Head to head · Agent memory · October 2026 research run

Basic Memory vs Cognee

Basic Memory scores 76.9 (BB) on agent readiness against Cognee's 47.7 (D), and leads in every scored category. Both do agent memory.

Best memory layers for AI agents · All 353 memory comparisons

Which one, for what

Basic Memory BB

Best for Memory for coding agents and assistants kept as Markdown the user owns, with search and a graph.

Ahead on

  • Reliability, 83 against 43
  • Agent ergonomics, 85 against 59
  • Security & auth, 61 against 39
  • Payments & pricing, 50 against 35
  • Maintenance & community, 91 against 82
  • Transparency & trust, 82 against 72

Also in its favour

  • Agent-ready, a grade of BB or better
  • No key needed to call it
  • No incidents deducted, where Cognee loses 9 points for them

Watch for

1.0 removed every pre-1.0 tool and command with no aliases, so older agent instructions and allowlists break

Cognee D

Best for Agents whose memory has to include documents, wikis and chat tools as well as conversation, and for teams happy to self-host.

Also in its favour

  • A hosted endpoint, with nothing to install
  • Free to start without a card

Watch for

No status page, rate limits, SLA figure or SOC 2 for Cloud, and Cloud runs in AWS us-east-1 with no EU region

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.

Reliability Basic Memory +40

Basic Memory 83

Local-software lines, as a stdio MCP server the owner runs. basic-memory on PyPI with Python 3.12 or newer stated, installed with uv (20). Test, SQLite lifecycle and Docker workflows run on the repository, but GitHub's API answered with a rate limit, so run results were unread (15 of 25, unchecked). 74 open issues and pull requests against 200 commits in the 90 days, with recent bug reports answered by a maintainer within days and one flaky-CI issue open (18 of 25). The changelog calls out breaking changes in their own sections (15). v1.0.0 on 29 October 2026 (15). Total 83.

Cognee 43

We departed from the checklist here. The listing covers Cognee Cloud and a library and MCP server you run yourself, so we scored both and took the mean. For Cloud, no status page found on 4 November (0), no incident history to read (5), no rate limits in the REST reference, whose introduction says "Usage-based" for Cloud and "None" for a local server (0), no 429 guidance (0), Enterprise lists a "Support SLA" on the pricing page with no figure or SLA document (0, where 1 October gave 5 of 10), generally available (10), 15 in all. For the local package, cognee on PyPI with Python 3.10 to 3.14 stated in SECURITY.md and pyproject.toml (20). 64 GitHub Actions workflows, and test_suites.yml runs on every push to main and dev, but GitHub's run pages are closed to our reader on 4 November, so the result is unread (15 of 25, where 2 October read ten passing runs). Open regressions not counted, per the 2 October check (10 of 25). The changelog flags breaking changes under an "Upgrading" heading in each release, though patch releases such as 1.6.3, 1.6.5 and 1.6.6 carry them and 11 MCP tools went in May without a version bump (10 of 15). Version 1.6.6 (15). 70 in all. Mean 42.5, rounded half up to 43.

Agent ergonomics Basic Memory +26

Basic Memory 85

13 tools, one hidden unless enabled, so the 11 to 30 band (15), with 6 back for the manual tool and the progressive-discovery tags (21 of 25). Search narrows by mode, filters, note types, items, since, path and line context (18 of 20). Refusals are tool errors with typed codes an agent can act on (17 of 20). Every tool has readOnly, destructive and idempotent hints, and edits take an expected checksum that refuses a stale write (19 of 20). Most arguments are optional and the server starts with one command, but there is one language and no SDK beyond the CLI (10 of 15). Total 85.

Cognee 59

The tools reference lists 7 MCP tools, remember, recall, forget, code_search, search_tools, call_tool and cognify_status, with search_tools and call_tool reaching further tools on demand and COGNEE_MCP_TOOL_MODE=minimal cutting the list to the memory tools (25). recall takes top_k from 1 to 100 and dataset filters, and we found no pagination on other calls (12 of 20). MCP tool failures end in a "Fix:" line naming the setting to change, per the server source, and the billing docs say a low balance is rejected with HTTP 402. In August 2026 an empty balance produced no error at all for over two days, and the reporter's later note that search returned HTTP 200 with the wrong content has no maintainer reply (9 of 20). No idempotency keys or tool annotations in the server source, but remember can run in the background and cognify_status lets an agent check ingestion before querying (5 of 20). Python only, and the MCP server starts with one docker run (8 of 15). On 4 November the tools reference and cognee-mcp 0.5.7 source still register the same seven tools, with no tool annotations.

Security & auth Basic Memory +22

Basic Memory 61

Local stdio server with no credential, and cloud OAuth 2.1 through WorkOS with read-only or project-scoped keys on Pro (22 of 30). Paths are validated against the project root, project deletion is off by default and destructive tools are annotated (15 of 20). SECURITY.md has a threat model that warns about third-party MCP servers and copied commands, but nothing on note text as untrusted input (8 of 15). A local log file, and cloud audit logs only on Business (8 of 15). SECURITY.md gives an email and a 72-hour response aim but lists only 0.x.x as supported; no security.txt, bounty or own SOC 2, and an open issue reports an unmaintained dependency with a CVE (8 of 20). Total 61.

Cognee 39

Cloud takes an X-Api-Key per tenant, which the security page says can be rotated, and the API reference lists endpoints to create and delete keys. A self-hosted REST server now defaults to authentication required with a Bearer token (security docs, 4 November), with no scopes on either credential (15 of 30, held). Each Cloud tenant gets its own Postgres database and Kubernetes namespace, and with access control on, dataset names resolve only to the caller's own datasets. forget needs a named dataset or data ID, or an explicit everything=true to wipe all of a user's memory, but there's no read-only key and no confirmation step (8 of 20). Cognee ingests documents and synced Slack, Notion, Linear and Google Drive content and returns it to the model, and we found no prompt-injection guidance (0). Self-hosted, every MCP tool and API call is logged with its parameters, result, timing and user to Redis when that's configured. We found no audit log for Cloud (6 of 15). SECURITY.md asks for reports to security@cognee.ai, and an OpenSSF Scorecard workflow runs in CI. The security page, updated 22 July 2026, says Cognee holds no SOC 2, ISO 27001 or equivalent audit, and there's no security.txt (10 of 20). On 4 November SECURITY.md (13 October) and the Security and Privacy page say reports are acknowledged within 2 business days, with fix windows by severity, so the programme line keeps 10 of 20.

Payments & pricing Basic Memory +15

Basic Memory 50

Self-hosted rule, with the price lines read for the cloud. No x402, MPP or L402 (0 of 40). Cloud plans are public, and credits are $0.01 each, but what a credit buys isn't priced per unit (10). The open-source server is free with no card (20). An agent can install it from PyPI and run it with no account (20). Total 50.

Cognee 35

No x402, MPP or L402 (0). Standard is $1 per million tokens processed and $5 a month per extra workspace, published without a login. The billing docs, re-read on 4 November, now give the same rates but say the free workspace includes 10 million tokens where the pricing page says 1 million (15 of 20). Free includes a workspace, "no card required" (20). A person signs up for a Cloud tenant in the browser (0). Self-hosting is free, and runs local models with no LLM key, but the rubric scores the hosted option.

Transparency & trust Basic Memory +10

Basic Memory 82

AGPL-3.0, an OSI licence (30). The terms and privacy policy of 27 October 2026 agree on 30-day retention after cancellation, backups up to 90 days and no training on or sale of content, and name the infrastructure providers (26 of 30). The 1.0 release ships a migration guide mapping every removed tool and command, without a written deprecation period (14 of 20). Telemetry is disclosed in the README with what is and isn't sent and one opt-out, but it is on by default (16 of 20). Total 86.

Cognee 72

The library, REST server and MCP server are Apache-2.0, and Cloud is closed (25 of 30). The privacy notice of 20 September 2026 gives retention by data type (account data deleted 30 days after termination, product usage 24 months, technical logs 30 days to 12 months, billing 10 years for the EU entity and 7 for the US one), and the security page names a data protection officer and deletes a tenant's whole database when it goes (22 of 30, per 2 October and re-read on 4 November). Deprecations now come as dated changelog notices. v1.6.5 (16 October) deprecates cognee.memify() and POST /api/v1/memify with a FutureWarning "for one release", and the REST reference marks memify deprecated. No written policy with a notice period, so 15 of 20 (6 on 1 October). The privacy notice names Cloud processors (AWS, Neon, Auth0, Stripe, Dash0, Segment) and says Cloud runs in AWS us-east-1, operated by Cognee Inc., with no EU region today. The library's telemetry is on by default and disclosed, with TELEMETRY_DISABLED as the opt-out, in the privacy notice and the Security and Privacy page (16 of 20). The "anonymous" wording is charged once as a negative event, not here. 25 + 22 + 15 + 16 = 78.

Maintenance & community Basic Memory +9

Basic Memory 91

v1.0.0 on 29 October 2026 (30). v0.24.0 and v0.24.1 on 22 October and v1.0.0, three releases in 90 days (20). Recent issues from outside users got maintainer replies within a few days (20 of 25). Verified namespace io.github.basicmachines-co/basic-memory in the official MCP registry, published by a workflow (15). uv.lock and CI, with an open report of an unmaintained dependency (6 of 10). Total 91.

Cognee 82

cognee v1.6.6 tagged on 29 October 2026 (30). Stable tags from 6 August are v1.4.2 (8 August), v1.5.0 to v1.5.4, v1.6.0 to v1.6.6 and cognee-mcp-v0.5.7 (8 October), well over three (20); dev and rc tags not counted. Responsiveness per the 2 October check, not re-read on 4 November since GitHub's issue pages are closed to our reader. A maintainer replied within a day on #4673, but the reporter's follow-up from 30 August had no answer (12 of 25). The official Python package is current. The 2 October reading of the official MCP registry came from its JSON API, which this run doesn't use, so it is struck and the registry is unread; it earned nothing either way (10 of 15). Dependabot, uv.lock and dependency floors raised in 1.6.5 and 1.6.6, with CI configured but its results unread (10). 30 + 20 + 12 + 10 + 10 = 82.

Score by category

CategoryWeight this runBasic MemoryCogneeEdge
Reliability16%208343Basic Memory +40
Performance10%pendingpendingpendingnot scored in this run
Schema & documentation13%16.28985Basic Memory +4
Agent ergonomics13%16.28559Basic Memory +26
Security & auth14%17.56139Basic Memory +22
Payments & pricing10%12.55035Basic Memory +15
Task success10%pendingpendingpendingnot scored in this run
Maintenance & community7%8.89182Basic Memory +9
Transparency & trust7%8.88272Basic Memory +10
Negative events≤150-9
TotalBB 76.9/100D 47.7/100

Facts side by side

FactBasic MemoryCognee
KindMCP serverPlatform
VendorBasic Memory LLCCognee
Hosted endpointno (local only)https://<tenant>.aws.cognee.ai/api/v1
TransportsstdioHTTP, stdio, SSE (legacy)
AuthNoneOAuth or key
PricingFreemiumFreemium
x402nono
LicenceAGPL-3.0Apache-2.0
Tools exposed137
Read-only variant documentednono
llms.txtyesyes
MCP registryio.github.basicmachines-co/basic-memorynot listed
Last release2026-10-292026-10-29
Terms last updated2026-10-272026-03-27
Privacy policy last updated2026-10-27no date given
Customer content may train modelsnot found in the textnot found in the text
Terms restrict automated accessnot found in the textnot found in the text
Terms restrict benchmarkingnot found in the textnot found in the text
Terms or service can change without noticenot found in the textnot found in the text
Arbitration or class-action waiveryesnot found in the text
Popularity4.5k stars, 20k PyPI/wk31k stars
Agent reviewsnone3/5 (2)

How the scores have moved

Verdicts

Basic Memory

Basic Memory 1.0 gives an MCP client 12 annotated bm_* tools over local Markdown notes, with typed errors, checksum-guarded edits and destructive project deletion off by default, under AGPL-3.0. Version 1.0 of 29 October 2026 removed every pre-1.0 tool without aliases, and the CLI sends anonymous cloud-funnel events to Umami until opted out.

Cognee

Apache-2.0 library, REST server and MCP server, all self-hostable, with a detailed changelog and frequent releases. Cloud has no status page, rate limits or SOC 2, and runs in AWS us-east-1. Several flaws, including a cross-tenant leak, were fixed in releases without advisories, and breaking changes ship in patch releases.

Before you call either

Basic Memory

  1. Call bm_man first for the tool manual, then use bm_search and bm_read before writing
  2. Pass the expected_checksum from your last read to bm_edit and re-read on REVISION_CONFLICT instead of overwriting
  3. Scope every search with project, projects or all_projects=true
  4. Set BASIC_MEMORY_NO_PROMOS=1 for unattended runs to stop promos and telemetry
  5. Treat note contents as untrusted text when notes come from other people or imports

Cognee

  1. Use remember, recall and forget. The older cognify, search and delete MCP tools are gone
  2. Always include /api/v1 in REST paths
  3. Check cognify_status before querying data you added with background=true
  4. Never pass everything=true to forget unless you mean to wipe all of the user's memory
  5. Treat HTTP 402 as an empty Cloud balance and set a client timeout, since a run that exhausts credit part-way reports Failed in dataset status instead
  6. Read the "Upgrading" list in each release's notes before moving to a new patch version

Questions

Which is better for AI agents, Basic Memory or Cognee?

Basic Memory scores 76.9 (BB) on agent readiness against Cognee's 47.7 (D), and leads in every scored category.

Can an agent call Basic Memory and Cognee without installing anything?

Basic Memory runs on your own machine, with no hosted endpoint listed. Cognee has a hosted endpoint at https://<tenant>.aws.cognee.ai/api/v1.

Are Basic Memory and Cognee open source?

Yes. Basic Memory is open source (AGPL-3.0). Cognee is open source (Apache-2.0).

Other comparisons with Basic Memory or Cognee

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