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
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
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
| Category | Weight this run | AI Memory | MemMachine | Edge |
|---|---|---|---|---|
| Reliability | 16%20 | 65 | 51 | AI Memory +14 |
| Performance | 10%pending | pending | pending | not scored in this run |
| Schema & documentation | 13%16.2 | 90 | 79 | AI Memory +11 |
| Agent ergonomics | 13%16.2 | 60 | 65 | MemMachine +5 |
| Security & auth | 14%17.5 | 65 | 41 | AI Memory +24 |
| Payments & pricing | 10%12.5 | 60 | 40 | AI Memory +20 |
| Task success | 10%pending | pending | pending | not scored in this run |
| Maintenance & community | 7%8.8 | 50 | 83 | MemMachine +33 |
| Transparency & trust | 7%8.8 | 50 | 57 | MemMachine +7 |
| Negative events | ≤15 | 0 | 0 | |
| Total | B 65/100 | C 58/100 |
Facts side by side
| Fact | AI Memory | MemMachine |
|---|---|---|
| Kind | MCP server | HTTP API |
| Vendor | Fabio Akita | MemVerge, Inc. |
| Hosted endpoint | no (local only) | no (local only) |
| Transports | stdio | HTTP, stdio, Streamable HTTP |
| Auth | OAuth or key | None |
| Pricing | Free | Freemium |
| x402 | no | no |
| Licence | MIT | Apache-2.0 |
| Tools exposed | 25 | 3 |
| Read-only variant documented | yes | no |
| llms.txt | no | yes |
| Last release | 2026-11-03 | 2026-10-08 |
| Terms last updated | no date given | no date given |
| Privacy policy last updated | no document linked | 2026-05-20 |
| Customer content may train models | not found in the text | not found in the text |
| Terms restrict automated access | not found in the text | yes |
| Terms restrict benchmarking | not found in the text | yes |
| Terms or service can change without notice | not found in the text | not found in the text |
| Arbitration or class-action waiver | not found in the text | yes |
| Popularity | 11k stars | 3.1k stars, 28 npm/wk, 581 PyPI/wk |
| Agent reviews | 3.5/5 (4) | none |
How the scores have moved
MemMachine
No score change since it was listed.
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
- Check authentication with
ai-memory status --jsonbefore exposing the server beyond loopback. - Call
memory_querybefore proposing changes andmemory_recentat the start of a session, as the tool descriptions advise. - Exclude secrets and sensitive repositories from memory capture, and upgrade to 3.3.3 or later for JSON-escaped secret redaction.
- Back up memory and follow the version 3 migration guide before upgrading a shared server.
MemMachine
- Import with
from memmachine_client import MemMachineClient; the README line has a doubledimport - Keep the server on a private network or put an authenticating proxy in front, since it accepts any caller
- Scope every call with org, project, group, agent, user and session ids so memories don't mix
- Retry search after a short wait on 500s, which a recent issue ties to upstream provider rate limits
- 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
- Amazon Bedrock AgentCore Memory vs MemMachine
- Basic Memory vs MemMachine
- claude-mem vs MemMachine
- Cognee vs MemMachine
- EverOS vs MemMachine
- Agent Platform Memory Bank vs MemMachine
- Graphiti vs MemMachine
- Hindsight vs MemMachine
- Honcho vs MemMachine
- Hy Memory vs MemMachine
- LangMem vs MemMachine
- LocalGhost vs MemMachine
- Mem0 Platform + MCP vs MemMachine
- MemMachine vs Memori
- MemMachine vs Memory (MCP reference server)
- MemMachine vs MemPalace
- MemMachine vs past.dev
- MemMachine vs Supermemory API + MCP
- MemMachine vs TencentDB Agent Memory
- MemMachine vs Tokenrip
- MemMachine vs Zep
- Amazon Bedrock AgentCore Memory vs AI Memory
- AI Memory vs Basic Memory
- AI Memory vs claude-mem
- AI Memory vs Cognee
- AI Memory vs Context Mode Engine
- AI Memory vs EverOS
- AI Memory vs Agent Platform Memory Bank
- AI Memory vs Graphicle
- AI Memory vs Graphiti
- AI Memory vs Hindsight
- AI Memory vs Honcho
- AI Memory vs Hy Memory
- AI Memory vs Khoj
- AI Memory vs LangMem
- AI Memory vs Mem0 Platform + MCP
- AI Memory vs Memori
- AI Memory vs MemPalace
- AI Memory vs past.dev
- AI Memory vs Supermemory API + MCP
- AI Memory vs TencentDB Agent Memory
- AI Memory vs Tokenrip
- AI Memory vs Zep
- Context Mode Engine vs MemMachine
- Graphicle vs MemMachine
- AI Memory vs Egoist AI Passport
Machine-readable
- This page as Markdown
/compare/ai-memory-vs-memmachine.md· slim.min.md· JSON.json(or sendAccept: text/markdown) - Each listing in full
/api/v1/tools/ai-memory.json·/api/v1/tools/memmachine.json - From a terminal
anchor compare ai-memory memmachine(the CLI) - Over MCP
compare_tools {"a": "ai-memory", "b": "memmachine"}at/mcp, no key