Batch 25 Notes — Yegge Beads ecosystem + FlowCoder + persistent agent-OS
Roster
| Slug | Stars | Lang | Dist | Hooks | Skills | MCP Tools | Status |
|---|---|---|---|---|---|---|---|
| gas-town | 15,581 | Go | cli-tool | 0 | 4 | 0 | Complete |
| beads-ui | 621 | TypeScript | npm-package | 0 | 0 | 0 | Complete |
| superbeads-wiggum | 31 | Bash | bash-script-bundle | 0 | 6 commands | 0 | Complete |
| flowcoder | 26 | Python | standalone-repo | 0 | 5 JSON cmds | 0 | Complete |
| onebrain | 10 | Markdown | claude-plugin | 1 | 34 | 0 | Complete |
| memsearch-cc | 1,841 | Python | claude-plugin | 4 | 1 | 0 | Complete |
| mcp-shrimp-task-manager | 2,103 | TypeScript | mcp-server | 0 | 0 | 16 | Complete |
| agentmemory | 18,016 | TypeScript | npm-package | 12 | 8 | 53 | Complete |
| mempalace | 52,851 | Python | python-package | 2 | 1 | 30 | Complete |
| ai-afterimage | 22 | Python | python-package | 2 | 0 | 0 | Complete |
Canonical Source Resolution
- beads-ui: Reddit ref — found via
gh search repos beads-ui. mantoni/beads-ui (621 stars) matched the "beads JSONL kanban UI" description. - superbeads-wiggum: Reddit ref — found via
gh search repos superbeads. EliaAlberti/superbeads-universal-framework (31 stars) was the only result with explicit "Wiggum Flavour" branding. - memsearch-cc: Reddit ref — found via
gh search repos memsearch. zilliztech/memsearch (1,841 stars) — Zilliz is the Milvus company, confirming this is the Milvus-backed memory search tool referenced in awesome-claude-code. - All other repos found directly by GitHub URL or org/repo slug.
Intra-Batch Analysis
Star Range
This batch spans the widest star range in the Phase B corpus: from 10 (onebrain) to 52,851 (mempalace). The top three (mempalace 52k, agentmemory 18k, gas-town 15k) are among the highest-starred frameworks in the entire corpus.
The Beads Sub-Ecosystem
gas-town, beads-ui, and superbeads-wiggum form a coherent Beads sub-ecosystem:
- beads (bd/bv CLI by mantoni, not in this batch) is the JSONL issue tracker
- beads-ui (mantoni, same author) is the web visualization layer
- gas-town (Yegge) is the multi-agent coordinator that uses Beads as its issue tracker
- superbeads-wiggum (EliaAlberti) is a community fork that adds CLAUDE.md integration and domain packs to the Beads CLI
Yegge's Beads philosophy ("everything is a Molecule, root sources only, no nudging") is the conceptual foundation, but each tool takes it differently: beads-ui is purely visual, gas-town uses it for agent task management, superbeads-wiggum adds slash-command workflow scaffolding.
Persistent Memory Systems (5 of 10)
Five frameworks in this batch are memory systems: agentmemory, mempalace, memsearch-cc, onebrain, ai-afterimage. They form a spectrum:
| Dimension | agentmemory | mempalace | memsearch-cc | onebrain | ai-afterimage |
|---|---|---|---|---|---|
| Storage | SQLite/KV (iii-engine) | ChromaDB + SQLite | Markdown + Milvus | Obsidian files | SQLite/PostgreSQL |
| Verbatim vs compressed | LLM-compressed | Verbatim | Raw markdown | Verbatim | Verbatim (code only) |
| Hooks | 12 (all events) | 2 (Stop+PreCompact) | 4 (all session lifecycle) | 1 (SessionStart) | 2 (Pre/PostToolUse) |
| Scope | All tool interactions | Code + conversations | All tool interactions | User ideas/tasks | Code files only |
| Knowledge graph | Yes (LLM-extracted) | Yes (SQLite temporal) | No | No | No |
| API key required | Optional (LLM features) | No (raw search) | Optional | No | No |
Deny-Then-Allow Hook Pattern
Two frameworks use this pattern: memsearch-cc and ai-afterimage. Both deny the first Write/Edit attempt to force the agent to read injected context. The pattern appears independently invented — memsearch-cc uses a shell script + Python approach while ai-afterimage uses a single Python file with hash-based state tracking.
Dynamic Instructions Pattern
mempalace's skill calls mempalace instructions <command> to fetch live instructions from the CLI at runtime. This is the only framework in the corpus to do this — prompt updates ship with pip upgrade not plugin reinstalls.
Benchmark Competition
agentmemory (95.2% R@5 LongMemEval-S) and mempalace (96.6% R@5 raw, 98.4% hybrid) publish competing benchmarks on the same dataset. mempalace achieves higher precision without any LLM; agentmemory closes the gap with LLM compression enabled. mempalace explicitly refuses to include side-by-side competitor comparisons, while agentmemory publishes a competitor comparison table.
Single-File Architectures
Two frameworks in this batch have notable single-file designs:
- ai-afterimage: entire hook logic in
hooks/afterimage_hook.py(~700 lines) - flowcoder: entire pipeline in a Tkinter Python app with a single JSON command format
FlowCoder Uniqueness
flowcoder is the only framework in Phase B that uses a visual desktop GUI (Tkinter) for workflow authoring. It auto-commits after every block execution, creating an audit trail of agent work. No other framework in the corpus auto-commits per-step.
OneBrain Scale
onebrain has 34 skills — the largest skill count in the Phase B corpus. Skills are directories (not flat .md files), each with a SKILL.md. The braindump skill is noteworthy: it classifies free-form user input into 6 categories (Task/Idea/Note/Project/Question/Feeling) and creates inbox files immediately without confirmation.
Cross-Batch Connections
- agentmemory competes directly with mempalace on LongMemEval benchmarks
- gas-town uses Beads (batch 25) + beads-ui (batch 25) as its data layer
- memsearch-cc uses Milvus — same vector database company (Zilliz) that hosts the memsearch repo
- mcp-shrimp-task-manager is closest to taskmaster-ai (batch 1) but adds process_thought enforcement
- ai-afterimage parallels memsearch-cc in deny-then-allow pattern but with code-only scope and churn tracking
Tier C Stubs
None required. All 10 frameworks were findable: 7 by direct URL, 3 via gh search repos with at most 2 attempts each.