Local AI Reasoning with Persistent Context Recall

On-device memory powering lasting AI context at the edge

Evaluate OpenInfer's local context engine directly from your terminal — built for privacy, determinism, and efficiency.

Download the CLI demo:

By downloading, you agree to the End User License Agreement (EULA).

The Problem

Most local AI reasoning systems are limited by short context windows and lack long-term memory. As conversations or tasks evolve, they lose awareness of prior interactions, forcing users to repeat context and reducing efficiency, personalization, and trust.

Existing solutions attempt to extend memory through cloud-based architectures, but these introduce latency, cost, and data privacy risks.

Our Solution

OpenInfer introduces a new paradigm for edge intelligence: a local inference engine powered by Mementos, an on-device memory layer that enables persistent context and long-term reasoning.

Mementos captures and recalls relevant context from every interaction, allowing AI systems to maintain continuity, learn from experience, and personalize responses, all without relying on the cloud.

With Mementos, users experience continuous, context-aware AI that runs entirely on-device or on-premise, preserving performance, privacy, and trust.

Core Capabilities

Persistent local memory that evolves with every interaction.

Seamless context recall across sessions with no performance degradation.

Fully local processing — no data leaves the device. No reliance on Cloud, No Cloud Costs.

Optimized retrieval architecture for fast, efficient inference.

Long-horizon reasoning across ongoing workflows.

Personalized intelligence powered by relevant Mementos recall.

Technical Advantages

OpenInfer Memento provides several technical and operational benefits:

Infinite Context via Persistent Memory

Extends effective context beyond native token limits, enabling long-term continuity across sessions.

Runtime Efficiency Through Cache Compression

Reduces GPU and system memory usage by summarizing low-importance tokens, cutting inference latency.

Local-First Architecture

Designed for on-device inference, eliminating dependency on cloud databases while preserving data privacy.

Unified Semantic Layer

Aligns cache and graph operations in a shared embedding space, ensuring meaning-preserving compression and retrieval.

Market Opportunity

As enterprises accelerate adoption of edge and on-prem AI, demand is rising for privacy-preserving, context-aware reasoning. Industries such as healthcare, finance, legal, and defense require intelligent systems that cannot depend on external servers for memory or inference.

By combining local reasoning with persistent memory, OpenInfer uniquely addresses the convergence of three critical market needs:

  • AI personalization without data exposure
  • Fast, private inference on local devices
  • Long-context reasoning for continuous workflows

Value Proposition

True Continuity

AI that remembers past interactions and builds on them, delivering context-aware reasoning over time.

Privacy

All memory and inference stay local; user data never leaves the device.

Lower Cost

No cloud dependency, no cloud bills, no vendor lock-in.

Data Sovereignty by Design

Ownership and control of data remain entirely with the user or enterprise.

Human-Like Intelligence

Adaptive, evolving behavior that feels natural, personal, and consistent.

Seamless Integration

Drop-in architecture that enhances existing local or on-prem AI systems.

Try It Out

Start a Chat

Chat with the Llama3.2 3B model enhanced with a prepopulated Mementos database:

openinfer-demo chat

Launch OpenInfer Studio

Launch the OpenInfer Studio – a web-based interface designed for developers to inspect, evaluate, and tune how Mementos influence the model's behavior:

openinfer-demo studio

View Licenses

openinfer-demo licenses
Share your feedback

Download the CLI demo:

By downloading, you agree to the End User License Agreement (EULA).

Next Steps

We're partnering with forward-thinking organizations to validate real-world use cases and scale integrations. Enterprises and developers interested in local, private, and context-aware AI are invited to collaborate with us in shaping the next generation of persistent memory for intelligent systems.