Resilient AI systems that recover under stress
Resilient AI Research builds open source resilience controllers (like Resilient-GNN-Controller) and memory systems that help agents retain what matters and recover from perturbations.
11×
Chaos survival improvement
62%
Success under stress
6
Resilience layers
Open
Source code
Works with Claude, GPT, and open source models.
How it works
Memory that stays useful over time.
A simple request path: enrich with memory, forward to the model, then convert important outcomes into durable memory.
Connect any provider
Pass your provider key + endpoint. We proxy requests without locking you in.
Retrieve memory
Short-, mid-, and long-term memory are pulled just-in-time for the request.
Convert and prioritize
We compress, transform, and rank memories so the model only sees what matters.
Write back learnings
Important outcomes are converted into durable memory for future tasks.
Capabilities
A memory layer for any LLM app.
Short-, mid-, and long-term memory with conversion and importance filtering baked into the request pipeline.
Short-, mid-, and long-term memory
A single interface for working memory, episodic memory, and durable knowledge.
Automatic memory conversion
Summarize and reshape raw context into useful, compact memories over time.
Importance filtering
Learn what's worth keeping. Drop noise. Keep signal.
Provider-agnostic proxy API
Claude, GPT, and open source models through the same request path.
Open source-first
Transparent primitives you can self-host, audit, and extend.
Built for production apps
Designed for latency, consistency, and evolving user context.
Open source
Transparent building blocks.
We ship primitives you can self-host, audit, and extend. Swap providers without rewriting your app.
Resilient-GNN-Controller
A biologically-inspired resilient RL controller: coping hormones, bounded state, and recovery across adversarial environments.
memory-proxy
Provider-agnostic proxy API with memory enrichment (coming soon).
memory-sdk
Client SDKs for integrating memory into apps quickly (coming soon).
Research
Applied research, shipped as product.
We focus on making memory predictable: what gets stored, how it’s converted, and how it’s retrieved.
Resilience controllers for RL agents
Bounded-state safety and recovery
Biologically-inspired coping signals
Graph-based multi-agent control
FAQ
Questions, answered.
Clear answers about providers, memory conversion, and self-hosting.
Does this work with any LLM?+
Yes. You pass your provider key + endpoint and the proxy enriches requests with memory before forwarding.
What is "memory conversion"?+
It's the process of transforming raw interaction history into compact, reusable memory (summaries, facts, preferences) that stays useful over time.
Can I self-host?+
That's the goal. The core primitives are designed to be open and deployable in your environment.
Do you store my provider API keys?+
V1 is designed so keys can be passed per request. The website waitlist does not collect provider keys.
Join the waitlist
We’re shipping a provider-agnostic memory proxy API plus open source components for memory conversion and importance filtering.