memtrace
About
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.
Details
- Author
- syncable-dev
- Categories
- Developer Tools, Knowledge Base, AI
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Setup
Install memtrace in your MCP client (Claude Desktop, Cursor, Windsurf, and others).
Repository: https://github.com/syncable-dev/memtrace-public
Follow the installation instructions in the repository README, then restart your MCP client.
Memtrace vs. general memory systems (Mem0, Graphiti)
Mem0 and Graphiti are strong conversational memory engines designed for tracking entity knowledge (e.g.User -> Likes -> Apples). They excel at that. For code intelligence specifically, the tradeoff is that they rely on LLM inference to build their graphs — which adds cost and time when processing thousands of source files.
Graphitiprocesses data throughadd_episode(), which triggers multiple LLM calls per episode — entity extraction, relationship resolution, deduplication. At ~50 episodes/minute (](https://github.com/syncable-dev/memtrace-public/blob/main/TELEMETRY.md)source), ingesting 1,500 code files takes1–2 hours.
Mem0processes data throughclient.add(), which queues async LLM extraction and conflict resolution per memory item (source). Bulk ingestion withinfer=True(default) means every file passes through an LLM pipeline. Throughput is bounded by your LLM provider's rate limits.
Bothaccumulate $10–50+ in API costs for large codebases because every relationship is inferred rather than parsed.
Memtrace takes a different approach:it indexes 1,500 files in 1.2–1.8 seconds for $0.00 — no LLM calls, no API costs, no rate limits. Native Tree-sitter AST parsers resolve deterministic symbol references (CALLS,IMPLEMENTS,IMPORTS) locally. The tradeoff is that Memtrace is purpose-built for code — it doesn't handle conversational entity memory the way Mem0 and Graphiti do.
Memtrace exposes a full structural toolkit via the Model Context Protocol.
Memtrace ships skills/guidance that teach agents how to use the graph. They fire automatically based on what you ask — no prompt engineering required.
Plus 8 workflow skills that chain multiple tools with decision logic:memtrace-first,codebase-exploration,change-impact-analysis,incident-investigation,refactoring-guide,continuous-memory,episode-replay, andsession-continuity.
Six scoring algorithms for different temporal questions:
UsesStructural Significance Budgetingto surface the minimum set of changes covering ≥80% of total significance.
Skills are workflow prompts that teach the agent how to chain tools. Kiro does not useSKILL.md, so Memtrace writes equivalent auto steering files instead.
dshcomes from@deepseek-ai/dsh, not from Memtrace.
npm install -g @deepseek-ai/dsh dsh plugin --profile web add github:syncable-dev/dsh-plugin-memtrace
npx -y @deepseek-ai/dsh plugin --profile web add github:syncable-dev/dsh-plugin-memtrace
That bundle registers Memtrace's skills and startsmemtrace mcpinside the Harness profile. First launch may fetch the Memtrace binary vianpx; pin a local install withnpm install -g memtraceandMEMTRACE_BIN=memtrace.
Handles everything — binary, 17 skills, MCP server, plugin, marketplace. One command, both editors.
claude plugin marketplace add https://github.com/syncable-dev/memtrace-public.git claude plugin install memtrace-skills@memtrace --scope user claude mcp add memtrace -- memtrace mcp
npm install -g memtracehandles everything automatically. Cursor v2.4+ reads the sameSKILL.mdformat as Claude.
For project-local install (skills travel with your repo):
npx memtrace-skills install --only cursor --local
Codex, Windsurf, VS Code, Hermes, OpenCode, and Kiro
The installer also writes skills/guidance and MCP configuration for the newer agent surfaces:
npx memtrace-skills install --only codex,windsurf,vscode,hermes,opencode,kiro
Install project-local config where supported:
npx memtrace-skills install --only codex,vscode,opencode,kiro --local
For Cline, Roo Code, or any client that only needs MCP tools, add this server manually:
{ "mcpServers": { "memtrace": { "command": "memtrace", "args": ["mcp"], "env": {} } } }
memtrace uninstall # removes skills, MCP server, plugin, settings npm uninstall -g memtrace
Already rannpm uninstallfirst? The cleanup script is at~/.memtrace/uninstall.js:
npm install -g memtraceships a small main package + a platform-specific binary (one of@memtrace/darwin-arm64,@memtrace/linux-x64,@memtrace/win32-x64). Ifmemtrace startever says"Could not find binary for your platform":
# Re-run install, asking npm to keep optional deps npm install -g memtrace --include=optional # Or refresh from latest memtrace install # built-in self-update npm install -g memtrace@latest --force # Or install the platform binary directly (Apple Silicon shown — swap for your platform) npm install -g @memtrace/darwin-arm64
This typically only happens on machines where npm is configured to skip optional dependencies (corporate npmrc, certain CI caches).
Programming:Rust · Go · TypeScript · JavaScript · Python · Java · C · C++ · C# · Swift · Kotlin · Ruby · PHP · Dart · Scala · Perl ·Lua— full AST: functions, classes, types, calls, complexity.
Infrastructure & config:YAML·HCL / Terraform·JSON·TOML·SQL(including PostgreSQLCREATE POLICYfor RLS, with cross-language edges from policies to Drizzle / Prisma / TS schema symbols).
Framework-aware scannerson top of the AST layer:
- Backend HTTP:Express · NestJS · Encore · Fastify · Vapor · Hummingbird · FastAPI · Flask · Django · Gin · Chi · Echo · Actix · Lapis · Kong · OpenResty · Rails routes
- Frontend / client:RTK Query · TanStack Query · SWR · URLSession · AsyncHTTPClient · axios · fetch · SwiftUI views
- CI / infra:GitHub Actions workflows (jobs, steps,needs:edges) · Terraform variables / modules / data sources · Helm charts · K8s manifests
- Package & dependency graphs:package.jsonscripts + deps ·Cargo.tomldeps ·pyproject.toml(best-effort)
- Database:PostgreSQL RLS policies + triggers + functions, with heuristic edges to ORM schema
Memtrace runs locally — first index is CPU/RAM intensive, subsequent queries and incremental indexing are much lighter.
Since v0.3.17 Memtrace ships withopt-outtelemetry that helps us catch crashes, regressions, and performance issues before someone files an issue.
- Collected:app-start events, indexing/embedding durations, panic reports, WARN/ERROR log lines from Memtrace's own crates.
- NOT collected:source code, file contents, symbol names, embeddings, repository names or paths, branch names, commit data.
- Sanitisation:every payload is run through a sanitiser that strips home-dir paths, token-shaped strings, and email addresses before it touches disk.
MEMTRACE_TELEMETRY=off memtrace start # per-run export MEMTRACE_TELEMETRY=off # permanent (~/.zshrc, ~/.bashrc)
Or in your editor's MCP config:"env": { "MEMTRACE_TELEMETRY": "off" }.
Full breakdown — including the on-disk queue layout, where data is stored on the receiving end, and how to inspect what would have shipped — is inTELEMETRY.md.
Proprietary EULA.Free to use during private beta and after general availability for individual developers. Indexer + database (MemDB) are closed-source.
Benchmark suite under MIT inbenchmarks/— fully reproducible, no proprietary code required to run them.
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