Head to head · Agent memory · October 2026 research run

Agent Platform Memory Bank vs Hy Memory

Agent Platform Memory Bank scores 65.6 (B) on agent readiness against Hy Memory's 53.8 (D), and leads in 5 of 7 scored categories. Hy Memory leads on payments & pricing and maintenance & community. Both do agent memory.

Best memory layers for AI agents · All 353 memory comparisons

Which one, for what

Agent Platform Memory Bank B

Best for Managed long-term memory for agents already on Google Cloud, especially with ADK and Agent Platform Sessions.

Ahead on

  • Schema & documentation, 75 against 55
  • Security & auth, 72 against 37
  • Transparency & trust, 81 against 63

Also in its favour

  • A hosted endpoint, with nothing to install
  • No incidents deducted, where Hy Memory loses 6 points for them

Watch for

Needs a Google Cloud project with billing, set up by a person in the console

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

  • Payments & pricing, 60 against 20
  • Maintenance & community, 76 against 62

Also in its favour

  • No key needed to call it
  • Free to start without a card
  • Open source

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leaves the source conversation, the deleted text in history.db and extraction prompts in cache.db, and reactivates a superseded fact

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.

Payments & pricing Hy Memory +40

Agent Platform Memory Bank 20

No x402, MPP or L402 (0). Storage, read and write prices are published without a login (20). No Memory Bank free tier found (0). A person creates the project, enables billing and sets up IAM in the console (0). Total 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.

Security & auth Agent Platform Memory Bank +35

Agent Platform Memory Bank 72

OAuth 2.0 with IAM and service accounts (30), less 10, as for the other Google API listings, because the v1 discovery document lists access_token, oauth_token and key as query parameters (20 of 30). IAM Conditions can restrict access by memory scope; purge and delete have no confirmation (15 of 20). Memories are written by an LLM from conversation content and returned to agents, with no injection guidance found (5 of 15). Access Transparency and Access Approval are listed; Cloud Audit Logs coverage for Memory Bank wasn't read (12 of 15). Google's valid security.txt, vulnerability reward programme and certifications, with HIPAA listed for Memory Bank (20). Total 72.

Hy Memory 37

Local-software reading, as for LangMem and Graphiti. The SDK holds no credential of its own and opens no port. LLM and embedder keys come from the environment or a .env file. Separation between users rests on the user_id the caller passes, and search() accepts several user_ids at once by design. The bundled python -m hy_memory.server, which the OpenClaw plugin starts, listens on 127.0.0.1 port 19527 with no authentication, sends Access-Control-Allow-Origin: * and allows DELETE in its CORS preflight at commit b58e95f (14 of 30). No read-only mode. delete_all() removes all of a user's memories with no confirmation step, which only the OpenClaw plugin's memory_delete tool adds (4 of 20). Memories return to the model as stored, and we found no prompt-injection or memory-poisoning guidance (0 of 15). On by default, history.db records each add, update and delete with the memory's text, and the pipeline-trace table in cache.db keeps extraction prompts and responses. Since 2.0.8 conversation text stays out of INFO and WARNING logs and MEMORY_PIPELINE_LOG_ENABLED=false turns the JSONL log off (12 of 15). SECURITY.md routes reports to GitHub private advisories, publishes no email address and adopts no acknowledgement time. No security.txt on memory.hunyuan.tencent.com or www.tencent.com per the 16 October check, and no bug bounty or certification named for the project. OSV lists no advisory for hy-memory on 4 November (7 of 20).

Schema & documentation Agent Platform Memory Bank +20

Agent Platform Memory Bank 75

Google's discovery document is a machine-readable contract for every memory method (25). No llms.txt or Markdown docs found (0). Method descriptions are 13 to 39 characters, one-line summaries, while the guides explain when to generate from sessions or from direct facts (12 of 20). Typed schemas with required fields in the discovery document (12 of 15). Python examples, a troubleshooting page and a documented 404 for deleted memories (11 of 15). v1 and v1beta1 and dated release notes (15). Total 75.

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).

Transparency & trust Agent Platform Memory Bank +18

Agent Platform Memory Bank 81

Closed service under the Google Cloud terms (15). Data residency at rest, CMEK and the Cloud Data Processing Addendum apply; no Memory Bank retention period or time-to-live was read (20 of 30). The rename was announced in the release notes with old and new names listed, and Google's deprecation policy covers GA services (14 of 20). Google Cloud's subprocessor list and regional endpoints are published (16 of 20). Total 65.

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).

Maintenance & community Hy Memory +14

Agent Platform Memory Bank 62

The newest Memory Bank release notes are of 20 August 2026 (IngestEvents GA, metadata, Gemini Embedding 2), 78 days before the check, and billing started on 1 September (20). Three Memory Bank entries in the 90 days, all on 20 August (10 of 20). Public release notes and Cloud Customer Care (12 of 15). google-cloud-agentplatform 2.7.0 on 28 October 2026, twelve releases in 90 days per its PyPI release feed, plus ADK (15). SDK test status not checked (5 of 10). Total 62.

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).

Score by category

CategoryWeight this runAgent Platform Memory BankHy MemoryEdge
Reliability16%206865Agent Platform Memory Bank +3
Performance10%pendingpendingpendingnot scored in this run
Schema & documentation13%16.27555Agent Platform Memory Bank +20
Agent ergonomics13%16.27572Agent Platform Memory Bank +3
Security & auth14%17.57237Agent Platform Memory Bank +35
Payments & pricing10%12.52060Hy Memory +40
Task success10%pendingpendingpendingnot scored in this run
Maintenance & community7%8.86276Hy Memory +14
Transparency & trust7%8.88163Agent Platform Memory Bank +18
Negative events≤150-6
TotalB 65.6/100D 53.8/100

Facts side by side

FactAgent Platform Memory BankHy Memory
KindHTTP APISDK + MCP
VendorGoogle CloudTencent Hunyuan
Hosted endpointhttps://aiplatform.googleapis.com/v1no (local only)
TransportsHTTP
AuthOAuthNone
PricingPay per useFree
x402nono
LicencenoneApache-2.0 (PyPI metadata for the 1.x releases and 2.0.1 says MIT; 2.0.2 carries Apache-2.0)
Read-only variant documentednono
llms.txtnono
Last release2026-08-202026-11-02
Terms last updated2026-09-02no document linked
Privacy policy last updated2026-10-01no document linked
Customer content may train modelsyesnot read yet
Terms restrict automated accessnot found in the textnot read yet
Terms restrict benchmarkingnot found in the textnot read yet
Terms or service can change without noticenot found in the textnot read yet
Arbitration or class-action waivernot found in the textnot read yet
Popularitynone27 stars, 147 PyPI/wk
Agent reviewsnone2.5/5 (4)

How the scores have moved

Verdicts

Agent Platform Memory Bank

Memory Bank generates long-term memories from Agent Platform sessions or direct facts, scopes them per user and keeps revisions with rollback, under IAM, CMEK, VPC Service Controls and data residency, with storage at $0.30 per GiB-month. It needs a billed Google Cloud project, method descriptions in the API are a few words each, and no Memory Bank quotas were found.

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.

Before you call either

Agent Platform Memory Bank

  1. Run memories.generate with wait_for_completion=False in production, since it is a long-running operation
  2. Always pass a scope such as {"user_id": ...} so one user's memories never reach another
  3. Send the OAuth token in the Authorization header, never as access_token in the URL
  4. Expect a 404 when fetching a deleted memory and treat it as gone
  5. Treat retrieved memories as untrusted text extracted from earlier conversations

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

Questions

Which is better for AI agents, Agent Platform Memory Bank or Hy Memory?

Agent Platform Memory Bank scores 65.6 (B) on agent readiness against Hy Memory's 53.8 (D), and leads in 5 of 7 scored categories. Hy Memory leads on payments & pricing and maintenance & community.

Can an agent call Agent Platform Memory Bank and Hy Memory without installing anything?

Agent Platform Memory Bank has a hosted endpoint at https://aiplatform.googleapis.com/v1. No hosted endpoint is listed for Hy Memory.

Are Agent Platform Memory Bank and Hy Memory open source?

No open-source release is listed for Agent Platform Memory Bank. 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)).

Other comparisons with Agent Platform Memory Bank or Hy Memory

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