Used by 500+ AI teams worldwide

Memory infrastructure that developers love

Sub-100ms memory recall with graph-based knowledge storage. Give your AI agents persistent memory that scales.

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Sub-100ms

Memory recall

99.9%

Uptime SLA

15M+

Memories processed

Built for production AI

Everything you need to give your agents intelligent, persistent memory

⚡

Lightning-fast recall

Semantic search with vector similarity and BM25. Find relevant memories instantly with intelligent ranking and graph traversal.

< 100ms latency

🧠

Graph-based knowledge

Memories form interconnected knowledge graphs. Your agents understand context and relationships automatically.

GraphRAG enabled

🔧

Zero infrastructure

One API call to get started. We handle vector databases, scaling, and maintenance.

5 minute setup

🏢

Enterprise-grade security

Multi-tenant isolation with SOC2 compliance. Encryption at rest and in transit. Audit logs included.

99.9% uptime SLA

📊

Real-time analytics

Track memory usage, search patterns, and agent behavior. Optimize performance with detailed metrics.

Live dashboards

🚀

Automatic scaling

From single bot to thousands of agents. Elastic scaling with predictable pricing.

Scales to millions

Loved by AI developers

Join hundreds of teams building smarter agents with MemTap

"MemTap's sub-100ms recall completely transformed our customer service bot. The graph connections let it understand context we never programmed explicitly."

Sarah Chen

CTO, AgentFlow

"Setup was literally one API call. Our agents now remember conversation context across sessions with <10ms overhead. Game changer for user experience."

Mike Rodriguez

Founder, ChatSupport.ai

"The semantic search is incredibly fast. Our agents recall relevant information from millions of memories in milliseconds. The GraphRAG feature is brilliant."

Alex Kumar

Head of AI, TechCorp

What developers are saying

@devbuilder

Just tried @memtap for agent memory - the setup is ridiculously easy and the search is lightning fast. Why did I spend weeks building this myself?

@aifounder

MemTap's graph memory approach is genius. Our chatbot now understands relationships between customer data automatically. Sub-100ms recall is cherry on top.

@startupCTO

Finally, agent memory infrastructure that just works. GraphRAG search finds connections we never thought to program. This is how memory should work.

@mlenginer

If you're building AI agents and not using @memtap, you're missing out. The graph-based approach is way ahead of traditional vector stores.

@aiproduct

MemTap handles our memory scaling so we can focus on the agent logic. From prototype to production in days, not months.

@techfounder

The 99.9% uptime SLA gives us confidence to build our entire product on MemTap. Enterprise features actually work as advertised.

Works with any agent framework

Universal memory API that integrates everywhere

OpenClaw Integration

# Install and start using immediately $ openclaw plugin install memtap # Agent gets instant memory agent.remember("User prefers technical docs over marketing copy") agent.recall("How should I write documentation?")

REST API

# Store memory curl -X POST https://api.memtap.ai/v1/memories \ -H "Authorization: Bearer $API_KEY" \ -d '{"content": "User likes concise answers", "type": "preference"}' # Recall with sub-100ms speed curl "https://api.memtap.ai/v1/recall?q=user+preferences" \ -H "Authorization: Bearer $API_KEY"

Python SDK (Beta)

# Coming soon $ pip install memtap import memtap client = memtap.Client(api_key) # Graph-based memory storage client.remember("User prefers Python over JavaScript") memories = client.recall("coding preferences")

Pricing that scales with you

Start small, scale to millions. Transparent pricing with no hidden costs.

Hobby

$5 free credits included

100 memories
1 agent
Basic search
Community support
REST API access

Start Free

Starter

$19

per month

10,000 memories
5 agents
Semantic search
Basic analytics
Email support
OpenClaw integration

Start Trial