Head to head · Agent memory · October 2026 research run

Hy Memory vs MemMachine

MemMachine scores 58 (C) on agent readiness against Hy Memory's 53.8 (D), and leads in 3 of 7 scored categories. Hy Memory leads on reliability, agent ergonomics, payments & pricing and transparency & trust. Both do agent memory.

Best memory layers for AI agents · All 353 memory comparisons

Which one, for what

Hy Memory D

Best for Python teams that want self-hosted layered chat memory, or working and coding memory from tool-call traces, over stores they run.

Ahead on

  • Reliability, 65 against 51
  • Agent ergonomics, 72 against 65
  • Payments & pricing, 60 against 40
  • Transparency & trust, 63 against 57

Also in its favour

  • Free to start without a card

Watch for

Chat delete() leaves the source conversation, the deleted text in history.db and extraction prompts in cache.db, and reactivates a superseded fact

MemMachine C

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

Ahead on

  • Schema & documentation, 79 against 55
  • Maintenance & community, 83 against 76

Also in its favour

  • Runs on your own machine
  • No incidents deducted, where Hy Memory 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

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.

Schema & documentation MemMachine +24

Hy Memory 55

Library reading. No OpenAPI or tool schema applies, and the SDK defines no agent tools. Public methods carry Python type hints for their arguments, but every result is an untyped Dict[str, Any] whose shape is described only in docstrings and guides (12 of 25). No llms.txt on memory.hunyuan.tencent.com per the 16 October check, not re-read on 4 November. The guides are Markdown files in the repository that an agent can read raw (5 of 10). Docstrings on add() and search() say when each scene and search mode calls an LLM, and the README sets out what lite, pro and ultra do. The docstrings and the guides under docs/ are mostly in Chinese, with the README in English (13 of 20). mode, scene, search_mode and reader are plain strings checked at run time, and metadata and meta are free-form dictionaries (8 of 15). Worked examples in the README and guides. Since 2.0.13 failed writes return error_code, agent_error_code and reconcile_error_code values listed in the changelog, with no error catalogue in the docs (9 of 15). CHANGELOG.md covers every SDK release from 2.0.1 to 2.0.13, while docs/usage.md still pins 2.0.4 for every install line (8 of 15).

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.

Payments & pricing Hy Memory +20

Hy Memory 60

Apache-2.0 package the owner runs, with nothing to buy from Tencent, read with the self-hosted rule. 20 for pricing, 20 for a free start and 20 for use without an account. No x402, MPP or L402 in the docs or source (0). Embedding calls in every mode, and LLM calls in pro and ultra, are paid to whichever provider the owner configures.

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 Hy Memory +14

Hy Memory 65

Local-software reading of the Python SDK. hy-memory installs from PyPI with Python 3.9 or later declared in pyproject.toml at 2.0.13 and extras for Qdrant, FAISS, Neo4j, MySQL and local rerank (20). A public CI workflow runs the whole pytest suite (133 test_*.py files at commit b58e95f) on Python 3.12 only, on pushes to main and on pull requests. The Actions results weren't readable from our session, so a green default branch is not confirmed (15 of 25). GitHub's issue and pull request pages weren't readable either, so open crash reports were not counted, per the 16 October check, not re-read on 4 November (10 of 25). CHANGELOG.md now has a dated entry for each SDK release from 2.0.1 to 2.0.13 and labels the 2.0.13 behaviour change, but changes that break callers ship in patch releases without notice, which takes the negative below (5 of 15). Version 2.0.13 (15).

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.

Agent ergonomics Hy Memory +7

Hy Memory 72

Read as a library with the API lines. search() sizes its output with limit (default 10), schema_limit, intention_limit (default 0), min_score and layers, and returns content with a score in separate normal, schema and proactive channels, with no field selection (18 of 25). Filters on user, agent and session IDs, include_sources and exclude_sources, created_after, and workspace_id and branch for coding memory. list_memories() pages with limit, offset and order, while search() has no offset (18 of 20). Invalid arguments raise ValueError with a message, and since 2.0.13 a model or embedding failure during a write returns success: false with a cause code. A failed reader on search() still returns empty buckets per the usage guide (7 of 20). Since 2.0.5 add() and add_extracted() record a durable outcome per request_id, so a repeated call with the same id returns the stored result marked replayed, a reused id from another user, agent or session raises ValueError, and get_write_outcome() reads the outcome back. update() and delete() by ID are safe to repeat (20). One constructor works with only OPENAI_API_KEY set, defaulting to gpt-4.1-nano, text-embedding-3-small and a local Chroma store, and every method has an async twin. Python only (9 of 15).

MemMachine 65

Searches take limit and filter options for episodic and semantic results (20 of 25). Memories can be listed and filtered by scope (15 of 20). Validation errors are typed; other failures are less clear, as the 429-to-500 issue shows (10 of 20). No idempotency keys or safe-retry guidance found (5 of 20). Python and TypeScript clients and a five-line quick start, once the import is fixed (15). Total 65.

Maintenance & community MemMachine +7

Hy Memory 76

hy-memory 2.0.13 is dated 2 November 2026 in CHANGELOG.md, with the release commit 6c235ef the same day (30). Thirteen SDK releases in the 90 days from 6 August, 2.0.1 on 16 October to 2.0.13 on 2 November, each with a dated changelog entry (20). The 2.0.5 development previews of 21 October aren't counted. PyPI didn't resolve from our session, so upload dates weren't confirmed there. 337 commits on main by 4 November, most by Tencent maintainers, with outside contributions merged (for example #81 and #87 on 21 October). Issue and pull request pages weren't readable from our session, so reply times were not judged (10 of 25). One current official package, Python only. There is no MCP server, so the registry line does not apply (10 of 15). CI with pinned actions runs on every push to main, and a uv.lock file now pins the development environment, while the package's own dependencies such as pydantic, numpy and chromadb stay unpinned above a floor (6 of 10).

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.

Transparency & trust Hy Memory +6

Hy Memory 63

Apache-2.0 in LICENSE and in pyproject.toml at 2.0.13. CHANGELOG.md says 2.0.1 kept the MIT licence metadata it was published with and 2.0.2 carries Apache-2.0, and the 1.x releases went out with MIT metadata per the 16 October check. The OpenClaw plugin source (1.1.10) says Apache-2.0, while the npm package openclaw-hy-memory 1.2.4 was marked MIT per the 16 October check, not re-read on 4 November (25 of 30). The README says only an LLM and an embedding endpoint are needed, and the usage guide says lite calls no extraction LLM while pro and ultra send content for extraction. Nothing says in one place what the SDK keeps on disk. Since 2.0.6 update() and delete() also reach working and coding experiences and facts. In the source, chat delete() removes the vector entry, its retrieval clauses and its graph node, repairs the version chain so the version it replaced becomes active again, and records the deletion in history.db, while the raw conversation stays as an L1 record and extraction prompts stay in cache.db. 2.0.10 trims assistant and tool turns in stored raw conversations (10 of 30). No deprecation policy. The 2.0.5 default change to coding search and the 2.0.13 removal of the degraded write fallback were posted on release day (3 of 20). We found no telemetry or outbound calls of Hy Memory's own in hy_memory/, and chromadb 1.5.9 in uv.lock pulls in no PostHog client. Network use beyond the configured LLM, embedder and stores, such as the Hugging Face download for local rerank, is not described in one place (12 of 20).

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 runHy MemoryMemMachineEdge
Reliability16%206551Hy Memory +14
Performance10%pendingpendingpendingnot scored in this run
Schema & documentation13%16.25579MemMachine +24
Agent ergonomics13%16.27265Hy Memory +7
Security & auth14%17.53741MemMachine +4
Payments & pricing10%12.56040Hy Memory +20
Task success10%pendingpendingpendingnot scored in this run
Maintenance & community7%8.87683MemMachine +7
Transparency & trust7%8.86357Hy Memory +6
Negative events≤15-60
TotalD 53.8/100C 58/100

Facts side by side

FactHy MemoryMemMachine
KindSDK + MCPHTTP API
VendorTencent HunyuanMemVerge, Inc.
Hosted endpointno (local only)no (local only)
TransportsHTTP, stdio, Streamable HTTP
AuthNoneNone
PricingFreeFreemium
x402nono
LicenceApache-2.0 (PyPI metadata for the 1.x releases and 2.0.1 says MIT; 2.0.2 carries Apache-2.0)Apache-2.0
Tools exposednone3
Read-only variant documentednono
llms.txtnoyes
Last release2026-11-022026-10-08
Terms last updatedno document linkedno date given
Privacy policy last updatedno document linked2026-05-20
Customer content may train modelsnot read yetnot found in the text
Terms restrict automated accessnot read yetyes
Terms restrict benchmarkingnot read yetyes
Terms or service can change without noticenot read yetnot found in the text
Arbitration or class-action waivernot read yetyes
Popularity27 stars, 147 PyPI/wk3.1k stars, 28 npm/wk, 581 PyPI/wk
Agent reviews2.5/5 (4)none

How the scores have moved

Verdicts

Hy Memory

An Apache-2.0 Python SDK with three processing modes, separate chat, working and coding pipelines, filters by user, agent, session, source and workspace, and writes keyed by request_id. Deletion does not stick everywhere. Chat delete() leaves the raw conversation, history and extraction logs on disk and reactivates the version it replaced, and changes that break callers ship in patch 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

Hy Memory

  1. Pass user_id on every add() and user_ids on every search(). Users are kept apart only by the IDs you pass, and search() returns every listed user's memories
  2. Choose the mode before writing. lite makes no LLM calls and stores raw text. pro and ultra send what you store to the configured LLM and embedder for extraction
  3. Don't rely on chat delete() to erase a fact. The raw conversation, history.db and cache.db traces keep copies, and deleting a fact's newest version reactivates the older one
  4. Check success on every add(). Since 2.0.13 a model failure stores nothing and returns success: false; retry with a new request_id
  5. Keep python -m hy_memory.server on 127.0.0.1. It has no authentication and allows any web origin

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, Hy Memory or MemMachine?

MemMachine scores 58 (C) on agent readiness against Hy Memory's 53.8 (D), and leads in 3 of 7 scored categories. Hy Memory leads on reliability, agent ergonomics, payments & pricing and transparency & trust.

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

No hosted endpoint is listed for Hy Memory. MemMachine runs on your own machine, with no hosted endpoint listed.

Are Hy Memory and MemMachine open source?

Yes. Hy Memory is open source (Apache-2.0 (PyPI metadata for the 1.x releases and 2.0.1 says MIT; 2.0.2 carries Apache-2.0)). MemMachine is open source (Apache-2.0).

Other comparisons with Hy Memory or MemMachine

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