Plugins

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.

Evolve skills, agents, prompts, code and plugins from inside DeepSeek Harness, with AgentDescent.

DSH Web UI 插件应用商店：目录浏览/搜索/一键安装/兼容徽章/已装列表。A plugin store for DSH Web UI — catalog browse, search, one-click install, compat badges, installed list.

A zero-residence context engine: large tool payloads are evicted from the live context into a queryable store, only pointers stay behind, and any payload can be reconstructed verbatim on demand — so per-turn prompt tokens stop growing with the transcript. It ships four capabilities: zr_recall rebuilds masked originals by tool-call id or session sequence from the persisted session log (the content is not resident, never lost), zr_ledger reports the attention integral and a three-part cost breakdown, zr_compact arms one forced compaction, and zr_fast runs long commands asynchronously and hands back a handle immediately.

The organ kernel of Agent-Body: plugins organized as one body. Each command is turned into a nerve impulse that deterministically innervates the organ which should handle it and names the capability to use; a heartbeat with a variable interval (faster on alerts, slower when idle) pumps operator directives, proprioception and homeostasis warnings system-wide; reflex arcs fire with zero model calls; failures go through deterministic attribution (missing tool / arguments / permission / timeout / network), a prescription, and a re-check loop, with argument errors never auto-retried. A missing organ is compensated by the one with the highest capability overlap, and tool schemas are gated by the current intent (84.7% measured reduction, reproducible in-repo). This entry is the kernel plugin package inside the agent-body repository.

The memory organ of Agent-Body: after 2 minutes of silence it enters light sleep and after 5 minutes deep sleep, where deterministic rules distil real experience into memory cards (pitfalls, playbooks, hotspots, unresolved issues, facts). Incoming commands recall related cards by keyword and inject them into context; cards unused for long decay on a half-life and are archived rather than deleted. It also de-noises body-wide homeostasis warnings: repeats inside a window are counted instead of re-printed, and a warning that keeps recurring without ever healing converges into a known steady-state offset and goes quiet. Consolidation and recall make no model calls.

Plugin coordination layer: plugins declare their capabilities, commands route to the owning plugin without a model call, tool schemas load on demand to cut prompt tokens, run history consolidates into long-term memory while idle, and failures are classified before any retry.

AI Company Framework: turn one sentence into a multi-agent company — 15 flat Skills (boss/pipeline/role-template/11 roles/generic customer-memory), 7 workflow templates, AgentTeams runtime + activity panel, employee sidebar with per-session isolation, Feishu bot bridge (boss and per-employee bots, group routing, autonomous push, human decision gate, external-contact guide), per-customer SQLite memory with three-layer memory (cross-group merge), /company mode switch; no RAG.

Math Agent Framework for DeepSeek Harness: symbolic derivation engine (CES / quadratic forms / ODE / PDE), SymPy verification, Lean 4 compiler-verified formal proofs, and QED-style multi-agent adversarial verification, exposed as native DSH tools through a TS shell bridging a Python engine. Requires cloning the math-agent-framework repo and `pip install -r requirements.txt`, with MATH_AGENT_HOME pointing at the repo root. Every result carries provenance (engine versions, seed, tolerances) and is marked untrusted data.

a demo plugin for a preset with builtin musescore wasm editor/preview engine for agent composing

给 DeepSeek Harness 补上时间轴记忆：定宽只追加的事实日志 + agent 自己维护的摘要金字塔，固定阅读预算下 recall 成本不随记忆量增长。

Microsoft Agent Framework (MAF) plugin for DeepSeek Harness: validate, run, and scaffold declarative agents and workflows (YAML).