Memory infrastructure that lets AI remember, reason, and scale like humans.
Used and Trusted by Developers From
Why AI Needs Better Memory
Persistent Memory
LLMs like GPT-4o and Claude do not have persistent memory. They forget previous conversations. Session are stateless.
Click to learn more
Poor Scalability
$10,000–$20,000/mo per deployment4x compute for every 2x context window140–300GB RAM for top LLMs
Click to learn more
Inaccuracy & Memory Loss
65–89% accuracy on factual recallCatastrophic forgetting wipes prior knowledge128k–200k tokens still loses facts
Click to learn more
Lack of Customizability
Most LLMs are closed platforms—users can't adjust, prioritize, or erase memory.
Click to learn more
Why use Recallr AI?
Plug in. Customize. Remember Better.
Modular & Customizable by Design
Swap or customize any component of Recallr’s modular memory system for your workflow.
Efficient Memory Storage
Recallr compresses and manages memory, reducing storage needs and saving you money.
Works with Any LLM
Recallr plugs into OpenAI, Anthropic, any LLM. Have freedom to choose and switch models as you grow.
Easy to Integrate
Add advanced memory to your app within minutes.
$ npm install recallrai
$ pip install recallrai
Better Memory
Recallr’s memory keeps conversations coherent and contextual, making AI feel real in long, ongoing chats.
Flexible for Any Use Case
From customer support to healthcare assistants, Recallr adapts to your domain and requirements.
Easy to Use
Integrate cognitive memory into your AI agents with just a few lines of code.
# Install the SDK (choose one)poetry add recallrai# orpip install recallraifromrecallraiimportRecallrAI# Initialize clientapi_key = "rai_yourapikey"project_id = "project-uuid"client = RecallrAI(api_key=api_key, project_id=project_id)
# Create or get useruser = client.create_user(user_id="user123", metadata={"name": "John Doe"})
# or, if the user already exists:user = client.get_user("user123")
# Create a sessionsession = user.create_session(auto_process_after_minutes=5)
# Add messages
session.add_user_message("Hello! How are you?")
session.add_assistant_message("I'm an assistant. How can I help you?")
# Retrieve context and process sessioncontext = session.get_context()
session.process()
Plug-and-play SDKs
Get started in minutes with our simple APIs
Customize what your AI remembers
Fine-tune memory retention and retrieval
No retraining or vector setup
Works with existing AI models
The future of Memory
Non-Rigid Memory Structure
When other rigid memory systems won't work for you, we will
Researched Memory Architecture
Integrates architecture from a multitude of research-backed approaches
Advanced Reasoning
Can recognize and handle conflict, temporal reasoning, and actively learn
Higher Accuracy
Outperforms other memory systems by +15%
AI Assistant
Memory
More Advanced Memory, Higher Accuracy
Recallr outperforms traditional memory systems with superior accuracy, reliability, and quality.
%
Improvement in long-term recall accuracy
.87%
Reduction in Memory Storage
.9%
Uptime SLA guarantee
Memory Comparison
Other Memory Systems
Memory Accuracy75-85%
ReasoningMinimal
Latency Added50–150ms
Recallr Memory
Memory Accuracy95-96%
ReasoningExcellent
Latency Added10-30ms
The Memory Layer of the AI Stack
Just like vector databases changed retrieval, Recallr changes how machines think and remember.
Recallr is building the foundational memory infrastructure that every AI application will need. This is the missing piece in the AI stack.
DC
Drew Colmenar
Co-Founder, Recallr AI
Market Opportunity
AI Memory Market$22.65Bby 2030
Growth Rate45%CAGR
Addressable Market$4.8Tby 2033
Competitive Advantage
Modular, customizable system for any use case
Proprietary memory architecture
30% better accuracy than vector systems
Easy to install and integrate
30+
Active Devs
5K+
API Calls/Month
99%
Uptime SLA
$40k+
Saved for Developers
Power Up Your Agent's Brain
Build AI that remembers. Join the waitlist or explore our docs today.