curated collections / memory-context
DSH Memory & Context Plugins (DeepSeek Harness)
Automated recall, project memory and context compression — the hottest direction in DSH right now.
The clearest signal in the DeepSeek Harness catalog: the top installable entries by stars are a memory stack — pre-step auto-recall, profile injection, session capture and project-level memory. These DSH plugins attack the real cost centers of long-running agents: context bloat, cross-task forgetting, and messy recall.
The strongest DeepSeek Harness (DSH) memory stack: auto-recall project memory first, then context compression — ranked below by stars and recency.
307 plugins62K stars
Synced from our GitHub list · Catalog updated 2026-08-20
Why it matters now
Highest-signal category — the most-starred installable entries (OpenViking-style memory, Hindsight-style project memory) are all memory.
Attacks real costs — deterministic compaction, cross-session memory and token savings map 1:1 to agent cost and confusion.
High ceiling — a quality memory layer is exactly what is hardest to copy vs. another theme or sidebar.
In this collection
Editor’s picks lead, then the rest of the theme is auto-discovered by GitHub topics and ranked by stars. Catalog data, refreshed on every sync.
- 1openvikingby volcengine
Self-evolving Context Database for AI Agents. Unify Agent Memory, Knowledge RAG and Skills.
Editor’s pickCuratedAgents, Automation & Workflows30.8K2,378 - 2memosby memtensor
Self-evolving memory OS for LLM & AI Agents: ultra-persistent memory, hybrid-retrieval, and cross-task skill reuse, with 35.24% token savings and DeepSeek Harness support.
Editor’s pickCuratedSessions & Messages10.8K1,000 - 3everosby evermind-ai
One portable memory layer for every AI agent: local-first, Markdown-native, user-owned, and self-evolving across apps, tools, and workflows.
Editor’s pickMCP & Protocols12.3K898 - 4mindmemosby mindscale-noah
Gives dsh agents persistent cross-session memory: auto-recalls relevant user/task context before each turn and writes back lessons after each turn, evolving memory via schema learning and skill distillation
Editor’s pickCuratedSessions & Messages94491 - 5mnemonby mnemon-dev
LLM-supervised persistent memory for AI agents — graph-based recall, cross-session knowledge, single binary. Works with DeepSeek Harness, Claude Code, OpenClaw, and any agent runtime.
Editor’s pickCLI & Terminal49463 - 6dsh-mnemonby omdsh-dev
Three-tier memory control plane for DeepSeek Harness: persistent runtime context, searchable project documents, pluggable long-term memory, smart routing, supervised agent workflows, WebUI, and headless tools.
Editor’s pickCuratedSessions & Messages1358 - 7engramoryby tinqiao-oss
A portable memory protocol for AI agents — load it as standing rules; a curation discipline + reference spec + optional cap hook.
Editor’s pickCuratedSessions & Messages16512 - 8graph-memoryby adoresever
Knowledge-graph memory core native to DSH (Cordis adapter, cross-session triples).
CuratedKnowledge & Research56280 - 9memtrace-publicby syncable-dev
Structural memory for AI coding agents. Bi-temporal graph, MCP-native, zero LLM calls. Cursor · Claude Code · Codex · DeepSeek Harness · Hermes · VS Code · Windsurf.
CuratedKnowledge & Research45941 - 10flowixby text2future
Registers the local flowix-cli MCP server so the agent can search, read, create, and edit Flowix memos and mind-map artifacts.
CuratedAgents, Automation & Workflows33142 - 11vibe-skillsby foryourhealth111-pixel
VibeSkills is a general-purpose Skill that automatically routes local Skills and intelligently orchestrates harness workflows.
Agents, Automation & Workflows2,924235 - 12memmy-agentby memtensor
🍙 A personal AI agent & local memory hub for all AI agents, gives every AI one shared, fully controlled memory and persistent context — all AI remember the same you. Now supports Claude Code, Codex, OpenClaw and Hermes Agent etc.
MCP & Protocols82991 - 13anolisaby alibaba
ANOLISA (Agentic Nexus Operating Layer & Interface System Architecture) | Agentic OS with runtime, security, observability, and Tokenless response compression for lower token usage and cost.
Knowledge & Research36494 - 14dsh-noemaby zseven-w
Noema long-term memory plugin for DSH: durable, inspectable agent memory with recall tools and a settings page.
CuratedSessions & Messages1167 - 15opencontextby melandlabs
A temporal context graph, a memory API, retrieval primitives, and a multiple-platform integration mesh — designed to be embedded into any host process.
CuratedSessions & Messages475 - 16deepseek-harness-sdr-pluginby xuxchloris
Controllable SDR digital employee for DSH: 9-stage foreign-trade lead-gen SOP driven by a server-side state machine, human approval gating on outreach drafts (hash-bound), lead dedupe, hybrid RAG knowledge base, and audit logging; Email/WhatsApp/CRM connectors default to dry-run with no real sends.
CuratedAgents, Automation & Workflows246 - 17dsh-mnemeby modusensus
Structured memory engine for DeepSeek Harness. Offline semantic search, entity-attribute-timeline, autoDream self-consolidation, and human-editable Markdown storage.
CuratedKnowledge & Research303 - 18dsh-context-doctorby zhenyu98
DSH 上下文注入审计：统计 AGENTS.md 指令链/技能目录/工具 schema/MCP 的 token 成本，检测跨文件重复与 rank shadow 冲突；Web UI 圆环面板 + context_audit 工具
CuratedPlugin Tooling164
Frequently asked questions
What does a memory plugin do in DeepSeek Harness?
It gives an agent long-term recall — storing notes, profiles or past turns — so it remembers context across sessions instead of starting fresh each time.
How does context compression help DSH agents?
It deterministically summarizes or truncates the conversation, shrinking token usage and preventing context bloat on long-running tasks.
What's the difference between profile memory and session memory?
Session memory lasts for one run; profile or project memory persists across runs and is injected each time so the agent keeps what it learned.
Which memory plugin should I start with?
Start with a project-memory plugin with auto-recall — the highest-signal install — then add compaction once your agents run long sessions.
Do memory plugins increase API costs?
They add a little overhead per recall, but usually lower total cost by avoiding re-reading large contexts and re-doing forgotten work.
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