LycheeMemory

Lightweight Long-Term Memory for LLM Agents.

Memory & Knowledge Python Grade D Listed Apache-2.0
Compare → Suggest correction
Listing state
Listed
HVTrust
29.4/100 · Grade D
Evidence coverage
Grade C · 2/5 signals
Last push
2026-07-17 · 12d ago
Recent change
New

Is LycheeMemory safe? LycheeMemory scores 29.4/100 (Grade D), ranked #404 of 441 tracked open-source AI agent projects, on evidence coverage C (2 of 5 independent signal types). The public evidence: no package-provenance attestation found; no OSSF Scorecard result yet; 7% of recent commits are signed; last pushed 2026-07-17. Every point is earned from checkable signals — never paid placement. How scoring works →

Quick Trust Read

Verdict
Thin or incomplete trust evidence. Review carefully before production use.
29.4/100 · Grade D
Strongest Signal
Maintenance
16.3/20
Weakest Signal
Safety / Integrity
0.4/25
What Would Improve It
Add or improve OSSF Scorecard coverage so safety checks are easier to verify.
Recent Changes
2026-07-28
Newly Listed
First tracked at rank #403
Maintainer Checklist
Add Scorecard coverage Expose the repository to OpenSSF Scorecard checks so supply-chain posture is easier to verify.
Publish provenance Add package provenance or release attestations so users can verify where shipped artifacts came from.
Increase signed commits Raise the share of verified-signed commits to make maintainer identity and release history easier to trust.
63.4
Activity sub-score · out of 100
29.4
HVTrust Score · out of 100
#404
Global Rank · of 441
#34

How to read this: HVTrust (0–100) weighs supply-chain signals (provenance, OSSF Scorecard, signed commits, open license) alongside real-world adoption. Grade D reflects the trust score band: A ≥ 80, B ≥ 65, C ≥ 50, D < 50. Evidence coverage C is separate — it grades how many independent signal types back the score (2 of 5), so a high score on thin evidence stays visible. Full methodology →

Signals refreshed 2026-07-29 00:30 UTC · Repo last pushed 12 days ago

Rank Trend

2026-07-28 2026-07-29

Activity & Reach

Stars
1.2k
Forks
15
Last Push
2026-07-17
12 days ago
Commits (4 wk)
18
Downloads (7d)
30
pypi
HN mentions (30d)
Open Issues
2
Rank Change
▼1
was #403

Analysis

HVTrust Dimensions

29.4 / 100 · 67.0% confidence
Safety / Integrity50% OSSF Scorecard · 30% provenance · 20% signed commits
0.4 / 25
Identity / Provenance60% listing status · 40% build provenance
10.8 / 18
Transparency50% declared license · 50% OSSF Scorecard
8.5 / 17
Maintenance60% last-push freshness · 40% commit activity
16.3 / 20
Adoption60% stars · 40% downloads, log-scaled
9.3 / 20

Activity Inputs

63.4 / 100
StarsRepository reach
18.4 / 30
FreshnessLast push recency
23.3 / 25
ActivityRecent commits
15.9 / 25
CommunityFork signal
5.6 / 20

Supply Chain Trust

Package Provenance
None
No package attestations found
OSSF Scorecard
Not available
Signed Commits
7%
of last 100 commits verified

Is LycheeMemory safe?

Public trust evidence for LycheeMemory is thin: several supply-chain signals are missing or weak. This does not mean the project is unsafe — it means an outside observer cannot easily verify the usual integrity checks. Treat with extra scrutiny.
Does LycheeMemory publish package provenance?
No published build provenance is currently detected for LycheeMemory. This is common for open-source projects but means consumers cannot independently verify that the package on the registry matches the GitHub source.
Does LycheeMemory have an OpenSSF Scorecard?
No OpenSSF Scorecard data is currently published for LycheeMemory. Maintainers can enable the Scorecard GitHub Action to get a public score; without it, automated supply-chain hygiene is harder for outsiders to verify.
Is LycheeMemory actively maintained?
Maintained. Last push was 12 days ago.
What license does LycheeMemory use?
LycheeMemory ships under Apache-2.0. A declared, OSI-approved license is one of the transparency signals HVTrust scores.
Are LycheeMemory's commits signed?
7% of the last 100 commits to LycheeMemory are verified-signed (GPG, SSH, S/MIME, or GitHub's signing flow). Signed commits help confirm that code was authored by who the commit claims.

Not a safety endorsement. HVTracker describes what public signals show, not whether a project is safe for your use case. Run your own security review before adopting in production.

Ranked neighbours in Memory & Knowledge

AI agent surface

Scored in HVTrust

These runtime-trust fields — detected from public repo docs and manifests — contribute a bounded adjustment to this project's HVTrust score alongside supply-chain evidence. The exact values each field can add or subtract are documented in the methodology → Compare this surface across every listed agent in the capability matrix →

MCP Server Support
high confidence
Implemented
LycheeMemory appears to expose MCP server capabilities.
Detailed evidence is not shown in the public view.
External Service Dependencies
high confidence
4 detected
Public provider/service dependencies detected.
Credential signal: API keys or service config markers documented.
Tool / Plugin Surface
medium confidence
Declared
Declared plugin/integration surface detected.
Detailed evidence is not shown in the public view.
Package Provenance Drift
low confidence
Unknown
Package source metadata is missing or inconclusive
Detailed evidence is not shown in the public view.
  • MCP signal live
  • External deps live
  • Tool / plugin surface live
  • Package provenance drift live

Maintain LycheeMemory?

HVTrust scores LycheeMemory from public signals only — we never contact maintainers first. If a signal is wrong, stale, or missing (provenance you publish, a Scorecard you run, signed releases), tell us and we'll review it. Corrections are public and tracked on GitHub.

Reputation Timeline

Listed 1
2026-07-28
Newly Listed
First tracked at rank #403

Embed Badge Badge guide for maintainers →

HVTrust 29.4 Grade D
Markdown:
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HTML:
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Other agents in Memory & Knowledge

Data sources
GitHub REST API (repo, commits, stars, forks, license) · PyPI / pypistats (downloads, provenance)
Each agent's signals refresh once daily across 6 staggered batches. Methodology v4.2 · Raw JSON