Proprietary EULA (private beta). Structural agent memory for AI coding agents — a bi-temporal knowledge graph installed as a DeepSeek Harness plugin via the dsh CLI, with fully local indexing, 25+ MCP tools, 17 skills and zero LLM calls.
DSH integration
Compatible
Author-claimed
Safety audit
Unaudited
Last verified
2026-08-21
License
NOASSERTION
01What can it help you accomplish?
Give DeepSeek Harness agents persistent structural agent memory of the codebase across sessions
A live bi-temporal knowledge graph of every function, class, call edge and version, queryable in milliseconds via 25+ MCP tools and 17 agent skills
Developers running coding agents on DeepSeek Harness who want shared, replay-aware code context without agents re-reading files every session
Assess blast radius and replay refactors with full causal awareness before changing code
Impact analysis with risk rating (`get_impact`), diff-to-symbol scope mapping (`detect_changes`), and six-mode temporal evolution queries
Engineers doing refactors, incident investigation or code review on large repos who need to know what breaks before they change it
Index a large codebase locally with zero LLM calls and zero API cost
A 50k-file repo indexed in under 90 seconds by Rust + Tree-sitter parsers — 20+ languages plus framework-aware scanners, fully local
Teams with large monorepos or strict privacy requirements who can't send source code through LLM APIs
02How to install into DeepSeek Harness
Prerequisites
- Node.js ≥ 18 (README requirements table)
- DeepSeek Harness CLI — `@deepseek-ai/dsh`, which provides the `dsh` command (installed globally or run via npx)
- Git repository history available — required for temporal analysis
- Private beta access — Memtrace rolls out access in batches via the waitlist at memtrace.io
Installation steps
- 01
Install DeepSeek Harness: `npm install -g @deepseek-ai/dsh`
$ npm install -g @deepseek-ai/dsh
- 02
Add the Memtrace plugin: `dsh plugin --profile web add github:syncable-dev/dsh-plugin-memtrace` (or the npx variant without a global CLI)
$ dsh plugin --profile web add github:syncable-dev/dsh-plugin-memtrace
- 03
Optional: pin a local binary with `npm install -g memtrace` and `MEMTRACE_BIN=memtrace` so the first launch doesn't fetch it via npx
$ npm install -g memtrace
- 04
Ask the agent to index the workspace, then pull blast radius, evolution, or an architecture briefing
Verify the integration
Not specified by the author
Rollback
- `memtrace uninstall` — removes skills, MCP server, plugin, settings
- `npm uninstall -g memtrace`; if npm uninstall already ran, the cleanup script is at `~/.memtrace/uninstall.js`
03DSH integration and capability boundaries
Installed as a DeepSeek Harness plugin via the dsh CLI (`dsh plugin --profile web add github:syncable-dev/dsh-plugin-memtrace`); the bundle registers Memtrace's skills and starts `memtrace mcp` inside the Harness profile.
Structural local indexing
any codebase — 20+ programming languages plus YAML / HCL / JSON / TOML / SQL and framework-aware scanners (Express, NestJS, FastAPI, Django, GitHub Actions, Terraform, …)→a live knowledge graph with symbols as nodes (functions, classes, interfaces, types, endpoints) and CALLS / IMPLEMENTS / IMPORTS / EXPORTS / CONTAINS edges, built deterministically with Rust + Tree-sitter — zero LLM calls
writes a local graph index / MemDB on disk; the first index is CPU/RAM intensiveBi-temporal engine
the indexed repo plus its Git history→time-travel queries via six scoring algorithms (impact, novelty, recency, directional, compound, overview) — every symbol carries its full version history
25+ MCP tools + 17 agent skills
natural-language requests from the agent (find / who-calls / what-changed / blast-radius / architecture)→hybrid BM25 + semantic search, relationship analysis, graph algorithms (PageRank, Louvain communities), Cypher queries — skills fire automatically based on what you ask
the DSH plugin bundle starts a local `memtrace mcp` server and registers skill files inside the Harness profileImpact analysis & cross-repo API topology
a symbol, diff, or service boundary→blast radius with risk rating, dead-code detection, complexity hotspots, and the HTTP call graph between repositories
network traffic limited to license validation, aggregate node/edge counts and opt-out crash telemetry — no source code, file paths or symbol names
04Who is it for? When not to use it?
Good for
- Developers running coding agents on DeepSeek Harness who want shared, replay-aware code context without agents re-reading files every session
- Engineers doing refactors, incident investigation or code review on large repos who need to know what breaks before they change it
- Teams with large monorepos or strict privacy requirements who can't send source code through LLM APIs
Not for
- Memtrace is in private beta — access is rolled out in batches via the waitlist at memtrace.io, so new users may need to wait for a cohort before they can use it.
- Proprietary EULA (GitHub reports NOASSERTION): the indexer and MemDB database are closed-source; free for individual developers during beta and after GA.
- The first index is CPU/RAM intensive — minimum 4 cores, 8 GB RAM, 5 GB disk and Node.js ≥ 18; 8+ cores and 16–32 GB RAM recommended for large monorepos.
05Compatibility, maintenance and safety notes
- Memtrace is in private beta — access is rolled out in batches via the waitlist at memtrace.io, so new users may need to wait for a cohort before they can use it.
- Proprietary EULA (GitHub reports NOASSERTION): the indexer and MemDB database are closed-source; free for individual developers during beta and after GA.
- The first index is CPU/RAM intensive — minimum 4 cores, 8 GB RAM, 5 GB disk and Node.js ≥ 18; 8+ cores and 16–32 GB RAM recommended for large monorepos.
Proprietary EULA per README (GitHub reports NOASSERTION) · actively maintained (latest release v1.1.5, 2026-08-20)
06Frequently asked questions
How does Memtrace integrate with DeepSeek Harness?
Install the Harness CLI first (`npm install -g @deepseek-ai/dsh`), then run `dsh plugin --profile web add github:syncable-dev/dsh-plugin-memtrace`. The bundle registers Memtrace's skills and starts `memtrace mcp` inside the Harness profile.
Is it a native dsh runtime or MCP?
It's installed as a DSH plugin. The plugin bundle starts a local `memtrace mcp` server inside the Harness profile and registers 17 agent skills that fire automatically based on what you ask — no prompt engineering required.
What do I need before installing?
Node.js ≥ 18 and the DeepSeek Harness CLI; Git repository history is required for temporal analysis. Memtrace is also in private beta, so you may need to join the waitlist at memtrace.io to get access.
Does my source code leave my machine?
No. Indexing runs entirely locally; the only network traffic is license validation, aggregate node/edge counts and opt-out crash telemetry — no source, file paths or symbol names. Disable telemetry with `MEMTRACE_TELEMETRY=off`.
What machine resources does indexing need?
The first index is CPU/RAM intensive: minimum 4 cores, 8 GB RAM and 5 GB disk; 8+ cores and 16–32 GB RAM are recommended for large monorepos. Subsequent queries and incremental re-indexing are much lighter.
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08Data and sources
Memtrace runs as a [DeepSeek Harness](https://github.com/deepseek-ai/deepseek-harness) plugin.
npx -y @deepseek-ai/dsh plugin --profile web add github:syncable-dev/dsh-plugin-memtrace
That bundle registers Memtrace's skills and starts `memtrace mcp` inside the Harness profile. First launch may fetch the…
This page is generated from the project’s public documentation, repository metadata and a structured parse of DSH Plugins; last verified on 2026-08-21. Found an error? Submit a correction.
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