#loop

Pay-per-call MCP server. Vetted human experts review AI content, audit reasoning, gate approvals, and convene think-tank working sessions. 9 paid tools $1–$15, USDC on Base via x402.

Open execution contract for agents doing business work. 6 operators following one spec. Approval-gated, tenant-isolated, MIT. Reference at chieflab.io/spec/v0.1.

Connect AI agents to verified humans for real-world tasks — search, book, and pay via MCP.

Connect AI agents to verified humans for real-world tasks — search, book, and pay via MCP.

ThumbGate is an agent governance MCP server that prevents costly AI mistakes before they execute. It provides pre-action gates, shared lessons across agents, and team-level safeguards for AI coding workflows. Features include Thompson Sampling for intelligent decision-making, feedback loops, and integrations with Claude, Cursor, and OpenCode.

Persistent memory for AI agents that survives across sessions. Store, recall, and semantically search memories. Includes loop detection that catches agents stuck repeating themselves, full audit trails, shared memory between agents, and a real-time dashboard. Works with Claude Code, Claude Desktop, and Cursor. Open source, free tier available.

MCP server for César Yagüe's Visual Medicine — 301 contemplative moving art works (seamless master loops up to 16K) across 13 collections, enriched with 109 deep vibrational capsules. 6 tools: search, browse, recommend for space, detail, overview, and direct inquiry to the artist.

Route tasks requiring human judgment to a vetted worker pool from any AI agent. Content moderation, refund decisions, data verification, quality assessments — when AI hits its limit, HumanRail routes to humans, verifies results, and pays workers via Lightning Network. 7 tools: create_task, get_task, wait_for_task, cancel_task, list_tasks, get_usage, health_check.

Stateless circuit breaker that detects recursive agent loops in MCP tool calls. Uses Bloom filter to identify repeated calls within 30 seconds. Pay $0.005 USDC via x402 on Base network to reset context and break the loop.

Human-in-the-loop API for autonomous AI agents. When your agent hits a CAPTCHA, needs KYC verification, a phone call, or any task requiring human judgment, SiliconBridge connects it to a real human operator. 22+ services starting at $1, with L402 payments, Stripe, and crypto support.

Human-in-the-loop API for AI agents. When your agent hits a CAPTCHA, 2FA prompt, identity check, or any human-only gate — SiliconBridge routes it to a real human operator who solves it in under 60 seconds. Result returned via API or webhook. Python SDK available: pip install siliconbridge

Structural observability for AI conversations and financial risk management. 17-channel pattern detection, loop/convergence/scattering detection, and OHLCV risk overlay with 31/31 validated drawdown reduction.

Agentwork solves your tasks with humans-in-the-loop

A Model Context Protocol (MCP) server implementing the Dual-Cycle Metacognitive Reasoning Framework for autonomous agents. This tool empowers agents with greater self-awareness and reliability through intelligent loop detection and experience acquisition.

Trigger a help request directly from your IDE via MCP and get human help in minutes

A Model Context Protocol (MCP) server implementation that exposes Pearl's AI and Expert services through a standardized interface

Intelligent Model Context Protocol (MCP) server for AI-assisted API development. Generate mock servers from OpenAPI specs with advanced logging, performance analytics, and server discovery. Optimized for AI development workflows with comprehensive testing insights and automated analysis.

MockLoop MCP is a powerful tool that helps developers create mock API servers effortlessly. 🚀 With just a few commands, you can set up a testing environment that mimics your production API. 💻

pydantic-ai-researcher is a research system that orchestrates an asynchronous loop between two specialized agents: a research agent and an evaluator agent. The research agent answers complex queries using external MCP servers, while the evaluator agent assesses and refines these answers, iterating until a satisfactory response is attained.

A simple chat loop that includes multple mcp servers.