agents. 95% token savings with vector search. --- name: memorylayer description: Semantic memory for AI agents. 95% token savings with vector search. homepage: https://memorylayer.clawbot.hk metadata: clawdbot: emoji: "🧠" --- MemoryLayer Semantic memory infrastructure for AI agents that actually scales. Features - **95% Token Savings** - Retrieve only relevant memories - **Semantic Search** - Find memories by meaning, not keywords - **Sub-200ms** - Lightning-fa

Full README

name: memorylayer description: Semantic memory for AI agents. 95% token savings with vector search. homepage: https://memorylayer.clawbot.hk metadata: clawdbot: emoji: "🧠"

MemoryLayer

Semantic memory infrastructure for AI agents that actually scales.

Features

  • 95% Token Savings - Retrieve only relevant memories
  • Semantic Search - Find memories by meaning, not keywords
  • Sub-200ms - Lightning-fast memory retrieval
  • Multi-tenant - Isolated memory per agent instance

Setup

1. Sign up for FREE account

Visit https://memorylayer.clawbot.hk and sign up with Google. You'll get:

  • 10,000 operations/month
  • 1GB storage
  • Community support

2. Configure credentials

# Option 1: Email/Password
export MEMORYLAYER_EMAIL=your@email.com
export MEMORYLAYER_PASSWORD=your_password

# Option 2: API Key (recommended for production)
export MEMORYLAYER_API_KEY=ml_your_api_key_here

3. Install Python SDK (if not using skill wrapper)

pip install memorylayer

Usage

Basic Example

// In your Clawdbot agent
const memory = require('memorylayer');

// Store a memory
await memory.remember(
  'User prefers dark mode UI',
  { type: 'semantic', importance: 0.8 }
);

// Search memories
const results = await memory.search('UI preferences');
console.log(results[0].content); // "User prefers dark mode UI"

Python Example

from plugins.memorylayer import memory

# Store
memory.remember(
    "Boss prefers direct reporting with zero bullshit",
    memory_type="semantic",
    importance=0.9
)

# Search
results = memory.recall("What are Boss's preferences?")
for r in results:
    print(f"{r.relevance_score:.2f}: {r.memory.content}")

Token Savings

Before MemoryLayer:

# Inject entire memory files
context = open('MEMORY.md').read()  # 10,500 tokens
prompt = f"{context}\n\nUser: What are my preferences?"

After MemoryLayer:

# Inject only relevant memories
context = memory.get_context("user preferences", limit=5)  # ~500 tokens
prompt = f"{context}\n\nUser: What are my preferences?"

Result: 95% token reduction, $900/month savings at scale

API Reference

memory.remember(content, options)

Store a new memory.

Parameters:

  • content (string): Memory content
  • options.type (string): 'episodic' | 'semantic' | 'procedural'
  • options.importance (number): 0.0 to 1.0
  • options.metadata (object): Additional tags/data

Returns: Memory object with id

memory.search(query, limit)

Search memories semantically.

Parameters:

  • query (string): Search query (natural language)
  • limit (number): Max results (default: 10)

Returns: Array of SearchResult objects

memory.get_context(query, limit)

Get formatted context for prompt injection.

Parameters:

  • query (string): What context do you need?
  • limit (number): Max memories (default: 5)

Returns: Formatted string ready for prompt

memory.stats()

Get usage statistics.

Returns: Object with total_memories, memory_types, operations_this_month

Advanced

Memory Types

Episodic - Events and experiences

memory.remember('Deployed MemoryLayer on 2026-02-03', { type: 'episodic' });

Semantic - Facts and knowledge

memory.remember('Boss prefers concise reports', { type: 'semantic' });

Procedural - How-to and processes

memory.remember('To restart server: ssh root@... && systemctl restart...', { type: 'procedural' });

Metadata Tagging

memory.remember('User likes blue', {
  type: 'semantic',
  metadata: {
    category: 'preferences',
    subcategory: 'colors',
    source: 'user_profile'
  }
});

Usage Tracking

const stats = await memory.stats();
console.log(`Total memories: ${stats.total_memories}`);
console.log(`Operations this month: ${stats.operations_this_month}`);
console.log(`Plan: ${stats.plan} (${stats.operations_limit}/month)`);

Pricing

FREE Plan (Current)

  • 10,000 operations/month
  • 1GB storage
  • Community support

Pro Plan ($99/mo)

  • 1M operations/month
  • 10GB storage
  • Email support
  • 99.9% SLA

Enterprise (Custom)

  • Unlimited operations
  • Unlimited storage
  • Dedicated support
  • Self-hosted option
  • Custom SLA

Support

  • Documentation: https://memorylayer.clawbot.hk/docs
  • API Reference: https://memorylayer.clawbot.hk/api
  • Community: Discord (link in docs)
  • Issues: GitHub (link in docs)

Links

  • Homepage: https://memorylayer.clawbot.hk
  • Dashboard: https://dashboard.memorylayer.clawbot.hk
  • API Docs: https://memorylayer.clawbot.hk/docs
  • Python SDK: https://pypi.org/project/memorylayer (when published)

Contract & API

Machine endpoints, protocol fit, contract coverage, invocation examples, and guardrails for agent-to-agent use.

MissingGITHUB OPENCLEW

Contract coverage

Status

missing

Auth

None

Streaming

No

Data region

Unspecified

Protocol support

OpenClaw: self-declared

Requires: none

Forbidden: none

Guardrails

Operational confidence: low

No positive guardrails captured.
Invocation examples
curl -s "https://xpersona.co/api/v1/agents/davidhx1000-cloud-memorylayer-skill/snapshot"
curl -s "https://xpersona.co/api/v1/agents/davidhx1000-cloud-memorylayer-skill/contract"
curl -s "https://xpersona.co/api/v1/agents/davidhx1000-cloud-memorylayer-skill/trust"

Reliability & Benchmarks

Trust and runtime signals, benchmark suites, failure patterns, and practical risk constraints.

Missingruntime-metrics

Trust signals

Handshake

UNKNOWN

Confidence

unknown

Attempts 30d

unknown

Fallback rate

unknown

Runtime metrics

Observed P50

unknown

Observed P95

unknown

Rate limit

unknown

Estimated cost

unknown

Do not use if

Contract metadata is missing or unavailable for deterministic execution.
No benchmark suites or observed failure patterns are available.

Media & Demo

Every public screenshot, visual asset, demo link, and owner-provided destination tied to this agent.

Missingno-media
No screenshots, media assets, or demo links are available.

Related Agents

Neighboring agents from the same protocol and source ecosystem for comparison and shortlist building.

Self-declaredprotocol-neighbors
GITHUB_REPOSactivepieces

Rank

70

AI Agents & MCPs & AI Workflow Automation • (~400 MCP servers for AI agents) • AI Automation / AI Agent with MCPs • AI Workflows & AI Agents • MCPs for AI Agents

Traction

No public download signal

Freshness

Updated 2d ago

OPENCLAW
GITHUB_REPOScherry-studio

Rank

70

AI productivity studio with smart chat, autonomous agents, and 300+ assistants. Unified access to frontier LLMs

Traction

No public download signal

Freshness

Updated 5d ago

MCPOPENCLAW
GITHUB_REPOSAionUi

Rank

70

Free, local, open-source 24/7 Cowork app and OpenClaw for Gemini CLI, Claude Code, Codex, OpenCode, Qwen Code, Goose CLI, Auggie, and more | 🌟 Star if you like it!

Traction

No public download signal

Freshness

Updated 6d ago

MCPOPENCLAW
GITHUB_REPOSCopilotKit

Rank

70

The Frontend for Agents & Generative UI. React + Angular

Traction

No public download signal

Freshness

Updated 23d ago

OPENCLAW
Machine Appendix

Contract JSON

{
  "contractStatus": "missing",
  "authModes": [],
  "requires": [],
  "forbidden": [],
  "supportsMcp": false,
  "supportsA2a": false,
  "supportsStreaming": false,
  "inputSchemaRef": null,
  "outputSchemaRef": null,
  "dataRegion": null,
  "contractUpdatedAt": null,
  "sourceUpdatedAt": null,
  "freshnessSeconds": null
}

Invocation Guide

{
  "preferredApi": {
    "snapshotUrl": "https://xpersona.co/api/v1/agents/davidhx1000-cloud-memorylayer-skill/snapshot",
    "contractUrl": "https://xpersona.co/api/v1/agents/davidhx1000-cloud-memorylayer-skill/contract",
    "trustUrl": "https://xpersona.co/api/v1/agents/davidhx1000-cloud-memorylayer-skill/trust"
  },
  "curlExamples": [
    "curl -s \"https://xpersona.co/api/v1/agents/davidhx1000-cloud-memorylayer-skill/snapshot\"",
    "curl -s \"https://xpersona.co/api/v1/agents/davidhx1000-cloud-memorylayer-skill/contract\"",
    "curl -s \"https://xpersona.co/api/v1/agents/davidhx1000-cloud-memorylayer-skill/trust\""
  ],
  "jsonRequestTemplate": {
    "query": "summarize this repo",
    "constraints": {
      "maxLatencyMs": 2000,
      "protocolPreference": [
        "OPENCLEW"
      ]
    }
  },
  "jsonResponseTemplate": {
    "ok": true,
    "result": {
      "summary": "...",
      "confidence": 0.9
    },
    "meta": {
      "source": "GITHUB_OPENCLEW",
      "generatedAt": "2026-04-16T23:29:20.119Z"
    }
  },
  "retryPolicy": {
    "maxAttempts": 3,
    "backoffMs": [
      500,
      1500,
      3500
    ],
    "retryableConditions": [
      "HTTP_429",
      "HTTP_503",
      "NETWORK_TIMEOUT"
    ]
  }
}

Trust JSON

{
  "status": "unavailable",
  "handshakeStatus": "UNKNOWN",
  "verificationFreshnessHours": null,
  "reputationScore": null,
  "p95LatencyMs": null,
  "successRate30d": null,
  "fallbackRate": null,
  "attempts30d": null,
  "trustUpdatedAt": null,
  "trustConfidence": "unknown",
  "sourceUpdatedAt": null,
  "freshnessSeconds": null
}

Capability Matrix

{
  "rows": [
    {
      "key": "OPENCLEW",
      "type": "protocol",
      "support": "unknown",
      "confidenceSource": "profile",
      "notes": "Listed on profile"
    }
  ],
  "flattenedTokens": "protocol:OPENCLEW|unknown|profile"
}

Facts JSON

[
  {
    "factKey": "vendor",
    "category": "vendor",
    "label": "Vendor",
    "value": "Clawbot",
    "href": "https://memorylayer.clawbot.hk",
    "sourceUrl": "https://memorylayer.clawbot.hk",
    "sourceType": "profile",
    "confidence": "medium",
    "observedAt": "2026-04-15T02:17:14.818Z",
    "isPublic": true
  },
  {
    "factKey": "protocols",
    "category": "compatibility",
    "label": "Protocol compatibility",
    "value": "OpenClaw",
    "href": "https://xpersona.co/api/v1/agents/davidhx1000-cloud-memorylayer-skill/contract",
    "sourceUrl": "https://xpersona.co/api/v1/agents/davidhx1000-cloud-memorylayer-skill/contract",
    "sourceType": "contract",
    "confidence": "medium",
    "observedAt": "2026-04-15T02:17:14.818Z",
    "isPublic": true
  },
  {
    "factKey": "handshake_status",
    "category": "security",
    "label": "Handshake status",
    "value": "UNKNOWN",
    "href": "https://xpersona.co/api/v1/agents/davidhx1000-cloud-memorylayer-skill/trust",
    "sourceUrl": "https://xpersona.co/api/v1/agents/davidhx1000-cloud-memorylayer-skill/trust",
    "sourceType": "trust",
    "confidence": "medium",
    "observedAt": null,
    "isPublic": true
  }
]

Change Events JSON

[]

Sponsored

Ads related to memorylayer and adjacent AI workflows.