Tempreon

Website

tempreon.com

Pricing URL

Official Tempreon Pricing

$ Details

freemium $19 / Monthly

Platforms

Web SaaS Online

Release Date

2026 April

Categories

#AI Tools #Knowledge Management #Productivity #Developer Tools #AI Chatbots #AI Assistant #AI Writing #Note Taking #AI

Startup details

Country

United States

State

UT

City

Saratoga Springs

Founder(s)

Brandon Briggs

SAME (Stateless Agent Memory Engine)

Website

statelessagent.com

Pricing URL

-

$ Details

-

Platforms

-

Release Date

-

Categories

#AI #AI Tools #Developer Tools #Productivity

Tempreon features and specs

• Cross-LLM memory
  Knowledge captured in one assistant is available in all of them — Claude, ChatGPT, Cursor, any MCP-capable client.
• Core Imprint
  A structured identity layer — who you are, how you work, what you care about — seeded in about 15 minutes.
• Knowledge Vault
  Your personal knowledge and files, stored once and retrievable by meaning, not just keywords.
• Learning System Layer
  Tempreon learns from your decisions and feedback over time — instincts, not just storage.
• One-URL connect (Bridges)
  Connect any MCP-capable client by pasting a Bridge URL; OAuth 2.1 handles authorization in your browser.
• Memory import
  Bring your existing ChatGPT or Claude memory with you — including via memhaul, our free open-source export CLI.
• You own your data
  Export everything, anytime. We monetize the service, never the custody.

SAME (Stateless Agent Memory Engine) features and specs

• Persistent Context for Stateless Systems
  SAME allows inherently stateless agents (like typical LLM API calls) to maintain continuity across sessions, enabling more coherent long-term interactions without requiring the underlying model to natively support memory.
• Scalability
  By decoupling memory storage from the agent's core processing, SAME can potentially scale independently, allowing multiple agent instances to share or access consistent memory stores without bottlenecking the agent's compute resources.
• Flexibility Across Models
  Since the memory engine operates externally to the AI model itself, it can theoretically be used with various LLMs or agent frameworks, making it adaptable rather than locked into a single vendor's ecosystem.
• Simplified Agent Architecture
  Developers can offload memory management complexity to SAME, allowing them to focus on core agent logic rather than building custom memory persistence solutions from scratch.
• Improved Personalization
  With persistent memory, agents can better tailor responses based on historical user interactions, preferences, and past context, leading to more relevant and personalized outputs over time.

Possible disadvantages of SAME (Stateless Agent Memory Engine)

• Limited Public Information
  As a relatively niche or newer product, there may be limited documentation, case studies, or third-party reviews available, making it harder to fully evaluate its reliability, performance, and real-world effectiveness before adoption.
• Potential Latency Overhead
  Introducing an external memory retrieval step for every agent interaction could add latency compared to fully stateless calls, especially if the memory store is large or the retrieval mechanism isn't optimized.
• Data Privacy and Security Concerns
  Storing persistent memory about user interactions raises questions about data privacy, security, and compliance with regulations like GDPR, especially if sensitive information is retained without clear user consent mechanisms.
• Integration Complexity
  Depending on the existing agent architecture, integrating an external memory engine like SAME may require non-trivial engineering work, including handling synchronization, consistency, and error states between the agent and memory store.
• Dependency Risk
  Relying on a third-party service for core memory functionality introduces a dependency risk—if the service experiences downtime, pricing changes, or discontinuation, it could significantly impact the reliability of agents built on top of it.