Adaptive Recall: Advanced Memory for Your AI Agents
Why Memory Decides How Good Your Agents Are
An agent is only as capable as what it can remember. The model supplies the reasoning, but memory supplies the context: what the user asked for last week, which approach already failed, the preferences that shape every answer, and the facts that keep responses grounded. When memory is shallow, agents repeat themselves, lose the thread across sessions, and surface results that are merely similar rather than genuinely relevant.
Most memory systems store text as vector embeddings and retrieve by cosine similarity. That works for simple lookups, but it treats every memory as a flat point in space with no sense of time, no sense of how often something has mattered, and no sense of how concepts connect. Adaptive Recall was built to close that gap, drawing on decades of cognitive science to model memory the way it actually behaves.
What Adaptive Recall Does Differently
Adaptive Recall runs four retrieval strategies in parallel on every query: vector similarity for meaning, temporal recency for what is current, full-text keyword for exact terms, and knowledge graph traversal for connected ideas. Instead of betting on a single method, it blends all four and learns which to weight for each kind of question.
Results are ranked with ACT-R activation modeling, a scoring approach grounded in thirty years of cognitive science research. Recency, how often a memory has been accessed, the strength of its entity connections, and its validated confidence all factor into which memories surface first, so the most useful information rises to the top rather than the most superficially similar.
The system also builds a knowledge graph automatically. As memories are stored, entities and the relationships between them are extracted and linked, turning your memory store into a network that can be traversed. A query can then find relevant information through connections, not just text matches, reaching answers that pure similarity search would never surface.
Memories are not static rows. Each one moves through a lifecycle, gaining or losing confidence as new evidence confirms or contradicts it, and fading naturally when it stops being accessed. On top of that, the system trains machine learning models on your real usage, validates every parameter change against your query history, and monitors its own retrieval quality. It genuinely gets better the more your agents use it.
All of this sits behind a simple interface. Eight tools cover everything your agents need: store, recall, update, forget, graph, status, snapshot, and feedback. It speaks the Model Context Protocol for Claude Code and other agent tooling, and plain HTTP REST for everything else, with bearer token auth and JSON in and out.
How It Works With Auto Learning Agents
Auto Learning Agents treats memory as a configurable backend. By default your agents use a local store powered by sentence-transformer embeddings and on-device vector search, which keeps everything fast and self-contained. When you want the cognitive scoring, the knowledge graph, and the self-improving retrieval that Adaptive Recall provides, you point the same memory backend at the Adaptive Recall service in your settings.
Nothing else changes. Every node in your system, from the master agent handling your prompts to the research and coding pipelines, keeps calling the same memory interface it always has. The store, recall, and graph operations your agents already use simply route to a more capable engine. You get production-grade memory across your entire fleet of agents by switching one setting, and you can move back just as easily.
Get Started With Adaptive Recall
Adaptive Recall has a free tier so you can connect it to your agents and feel the difference before you commit, with no credit card required. If your agents are doing real work and their memory needs to keep up, it is the upgrade built for exactly that.