"Exploring Memory Persistence in DeepSeek Technology" matching MCP servers:

Enables Codex to delegate separable work to DeepSeek Flash/Pro agents through start, continue, status, and cancel tools, while Codex retains tool execution, authorization, and final verification.

Enables AI coding agents like Claude Code or Codex to delegate tasks to a DeepSeek Harness subagent with its own context window, providing tools for task delegation, result waiting, continuation, and supervision with sandboxed execution.

Enables Codex to rank available tools and skills, rerank code and documentation search results, and triage saved output artifacts using Jev-based relevance scoring while leaving final decisions to Codex.

An MCP relay that adds a use_jev tool to every connected server, letting the Jev model autonomously select and execute tool calls while the calling model supplies any required text values. It enables agents to delegate multi-step tool use across all configured MCP servers through a single unified interface.

Enables Codex to manage a local Claude Code companion through MCP, implementing a dual-agent workflow where Codex handles reasoning and review while Claude Code performs engineering tasks.

A Codex MCP server that delegates exploration and implementation tasks to DeepSeek via Claude Agent SDK, supporting subagent types and task persistence.

Allows seamless integration of DeepSeek's language models with MCP-compatible applications like Claude Desktop, supporting features such as model selection, temperature control, and multi-turn conversations with automatic model fallback.

Enables ChatGPT (or any MCP client) to delegate coding tasks to a local Hermes-backed agent with async job management, supporting read-only investigation, implementation, and continuation of sessions via secure MCP tunnel.

Enables Claude Code to control Firefox through tools for tabs, navigation, clicks, typing, scrolling, screenshots, page reading, forms, JavaScript, and console/network logs.

A Model Control Protocol server implementation that allows Claude Desktop to use Deepseek models running in Docker, enabling seamless integration between Claude Desktop and Deepseek's language models.

Enables Codex to start the local DeepSeek Harness Web UI, delegate coding tasks into visible sessions, and review workspace changes.

Enables MCP-capable AI harnesses to interact with DeepSeek Web using an authenticated browser session, supporting prompts, deep think, search, image recognition, file attachments, and local SKILL.md injection.

Enables Codex to launch and orchestrate local agent sessions, delegate tasks, return clickable live web links, and independently verify actual workspace changes.

Enables MCP clients to submit prompts through a signed-in DeepSeek browser session, retrieve responses, start new chats, check status, and stop operations, with optional Telegram forwarding.

Enables users to describe an LLM fine-tuning workload once and receive ranked, task-aware GPU recommendations across a dozen cloud providers based on required VRAM/FLOPs, cost, and runtime. It surfaces the cheapest, fastest, and balanced options through MCP so coding assistants can plan training hardware without manual price comparison.

Lets MCP clients like Codex delegate independent sub-tasks to DeepSeek as a sub-agent via a single tool, with optional context, custom system prompts, and configurable model/temperature settings.

MCP server that lets AI agents discover, inspect, install, and run DeepSeek Harness plugins via MCP, bridging the gap between the MCP protocol and Cordis-based plugin bundles.

Connects a DOT main chat to an already-running Harness instance so it can submit native tasks with progress, replies, queue controls and verified cancellation, page through original history, run keyword history searches, and discover project files with bounded line/tail reads and literal content search. It also handles native interactions, permission reconciliation and explicitly scoped deployment planning and evidence reads, with stable run/operation IDs and idempotency keys for recovery after timeouts or disconnects.

Adds OrgX MCP tools to a DeepSeek Harness profile and runs a workspace-bound OrgX gateway peer through Harness headless mode.

Enables AI agents to persist and restore checkpoint state, supporting crash recovery and replay for LangGraph-based workflows.

A shared memory and coordination server for multiple AI coding agents, built on the Model Context Protocol (MCP).

Provides MCP tools for persistent, cross-session memory via Cloudflare Agent Memory, enabling agents to store, recall, ingest, summarize, and manage memory namespaces.

Proxies every Playwright MCP tool so agents drive a real browser, while automatically attaching per-site lessons, self-firing fixes, saved procedures and the site's observed internal API to each navigation. Later sessions on the same domain start where the previous one stopped, without anyone having to write or request the notes.

This MCP server enables Codex to delegate bounded subtasks to DeepSeek Harness through MCP, with persistent JSON mailbox messaging and proactive task initiation via Codex app-server. It provides tools for delegation, message exchange, and task status tracking.

Enables AI tools and agents to share a persistent memory space, so a decision, task, or encrypted secret saved in one client can be searched, recalled, and handed off from another. It also tracks goals day by day, moves unfinished work between sessions, and keeps exports, trash recovery, and per-account isolation under the user's control.

A lightweight MCP server that records and reuses prompt gaps — essential context missing from initial instructions — so your AI assistant learns from every session using a plain JSONL file tracked by Git.

Enables agents to search a lightweight catalog, inspect permissions, lazily start trusted MCP servers, and call child tools without keeping all schemas in context. It also loads approved skills on demand and routes third-party additions through a human approval queue.

Provides a centralized MCP-based system for managing and accessing multi-project memory banks remotely, with features like project isolation, file structure validation, and type-safe operations.

A persistent memory implementation using a local knowledge graph that lets Claude remember information about users across conversations.

Provides a shared context layer for AI agent teams to improve token efficiency through context deduplication and incremental state sharing. It enables multiple agents to coordinate tasks, share real-time discoveries, and manage dependencies while significantly reducing redundant data transmission.