DeepSeek Harness chat history and session management: search archives, restore conversations, and delete safely

DSH 插件：便捷无感的同窗口内对话回退，从不新建分支；自带轻量工作区备份，可一并还原文件（Claude Code /rewind 语义）。 · DSH plugin: effortless in-window conversation rewind — never forking a new session; ships a lightweight workspace backup that restores files together with the rewind (Claude Code /rewind semantics).

Mneme — the memory layer for your codebase. Knows the WHY, the WHAT, the WHERE-IT-BREAKS.

One chat for everything in DeepSeek Harness (DSH): no more hunting for old conversations. Each message goes to the topic it belongs to, with its own context.

Browse every recent Claude Code session across all your projects and jump back into any of them. Shows project path, summary, git branch, model, turn count, and a ready-to-paste resume command. --open re-opens a whole working set in terminal tabs. Local files only: no network, no telemetry.

Token-saving proxy for Claude Code: renders bulky context (system prompt, tool docs, old history) as dense PNGs to cut input tokens. Runs on Node and Cloudflare Workers.

Context-as-image compression proxy for LLMs: renders bulky context (system prompt, tool docs, history) as dense PNG pages with exact per-provider billing math (Anthropic/OpenAI/Gemini). Node and Cloudflare Workers. Part of the OmniRoute family.

WorkBuddy console for DeepSeek Harness, in one plugin: the account/model pool (merged from dsh-workbuddy-xdpool), the skill market with an in-composer skill picker, batch check-in, pending tasks, session health, history and credit usage. Bilingual.

Graph memory for AI agents — decisions, context, and session history that survive across every conversation. Works with any LLM.

Your team and your AI agents work from the same context: tasks, their full history and time as plain files in your git repository

Connects ChatGPT to local files, terminals, and the desktop through MCP with Chrome integration, letting users read and edit project files, run commands, and follow tool results as they arrive. It also adds durable multi-agent workers, Goal/Loop task tracking, mid-task corrections, and Compact & Resume for carrying session and worker history into a fresh chat.

Enables AI agents and LLMs to persist facts, preferences, and observations in a local SQLite store and to build and traverse subject-predicate-object knowledge graphs across sessions. It provides fuzzy keyword search and graph queries so assistants retain context and recall prior project history without repeating prompts.

Enables MCP clients to coordinate local AI model runs by monitoring RAM, system commit, and VRAM, queueing tasks across clients, performing admission checks before inference, renewing leases, completing and releasing runs, and reporting status, history, and health while preserving original generation quality parameters. It also supports controlled start/stop and optional idle application memory reclamation.

Enables MCP clients to discover NVIDIA, AMD, and Intel GPUs, monitor utilization, memory, temperature, power, clocks, and processes, retain bounded local history, and estimate whether AI models fit available VRAM before loading them.