OpenVikingVolcengine's context database: memories, resources and skills as one `viking://` filesystem agents ls, tree and grep — L0/L1/L2 tiers, traceable retrieval, sessions distilled into memory.
Why switchBoth unify memory, files and skills into one human-readable store rather than an opaque index. OpenViking runs a `viking://` filesystem with L0/L1/L2 tiers and traceable retrieval; EverOS keeps plain Markdown as truth with local indexes and offline reflection. Filesystem metaphor versus editable files.
Full comparison → AcontextSkill memory layer for agents: auto-captures learnings from runs into plain Markdown skill files you can read, edit, git and share across frameworks — memory without an opaque store.
Why switchShared bet on Markdown-as-memory. acontext stays minimal — learnings become skill files you git and share; EverOS is a runtime with episodes, profiles, a knowledge wiki, vector indexes and background consolidation. acontext when git is enough, EverOS when you want recall infrastructure.
Full comparison → MemMachineLong-term memory layer for AI agents — episodic (graph), profile (SQL) and working memory behind Python/TS SDKs, REST and MCP; ships LangChain, LangGraph, CrewAI and LlamaIndex integrations.
Why switchSame job, opposite substrate: MemMachine puts episodic memory in a graph and profiles in SQL behind SDKs and REST; EverOS puts everything in Markdown you can edit by hand. Choose by whether memory should be a service or a folder.
Full comparison → MemoriaRust memory layer for AI agents with Git-style version control — snapshot, branch, merge and rollback over MatrixOne's copy-on-write engine, plus hybrid vector + full-text retrieval.
Why switcheveros keeps memory as canonical Markdown you can open and edit; memoria treats it as a versioned database â snapshot, branch, merge, rollback. Readable artifacts vs auditable history.
Full comparison → brain.mdFile-based durable memory for coding agents: brain-setup scaffolds a BRAIN.md protocol + brain/ directory of decisions, requirements and constraints — plain Markdown in your repo, written via CLI.
Why switchBoth make a repo-visible Markdown protocol the memory. brain.md is a lightweight convention plus CLI for decisions, requirements and constraints; EverOS adds a server, vector recall and self-evolving skills on top of the same instinct.
Full comparison → claude-memCross-harness session memory: hooks capture what the agent does, an LLM compresses it into observations, and the next session gets the relevant ones back via progressive-disclosure MCP tools.
Why switchEveros keeps memory as canonical Markdown you can read, edit and git across any agent; claude-mem keeps it in SQLite + Chroma behind a worker API and web viewer. Portability and auditability versus search quality and automation.
Full comparison → hindsightAgent memory that learns, not just recalls: retain/recall/reflect API over Postgres, SOTA on LongMemEval. Self-host via Docker with UI; Python/TS clients, any LLM provider.
Why switchBoth are the one memory layer you point every agent at; everos keeps canonical Markdown indexed locally, hindsight is a learning retain/recall/reflect service on Postgres.
Full comparison →