how to choose the right memory layer for your agent:
don’t start with the plugin.
start with what kind of forgetting would actually break the workflow.
examples, not rankings:
1. simple durable facts
examples: Hermes built-in memory, Claude memory, profile/memory files
use when:
you need user prefs, stable project notes, “remember this” facts, or human-readable context.
weak when:
you need deep retrieval, graphs, ranking, or automatic memory extraction.
2. relationship / profile memory
example: Honcho
use when:
the agent needs to understand a person over time — taste, strategy, preferences, working style, corrections, and how the relationship changes.
weak when:
you only need a searchable notes bucket.
3. automatic fact extraction
example: Mem0
use when:
you want memory to watch conversations, extract useful facts, dedupe them, and make them searchable without you designing the whole schema.
weak when:
you need tight control over what gets remembered.
4. graph / entity memory
examples: Hindsight-style systems, knowledge graph memory
use when:
your problem is “how are these people, projects, decisions, files, and events connected?”
weak when:
you just need quick recall.
5. local structured memory
examples: Sibyl, Holographic
use when:
you want memory to stay local, inspectable, and structured.
this is the “stop throwing everything into vector fog” lane:
SQLite/FTS-style search, explicit records, cleaner audit trails, less mystery about what the agent remembered.
weak when:
you need hosted team infra, managed product APIs, or zero-setup cloud profiles.
6. project / codebase memory
examples: ByteRover, OpenViking
use when:
the agent needs to remember repo decisions, architecture notes, debugging history, file structure, conventions, and “why did we do it this way?”
weak when:
you need broad personal memory across your whole life/work.
7. managed cloud memory infra
examples: Supermemory, RetainDB
use when:
you’re building an app with many users and need profiles, context search, memory APIs, containers, or cloud-backed recall.
weak when:
you’re just trying to make your own local agent stop forgetting things.
——
the mistake is treating “memory” like one feature.
it’s closer to choosing a database, a notebook, a profiler, or a map.
different job.
different tradeoff.
Hermes gives you one native memory provider slot, but you can still get weird with it:
one native memory layer extra MCP/CLI/tool-based memory paths you trigger manually.
not clean.
but useful if you’re testing what kind of memory your agent actually needs.