AI Goofish Monitor

AI Manager is a local-first desktop application for installing, updating, configuring, and repairing AI coding command-line tools on a user's computer. It provides a single interface for checking installed tools, managing versions, connecting tools to reviewed or custom HTTPS AI-service endpoints, and viewing the effective endpoint and environment-variable overrides without editing shell profiles or PATH files. Its sidebar includes software management, API endpoints, skills, MCP connections, global prompts, sessions, and settings. It installs skills from trusted GitHub catalogues or local ZIP files, supports stdio, HTTP, and SSE MCP servers, and searches local coding-tool sessions that can be resumed in a terminal. Tool behavior is controlled by a capability registry, and the application uses local signals for checks rather than claiming a process is healthy without evidence. The application is built with Tauri 2 and React 18 and is released under the GNU AGPL-3.0-or-later. It has no analytics or telemetry SDK, stores product data locally, and offers signed update verification and platform-specific installers. Provider credentials are currently stored in plaintext in the local database, backups, and SQL configuration exports. AI Manager began as a fork of CC Switch, with inherited portions retaining their original MIT licensing.

NiubiGEO is an open-source, self-hosted tool for tracking how AI models describe products and brands, which competitors they name, and which sources they cite. It accepts a domain, queries selected OpenRouter models with optional web search, and stores each model's original answer, descriptions, competitor associations, keywords, citations, ordinary URLs, errors, and execution conditions. Users can confirm model-derived competitors and keywords for follow-up tests that omit the target brand's name, repeat measurements, and schedule monitoring runs. Results are organized into separate projects for different domains and retain historical records so findings can be traced back to the underlying answers and evidence. The project states that it does not track traditional search-engine rankings and that offline and web-enabled tests should be interpreted separately. The Community Edition is free, open source, and licensed under Apache-2.0. It runs locally with Node.js 22 or later, requires an OpenRouter API key for testing, and can also be deployed with Docker; users pay the model, search-service, and hosting costs they incur. The project also offers paid AI testing by real people and GEO optimization services.

An Apache-2.0-licensed open-source Go SDK from Grafana for building AI-powered backends and agents. It provides GenerateText and StreamText APIs for calling language models, returning complete or streamed responses, executing Go functions as tools, generating schema-validated structured output, and running multi-step tool workflows, with approval for consequential tool actions. The SDK supports Anthropic, Amazon Bedrock, OpenAI, OpenAI-compatible APIs, and Grafana's hosted endpoint, along with retries, configurable timeouts, fallbacks, logging, Prometheus metrics, and Agent Observability. It follows the design of Vercel's AI SDK and is wire-compatible with its TypeScript React frontend hooks, including useChat, useCompletion, and useObject, allowing Go backends to stream Server-Sent Events directly to an existing React frontend without a protocol adapter.

Agent Reach is an open-source command-line capability layer for AI agents. It selects, installs, configures, checks, and routes tools that let command-line agents read and search websites and services including web pages, YouTube, RSS, GitHub, Twitter/X, Reddit, Bilibili, Facebook, Instagram, Xiaohongshu, LinkedIn, V2EX, and Xueqiu. It uses an ordered list of backends for each channel and probes them for actual availability, selecting the first usable option and reporting failures and repair guidance through `agent-reach doctor`. The repository lists integrations such as Jina Reader for web pages, yt-dlp for YouTube transcripts, `gh` for GitHub, feedparser for RSS, Exa through mcporter for web search, and alternative CLI or browser-session-based routes for sites requiring authentication. Agents invoke the upstream tools directly rather than through a data-wrapping layer. The CLI supports environment checks, explicit system installation, dry runs, updates, and uninstalling. Credentials and cookies are documented as being stored locally in `~/.agent-reach/config.yaml` with owner-only permissions; authenticated services may require user-provided cookies or browser sessions. The project is distributed under the MIT license.

PanWatch is a self-hosted AI stock-monitoring assistant for A-shares, Hong Kong stocks, and U.S. stocks. It provides multi-account portfolio management, market and opportunity views, simulated trading, technical indicators, price alerts, stock research, and notifications through Telegram, WeCom, DingTalk, Lark, Bark, or custom webhooks. Its deep-analysis workflow integrates the TradingAgents framework: technical, sentiment, news, and fundamental analyst agents produce analyses, which proceed through bullish/bearish debate, risk review, and portfolio-manager decision synthesis. The system also includes scheduled pre-market, intraday, and post-market agents, plus AI-filtered financial-news collection. Alerts can combine price, percentage-change, turnover, and volume-ratio conditions with AND/OR rules and scheduling limits. PanWatch runs through Docker or local Python and supports a progressive web app for mobile use. It uses FastAPI, SQLAlchemy, APScheduler, the OpenAI SDK, React, TypeScript, and Tailwind CSS; it supports OpenAI-compatible providers including OpenAI, Zhipu, DeepSeek, and Ollama. Optional OpenTelemetry export covers agent runs, LLM calls, and TradingAgents nodes. The project is released under the MIT license.