This repository implements an agentic crypto trading platform called AI Market Maker (AIMM). Despite the name, the code is not a low-latency market-making engine; it is a multi-agent hedge-fund-style trading stack that combines LLM-driven research agents with deterministic quant logic for backtesting, paper trading, and live trading. The core execution path is a LangGraph workflow: nine Tier-0 specialist agents (monetary, news, TA, on-chain, order flow, etc.) produce structured signals, a weighted arbitrator fuses them into trade intents, a Risk Guard vetoes dangerous proposals, and portfolio management routes execution through an OMS with an in-memory or SQLite ledger. The platform includes a FastAPI backend with REST and streaming endpoints, a Next.js/React dashboard, Docker deployment, Alembic migrations, CI, and a large test suite.
This repository stores design proposals and technical decisions for the OpenClaw agent platform. It contains RFCs covering architecture, protocols, and implementation plans for features such as localization, computer use, sender authentication, enterprise control plane, and plugin SDKs. While not an application itself, it serves as the authoritative specification source for the OpenClaw project. The RFCs are well-structured with YAML frontmatter, sidecar assets, and implementation plans, demonstrating high-quality design documentation. The repository also includes GitHub Actions workflows for process automation and a pull request template.
This repository is a collection of Agent Skills for AI coding assistants (Claude Code, Codex, OpenCode) that enables them to interact with Obsidian vaults. It contains six skill definitions: obsidian-markdown (creating Obsidian Flavored Markdown with wikilinks, embeds, callouts, properties), obsidian-bases (creating database-like .base files with views, filters, formulas), json-canvas (creating visual .canvas files with nodes and edges), obsidian-cli (interacting with Obsidian via CLI for note management and plugin development), defuddle (extracting clean markdown from web pages), and knap (rendering Markdown templates from structured data). Each skill is a self-contained markdown file following the Agent Skills specification, providing detailed syntax, examples, and validation steps. The repository is purely documentation with no executable code, serving as a knowledge base that AI agents use to perform Obsidian-related tasks on behalf of users.
ClawManager is a full-stack, Kubernetes-native control plane for managing AI agent runtimes (OpenClaw, Hermes, OpenCode, DeepSeek Harness, Workbuddy). It provides instance lifecycle management, secure AI gateway proxying with governance and cost tracking, skill management (import, versioning, hub), team collaboration with leader-mediated orchestration, and a comprehensive security protection platform (SecPlane) covering runtime behavior, egress policy, intrusion detection, and compliance reporting. The backend is built in Go with Gin, uses MySQL for persistence, Redis for real-time events, and the Kubernetes client-go library for orchestration. The frontend is a React 19 SPA with TypeScript, Vite, Tailwind CSS, and i18n support for five languages. The repository also includes Playwright-based E2E tests, Docker Compose local development stacks, and a Hermes desktop web component with its own build pipeline. While the codebase demonstrates solid architecture (repository pattern, service layer, middleware), some areas show drift (many SQL migrations, hardcoded dev secrets, incomplete feature flags) and the sheer number of runtime types and configurations introduces complexity.
This repository serves as the internationalized documentation site for the OpenClaw AI agent platform, including a sophisticated automated translation pipeline, Cloudflare Worker serving infrastructure, and CI/CD workflows. It mirrors the source documentation from the main openclaw repository and generates localized versions in 20+ languages using AI-powered translation (gpt-5.6). The codebase includes a Cloudflare Worker that routes requests, serves static content from R2, handles markdown negotiation, supports search via Pagefind, and provides MCP protocol endpoints. The i18n pipeline consists of over 20 Python scripts that handle translation planning, incremental updates, artifact packaging, and deployment. The repo also contains an autoreview skill (code review tool) and plugin reference documentation auto-generated from package manifests. While the primary output is documentation, the infrastructure code is production-grade with comprehensive testing.
EvoNexus is an AI-powered business operating system with a dashboard for managing various applications and services. It has features for managing systems, knowledge base, and user authentication, and uses Docker for containerization.
OpenCrew is a multi-agent operating system designed to extend OpenClaw into a manageable AI team. It enables users to create specialized agents (Chief of Staff, CTO, Builder, CIO, Knowledge Officer, Operations, Research) that collaborate through messaging platforms like Slack, Discord, and Feishu. The system implements structured collaboration through role-based channels, thread-based task isolation, and agent-to-agent communication protocols. Key features include autonomy levels (L0-L3), task classification (QAPS), two-mode agent collaboration (delegation and discussion), and three-layer knowledge distillation. The application is actively maintained with recent updates focusing on cross-agent collaboration capabilities.
ChatClaw is a desktop AI assistant application built with Wails v3 (Go backend + Vue 3 frontend) that provides a local-first, multi-platform AI agent experience. It integrates with multiple messaging platforms (WeChat, QQ, DingTalk, Feishu, WeCom, WhatsApp) through gateway adapters and manages AI agents using the CloudWeGo Eino framework. The application features a knowledge base with full-text search (FTS5) and vector embeddings (sqlite-vec), document processing pipeline (parsing, splitting, RAPTOR hierarchical summarization), scheduled tasks with cron execution, a skill marketplace, MCP tool support, and native desktop capabilities like window snapping, floating ball, and text selection popups. It also bundles an OpenClaw Node.js runtime for advanced agent workflows. The frontend is Vue 3 with TypeScript, Pinia, and shadcn-vue UI components. The architecture is a monolith with service-oriented backend and multiple WebView windows. Note: The repo shows signs of hybridity as it includes substantial data/research components (RAPTOR, embeddings, document processing) but these serve the application's core functionality.
This repository is a curated community collection of 42 real-world use cases for OpenClaw (an AI agent framework), organized as markdown documentation. It is NOT a data pipeline, application, or research codebase itself β rather, it's an 'awesome list' style knowledge base documenting how practitioners use OpenClaw for tasks spanning social media automation, content pipelines, infrastructure management, personal productivity, research, and finance. Each use case file describes a workflow pattern, required skills/plugins, setup prompts, and architectural insights. The repo serves as a pattern catalog for agent-based automation. Classification as 'data_research' reflects the content's focus on data/engineering workflows (RAG, ETL, research pipelines, analytics), though the repo itself contains only documentation, not executable code or data.
This repository implements a meta-skill (ex-skill) that generates digital personas of former romantic partners by analyzing chat histories from WeChat and iMessage. It is designed to be consumed by AI agent runtimes like OpenClaw, Claude Code, or DeepSeek Harness via the AgentSkills standard. The system guides users through conversational intake of relationship details (astrology, MBTI, attachment style), automatically decrypts and extracts WeChat/iMessage chat databases, analyzes communication patterns using LLM prompt templates, and writes a structured persona skill that can be interactively chatted with. The codebase consists of a SKILL.md orchestrator, six detailed LLM prompts (intake, analysis, persona builder, merger, correction handler), four Python tools (database decryption, message parsing, skill file management, version management), and an example persona. It is a specialized tool for cyber-immortality and digital persona creation, blending reverse engineering with psychological profiling.
OpenClaw RTT is a data repository that stores normalized round-trip time (RTT) performance test results for the OpenClaw multi-channel messaging integration platform. It contains structured JSONL files under `data/channels/` for Telegram, Slack, Discord, and WhatsApp, alongside raw `result.json` files under `runs/`. The data is collected from automated QA canary and release harness runs executed via `pnpm openclaw qa <channel>`, with all tests using a mock OpenAI provider. The repository's primary function is to enable version-to-version performance regression tracking across chat channels, with summaries displayed in the README dashboard. The codebase includes import and summary scripts, validation logic, and CI workflows, but the majority of content is historical test result data. Notable patterns include consistent JSONL schemas, polling configurations, and resource measurement (max RSS, elapsed time). Many channel tests, especially Slack and WhatsApp, show persistent failures, indicating integration issues or broken test infrastructure.
This repository is the community governance and operations hub for the OpenClaw project. It contains no application code; instead, it provides markdown documentation for community team guides, moderation policies, incident playbooks, onboarding, and bot management. The only executable artifact is a GitHub Actions workflow (`clawsweeper-dispatch.yml`) that dispatches events to an external ClawSweeper bot for automated GitHub QA and merge assistance. The repo also includes well-structured issue templates for policy and rules changes. It is not a software application in the traditional sense, but rather a set of operational documents and GitHub configuration files that support the OpenClaw community. The quality score reflects the lack of reusable software components and the documentation-only nature, though the workflow demonstrates moderate engineering craftsmanship.
This repository is a curated, categorized directory of over 10,000 Apify actors (web scraping and automation APIs), organized into thematic folders (e.g., AI, Jobs, Real Estate, Videos) with affiliate tracking links. It is primarily a static reference catalog intended for integration with the OpenClaw AI assistant, but also includes two Node.js scripts that fetch actor metadata from the Apify Store API and regenerate the markdown files. The repo is a hybrid: the scripts form a simple data pipeline (fetch, filter, generate), while the bulk of the content is a manually curated list. The code quality is basicβscripts are functional but tightly coupled to the Apify API and output format. The value lies in the aggregation and categorization of APIs, not in the software craftsmanship. The list is large but contains duplicates, placeholder entries, and potential staleness due to reliance on third-party actor availability.
This repository is a generated performance report archive for the OpenClaw project. It does not contain any source code, applications, or data pipelines. Instead, it stores structured benchmark outputs (JSON, Markdown, tar.gz bundles) produced by GitHub Actions workflows from the openclaw/openclaw repository. The files document Gateway boot timing, agent-turn latency, CPU/RSS usage, CLI startup performance, and QA scenario results across multiple OpenClaw versions (v2026.4.x, v2026.5.x, main). Three benchmark lanes are tracked: mock-provider (deterministic fake OpenAI provider), mock-deep-profile (CPU/heap/trace artifacts), and live-gpt54 (real GPT-5.4 turns when credentials are available). The repository serves as a historical performance regression tracker, not a reusable software component.
This repository claims to be a Solana trading bot hub but contains ZERO actual trading bot code. The entire codebase consists of only 3 files: .gitignore, README.md, and one image asset. The README is purely a marketing landing page with affiliate/referral links to external trading platforms (Axiom, Odin Bot, Bloom, Trojan, GMGN, Padre, etc.). There are no Python, TypeScript, Rust, or any other programming language source files. No package manifests, no configuration files, no actual bot implementations. This is effectively a placeholder repository designed to drive traffic to affiliate partners, not a functional software project. The repository structure suggests it was created solely for SEO/marketing purposes with no intention of providing reusable code or actual trading bot implementations.
The ZeroClaw-Android repository is an archived Android application project that was intended as an experiment to create a home for AI agents on Android. The README explicitly states the project is archived due to scope concerns and safety issues with the largely AI-generated codebase. No actual source code is visible in the provided file tree beyond the README and a ZIP archive (ZeroClaw-Android-main.zip) whose contents cannot be inspected. The repository consists almost entirely of Git metadata, indicating the source code is not directly accessible for analysis. Without access to the application code, it is impossible to assess architecture, functionality, or code quality. The project is marked as abandoned with no maintenance activity.