Memory & Learning
AgentDB, HNSW, ReasoningBank, SONA, micro-LoRA adapters, cross-session memory — memory and learning owned by the harness, not the model.
AgentDB and Vector Stores in Harnesses
AgentDB (ruflo’s purpose-built vector database) and vector stores generally are the harness’s substrate for semantic recall — embeddings of past trajectories, code snippets, documents, and decisions are kept in a queryable index so the agent can retrieve relevant memories on demand.
Cross-Session Memory Strategies
Cross-session memory strategies decide what an agent remembers between conversations — the durable artifacts (configuration files, summaries, trajectories, adapters) and the policies for writing, retrieving, and aging them; this is one of the highest-leverage UX dimensions of any harness.
Harness-Owned Memory
Durable agent memory — across turns, sessions, machines, and users — is owned by the harness, not the model; this is one of the harness’s load-bearing responsibilities and a major axis on which harnesses differentiate.
HNSW for Agent Recall
HNSW (Hierarchical Navigable Small World) is the dominant approximate-nearest-neighbor index used by agent vector stores — it is the data structure underneath AgentDB, Pinecone, Qdrant, Weaviate, and most production memory layers, and understanding its trade-offs explains a lot about why agent recall feels the way it does.
Memory Portability Across Harnesses
Memory portability — whether the artifacts you’ve built up in one harness work in another — is partial in 2026: configuration files (CLAUDE.md, AGENTS.md, .cursorrules) are convergent enough to copy-with-edits; vector stores and trajectory stores are mostly per-harness; adapters are model-specific; expect a portability gradient, not a clean abstraction.
Micro-LoRA Adapters at the Harness Layer
Micro-LoRA adapters are small, project-scoped low-rank fine-tunes (typically <50MB) that the harness can load on top of a base model to bias it toward the project’s conventions, vocabulary, and successful trajectories — emerging in 2026 as a way to give agents a kind of parametric memory without the cost of full fine-tuning.
ReasoningBank
ReasoningBank is ruflo’s named pattern for storing whole trajectories — the sequence of (state, decision, outcome) tuples an agent produced — as memory the system can replay or learn from; it is a specialized vector store optimized for trajectory-shaped data and a key driver of ruflo’s claimed self-learning behavior.
SONA: Self-Learning Neural Patterns
SONA is ruflo’s pattern-matching layer that learns which strategies tend to succeed for which task signatures, sitting one level above ReasoningBank — instead of replaying trajectories verbatim, SONA distills them into reusable patterns that bias the agent’s planner toward known-good moves.
Trajectory Learning
Trajectory learning is the family of techniques that learn from full agent rollouts (state-action-outcome sequences) rather than from isolated examples — it includes simple replay (store trajectories, retrieve at run time) and stronger forms (parametric updates via fine-tuning or LoRA on successful trajectories).