GLIA — A holographic memory for AI agents that isn't a graph and isn't RAG

GLIA, a new open-source tool designed to give AI coding agents long-term memory by storing knowledge as 1024-dimensional vector patterns (called "glyphs") rather than using traditional text chunks (RAG) or rigid graph structures. Unlike RAG, which fails at associative reasoning, and graphs, which are brittle and hard to maintain, GLIA uses holographic encoding via circular convolution to represent relationships without storing explicit edges, allowing it to perform structural analogies across a codebase. In benchmarks across three real-world projects, GLIA outperformed graph-based retrieval by 2.5x in accuracy and remained competitive with BM25 keyword matching while offering zero-cost, offline, and plastic memory capabilities.

Every AI coding agent I've used Cline, Claude, Cursor, etc has the same problem: it forgets everything between sessions. You fix a complex race condition on Monday, and on Tuesday the agent suggests the same broken pattern again. RAG Retrieval-Augmented Generation is the standard fix. You chunk files, embed them, and search by similarity. It works for direct questions. But it fails at associative reasoning . It can't connect "rate limiting fails open" with "shared Redis connection pool" if those concepts never appear in the same text chunk. The relationship exists in the architecture, but RAG is blind to it. Graphs are the other option. Nodes and edges. Better at relationships, but rigid. If you delete 30% of your edges, you lose entire paths. And let's be honest: maintaining a massive knowledge graph for a changing codebase is a schema nightmare. I wanted something different. Something that behaves more like a brain than a database. So I built GLIA. What GLIA actually does GLIA stores knowledge as 1024-dimensional vectors. Not text chunks. Not nodes. Patterns. In GLIA, every piece of knowledge a function, a decision, a bug fix is a glyph — a distributed pattern across 1024 dimensions. No single dimension carries the meaning; the meaning is the "interference pattern" of the whole vector. Holographic Binding The "Secret Sauce" Relationships aren't stored as edges in a table. They are encoded holographically using Circular Convolution. Think of it as "folding" two patterns into each other. When you bind A, B , you create a new vector that is mathematically related to both but looks like noise to anything else. - Superposition : You can add thousands of these bindings into the same 1024-d vector space. They don't overwrite each other; they coexist as interference patterns. - Unbinding : Later, if you have A and the "memory substrate", you can mathematically unbind them to recover B. This means GLIA has Zero Edges . If you open the database, there is no relationships table. The architecture is the memory. Why this beats a Graph Three things a graph can't do that GLIA does natively: - Graceful Degradation : If you corrupt 30% of a glyph's dimensions, its similarity only drops to ~0.85. It's still recognizable. In a graph, deleting 30% of edges destroys entire paths permanently. - Analogical Reasoning : Because it's a vector space, king - man + woman produces a vector close to queen . In a codebase, this translates to structural analogies across different modules without explicit links. - Hebbian Plasticity : GLIA isn't static. When a pattern "resonates" is retrieved , it gets stronger. Patterns that aren't used decay over time and eventually fade to zero. The memory auto-cleans itself. The Benchmarks Rigorous & Reproducible I didn't just "feel" it was better. I benchmarked GLIA v2 against a Graph Spreading Activation and BM25 the algorithm behind Elasticsearch across 3 real-world projects Python backend, ML pipeline, React frontend . I used 21 multi-hop questions per project — questions that require connecting 3 or 4 different files or concepts to answer correctly. | Metric | GLIA | Graph V1 | BM25 | |---|---|---|---| MRR Avg | 0.851 | 0.344 | 0.870 | Token Savings | 97.8% | — | — | Latency | 94ms | 2ms | <1ms | Edges in DB | 0 | 696 | 0 | The result: GLIA outperforms the graph-based approach by 2.5x in retrieval accuracy MRR . It stays within ~2% of BM25 — a 30-year-old algorithm optimized specifically for keyword matching — while providing associative capabilities that BM25 could never dream of. Against RAG with Gemini embeddings: RAG wins on precision by ~10%, but GLIA wins on cost $0 , offline capability , and plasticity learning as it goes . How to use it GLIA is a local-first tool. No API keys required for the core engine. git clone https://github.com/FelipeFariasAlfaro/glia.git cd glia && pip install -e . cd your-project python -m glia init python -m glia scan AST parsing, $0, instant python -m glia recall "session expiration bug" It exposes an MCP Model Context Protocol server , so it plugs directly into Cline, Cursor, or Claude Desktop. Your agent gets a "long-term memory" that actually understands how your project is wired. { "mcpServers": { "glia": { "command": "python", "args": "-m", "glia.mcp server" , "env": { "GLIA WORKSPACE": "/path/to/your/project" } } } } Once connected, the agent gets glia recall , glia learn , glia scan , glia forget , and glia changes . Tell it in your custom instructions to query GLIA before answering and to teach it after completing tasks — the memory grows on its own from there. What's the catch? Single-hop precision. If you ask "what does exactly this line do?", RAG with high-end embeddings will be more surgical. GLIA is a Structural Memory . It's designed to help an agent understand the relationships and history of a project, not to replace a grep tool. The Stack - Binding : Circular Convolution via FFT Fast Fourier Transform . - Encoding : Deterministic hash-projection + Synonyms + Stemming. - Storage : SQLite BLOBs No edge tables, no complex joins . - Plasticity : Hebbian reinforcement + Temporal decay. Everything is open source MIT . If you're tired of your AI agents having the memory of a goldfish, give it a try.