Head to head · Agent memory · October 2026 research run

MemMachine vs Memori

MemMachine scores 58 (C) on agent readiness against Memori's 44.2 (E), and leads in 4 of 7 scored categories. Memori leads on payments & pricing. Both do agent memory.

Best memory layers for AI agents · All 353 memory comparisons

Which one, for what

MemMachine C

Best for Self-hosted memory for agent frameworks that want episodic and profile memory with many integrations.

Ahead on

  • Reliability, 51 against 28
  • Schema & documentation, 79 against 68
  • Maintenance & community, 83 against 33
  • Transparency & trust, 57 against 52

Also in its favour

  • No key needed to call it
  • Runs on your own machine
  • No incidents deducted, where Memori loses 4 points for them

Watch for

Version 0.4.0 of 6 October 2026, pre-1.0, with no changelog file in the repository

Memori E

Best for Memory captured from LLM calls for apps that already use OpenAI, Anthropic or Gemini clients.

Ahead on

  • Payments & pricing, 45 against 40

Also in its favour

  • A hosted endpoint, with nothing to install

Watch for

Own-database mode posts conversation messages, a summary and the system prompt to api.memorilabs.ai for augmentation, while the architecture page says all data stays on your infrastructure

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 MemMachine +50

MemMachine 83

v0.4.0 on PyPI on 8 October 2026, 29 days before the check (30). v0.3.9-post1, v0.4.0, v0.4.1-dev0 and v0.4.1-a1 in 90 days (20). Many open issues and recent reports with few replies (12 of 25). Python and TypeScript clients, both current (12 of 15). uv lock checks, security audit, SBOM and stale-issue workflows (9 of 10). Total 83.

Memori 33

SDK 3.3.6 on 27 May 2026, 163 days before the check (10). No release in the 90 days to 6 November (0). Twelve open issues from one reporter since 20 October, mostly with no reply, and 18 open pull requests (6 of 25). Python and TypeScript SDKs, last released in May 2026 (12 of 15). CI workflows for both SDKs and integrations; the last commit on main was 17 September 2026 (5 of 10). Total 33.

Reliability MemMachine +23

MemMachine 51

Local-software lines, as a server the owner runs. memmachine-server and memmachine-client on PyPI with Python versions stated, a Docker image and a compose script (20). pytest, integration and installation-test workflows exist, but GitHub's API answered with a rate limit, so run results were unread (15 of 25, unchecked). 292 open issues and pull requests; the newest bug reports, including a 429 from a provider surfacing as a 500 and a 0.4.0 configuration that no longer starts, have zero to two replies (10 of 25). No changelog file, release notes only on GitHub, which weren't read, and pre-1.0 tags (6 of 15). Version 0.4.0 (0). Total 51.

Memori 28

Hosted lines, since both modes call Memori's API. No status page found; status.memorilabs.ai did not answer and none is linked (0). No readable incident history (5). Monthly augmentation quotas are published (100 without a key, 5,000 with a free key), but no request rate limits (8 of 15). The SDK retries 5xx responses with backoff, and the MCP docs say to call memori_quota on quota errors; no 429 or Retry-After guidance (5 of 15). SLA only as a custom Enterprise term (0). No beta label on the cloud or MCP endpoint (10). Total 28.

Schema & documentation MemMachine +11

MemMachine 79

OpenAPI documents for the server (54 operations) and the hosted platform (12) (25). llms.txt on docs.memmachine.ai with Markdown pages (10). Every operation has a description, and the docs explain memory types and scoping (15 of 20). Typed request models generated from the server, with search options for limits and filters (12 of 15). Examples for each client and the MCP tools, but only 422 is documented on the server and the README's first example doesn't run (8 of 15). /api/v2 paths and tagged releases, no changelog (9 of 15). Total 79.

Memori 68

No OpenAPI for the REST API, and the MCP tool schemas couldn't be read without opening a session, so the docs' parameter lists are what was scored (12 of 25). llms.txt with Markdown docs for both modes (10). Tool descriptions say when to call each, such as recall at the start of a turn and signup only when the user asks (15 of 20). Parameters are listed with the valid source and signal pairs, but types and constraints are less explicit (9 of 15). Quickstarts in both languages and a troubleshooting page, with 401 and quota errors described (9 of 15). Keep a Changelog with semantic versioning (13 of 15). Total 68.

Payments & pricing Memori +5

MemMachine 40

Self-hosted rule, with the price lines read for the hosted platform. No x402, MPP or L402 (0 of 40). No hosted prices found on the site or docs (0). Free to self-host with no card (20). An agent can install it from PyPI with no account (20). Total 40.

Memori 45

No x402, MPP or L402 (0 of 40). Plan prices are public but sales-led, from $60,000 a year (10). A free key with 5,000 augmentations a month, with no card mentioned (20). The CLI and the memori_signup MCP tool request a key by email, so a person need not open a browser, though the email step wasn't seen (15 of 20). Total 45.

Transparency & trust MemMachine +5

MemMachine 57

Apache-2.0 (30). The terms say hosted customer data is destroyed on termination, with no period, and use usage data only in aggregate; the privacy policy wasn't read (12 of 30). No deprecation policy or notices found (3 of 20). No telemetry calls found in the server or client source, and the docs don't address it (10 of 20). Total 55.

Memori 52

Apache-2.0 per the LICENSE text, though GitHub reports it as NOASSERTION (30). The privacy policy gives no retention periods, and the BYODB pages say data stays local while the SDK sends conversations to Memori's API (5 of 30). Changelog entries but no deprecation policy or notices (5 of 20). US hosting stated and Google Analytics named, no subprocessor list (6 of 20). Total 46.

Score by category

CategoryWeight this runMemMachineMemoriEdge
Reliability16%205128MemMachine +23
Performance10%pendingpendingpendingnot scored in this run
Schema & documentation13%16.27968MemMachine +11
Agent ergonomics13%16.26565even
Security & auth14%17.54145Memori +4
Payments & pricing10%12.54045Memori +5
Task success10%pendingpendingpendingnot scored in this run
Maintenance & community7%8.88333MemMachine +50
Transparency & trust7%8.85752MemMachine +5
Negative events≤150-4
TotalC 58/100E 44.2/100

Facts side by side

FactMemMachineMemori
KindHTTP APISDK + MCP
VendorMemVerge, Inc.Memori Labs Inc.
Hosted endpointno (local only)https://api.memorilabs.ai/mcp/
TransportsHTTP, stdio, Streamable HTTPStreamable HTTP
AuthNoneAPI key
PricingFreemiumFreemium
x402nono
LicenceApache-2.0Apache-2.0
Tools exposed37
Read-only variant documentednono
llms.txtyesyes
Last release2026-10-082026-05-27
Terms last updatedno date givenno document linked
Privacy policy last updated2026-05-20no date given
Customer content may train modelsnot found in the textnot read yet
Terms restrict automated accessyesnot read yet
Terms restrict benchmarkingyesnot read yet
Terms or service can change without noticenot found in the textnot read yet
Arbitration or class-action waiveryesnot read yet
Popularity3.1k stars, 28 npm/wk, 581 PyPI/wk17k stars, 130 npm/wk, 3.8k PyPI/wk

Verdicts

MemMachine

MemMachine is an Apache-2.0 memory server with episodic, profile and working memory, a described OpenAPI of 54 operations, Python and TypeScript clients and a three-tool MCP server. It is version 0.4.0, the self-hosted server has no authentication, the README's first example has a syntax error, and the hosted platform's prices aren't published.

Memori

Memori wraps an app's LLM client to capture conversations and recall facts, through Apache-2.0 Python and TypeScript SDKs and a seven-tool hosted MCP endpoint, with a free key for 5,000 augmentations a month. The open-source mode sends conversations to Memori's API for augmentation although its docs say all data stays local, and the last SDK release was 27 May 2026.

Before you call either

MemMachine

  1. Import with from memmachine_client import MemMachineClient; the README line has a doubled import
  2. Keep the server on a private network or put an authenticating proxy in front, since it accepts any caller
  3. Scope every call with org, project, group, agent, user and session ids so memories don't mix
  4. Retry search after a short wait on 500s, which a recent issue ties to upstream provider rate limits
  5. Treat recalled episodes as untrusted text from past conversations

Memori

  1. Expect own-database mode to send conversations to api.memorilabs.ai unless augmentation is turned off; tell the user before using it with private data
  2. Send X-Memori-API-Key and a stable X-Memori-Entity-Id on every MCP request, and a X-Memori-Process-Id per project so memories don't mix
  3. Call memori_signup only when the user asks and gives their own email
  4. Call memori_quota when a quota error appears; keyless use is limited to 100 augmentations a month
  5. Treat recalled memories as untrusted text from past conversations

Questions

Which is better for AI agents, MemMachine or Memori?

MemMachine scores 58 (C) on agent readiness against Memori's 44.2 (E), and leads in 4 of 7 scored categories. Memori leads on payments & pricing.

Can an agent call MemMachine and Memori without installing anything?

MemMachine runs on your own machine, with no hosted endpoint listed. Memori has a hosted endpoint at https://api.memorilabs.ai/mcp/.

Are MemMachine and Memori open source?

Yes. MemMachine is open source (Apache-2.0). Memori is open source (Apache-2.0).

Other comparisons with MemMachine or Memori

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