Approach to Self-Improving Memory in Local AI Agents
Memory Architecture for Persistent AI Agents
A developer on r/LocalLLaMA has shared their approach to creating AI agents that don't repeat mistakes across sessions. The core problem addressed is that every session starts from zero, with context windows resetting and corrections being lost between sessions.
Memory Implementation
The system uses markdown as the source of truth instead of a database. MEMORY.md is human-editable - delete a line in vim and the agent forgets it. SQLite and FAISS (HNSW, 768-dim) are derived caches that are rebuildable from markdown anytime. This allows users to version-control their agent's memory with git.
Episode Scoring and Rule Learning
Each execution gets scored +1/-1 and saved as an episode. On similar future tasks, relevant episodes get pulled into context. When the same error signature (SHA256 of tool name + normalized error) shows up twice within 7 days, a rule learner generates a one-line prevention rule.
Rules start at 0.40 confidence and need 0.60 to actually get injected into future prompts. Success bumps confidence +0.03, failure drops it -0.05. Rules that don't help eventually decay away.
Trust Escalation System
Instead of configuring permission levels upfront, the agent tracks approval patterns. 5 approvals at 90%+ rate = auto-promote. One revert = demote back. There's a shadow mode for auditing.
Task Decomposition and Safety
Complex goals become a DAG (Directed Acyclic Graph). Circular dependencies are caught via topological sort, failure cascades to dependents via DFS (Depth-First Search). A completion gate checks 18 requirements (R01-R18) - did the agent actually read files, write changes, verify results, stay in the workspace?
Safety features include 43 bash risk patterns, dual-pass analysis (raw + decoded), fail-closed design (Guardian crash = deny), and minimum writable depth of 3 to prevent rm -rf /.
The developer is seeking feedback on whether the confidence decay on rules feels right and whether the +0.03/-0.05 asymmetry is optimal. They're also wondering if there are better alternatives to HNSW for this scale (typically <10k episodes).
📖 Read the full source: r/LocalLLaMA
👀 See Also
Atlas: World Labs' Omni World Model for Spatial Intelligence
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Monarch v3: NES-Inspired KV Paging for 78% Faster LLM Inference
Monarch v3 implements NES-inspired memory paging for transformers, achieving 78% faster inference (17.01 to 30.42 tok/sec) on a 1.1B parameter model with nearly zero VRAM overhead. The open-source algorithm splits KV cache into hot and cold regions with compression and promotion mechanisms.
Skillware adds synthetic data generator with entropy scoring for local model fine-tuning
Skillware has released a new synthetic data generator skill that uses zlib compression-ratio heuristics to score output diversity, helping prevent model collapse. The tool works out-of-the-box with Ollama, supports Gemini/Anthropic for high-reasoning batches, and outputs JSON batches for .jsonl fine-tuning pipelines.
Obsidian Integration for Persistent Memory in OpenClaw and Claude Code
A Reddit user demonstrates how connecting OpenClaw and Claude Code to an Obsidian vault creates persistent long-term memory across sessions. The setup automatically links memories, context, project files, and notes, with all instances able to access shared memory when needed.