ShadowGraph
OfficialRelated Servers
Alternatives to ShadowGraph
No user-submitted related servers found.
Related Servers
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- FlicenseBqualityAmaintenanceMemory for LLM agents: a local graph where the agent keeps decisions, tasks, hypotheses and reasoning between sessions, and gets a summary of what is open and overdue on every return to a project. Works with Claude Code, Codex, Cursor and opencode. Single Rust binary, no embedding model or LLM calls. Source-available (BSL 1.1), commercial use permitted.7140-

litopysofficial
AlicenseNot gradedqualityBmaintenancePersistent graph-based memory for AI agents, stored as plain markdown — no vector DB. Typed nodes and 11 relation types via 5 MCP tools (search, get, create, link, related), stdio and HTTP/SSE transports.3MIT- AlicenseAqualityBmaintenanceLocal-first, source-traceable memory for AI agents — no LLM at ingest, $0 per message, zero data egress. Gives Claude Code, Cursor, and any MCP client one shared persistent memory with semantic recall, belief revision, selective forgetting, and a provenance guard that blocks acting on stale or unconfirmed memories.2317 PyPI15MIT
- AlicenseAqualityAmaintenanceSelf-hosted decision memory for AI coding agents. Captures decisions with the alternatives you rejected, and warns before an agent re-proposes a rejected approach.482Apache 2.0
- FlicenseNot gradedqualityCmaintenanceLocal-first cross-agent memory for AI coding agents. Persistent, shared memory over MCP — what you tell one agent can be recalled by another — with all data stored in a single local SQLite file, no cloud and no API keys.-
TDQS
Scored across 27 tools
Most tools have clearly distinct purposes and the descriptions cross-reference related tools, so an agent can usually select correctly. However review vs review_signals and search/retrieve/recall are close enough that a model needs to read carefully to avoid selecting the wrong read/evaluate variant.
All tools share the shadowgraph_ prefix and snake_case style, which gives the surface a consistent feel. But the pattern mixes bare verbs (review, rebuild, search), noun phrases (context, journal, confidence_evidence), and verb_noun forms (record_decision, purge_preview), so it is not fully predictable.
27 tools is above the threshold for a heavy surface, and several clusters are over-fragmented: search/retrieve/recall/context/traverse, review/review_signals, and purge/purge_preview could be consolidated. While the domain is broad, the count feels excessive for an agent to navigate.
The core decision/memory lifecycle is well covered: record, recall, update, supersede, review, backup, restore, purge. The main gaps are the explicitly missing unlink operation for relationships and no direct apply tool for repair_plan, though agents can work around these via other tools.