Vulcanus
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Alternatives to Vulcanus
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- AlicenseNot gradedqualityCmaintenanceEnables AI assistants to build and maintain a shared, git-backed Markdown wiki: they search, file, update and link notes with YAML frontmatter, keep verbatim sources, and optionally read code repos, so knowledge one assistant records is available to all the others. Runs without LLM calls, embeddings or a database, with the vault's own files defining page types, instructions and tools.MIT
- AlicenseAqualityAmaintenanceLocal, searchable project memory for AI coding agents. Markdown source of truth, MCP interface, safe structured updates313Apache 2.0
- AlicenseNot gradedqualityBmaintenanceExposes a living systems memory vault to autonomous agents, letting them search notes with BM25 ranking, read sections within token budgets, extract API/port/schema contracts, compute dependency blast radius, and run machine-verifiable invariant assertions before mutating code. It also supports surgical frontmatter edits, schema linting, wikilink integrity checks, and worklog recording, over stdio MCP.MIT
- AlicenseNot gradedqualityDmaintenanceProvides long-lived, cross-project technical memory for AI agents via markdown cards stored in git and indexed by SQLite, enabling search, retrieval, and human-reviewed knowledge management.ISC
- AlicenseBqualityBmaintenanceEnables AI agents to capture, search, relate, review, and safely update a local-first Markdown knowledge vault through typed operations such as inventory, retrieve, run_view, update, add_attachment, trash, and restore. Agents get validated access to personal notes and attachments without a generic shell or arbitrary filesystem access.28Apache 2.0
- AlicenseNot gradedqualityBmaintenanceGives AI agents a long-term memory layer over plain Markdown notes, letting them run ranked, citable searches, record new facts, and inspect conflicts and stale entries. Results carry freshness labels, supersession history, and audit fields like search timestamp and no-match status so agents can prove a search actually ran.MIT
TDQS
Scored across 8 tools
The read tools (recall, search, list_projects) overlap in purpose but descriptions cross-reference each other well, steering the agent to the cheapest correct call. append_rule vs append_decision vs update_capsule are clearly differentiated by what they write. doctor vs vault_status is explicitly disambiguated (full findings vs summary).
Four tools use a clean verb_noun pattern (list_projects, append_rule, append_decision, update_capsule), but the read/inspect tools break it with bare verbs (recall, search, doctor) and one noun phrase (vault_status). Still readable and mostly predictable, with only minor deviations.
Eight tools is well-scoped for a knowledge-vault server, covering retrieval, inspection, and targeted writes without redundancy. Each tool earns its place in the read/write/validate triad.
Core lifecycle is covered: read (recall, search, list_projects), validate (doctor, vault_status), and targeted writes (append_rule, append_decision, update_capsule) with supersede handling. Project/note creation and deletion are absent, but those appear intentionally delegated to the terminal, leaving only minor gaps.