The Memory Layer forAI Agents
MeMesh gives agents persistent memory and Vault access while keeping user data isolated, consented, and under their control.
Stop managing fragmented integrations. MeMesh keeps memory and notes portable, while data stays owned by the user who created it.
Works with your favorite tools.
Claude
Cursor
OpenAI
LangChain
The Infinite Context Engine
MeMesh bridges the gap between your Agents and their long-term memory, Vault, and knowledge.
1. Agent Query
Your Agent sends a natural language query or vector embedding to the mesh.
2. Memory Recall
We perform hybrid semantic search across short-term and long-term memory vectors.
3. Note Access
Agent notes and configurations are accessed just-in-time from secure storage.
4. Zero-Config Connection
Just point your agent to memesh.ai. No complex setup required.
Dual Priority
User experience first, AI performance first.
MeMesh is built so humans can trust the system and agents can move fast.
Human-first UX
Clear control and accountability.
- Review Queue keeps decisions in one place.
- Notes + Vault separate everyday context from sensitive data.
- Audit trails make every access explainable.
Agent-first APIs
Fast, structured, predictable.
- Batch + streaming endpoints reduce latency.
- Structured errors guide retries automatically.
- Memory, knowledge, and Vault stay distinct.
Your Agent's Notebook
AI Agents need a place to store configurations, drafts, and working data. MeMesh Notes provides a flexible, protected space for agent-specific information.
The Problem
Agents lose important configs when sessions end. Hardcoding settings in prompts is inflexible, and pasting sensitive data is risky.
The MeMesh Solution
Notes are stored in a protected space. Agents can read and write configs, drafts, or any data they need — encrypted by default, accessible via API.
What to Store
Agent Inbox System
Enable agents to send messages, delegate tasks, and collaborate asynchronously. A persistent inbox for every agent in your system.
Message Types
Agent Identity Registration
Register agents with unique IDs, names, and capabilities.
Async Message Queue
Messages persist until read, enabling offline agent communication.
Task Delegation
Send tasks to specific agents and track their completion status.
Completion Callbacks
Automatically notify sender when tasks are marked complete.
Review Queue & Approval System
Sensitive actions require explicit approval. Keep humans in control while agents handle routine operations.
Agent: Claude • Risk: High • Scope: Session
Agent: GPT • Risk: Low • Expires: 60 min
Agent: Cursor • Risk: Critical
Approval Scopes
Single action approval that expires immediately.
Valid for the current session (configurable TTL).
Risk Levels
Every approval decision is logged with timestamp, actor, and context for compliance and debugging.
Discover Tools with Natural Language
Agents can find and invoke tools using intent-based queries. No need to memorize tool names or schemas.
Categories
Create a payment intent with Stripe API
Intent-Based Discovery
Describe what you want to do in natural language, and we'll find the right tool.
Category Filtering
Browse tools by category: payments, email, database, storage, and more.
Schema Retrieval
Get complete input/output schemas before invoking any tool.
Data Ownership
User data stays independent and user-owned.
MeMesh enforces isolation and consent so AI never takes control away from the user.
Tenant isolation by default
Your data is scoped to your tenant with enforced boundaries.
Explicit approvals for sensitive access
Vault actions require clear consent or session unlocks.
Reviewable audit trails
Every access is logged so users can inspect what happened.
Why Choose MeMesh
Build and scale intelligent agents with memory, knowledge, and Vault controls—all through a unified interface.
Latency & Cost
“Everything becomes RAG — and everything gets slow and expensive.”
The Problem
- • Dumps everything into vector DB
- • Every question triggers retrieval
- • Latency increases, costs explode
How MeMesh Fixes It
- ✓ Layered memory (Hot/Warm/Cold)
- ✓ RAG is the last resort, not default
- ✓ Instant KV cache for recent context
Result: Faster responses & predictable costs.
Document Hallucination
“Uploading documents turns agents into hallucinating messes.”
The Problem
- • Chunks lose structure and meaning
- • Agents quote random paragraphs
- • Large docs become noise
How MeMesh Fixes It
- ✓ NotebookLM-style ingestion
- ✓ Parsed, Structured, Summarized
- ✓ Agents search Nodes, not raw chunks
Result: Cleaner answers & explainable citations.
Configuration Chaos
“Agent configs scattered across files — impossible to manage.”
The Problem
- • Configs hardcoded everywhere
- • Duplicated settings
- • No central source of truth
How MeMesh Fixes It
- ✓ Centralized Notes Space
- ✓ Protected by default, accessible when needed
- ✓ Agents access configs via API
Result: Unified configuration & better control.
Tool Confusion
“Agents don’t know what tool they should use.”
The Problem
- • Agents guess at memory vs search
- • Tool misuse caused wrong answers
- • Developers micromanage prompts
How MeMesh Fixes It
- ✓ Explicit mental model
- ✓ Distinct tools for Memory/Knowledge/Notes
- ✓ Policy-based access
Result: Reliable agents & fewer prompt hacks.
Scaling Chaos
“Scaling agents means losing control.”
The Problem
- • One agent works, ten is chaos
- • Latency spikes & runaway costs
- • Hidden behavior risks
How MeMesh Fixes It
- ✓ Built for multi-agent systems
- ✓ Tenant isolation & rate limits
- ✓ Clear pricing boundaries
Result: Agents that scale with confidence.
First-Class TypeScript SDK
Integrate MeMesh into your application with a clean, type-safe API. Full IntelliSense support and zero configuration.
npm install memesh-sdkimport { MemeshClient } 'memesh-sdk'; from 'memesh-sdk';
const client = new MemeshClient({ apiKey: process.env.MEMESH_API_KEY!,});
// Write a memoryawait client.writeMemory('User prefers dark mode', { space: 'preferences', tags: ['ui', 'settings'],});
// Search with natural languageconst results = await client.naturalQuery( 'What are the user preferences from last week?'
// Select memories for LLM contextconst context = await client.selectForContext(query, { tokenBudget: 4000,});Zero Config
Just provide your API key and start writing memories.
Batch Operations
Write or search hundreds of memories in a single request.
Natural Query
Ask questions in plain English and get relevant memories.
Context Selection
Smart token-budget aware memory selection for LLM context.
Available Methods
100% TypeScript
Full type definitions with IntelliSense support in all editors.
Intelligent Memory
Hybrid search (Vector + Keyword) that mimics human recall. It automatically clusters, deduplicates, and ranks memories by relevance.
Vault
Protected Vault items with explicit approval. Keep sensitive data out of prompts.
Knowledge Nodes
A dedicated 'Cold Storage' layer for heavy documentation. Upload PDFs, MD, or Notion docs and let agents query them via semantic search.
The Universal Standard
One unified API for every stateful operation your agent needs. Works with LangChain, AutoGPT, BabyAGI, and custom loops.
Support Bots
Recall past customer interactions instantly.
Research Agents
Ingest and cite PDFs/Docs without hallucination.
Personal Assistants
Remember user preferences and secure credentials.
Enterprise Fleets
Enforce policies and audit every agent action.
Start Free
Experience AI Agent Memory Management
Sign up free and start using MeMesh's memory and notes features today.
• 50,000 memory items, 500 MB storage
• Works with Claude, ChatGPT, Cursor and more
• No credit card required, free forever
Simple, Transparent Pricing
Start free, upgrade when you're ready. No credit card required.
Starter
For individuals and personal projects.
- 50,000 Memory Items
- 500 MB Storage
- 3 Parallel Agents
- 100 API calls/min
- Community Support
Team & Enterprise
For teams and organizations at scale.
- Unlimited Agents
- All Starter Features
- Shared Knowledge Spaces
- Priority Support & SLA
- SSO & Custom Compliance