Run DeepSeek as a real sub-agent inside Claude Code / Codex CLI — not just a single LLM call. DeepSeek gets its own 7-tool agent loop (Read/Write/Edit/Bash/Glob/Grep/NotebookEdit) inside a sandboxed workspace.
Local-first external brain for Claude Code, Codex, and any MCP client. Stores decisions, entities, and session artifacts in one SQLite file and exposes MCP tools for
recall, page, promote, review, graph-query, and source-status.
Provides real-time oversight for Claude Code by connecting it to an OpenClaw agent that reviews, approves, blocks, or modifies actions before they execute.
Mandatory human approval gate for autonomous AI agents. Before any critical, irreversible, or
financially significant action executes — file deletion, production deployment, financial transaction
— the agent calls oracle_validate. A human receives a real-time Telegram notification with full
context and taps Approve or Reject. The agent waits for the decision before proceeding.
An MCP server that enables local AI models to receive guidance from remote 'senior' AI providers like OpenAI, Anthropic, and Gemini to solve programming problems. It features intelligent multi-turn dialogue management, context synchronization, and automated session history tracking.
Enables a host AI agent to trigger a multi-agent debate, running N-rounds where agents critique each other's answers, then synthesizes a consensus recommendation with ranked options.
Human-in-the-loop approval gate for AI agents. Your agent calls submit_approval before any irreversible action; a human reviews on a branded page; a signed webhook fires back with the decision.
Provides a hybrid memory architecture with a thin SQLite index and Markdown cold storage, enabling AI agents to write, query, link, and rebuild long-term memories via MCP tools, model-agnostic and zero third-party dependencies.
Enables AI agents to discover, compare, and select the best AI models across multiple providers based on pricing, performance, and capabilities, with real-time cost estimation and benchmarking.
Enables AI agents to orchestrate security research workflows by connecting to containerized security tools via MCP, allowing automated vulnerability analysis and pipeline execution.
Enables agentic coordination by connecting humans and AI agents through group messaging, project tracking, and milestone management. It provides tools for consensus voting, progress checkpoints, and multi-session collaboration across various agentic platforms.
An MCP server for managing work logs, research results, and task checkpoints to enable seamless collaboration and state recovery between AI agents. It provides a persistent memory layer for tracking project history and resuming workflows across different sessions or tools.
Persistent, consensus-validated institutional memory for AI agents. Gives LLMs real memory that survives across sessions - validated through BFT consensus, not just dumped to a file.
Enables AI models to dynamically create and execute their own custom tools through a meta-function architecture, supporting JavaScript, Python, and Shell runtimes with sandboxed security and human approval flows.
An MCP server that enables AI agents like Claude to play Minesweeper through natural language interaction, connecting to a separate Minesweeper game server.