Memtrace gives AI coding agents structural memory — your codebase as a live knowledge graph so agents stop re-deriving code structure from scratch
Memtrace gives AI coding agents structural memory — your codebase as a live knowledge graph so agents stop re-deriving code structure from scratch and start reasoning from fact. Exposed over MCP by the memtrace mcp server, that capability becomes something an assistant can invoke while it works, not something you go and do afterwards.
The server ships on npm as memtrace-skills, so your MCP client can launch it on demand — there is no separate build step. Add the server block to your client's configuration, restart it, and the tools register themselves.
Everything the assistant can do here goes through one of these:
find_code — hybrid BM25 + semantic + RRFfind_symbol — exact / fuzzy with Levenshteinanalyze_relationships — callers, callees, hierarchy, importsget_symbol_context — 360° view in one callget_impact — blast radius with risk ratingdetect_changes — diff-to-symbols scope mappingfind_dead_code — zero-caller detectionfind_most_complex_functions — complexity hotspotscalculate_cyclomatic_complexity — get_repository_statsget_evolution — 6 scoring modesget_timeline — full version historyfind_bridge_symbols — betweenness centralityYou will need one environment variable: MEMTRACE_TELEMETRY. Keep credentials in your client's env block or a secrets manager rather than in a file you might commit.
Memtrace runs locally — first index is CPU/RAM intensive, subsequent queries and incremental indexing are much lighter. | | Minimum | Recommended | |---|---|---| | CPU | 4 cores | 8+ cores for large monorepos | | Memory | 8 GB RAM | 16–32 GB RAM | | Disk | 5 GB free | 10–20 GB free | | GPU | Not required | Not required | | Node.js | ≥ 18 | Current LTS | | Git | Required for temporal analysis |
This sits in the knowledge and memory group, where several servers overlap in what they claim to do but differ sharply once you actually set them up. Memtrace's toolset — find_code, find_symbol, analyze_relationships and 11 more — is a fair guide to whether it matches your workflow.
We check each listing at SyncDev against the project's documentation before it goes live — if something here drifts out of date, it is a bug worth reporting.
| Tool | What it does |
|---|---|
| find_code | hybrid BM25 + semantic + RRF |
| find_symbol | exact / fuzzy with Levenshtein |
| analyze_relationships | callers, callees, hierarchy, imports |
| get_symbol_context | 360° view in one call |
| get_impact | blast radius with risk rating |
| detect_changes | diff-to-symbols scope mapping |
| find_dead_code | zero-caller detection |
| find_most_complex_functions | complexity hotspots |
| calculate_cyclomatic_complexity | get_repository_stats |
| get_evolution | 6 scoring modes |
| get_timeline | full version history |
| find_bridge_symbols | betweenness centrality |
| find_central_symbols | PageRank / degree |
| list_communities | Louvain modules |
{
"mcpServers": {
"github-com-syncable-dev-memtrace-public-tree-main": {
"command": "npx",
"args": ["-y", "memtrace-skills"],
"env": {
"MEMTRACE_TELEMETRY": "your-value"
}
}
}
}Add to claude_desktop_config.json, then restart Claude Desktop.
Memtrace runs locally — first index is CPU/RAM intensive, subsequent queries and incremental indexing are much lighter. | | Minimum | Recommended | |---|---|---| | CPU | 4 cores | 8+ cores for large monorepos | | Memory | 8 GB RAM | 16–32 GB RAM | | Disk | 5 GB free | 10–20 GB free | | GPU | Not required | Not required | | Node.js | ≥ 18 | Current LTS | | Git | Required for temporal analysis |
| Variable | Description | Required |
|---|---|---|
| MEMTRACE_TELEMETRY | Configuration value read at startup. | Optional |
A knowledge graph your assistant keeps between sessions — entities, relations and observations that persist.
Kill hallucinated APIs — version-accurate, up-to-date library documentation injected straight into context.
Your workspace, on speaking terms with AI — search, read and write Notion pages and databases.
A structured scratchpad for hard problems — stepwise reasoning with revisions, branches and visible logic.
Symbol-level code navigation, refactoring and memory for coding agents — the IDE brain your assistant has been missing.
Chat with your second brain — search, read and write vault notes through the Local REST API.