EverMemOS organises memory into cells and scenes and checks its own answers. recall keeps the chats and searches them.

EverMemOS, now shipping as EverOS, is an open-source memory runtime: a model splits conversations into episodes and memory cells, consolidates them into scenes and a profile, and at query time a model judges whether what it found is enough. recall indexes the chats Claude Code, Codex and Cursor already keep, with no model calls, and writes notes overnight with a model you choose.

Checked 17 Oct 2026recall 0.5.1EverMemOS on GitHub ↗

01At a glance

EverMemOS set out to be an operating system for memory: conversations cut into episodes by a model, each into cells, cells gathered into scenes, a profile on top, and a model at query time deciding whether what came back is enough. Its 1.0 rewrite, EverOS, drops four databases for markdown, SQLite and LanceDB, closer to recall in shape.

recall was built the small way from the start: the chats your coding agents already keep are the memory, indexed with no model, searched in milliseconds, distilled into notes once a night.

Table 1 · recall and EverMemOSThe short answers; the sections below take each in turn.

	recall	EverMemOS
Runs	On your machine	Self-hosted (1.0: markdown, SQLite, LanceDB), or EverOS Cloud
Licence	MIT	Apache 2.0
Model calls to store a chat	0	At ingest and at query time: boundaries, extraction, a sufficiency check
A search takes	11 ms local, 47 with embeddings	Under 500 ms p95, a vendor post's claim
Price	Free	Free in beta; $25 a month after; enterprise custom
Chat text leaves your machine	No, with a local model	Yes: to the model behind ingest and query, OpenRouter by default
Claude Code, Codex, Cursor	MCP, skill, session hook	MCP server, Claude Code plugin, Cursor

02Where both were measured

EverMemOS's paper gives 83.0% on LongMemEval with GPT-4.1-mini answering, on about 2.8k tokens a question by a column it does not define, or 6.7k for the full prompt. recall 0.4.1 scores 88.4% with Claude Haiku 5.5 answering and judging, reading ~44k tokens of whole chats. Different models and judges, so the five points are loose; recall's retrieval alone finds an answering session in the top five 98.7% of the time.

LoCoMo is where EverMemOS's claim is strongest, 93.05%, and where the doubt is: a reproduction with the documented stack scored 38.38%, and the issue closed without a cause. An audit also found 6.4% of LoCoMo's answer key wrong and a full-context GPT-4.1-mini scoring 92.62%. recall measures LoCoMo as retrieval, 92.1% with an answering session in the top five, so there is no answer figure to set beside it.

recall 0.5.1 and EverMemOSLongMemEval: answers correct and the context handed to the model, as each reports it.↑ Higher is better, except context

Fig 1

88.4%of LongMemEval questions answered correctly, Claude Haiku 5.5 reading recall's top 10 chats

+5.4points over EverMemOS, with a different answer model and judge

Answers correctLongMemEval_S

recall 0.4.188.4%
EverMemOS83.0%

Context a questiontokens handed to the answer model↓ fewer

EverMemOS · token column~2.8k
EverMemOS · full prompt~6.7k
recall 0.4.1 · top 10~44k
recall 0.4.1 · windows~17.5k

LongMemEval_S · EverMemOS's paper, GPT-4.1-mini · recall 0.4.1, all 500 questions

EverMemOS's token column is undefined in its paper; its Table 8 puts the full prompt at about 6.7k. LoCoMo is left out: EverMemOS reports 93.05% and a reproduction scored 38.38%, and recall measures retrieval there, not answers.

03Cost

EverMemOS spends model calls twice: at ingest, for boundary detection, extraction and consolidation, and at query time, for the sufficiency check, which reran retrieval on 31% of LoCoMo questions in its paper. The runtime is free to self-host with your OpenRouter key; the cloud is free in beta and $25 a month after.

recall spends no model calls to index or search. The nightly notes are its one model cost, with a model you choose: nothing with a local one, covered by a subscription, batch prices on an API key.

Table 2 · CostWhat storing and reading memory costs.

	recall	EverMemOS
Model calls to store a chat	0	At ingest and at query time: boundaries, extraction, a sufficiency check
Price	Free	Free in beta; $25 a month after; enterprise custom
To take in 300 sessions	$0, no model calls	Model calls per episode and per query
Cost of memory	One nightly run, your model	Your OpenRouter bill, or the cloud plan

04Latency

EverMemOS's paper measures no latency; a vendor post claims under 500 ms at the 95th percentile for retrieval. A query that fails the sufficiency check runs a second round, with a model call in between.

recall's search is a local index with no model in the path: 11 ms at the median on ~2,800 chats, 47 ms with the embedding model, and 69 ms at the 95th percentile over a LongMemEval history.

Table 3 · LatencyWhat an agent waits for.

	recall	EverMemOS
A search takes	11 ms local, 47 with embeddings	Under 500 ms p95, a vendor post's claim
Wait at write time	None: reads saved chats	Boundary detection and extraction by a model

05Privacy

Self-hosted EverOS keeps its files on your machine and sends conversations to the model behind ingest and query, through OpenRouter by default, a hosted broker; a local model is possible but not the path the docs take. EverOS Cloud states no data region.

recall keeps the index and the notes under your home folder, reads chats read-only, redacts secrets before indexing, sends no telemetry, and makes no network calls except to the model you chose for the nightly notes. With a local model, nothing leaves.

Table 4 · PrivacyWhere your chat text goes.

	recall	EverMemOS
Runs	On your machine	Self-hosted (1.0: markdown, SQLite, LanceDB), or EverOS Cloud
Licence	MIT	Apache 2.0
Chat text leaves your machine	No, with a local model	Yes: to the model behind ingest and query, OpenRouter by default
Compliance	Nothing to certify: no service	No region stated for the cloud
Telemetry	None	Not stated

06How memory is built

EverMemOS's memory is layered: episodes cut by topic, cells inside them of three kinds, scenes that group cells, a profile on top, and in 1.0 all of it as markdown over SQLite and LanceDB. Retrieval fuses BM25 and dense search, reranks, then asks a model whether the result will do.

recall keeps every chat whole in a full-text index, ranked five ways and fused, and keeps notes apart from it in markdown under git, consolidated rule by rule once a night. There is no model at query time; the agent that called recall is the reader, and it can search again.

Table 5 · How memory is builtWhat is kept, and in what shape.

	recall	EverMemOS
Captured by	Reading the agents' own chat files	Your code or an MCP client sending messages
Memory is	Markdown notes in git, plus an index of every chat	Memory cells in scenes, with a profile
Retrieval	Full-text index, five rankings fused; optional local embeddings	BM25 and dense, fused, reranked, then a model checks sufficiency
You can edit it	Yes, and the edit wins	In the files, since 1.0

07Which to pick

Pick EverMemOS if

You want a memory runtime with typed cells, scenes and a profile, and a model checking retrievals at query time.
You are building on its MCP server or plugins and the per-message model calls through OpenRouter suit you.
You want a cheap hosted option once the beta ends.

Pick recall if

You want your coding agents to remember with no model calls to index or search, and no broker in the path.
You want figures you can reproduce yourself rather than a disputed leaderboard.
You want memory as readable markdown in git, from the chats you already have.

08Questions

Is EverMemOS the same as EverOS?

EverOS is EverMemOS's 1.0 rewrite, released in June 2026: the same project and repository, now a local-first Python library on markdown, SQLite and LanceDB. The paper and the benchmark figures describe the earlier system.

Is the LoCoMo score real?

EverMemOS reports 93.05%. One reproduction with the documented stack scored 38.38% on the full set, others could not settle it, and the issue closed without a cause. An independent audit also found 6.4% of LoCoMo's answers wrong and a full-context GPT-4.1-mini scoring 92.62%, so the benchmark separates little above 90. recall reports LoCoMo as retrieval, 92.1% with an answering session in the top five, reproducible with recall eval locomo.

Which uses fewer tokens?

EverMemOS, by its own figures: 2.8k tokens a question by an undefined column, or 6.7k for the full prompt, against recall's 44k reading ten whole chats or 17.5k reading matched windows. recall spends those tokens at read time instead of model calls at write time; on coding issues an agent takes in about 7.8k a question.

Do both support Claude Code and Cursor?

Yes. EverOS has an MCP server, a Claude Code plugin and others; recall has an MCP server, a skill and session-start hooks, and reads the transcripts the agents already write.

SourcesEverMemOS, now EverOS, on GitHub · github.com/EverMind-AI/EverMemOSEverMemOS: a self-organizing memory operating system, 2026 · arxiv.org/abs/2601.02163EverOS Cloud · docs.evermind.ai/cloud/overviewEverMind: pricing and latency claims · evermind.ai/blogs/evermind-vs-memThe LoCoMo reproduction, issue 73 · github.com/EverMind-AI/EverMemOS/issues/73LoCoMo audit · github.com/dial481/locomo-auditrecall's chat benchmark runs, with the other systems' figures · github.com/alirng/recall/blob/main/docs/research/retrieval-benchmarks.md