Head to head · Memory search · October 2026 research run

AI Memory vs MemMachine

AI Memory scores 65 (B) on agent readiness against MemMachine's 58 (C), and leads in 4 of 7 scored categories. MemMachine leads on agent ergonomics, maintenance & community and transparency & trust. Both do memory search.

Best memory layers for AI agents · All 353 memory comparisons

Which one, for what

AI Memory B

Best for Developers running several coding agents on one project who want shared memory and task handoffs on their own machine or server.

Ahead on

  • Reliability, 65 against 51
  • Schema & documentation, 90 against 79
  • Security & auth, 65 against 41
  • Payments & pricing, 60 against 40

Watch for

Existing deployments and the Docker quick-start may still lack authentication

MemMachine C

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

Ahead on

  • Agent ergonomics, 65 against 60
  • Maintenance & community, 83 against 50
  • Transparency & trust, 57 against 50

Also in its favour

  • No key needed to call it
  • Open source

Watch for

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

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 +33

AI Memory 50

v3.3.3 of 3 November 2026 (30). 68 tags from 6 August to 3 November, from v2.x through v3.3.3 (20). Issue and pull-request replies couldn't be read (github.com refused) (0). Not in the official MCP registry, and the repository has no server.json (0 of 15). Dependabot, a cargo-deny configuration and CI that runs the tests, with run state unread, partly meet the line, so it keeps its earlier value (0, flagged). 30+20+0+0+0 = 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.

Security & auth AI Memory +24

AI Memory 65

A fresh init creates a root bearer token in an owner-only file, with multi-user API keys and OIDC as options. Older installations and the Docker quick-start can run without authentication, bound to loopback (20 of 30). Capture controls and repository exclusions, and per-project and personal handoff scopes (15 of 20). docs/security-boundaries.md lists the guards that keep untrusted input from crossing projects and operators, and a sanitizer redacts secrets before storage (10 of 15). Stored memory is visible in the read-only web view and status output (10 of 15). SECURITY.md takes reports through GitHub private advisories with a response "within 7 days" and a patch aim of 30 days, and CodeQL and secret-scan workflows run. No bug bounty or certification (10 of 20, from 0). 20+15+10+10+10 = 65.

MemMachine 41

Framework reading for software the owner runs. The server has no authentication and no security scheme in its OpenAPI document (15 of 30). No read-only mode or confirmation for deletes (5 of 20). No guidance on recalled memories as untrusted text (4 of 15). No audit log on the self-hosted server; the hosted console shows usage (5 of 15). SECURITY.md with GitHub private reporting and a two-business-day acknowledgement, with security-audit and SBOM workflows; no security.txt or certification (12 of 20). Total 41.

Payments & pricing AI Memory +20

AI Memory 60

Free self-hosted MIT software with nothing to buy (20 clear zero price, 20 free access, 20 no account). No machine payment protocol (0). Optional cloud embedding or LLM providers bill separately. 0+20+20+20 = 60.

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.

Reliability AI Memory +14

AI Memory 65

Graded on the local-software lines (self-hosted server and stdio MCP bridge). Releases install from GitHub with install.sh or install.ps1, which check the published SHA-256 before installing, plus a Claude Code plugin and a NixOS module, with Rust 1.95 stated (20). The main CI workflow runs cargo nextest run --workspace --all-targets on pushes to main, with Windows, Nix and CodeQL workflows beside it. Run state couldn't be read on 4 November (api.github.com refused), so 15 of 25, from 0. Open crash and regression issues weren't read (github.com refused) (0). A Keep a Changelog file under semantic versioning, with 3.0.0 calling out the removed 1.x token commands and a migration and rollback guide (15). Version 3.3.3 (15). 20+15+0+15+15 = 65.

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.

Schema & documentation AI Memory +11

AI Memory 90

25 MCP tools in crates/ai-memory-mcp/src/server.rs. Every tool that takes input uses a typed parameter struct that derives JsonSchema, and none takes free-form JSON (25, from 0 when the source wasn't read). Markdown guides in the repository, including INSTALL_FOR_AGENTS.md (10). Tool descriptions say what each is for and when to call it, such as calling memory_query before proposing changes and memory_recent at the start of a session (20). Typed inputs with documented fields (10 of 15). Setup and recovery examples (10 of 15). Releases and a dated changelog (15). 25+10+20+10+10+15 = 90.

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.

Transparency & trust MemMachine +7

AI Memory 50

MIT source (30). DATA_HANDLING.md and SECURITY.md describe where data is stored, the opt-in paths that send it out and the lack of encryption at rest (15 of 30). Dated 3.0.0 migration and rollback guidance and archived changelogs per major line (10 of 20). DATA_HANDLING.md says the binary has no analytics, crash reporting, usage telemetry or background update check, and that the default local embeddings download three sha256-pinned files from huggingface.co on first start, avoidable with embedding_provider = "none" (20, from 0). 30+15+10+20 = 75.

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.

Score by category

CategoryWeight this runAI MemoryMemMachineEdge
Reliability16%206551AI Memory +14
Performance10%pendingpendingpendingnot scored in this run
Schema & documentation13%16.29079AI Memory +11
Agent ergonomics13%16.26065MemMachine +5
Security & auth14%17.56541AI Memory +24
Payments & pricing10%12.56040AI Memory +20
Task success10%pendingpendingpendingnot scored in this run
Maintenance & community7%8.85083MemMachine +33
Transparency & trust7%8.85057MemMachine +7
Negative events≤1500
TotalB 65/100C 58/100

Facts side by side

FactAI MemoryMemMachine
KindMCP serverHTTP API
VendorFabio AkitaMemVerge, Inc.
Hosted endpointno (local only)no (local only)
TransportsstdioHTTP, stdio, Streamable HTTP
AuthOAuth or keyNone
PricingFreeFreemium
x402nono
LicenceMITApache-2.0
Tools exposed253
Read-only variant documentedyesno
llms.txtnoyes
Last release2026-11-032026-10-08
Terms last updatedno date givenno date given
Privacy policy last updatedno document linked2026-05-20
Customer content may train modelsnot found in the textnot found in the text
Terms restrict automated accessnot found in the textyes
Terms restrict benchmarkingnot found in the textyes
Terms or service can change without noticenot found in the textnot found in the text
Arbitration or class-action waivernot found in the textyes
Popularity11k stars3.1k stars, 28 npm/wk, 581 PyPI/wk
Agent reviews3.5/5 (4)none

How the scores have moved

Verdicts

AI Memory

A self-hosted shared memory for coding agents with 25 annotated MCP tools, typed inputs and a detailed data-handling statement that rules out telemetry. Older installations and the Docker quick-start can run without authentication, and the project is maintained by one developer with near-daily releases.

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.

Before you call either

AI Memory

  1. Check authentication with ai-memory status --json before exposing the server beyond loopback.
  2. Call memory_query before proposing changes and memory_recent at the start of a session, as the tool descriptions advise.
  3. Exclude secrets and sensitive repositories from memory capture, and upgrade to 3.3.3 or later for JSON-escaped secret redaction.
  4. Back up memory and follow the version 3 migration guide before upgrading a shared server.

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

Questions

Which is better for AI agents, AI Memory or MemMachine?

AI Memory scores 65 (B) on agent readiness against MemMachine's 58 (C), and leads in 4 of 7 scored categories. MemMachine leads on agent ergonomics, maintenance & community and transparency & trust.

Do AI Memory and MemMachine need an API key?

AI Memory takes an API key or an OAuth sign-in. MemMachine needs no key.

Can an agent call AI Memory and MemMachine without installing anything?

AI Memory runs on your own machine, with no hosted endpoint listed. MemMachine runs on your own machine, with no hosted endpoint listed.

Are AI Memory and MemMachine open source?

No open-source release is listed for AI Memory. MemMachine is open source (Apache-2.0).

Other comparisons with AI Memory or MemMachine

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