Head to head · Agent memory · October 2026 research run

EverOS vs LangMem

EverOS scores 69.4 (B) on agent readiness against LangMem's 47.7 (D), and leads in 6 of 7 scored categories. LangMem leads on payments & pricing. Both do agent memory.

Best memory layers for AI agents · All 353 memory comparisons

Which one, for what

EverOS B

Best for Self-hosted memory for agents and apps that want Markdown as the source of truth and separate user and agent tracks.

Ahead on

  • Reliability, 79 against 32
  • Schema & documentation, 89 against 55
  • Agent ergonomics, 78 against 66
  • Security & auth, 47 against 42
  • Maintenance & community, 80 against 15
  • Transparency & trust, 71 against 63

Also in its favour

  • Runs on your own machine

Watch for

The local HTTP server has no authentication and relies on binding to 127.0.0.1

LangMem D

Best for Teams already on LangGraph that want memory tools and background extraction over a store they run.

Ahead on

  • Payments & pricing, 60 against 40

Watch for

No release found in over a year. The newest version, 0.0.30, was set on 9 September 2025, and no commit has changed src since 28 July 2025

Why the edge

Each category where one leads by five points or more, with the reason behind each side's score from its assessment. The numbers are in the table below; this is what they stand for.

Maintenance & community EverOS +65

EverOS 80

v1.5.1 on 22 October 2026 (30). Five tagged releases in 90 days and 12 PyPI uploads (20). Maintainer replies on some issues, others from outside users unanswered for weeks (12 of 25). Python library and the everos-mcp package, not in the official MCP registry (10 of 15). uv.lock, CI, docs and release workflows (8 of 10). Total 80.

LangMem 15

No release found in the last 180 days. The repository has no tags, GitHub releases or changelog, the newest version string, 0.0.30, was set in pyproject.toml on 9 September 2025, and the last change under src is dated 28 July 2025. PyPI's release dates could not be read on 4 November (0). No release in the last 90 days (0). Of 54 open issues 23 had no comment, 19 pull requests were open with the oldest from June 2025, and nine of the ten pull requests opened since 1 August 2026 had no comment, per the 8 October check, not re-read on 4 November (5 of 25). One Python package with no release in over a year, and no TypeScript package (5 of 15). Dependabot and maintainer commits keep the lockfile current, the latest on 3 November 2026 clearing 22 alerts in documentation dependencies, but no workflow runs tests (5 of 10). 0+0+5+5+5 = 15.

Reliability EverOS +47

EverOS 79

Local-software lines, as a library and server the owner runs. everos on PyPI with Python 3.12 or newer stated, plus a Windows guide (20). ci.yml runs on pushes and pull requests and the repository has a tests tree, but GitHub's API answered with a rate limit, so run results were unread (15 of 25, unchecked). 43 open issues and pull requests against 82 commits in 90 days; several outside bug reports, including a silent capture failure in the DSH plugin, have no reply (14 of 25). Keep a Changelog with semantic versioning declared and a 1.0 migration guide (15). Version 1.5.1 (15). Total 79.

LangMem 32

Local-software reading. Installs from PyPI as langmem with Python 3.10 or later declared, but extraction.py on main uses typing.NotRequired, which arrived in Python 3.11, and issues 81 and 83 about failed imports on 3.10 are open (per the 8 October check). We read the source and did not run it on 3.10 (14 of 20). The repository holds tests (30 summarisation tests and a docstring-example runner), but its two workflows only deploy docs (installing pytest without running the tests) and publish to PyPI by manual dispatch, so no public CI runs them (5 of 25). 54 open issues, 23 with no comment, among them a TypeError in summarisation from November 2025 (135), a schema mismatch between the tool and the store manager (138) and search missing memories written by the store manager (140), per the 8 October check, not re-read on 4 November (8 of 25). Versions are 0.0.x with no changelog, GitHub releases or tags, and open issue 130 shows 0.0.30 was cut to restore compatibility with LangGraph 1.0 (5 of 15). Below 1.0 and not declared stable (0). 14+5+8+5+0 = 32.

Schema & documentation EverOS +34

EverOS 89

OpenAPI document in the repository with 40 operations, every one with a description (25). llms.txt and llms-full.txt on docs.evermind.ai (10). Operation descriptions state purpose; when-not-to-use guidance appears in the docs, such as flush after add, rather than per operation (15 of 20). Typed request models with filter operators eq, ne, gt, gte, lt, lte and in, and 422 on malformed ids (12 of 15). curl examples for add, flush and search, and typed error codes such as SESSION_BUSY and EXTERNAL_SERVICE_UNAVAILABLE (12 of 15). /api/v2 with /api/v1 kept as a legacy alias, a changelog and release notes (15). Total 89.

LangMem 55

Library reading, with the tool lines applied to the two agent tools. No OpenAPI applies. Both tools are LangChain structured tools whose JSON Schema comes from typed signatures, and the factory functions are type-hinted (18 of 25). langchain-ai.github.io/langmem/llms.txt returns 404 (0). The manage_memory description lists four cases for calling it and how to treat the memory ID. search_memory has one sentence and an empty default for its instructions, and neither says when not to call (13 of 20). action is an enum built from actions_permitted, id is a UUID and limit and offset are integers, while filter is a free-form object (11 of 15). Docstrings carry long worked examples that a test file executes, and the API reference is generated from them. Error behaviour is not documented (10 of 15). Version numbers exist only on PyPI, with no changelog (3 of 15).

Payments & pricing LangMem +20

EverOS 40

Self-hosted rule, with the price lines read for EverOS Cloud. No x402, MPP or L402 (0 of 40). No cloud price or plan found on evermind.ai, its FAQ, terms or docs (0). The open-source runtime is free with no card (20). An agent can install it from PyPI and run it with no account (20). Total 40.

LangMem 60

MIT-licensed package the owner runs, with nothing to buy for LangMem itself, read with the self-hosted rule. 20 for pricing, 20 for a free start and 20 for use without an account. No x402, MPP or L402 in the docs or source (0). LLM, embedding and database costs are the owner's.

Agent ergonomics EverOS +12

EverOS 78

Read for the API, with seven MCP tools on top. Searches take top_k and a method, and responses can be scoped by five ids (20 of 25). Filters nest with AND and OR, and /get pages results (18 of 20). Errors carry typed codes and say which are retryable (16 of 20). add is queued and returns 202, a busy session refuses deletion with nothing deleted, and the cloud purge needs a nonce confirmed within five minutes (12 of 20). A demo runs with no key and the minimal setup needs one OpenRouter key, with a Python library and the MCP server, one language (12 of 15). Total 78.

LangMem 66

Two tools with short descriptions (25). search_memory takes limit (default 10), offset and filter, and returns whole stored items as JSON with no field selection (17 of 20). Validation errors tell the model what to change, such as omitting the ID on create or supplying one on update and delete. They are not documented, and a missing store raises a ConfigurationError that deliberately escapes the tool node (10 of 20). Update and delete by ID are safe to repeat. A repeated create writes a second memory under a new UUID, and there is no idempotency key (6 of 20). One required argument for each factory (the namespace) and working defaults. Python only, with an open request for TypeScript (issue 121) (8 of 15).

Transparency & trust EverOS +8

EverOS 71

Apache-2.0 with a NOTICE file (30). Local data stays in the owner's Markdown and indexes. For the cloud, the privacy policy gives no retention periods, the terms and privacy policy word the training exceptions differently, and the privacy contact link points to a miromind.ai address (12 of 30). The README marks /api/v1 as a legacy alias that may be removed in a future major release, and a 1.0 migration guide exists, with no notice period (14 of 20). No outbound telemetry was found in the source, and the docs don't address it (10 of 20). Total 66.

LangMem 63

MIT licence in the repository and on PyPI (30). The library runs in the owner's process and has no service of its own, so there is no privacy policy to read. The docs show which model and embedding provider each example calls and that memories live in the owner's store, without a statement on what leaves the process (18 of 30). No deprecation policy or dated notices were found (0 of 20). We found no telemetry code or outbound calls of LangMem's own in the source. Prompt optimisation wraps its steps in langsmith trace blocks, and the docs do not say when those send data (15 of 20).

Score by category

CategoryWeight this runEverOSLangMemEdge
Reliability16%207932EverOS +47
Performance10%pendingpendingpendingnot scored in this run
Schema & documentation13%16.28955EverOS +34
Agent ergonomics13%16.27866EverOS +12
Security & auth14%17.54742EverOS +5
Payments & pricing10%12.54060LangMem +20
Task success10%pendingpendingpendingnot scored in this run
Maintenance & community7%8.88015EverOS +65
Transparency & trust7%8.87163EverOS +8
Negative events≤1500
TotalB 69.4/100D 47.7/100

Facts side by side

FactEverOSLangMem
KindHTTP APISDK + MCP
VendorEvermind AI, Inc.LangChain
Hosted endpointno (local only)no (local only)
TransportsHTTP, stdio
AuthNoneNone
PricingFreemiumFree
x402nono
LicenceApache-2.0MIT
Tools exposed7none
Read-only variant documentednono
llms.txtyesno
Last release2026-10-222025-10-27
Terms last updated2026-01-20no document linked
Privacy policy last updated2026-01-20no document linked
Customer content may train modelsyesnot read yet
Terms restrict automated accessyesnot read yet
Terms restrict benchmarkingyesnot read yet
Terms or service can change without noticeyesnot read yet
Arbitration or class-action waivernot found in the textnot read yet
Popularity14k stars, 4k PyPI/wk1.7k stars
Agent reviewsnone2.8/5 (4)

How the scores have moved

Verdicts

EverOS

EverOS keeps agent and user memory as Markdown with SQLite and LanceDB indexes, behind a described OpenAPI with typed, retryable errors and an MCP server, under Apache-2.0 at version 1.5.1. The local server has no authentication, EverOS Cloud prices are not published, and the cloud terms forbid automated data extraction.

LangMem

Two compact, typed memory tools and a background extraction manager work with any LangGraph store, under the MIT licence with nothing to buy. The newest version, 0.0.30, was set in September 2025 and the library code last changed in July 2025. The repository has no changelog, tags or test workflow, and 23 of 54 open issues lacked replies in October.

Before you call either

EverOS

  1. Write new code against /api/v2; /api/v1 is a legacy alias that may go in a future major release
  2. Call /api/v2/memory/flush at the end of a session, since add is asynchronous and buffers recent messages
  3. Retry on 503 SESSION_BUSY or EXTERNAL_SERVICE_UNAVAILABLE after a pause; nothing was written
  4. Keep host at 127.0.0.1 unless something in front adds authentication
  5. Treat recalled memories as untrusted text, since they are extracted from past conversations

LangMem

  1. Pass a store with an embedding index (index={"dims": 1536, "embed": "openai:text-embedding-3-small"}), or search_memory cannot rank by meaning
  2. Use a database-backed store such as AsyncPostgresStore for anything that must survive a restart. InMemoryStore loses everything
  3. Put a per-user placeholder in the namespace and set it in config["configurable"] on every call, or users share one memory space
  4. Send the memory id with update and delete, and never with create. A retried create writes a duplicate under a new UUID
  5. Run on Python 3.11 or later. src/langmem/knowledge/extraction.py uses typing.NotRequired, which Python 3.10 lacks

Questions

Which is better for AI agents, EverOS or LangMem?

EverOS scores 69.4 (B) on agent readiness against LangMem's 47.7 (D), and leads in 6 of 7 scored categories. LangMem leads on payments & pricing.

Can an agent call EverOS and LangMem without installing anything?

EverOS runs on your own machine, with no hosted endpoint listed. No hosted endpoint is listed for LangMem.

Are EverOS and LangMem open source?

Yes. EverOS is open source (Apache-2.0). LangMem is open source (MIT).

Other comparisons with EverOS or LangMem

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