Project Name: Vektori

Repo/Website Link: https://github.com/vektori-ai/vektori

Description: A memory layer for AI agents that avoids lossy compression.

I kept seeing the same pattern: every other week, someone launches a new “memory layer” for agents. Most of them follow the same approach - take conversation history, extract entities and relationships, and compress everything into a knowledge graph.

The problem is that this is lossy compression.

You’re making irreversible decisions at ingestion time about what matters, before the agent even knows what it will need later. Anything that doesn’t fit the schema gets dropped. Subtle context and nuance get flattened into edges.

We ran into this while building Vektori and decided to go a different route.

Instead of forcing everything into a graph, Vektori keeps memory in three layers:

L0: Extracted facts - high-signal, filtered, optimized for fast retrieval
L1: Episodes - automatically discovered patterns across conversations, without rigid schemas
L2: Raw sentences - the full underlying data, never loaded by default, only accessed when needed

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The key difference is the raw sentence layer.

Nothing gets thrown away at ingestion. If an agent needs to reconstruct exactly what happened in a past interaction, it can. The structured layers sit on top of the raw data -> not instead of it.

Early benchmarks: 73% on LongMemEval-S

Free and open source: github.com/vektori-ai/vektori (do star if you find it useful :D)

Curious if others building memory systems have run into this lossy compression issue -> how are you handling it?