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v1.0 Launch Runbook#

Step-by-step plan to take memgar from "code on a branch" to "stable PyPI release + signed feed live + public announcement." Targets the maintainer (slcxtor); not user-facing.

Pre-flight checklist#

The launch-prep PR (#NN — this one) ships:

  • pyproject.toml version 0.5.6 → 1.0.0
  • pyproject.toml classifier Development Status :: 4 - Beta → 5 - Production/Stable
  • CHANGELOG.md v1.0.0 entry consolidating Tier 1-3 + integrations + cloud foundation
  • README.md honest-baseline disclaimer (80% recall / 9% FPR on internal gold corpus, pre-1.0 caveat)
  • similarity_layer.py threshold calibration (kills SIM-001 false positive)
  • examples/quickstart.py — 60-second end-to-end demo
  • This runbook

The launch-prep PR does NOT publish PyPI or create the v1.0.0 git tag — those are explicit operator actions below.

Phase 1 — Repository state (10 minutes)#

  1. Merge the launch-prep PR 
    # in this PR's web UI: Squash and merge
  2. Merge the feed key rotation PR (#63) if not already 
    # ensures feeds/memgar-feed.json.gz reflects 807 patterns
  3. Close stale PRs that won't make it into v1.0

  4. 32 (feat(enforcement)) — 11 days old, big diff, likely conflicts → close with comment "deferred to v1.1, see roadmap"#

  5. 35 (docs: polish README quickstart) — superseded by the launch-prep PR — close#

  6. 51 (fix feed loader network failure fallback) — small, decide on merits → merge or close#

Phase 2 — Threat feed live (5 minutes)#

  1. Set the Ed25519 private-key secret 
    # The base64-encoded PEM is in your password manager (entry from
# 2026-05-19 — `memgar feed signing key (Ed25519) — rotated`)
echo '<paste base64 PEM here>' | gh secret set MEMGAR_FEED_PRIVATE_KEY_PEM \
    --repo slcxtor/memgar
  2. Trigger the first real feed release 
    gh workflow run feed-publish.yml \
    -f version_bump=patch \
    --repo slcxtor/memgar
  3. Verify (after ~2 min) 
    gh release view feed-v1.0.2 --repo slcxtor/memgar
# Asset: memgar-feed.json.gz (~165 KB)
  4. Verify client side 
    pip install -U memgar
memgar feed sync
memgar feed status
# → feed_version=1.0.2  pattern_count=807  signed=True

Phase 3 — PyPI publish (5 minutes)#

The .github/workflows/publish.yml (or equivalent — confirm the actual filename) is wired for trusted publishing. Tag triggers it.

  1. Tag v1.0.0 
    git checkout main
git pull
git tag -a v1.0.0 -m "memgar v1.0.0 — first stable release"
git push origin v1.0.0
  2. Watch the workflow 
    gh run watch --repo slcxtor/memgar
  3. Verify on PyPI 
    pip install memgar==1.0.0
memgar version
# → memgar 1.0.0

Phase 4 — GitHub Release (3 minutes)#

  1. Create the release notes
  2. GitHub UI → Releases → "Draft a new release"
  3. Tag: v1.0.0 (already exists from Phase 3)
  4. Title: memgar v1.0.0 — first stable release
  5. Body: paste from CHANGELOG.md v1.0.0 section
  6. Attach: a copy of feeds/memgar-feed.json.gz (optional convenience)
  7. Mark as the latest release

Phase 5 — Announcement (variable)#

This is the bit that's all signal-noise tradeoff. Honest framing is the biggest predictor of how the post lands.

Channels in order of leverage#

  1. r/LLMDevs — small, technical, very memgar-adjacent
  2. r/MachineLearning — high reach, low memgar-relevance; expect skepticism
  3. Hacker News — high reach, high pickiness; "Show HN" is the right framing
  4. AI safety mailing lists (LessWrong, MITRE working groups) — slow but high-signal
  5. Twitter/X — quick, low-bar; thread format with concrete examples
  6. OWASP ASI slack (if exists) — directly relevant audience
  7. A few cold emails to AI agent teams you know — explicit "design partner" framing

Suggested copy (one example, adapt freely)#

Title (HN / r/LLMDevs):

Show HN: Memgar — open-source memory poisoning defense for AI agents (Pre-1.0 → 1.0)

Body opener:

Memgar is an open-source library for detecting and blocking memory poisoning in AI agents — the class of attacks where a poisoned RAG chunk, conversation message, or tool result quietly influences every future read.

We just shipped v1.0. The honest summary: - 807 patterns covering 7 phases of a memory-poisoning kill chain - 4-layer detection (pattern + semantic + LLM + behavioral baseline) - Drop-in security wrappers for Mem0, Letta, Pinecone, Chroma, Qdrant, Weaviate, LangChain, LlamaIndex, CrewAI, AutoGen, OpenAI Assistants, MCP - Ed25519-signed weekly threat feed with automated MITRE/CVE/OWASP sync - On our internal corpus (95 attacks + 49 benign) we measure ~80% recall and ~9% FPR. No public benchmark for memory poisoning exists yet — treat any vendor's number as preliminary, ours included.

Not production-tested at scale. Looking for design partners.

The "not production-tested" line is what makes the rest believable. Don't skip it.

Don't do#

  • Don't claim "battle-tested" or "enterprise-ready"
  • Don't use "industry-leading" or "best-in-class"
  • Don't claim a specific customer unless you have their written go-ahead
  • Don't quote precise FPR numbers without the corpus caveat in the same breath

Phase 6 — Post-launch (week 1)#

  • Watch issues + DMs daily for the first week
  • Each "won't install" or "false positive on my data" report becomes a curated GitHub issue with triage label
  • Patch obvious wins fast (small versions: 1.0.1 / 1.0.2)
  • Open one blog post 7-10 days in: "What we learned from 100 issues" — early honesty signals
  • If 3+ people ask for the same integration / feature, prioritise it for 1.1

Rollback paths#

Problem Action
v1.0.0 has a critical bug pip install memgar==0.5.6 is still on PyPI; yank 1.0.0 via pypi-yank
Feed signed with wrong key Roll back verifier.py to old key constant, push 1.0.1
HN front-page traffic crushes infra docs.memgar.com is static — no infra to crush. PyPI handles its own load
Embarrassing tweet from someone Don't engage publicly; fix the underlying issue, ship 1.0.1, link to the fix

What 1.1 should bring (already in the roadmap)#

  • Cloud control plane (currently on the claude/cloud-saas-foundation branch — revive when there's a customer asking)
  • Production-grade transformer model (training data + ONNX artifact)
  • arXiv paper monitor (LLM-assisted pattern extraction)
  • Community submission portal
  • Postgres adapter for cloud TenantStore
  • WebSocket telemetry streaming
  • SOC 2 controls