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memsearch

Persistent semantic memory layer for DeepSeek Harness backed by Markdown and Milvus.

UI & ProductivityTerminal & TUIDeveloper ToolsBrowser & WebSkills & WorkflowsRemote Execution
2.8kGitHub Stars268ForksUpdated2026-10-19

Install

$ dsh plugin --profile web add @zilliz/memsearch-dsh

Plugin Overview & Capabilities

AI-assisted organization based on the public repository snapshot. The content must be grounded in source evidence and does not replace compatibility or security verification.

source-grounded
memsearch provides persistent, cross-platform semantic memory for DeepSeek Harness and other AI coding agents. Backed by Markdown files as the primary source of truth and Milvus as a rebuildable shadow vector index, it supports automatic session turn capture, pre-step contextual memory injection, hybrid BM25 and dense vector search, and skill-based memory recall.

Key Capabilities

  • Automatic conversation capture and turn summarization into daily Markdown logs
  • Pre-step memory injection before the first model step when relevant context exists
  • Semantic hybrid search combining dense ONNX vector embeddings and BM25 sparse recall with RRF reranking
  • 3-layer progressive retrieval across search hits, section expansions, and raw transcripts
  • Registered memory-recall skill for natural language or explicit agent recall
  • Procedural skill distillation that drafts reusable Agent Skills from captured workflows
  • Optional background maintenance for durable PROJECT.md and USER.md project context
  • Web profile dock component for inspecting skill candidates and browsing memory files

Useful For

  • Recalling past debugging solutions, configuration decisions, and architectural rationales across sessions
  • Sharing conversational context and memory across DeepSeek Harness, Claude Code, and Codex
  • Automatically converting multi-step debugging or deployment procedures into reusable agent skills
  • Maintaining long-lived project state and user preferences across disjoint harness executions

Who It Fits

  • DeepSeek Harness developers working on multi-session software projects
  • Engineers using multiple AI coding agents who need unified memory
  • Agent workflow authors looking to persist context and distill repeatable skills

Documented Limitations

  • Requires installing the companion Python package (memsearch) via uv, pipx, or pip alongside the DSH plugin
  • First run with local ONNX embeddings requires downloading an embedding model (~558 MB)
  • External vector hosting requires network connectivity and credentials for Zilliz Cloud or a self-hosted Milvus instance
  • Interactive memory dock features require the DeepSeek Harness web profile

DSH Compatibility

Version-specific runtime evidence collected by DSH Plugin. A missing result means we have not tested that combination yet.

Not tested yetNo runtime compatibility tests have been published yet.

Security Signals

Objective signals discovered from package metadata and source inspection. These are not a guarantee that a plugin is safe.

License Declared

GitHub reports the repository license as MIT.

info
Package Manifest Not Found

No root package.json was captured in the latest GitHub snapshot.

info
Source Available

Public GitHub source metadata is available for this registry snapshot.

info

Source & Registry Notes

Public provenance, Registry classification, and the latest source check for this entry, kept separate from runtime verification.

Source repository
zilliztech/memsearch
Registry source
GitHub · dsh-plugin topic
Registry classification
Plugin
Source checked
2e64124 · 2026-10-19

This project is independently indexed from public source information. DSH Plugin is not affiliated with DeepSeek or the plugin author. Always check the author repository before installation.

Repository Activity

GitHub stars
2.8kGitHub stars
Forks
268
Open issues
273
Last commit
2026-10-19
Last release
2026-09-24
For maintainers

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