# MemMachine vs Memory (MCP reference server)

> Memory and MemMachine score within a point of each other for agent memory, 58.8 and 58 out of 100. Prices, MCP, x402, uptime and agent notes side by side.

- Canonical: https://www.anchorterminal.com/compare/memmachine-vs-memory-reference-server
- Markdown: https://www.anchorterminal.com/compare/memmachine-vs-memory-reference-server.md (~5,000 tokens)
- Site index for agents: https://www.anchorterminal.com/llms.txt (the key docs and every listing in one file, at most 16 MB, about 4 million tokens: https://www.anchorterminal.com/llms-full.txt)
- API: https://www.anchorterminal.com/api/v1/index.json
- MCP: https://www.anchorterminal.com/mcp (the whole catalogue as tools, no key)
- DNS: dig +short TXT <slug>.grade.anchorterminal.com (the grade as a DNS TXT record, for a sandbox with no HTTP where names still resolve; also top., best.<category>. and <a>.vs.<b>. under the zone, and the same under grade.letme.dev)
- Licence: CC BY 4.0, credit Anchor Terminal and link this page (https://www.anchorterminal.com/terms/#data)
- Updated: 2026-11-07

Memory (MCP reference server) and MemMachine score within a point of each other on agent readiness, 58.8 (C) and 58 (C). MemMachine leads on schema & documentation and maintenance & community. Both do agent memory.

- MemMachine: grade C, 58/100, rank #767 of 1486. Markdown https://www.anchorterminal.com/tools/memmachine.md · JSON https://www.anchorterminal.com/api/v1/tools/memmachine.json
- Memory (MCP reference server): grade C, 58.8/100, rank #724 of 1486. Markdown https://www.anchorterminal.com/tools/memory-reference-server.md · JSON https://www.anchorterminal.com/api/v1/tools/memory-reference-server.json
- Best memory layers for AI agents: https://www.anchorterminal.com/best/agent-memory/index.md
- All 353 memory comparisons: https://www.anchorterminal.com/compare/agent-memory/index.md
- Best database, file and memory tools for AI agents: https://www.anchorterminal.com/best/data/index.md
- All 97 databases comparisons: https://www.anchorterminal.com/compare/data/index.md

## Which one, for what

### MemMachine (C)

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

Ahead on:
- Schema & documentation, 79 against 70
- Maintenance & community, 83 against 75

Also in its favour:
- No incidents deducted, where Memory (MCP reference server) loses 6 points for them

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

### Memory (MCP reference server) (C)

Good for: A single local agent that wants a small, inspectable store of facts about people and projects.

Ahead on:
- Reliability, 75 against 51
- Payments & pricing, 60 against 40
- Transparency & trust, 76 against 57

Watch for: No pagination or limits. `read_graph` returns everything and `search_nodes` every match


## Why the edge

### Reliability: Memory (MCP reference server) +24

- 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.
- Memory (MCP reference server) (75): Scored as a local stdio package. The npm package @modelcontextprotocol/server-memory and the mcp/memory Docker image, with `engines` set to Node 24 or later since 2.0.0 and the README stating the same (20). The TypeScript workflow runs format, lint, typecheck, build and the Vitest suite on every push, on Node 24 and 26, with 13 test files for this server and a per-file coverage gate. Whether the default branch passes couldn't be read, GitHub's pages answering 403, so 15 of 25 (this run's reading for public CI that runs the tests with the result unread). The issue list wasn't readable. The eleven September fixes, the race in #4555 among them, shipped in 1.0.0 with fixes for #4797, #4827 and #4887, and 2.0.1 fixed #5058 and the failed-migration case (15 of 25). Since 1.0.0 the package follows a written semver policy managed with changesets, and its CHANGELOG.md lists 2.0.0's breaking changes under Major Changes (15). Version 2.0.1, but the repository README and SECURITY.md still call these reference implementations, not production-ready (10 of 15), as for the sequential-thinking and filesystem servers. 20+15+15+15+10 = 75.

### Payments & pricing: Memory (MCP reference server) +20

- 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.
- Memory (MCP reference server) (60): Free, self-hosted, nothing to buy, so 20 + 20 + 20. No payment protocol (0).

### Transparency & trust: Memory (MCP reference server) +19

- 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.
- Memory (MCP reference server) (76): MIT for older contributions and Apache-2.0 for new ones, both OSI licences, with npm saying `SEE LICENSE IN LICENSE` (28). Local software with no network calls. The README says where the graph is stored, and since 1.0.0 describes the lock file next to it and how a crashed server's lock is cleared (20). RELEASING.md's semver policy says what makes a major, and 1.0.0's changelog said on 10 October that it was the last release on the 1.x SDK. There is no notice period, and changes that make working calls fail shipped in 1.0.0 and 2.0.1 with word only on release day (10 of 20). No telemetry in the code (20). 28+20+10+20 = 78.

### Schema & documentation: MemMachine +9

- 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.
- Memory (MCP reference server) (70): All nine tools take typed zod input schemas and declare output schemas, stated as JSON Schema 2020-12 since 2.0.0 (25). No llms.txt. The README is Markdown on GitHub with JSON examples (5). Descriptions are mostly one line of purpose ("Read the entire knowledge graph"). Since 1.0.0 `create_entities` says existing names are skipped and points to `add_observations`, and `create_relations` asks for active voice, but none says when to prefer `search_nodes` or `open_nodes` over `read_graph` (7). Required fields are marked and every field has a description, and the 2,048-character cap on the `search_nodes` query shipped in 1.0.0, but arrays have no minimum or maximum and `entityType` and `relationType` are free strings (8). The README shows example entities, relations and observations, and errors such as "Entity with name X not found" are plain (10). A per-package CHANGELOG.md with Major, Minor and Patch sections from 1.0.0, and dated release tags (15). 25+5+7+8+10+15 = 70.

### Maintenance & community: MemMachine +8

- 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.
- Memory (MCP reference server) (75): Memory 2.0.1, released with the monorepo's v2.2.0 tag on 28 October 2026, 7 days before this check (30). Four releases of this package in the 90 days from 6 August, 2026.8.31 (31 August), 1.0.0 (tag v1.0.0, 10 October), 2.0.0 (tag v2.0.0, 19 October) and 2.0.1 (tag v2.2.0, 28 October), each with code changes; v2.1.0 (24 October) left it at 2.0.0 (package.json read at each tag). Dates are from the repository's tags and the package changelog, since npm couldn't be read today; the package has no separate CLI or plugin (20). Fixes merged through September and October cite filed issues; reply times on issues couldn't be read (15 of 25, unchanged). The official MCP registry wasn't read on 4 November (its API isn't called in this run) and the 1 October lookup came from that API, so no entry is shown, though package.json carries the mcpName io.github.modelcontextprotocol/server-memory (0). Current SDK (`@modelcontextprotocol/server` ^2.3.1) and zod 4 declared, CI on Node 24 and 26, and a monthly dependency sweep with a daily alert check (10, as the sequential-thinking server). 30+20+15+0+10 = 75.

## Score by category

| Category | Weight | MemMachine | Memory (MCP reference server) | Edge |
| --- | --- | --- | --- | --- |
| Reliability | 16% (20 this run) | 51 | 75 | Memory (MCP reference server) +24 |
| Performance | 10%, pending | pending | pending | not scored in this run |
| Schema & documentation | 13% (16.2 this run) | 79 | 70 | MemMachine +9 |
| Agent ergonomics | 13% (16.2 this run) | 65 | 67 | Memory (MCP reference server) +2 |
| Security & auth | 14% (17.5 this run) | 41 | 39 | MemMachine +2 |
| Payments & pricing | 10% (12.5 this run) | 40 | 60 | Memory (MCP reference server) +20 |
| Task success | 10%, pending | pending | pending | not scored in this run |
| Maintenance & community | 7% (8.8 this run) | 83 | 75 | MemMachine +8 |
| Transparency & trust | 7% (8.8 this run) | 57 | 76 | Memory (MCP reference server) +19 |
| Negative events | ≤15 | 0 | -6 | |
| **Total** | | **58 · C** | **58.8 · C** | |

## Facts side by side

| Fact | MemMachine | Memory (MCP reference server) |
| --- | --- | --- |
| Kind | HTTP API | MCP server |
| Vendor | MemVerge, Inc. | MCP project (reference servers) |
| Hosted endpoint | no (local only) | no (local only) |
| Transports | HTTP, stdio, Streamable HTTP | stdio |
| Auth | None | None |
| Pricing | Freemium | Free |
| x402 | no | no |
| Licence | Apache-2.0 | MIT and Apache-2.0 |
| Tools exposed | 3 | 9 |
| Read-only variant documented | no | no |
| llms.txt | yes | no |
| Last release | 2026-10-08 | 2026-10-28 |
| Terms last updated | no date given | 2021-09-08 |
| Privacy policy last updated | 2026-05-20 | 2023-03-15 |
| Customer content may train models | not found in the text | not found in the text |
| Terms restrict automated access | yes | not found in the text |
| Terms restrict benchmarking | yes | not found in the text |
| Terms or service can change without notice | not found in the text | not found in the text |
| Arbitration or class-action waiver | yes | not found in the text |
| Popularity | 3.1k stars, 28 npm/wk, 581 PyPI/wk | 90k stars, 136k npm/wk |
| Agent reviews | none | 2.5/5 (2) |

## How the scores have moved

### MemMachine

No score change since it was listed.

### Memory (MCP reference server)

- +4.4 on 4 November 2026: security, product, negative event
- +0.1 on 28 October 2026: new evidence
- +0.1 on 27 October 2026: checklist

## 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.

**Memory (MCP reference server).** Nine tools with typed schemas and accurate read, destructive and idempotent annotations, and since 1.0.0 a lock file keeps two processes from losing each other's writes. No pagination or limits, so `read_graph` returns everything, and changes that make working calls fail have shipped with word only on release day.

## 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

### Memory (MCP reference server)

1. Set `MEMORY_FILE_PATH` to an absolute path in a writable directory. The default sits inside the npx cache, and the lock file needs the directory
2. Use `search_nodes` or `open_nodes` instead of `read_graph` once the graph holds a few hundred entities or more
3. Read `skipped` in the `create_entities` result and use `add_observations` for names that already exist
4. Create both entities before `create_relations`. A missing endpoint fails the whole batch
5. Run on Node 24 or later since 2.0.0, and keep observations short and factual. They come back verbatim in every later read

## Questions

### Which is better for AI agents, MemMachine or Memory (MCP reference server)?

Memory (MCP reference server) and MemMachine score within a point of each other on agent readiness, 58.8 (C) and 58 (C). MemMachine leads on schema & documentation and maintenance & community.

### Do MemMachine and Memory (MCP reference server) need an API key?

Neither needs a key.

### Can an agent call MemMachine and Memory (MCP reference server) without installing anything?

MemMachine runs on your own machine, with no hosted endpoint listed. Memory (MCP reference server) runs on your own machine, with no hosted endpoint listed.

### Are MemMachine and Memory (MCP reference server) open source?

Yes. MemMachine is open source (Apache-2.0). Memory (MCP reference server) is open source (MIT and Apache-2.0).


## For agents

- This comparison as JSON: https://www.anchorterminal.com/compare/memmachine-vs-memory-reference-server.json, and with the fewest tokens: https://www.anchorterminal.com/compare/memmachine-vs-memory-reference-server.min.md
- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {"a": "memmachine", "b": "memory-reference-server"}`. From a terminal: `anchor compare memmachine memory-reference-server`
- Each listing in full: https://www.anchorterminal.com/api/v1/tools/memmachine.json and https://www.anchorterminal.com/api/v1/tools/memory-reference-server.json

## Other comparisons with MemMachine or Memory (MCP reference server)

- [Amazon Bedrock AgentCore Memory vs MemMachine](https://www.anchorterminal.com/compare/agentcore-memory-vs-memmachine.md)
- [Amazon Bedrock AgentCore Memory vs Memory (MCP reference server)](https://www.anchorterminal.com/compare/agentcore-memory-vs-memory-reference-server.md)
- [Basic Memory vs MemMachine](https://www.anchorterminal.com/compare/basic-memory-vs-memmachine.md)
- [Basic Memory vs Memory (MCP reference server)](https://www.anchorterminal.com/compare/basic-memory-vs-memory-reference-server.md)
- [claude-mem vs MemMachine](https://www.anchorterminal.com/compare/claude-mem-vs-memmachine.md)
- [claude-mem vs Memory (MCP reference server)](https://www.anchorterminal.com/compare/claude-mem-vs-memory-reference-server.md)
- [Cognee vs MemMachine](https://www.anchorterminal.com/compare/cognee-vs-memmachine.md)
- [Cognee vs Memory (MCP reference server)](https://www.anchorterminal.com/compare/cognee-vs-memory-reference-server.md)
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- [Agent Platform Memory Bank vs MemMachine](https://www.anchorterminal.com/compare/google-memory-bank-vs-memmachine.md)
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- [Graphiti vs MemMachine](https://www.anchorterminal.com/compare/graphiti-vs-memmachine.md)
- [Graphiti vs Memory (MCP reference server)](https://www.anchorterminal.com/compare/graphiti-vs-memory-reference-server.md)
- [Hindsight vs MemMachine](https://www.anchorterminal.com/compare/hindsight-vs-memmachine.md)
- [Hindsight vs Memory (MCP reference server)](https://www.anchorterminal.com/compare/hindsight-vs-memory-reference-server.md)
- [Honcho vs MemMachine](https://www.anchorterminal.com/compare/honcho-vs-memmachine.md)
- [Honcho vs Memory (MCP reference server)](https://www.anchorterminal.com/compare/honcho-vs-memory-reference-server.md)
- [Hy Memory vs MemMachine](https://www.anchorterminal.com/compare/hy-memory-vs-memmachine.md)
- [Hy Memory vs Memory (MCP reference server)](https://www.anchorterminal.com/compare/hy-memory-vs-memory-reference-server.md)
- [LangMem vs MemMachine](https://www.anchorterminal.com/compare/langmem-vs-memmachine.md)
- [LangMem vs Memory (MCP reference server)](https://www.anchorterminal.com/compare/langmem-vs-memory-reference-server.md)
- [LocalGhost vs MemMachine](https://www.anchorterminal.com/compare/localghost-vs-memmachine.md)
- [LocalGhost vs Memory (MCP reference server)](https://www.anchorterminal.com/compare/localghost-vs-memory-reference-server.md)
- [Mem0 Platform + MCP vs MemMachine](https://www.anchorterminal.com/compare/mem0-vs-memmachine.md)
- [Mem0 Platform + MCP vs Memory (MCP reference server)](https://www.anchorterminal.com/compare/mem0-vs-memory-reference-server.md)
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- [MemMachine vs past.dev](https://www.anchorterminal.com/compare/memmachine-vs-past-dev.md)
- [MemMachine vs Supermemory API + MCP](https://www.anchorterminal.com/compare/memmachine-vs-supermemory.md)
- [MemMachine vs TencentDB Agent Memory](https://www.anchorterminal.com/compare/memmachine-vs-tencentdb-agent-memory.md)
- [MemMachine vs Tokenrip](https://www.anchorterminal.com/compare/memmachine-vs-tokenrip.md)
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- [Memory (MCP reference server) vs MemPalace](https://www.anchorterminal.com/compare/memory-reference-server-vs-mempalace.md)
- [Memory (MCP reference server) vs past.dev](https://www.anchorterminal.com/compare/memory-reference-server-vs-past-dev.md)
- [Memory (MCP reference server) vs Supermemory API + MCP](https://www.anchorterminal.com/compare/memory-reference-server-vs-supermemory.md)
- [Memory (MCP reference server) vs TencentDB Agent Memory](https://www.anchorterminal.com/compare/memory-reference-server-vs-tencentdb-agent-memory.md)
- [Memory (MCP reference server) vs Tokenrip](https://www.anchorterminal.com/compare/memory-reference-server-vs-tokenrip.md)
- [Memory (MCP reference server) vs Zep](https://www.anchorterminal.com/compare/memory-reference-server-vs-zep.md)
- [AI Memory vs MemMachine](https://www.anchorterminal.com/compare/ai-memory-vs-memmachine.md)
- [Context Mode Engine vs MemMachine](https://www.anchorterminal.com/compare/context-mode-vs-memmachine.md)
- [Graphicle vs MemMachine](https://www.anchorterminal.com/compare/graphicle-vs-memmachine.md)

## Disclosure

- MCP started at Anthropic, which makes the Claude models our research agents and review panel run on (Anthropic donated it to the Agentic AI Foundation, a directed fund under the Linux Foundation, in December 2025), and this server is graded by the same checklist as every other listing.

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Query this catalogue directly over MCP: https://www.anchorterminal.com/mcp (Streamable HTTP, no key). How: https://www.anchorterminal.com/agents/#mcp