# MemMachine vs Memori

> MemMachine scores 58 (C) to Memori's 44.2 (E) for agent memory. Prices, MCP, x402, uptime and agent notes side by side.

- Canonical: https://www.anchorterminal.com/compare/memmachine-vs-memori
- Markdown: https://www.anchorterminal.com/compare/memmachine-vs-memori.md (~4,100 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

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.

- MemMachine: grade C, 58/100, rank #771 of 1510. Markdown https://www.anchorterminal.com/tools/memmachine.md · JSON https://www.anchorterminal.com/api/v1/tools/memmachine.json
- Memori: grade E, 44.2/100, rank #1231 of 1510. Markdown https://www.anchorterminal.com/tools/memori.md · JSON https://www.anchorterminal.com/api/v1/tools/memori.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

## 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:
- 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)

Good 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

### 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

| Category | Weight | MemMachine | Memori | Edge |
| --- | --- | --- | --- | --- |
| Reliability | 16% (20 this run) | 51 | 28 | MemMachine +23 |
| Performance | 10%, pending | pending | pending | not scored in this run |
| Schema & documentation | 13% (16.2 this run) | 79 | 68 | MemMachine +11 |
| Agent ergonomics | 13% (16.2 this run) | 65 | 65 | even |
| Security & auth | 14% (17.5 this run) | 41 | 45 | Memori +4 |
| Payments & pricing | 10% (12.5 this run) | 40 | 45 | Memori +5 |
| Task success | 10%, pending | pending | pending | not scored in this run |
| Maintenance & community | 7% (8.8 this run) | 83 | 33 | MemMachine +50 |
| Transparency & trust | 7% (8.8 this run) | 57 | 52 | MemMachine +5 |
| Negative events | ≤15 | 0 | -4 | |
| **Total** | | **58 · C** | **44.2 · E** | |

## Facts side by side

| Fact | MemMachine | Memori |
| --- | --- | --- |
| Kind | HTTP API | SDK + MCP |
| Vendor | MemVerge, Inc. | Memori Labs Inc. |
| Hosted endpoint | no (local only) | `https://api.memorilabs.ai/mcp/` |
| Transports | HTTP, stdio, Streamable HTTP | Streamable HTTP |
| Auth | None | API key |
| Pricing | Freemium | Freemium |
| x402 | no | no |
| Licence | Apache-2.0 | Apache-2.0 |
| Tools exposed | 3 | 7 |
| Read-only variant documented | no | no |
| llms.txt | yes | yes |
| Last release | 2026-10-08 | 2026-05-27 |
| Terms last updated | no date given | no document linked |
| Privacy policy last updated | 2026-05-20 | no date given |
| Customer content may train models | not found in the text | not read yet |
| Terms restrict automated access | yes | not read yet |
| Terms restrict benchmarking | yes | not read yet |
| Terms or service can change without notice | not found in the text | not read yet |
| Arbitration or class-action waiver | yes | not read yet |
| Popularity | 3.1k stars, 28 npm/wk, 581 PyPI/wk | 17k 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).


## For agents

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

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- [Hy Memory vs Memori](https://www.anchorterminal.com/compare/hy-memory-vs-memori.md)
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- [LocalGhost vs Memori](https://www.anchorterminal.com/compare/localghost-vs-memori.md)
- [Mem0 Platform + MCP vs MemMachine](https://www.anchorterminal.com/compare/mem0-vs-memmachine.md)
- [Mem0 Platform + MCP vs Memori](https://www.anchorterminal.com/compare/mem0-vs-memori.md)
- [MemMachine vs Memory (MCP reference server)](https://www.anchorterminal.com/compare/memmachine-vs-memory-reference-server.md)
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- [Graphicle vs Memori](https://www.anchorterminal.com/compare/graphicle-vs-memori.md)

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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