thehardwarestore.ai

Caclawphony is an Elixir/OTP application that automates pull request triage, review, and merging by orchestrating AI agents (Codex). It polls a Linear project board for issues in configured states, creates isolated workspaces per issue, and runs phase-specific agent prompts (triage, review, prepare, merge, closure). A real-time terminal dashboard and optional Phoenix LiveView web UI provide observability into running agents, token usage, and rate limits. The system is driven by a single WORKFLOW.md file that serves as both configuration (YAML front matter) and per-issue prompt template. The architecture follows OTP GenServer patterns: an orchestration loop polls the tracker, dispatches to AgentRunner instances that manage Codex subprocesses via stdin/stdout JSON-RPC, and reconciles state transitions. External integrations include the Linear GraphQL API, GitHub CLI for PR operations, and Telegram for notifications. The repository includes extensive test coverage, CI/CD workflows, and reusable patterns such as a tracker behaviour, snapshot testing support, and a spec coverage linting mix task. While designed for the openclaw/openclaw repository with hardcoded Linear identifiers and defaults, the plugin architecture (tracker adapters, workspace hooks, prompt templates) supports customization. The codebase is actively maintained with modern Elixir tooling (Phoenix, Bandit, NimbleOptions) and demonstrates solid software engineering practices, though its tight coupling to specific Linear states and the prototype nature noted in README limit immediate reuse outside the originating project.

rsclaw is a production-grade AI agent gateway and runtime written in Rust, with a React/Tauri desktop UI. It provides a local-first autonomous agent engine that routes messages through 13+ messaging channels (WeChat, Telegram, Discord, Slack, Feishu, etc.) to any of 15+ LLM providers (Anthropic, OpenAI, Gemini, Grok, DeepSeek, etc.). The core architecture is a WebSocket gateway that manages agent sessions, tool execution, memory persistence, skill invocation, and a knowledge base RAG pipeline. Key subsystems include a computer-use GUI agent (native + browser + ADB + iPhone Mirror operators), a plugin system (WASM, JS subprocess, Codex bundles), an agent-to-agent (A2A) protocol for distributed orchestration, and a three-tier memory store (redb KV + Tantivy FTS + HNSW vectors). The binary is a single compiled crate (~21MB) with no Node.js runtime dependency for the backend. The accompanying Next.js web UI and Tauri desktop shell provide a ChatGPT-like chat interface, memory management, model configuration, and computer-use overlays. While primarily an application, the knowledge base and memory subsystems incorporate significant data engineering patterns: content-aware chunking, hybrid vector/FTS search, entity extraction, and a job-based ingestion pipeline with leadership/leaderboard semantics.

Eko is a TypeScript framework for building production-ready AI agents that can automate workflows across browser, Node.js, and web extension environments. The core orchestration engine (Eko class) takes a natural language task, uses an LLM to generate a multi-agent workflow plan encoded as XML, then executes it with parallel/serial agent execution, replanning, and human-in-the-loop support. The system provides a clean abstraction over LLM providers (OpenAI, Anthropic, etc.) with retry and fallback, a ReAct tool-calling loop, and built-in browser automation agents that operate via screenshots or DOM labels. It also includes MCP client implementations (SSE, HTTP, stdio) for integrating external tools, and a chat agent for conversational interactions with tool delegation. The architecture is modular with separate npm packages: @eko-ai/eko-core (core engine), @eko-ai/eko-nodejs (Playwright-based browser agent), @eko-ai/eko-extension (Chrome extension agent), and @eko-ai/eko-web (web environment agent). The repository includes working examples (Chrome extension with side panel, Node.js automation using Playwright, and a React web app). The framework is designed for developers who want to automate complex, multi-step tasks using language models, with a focus on reliability, human oversight, and extensibility via custom agents and tools.

Integuru is a Python application that reverse-engineers API requests from browser traffic to generate runnable integration code. It takes a HAR file (HTTP archive) containing browser network requests and cookie data, then uses LLMs to analyze these requests to identify which endpoint performs a user-specified action. The system builds a dependency graph to determine what prerequisite requests are needed to gather dynamic parameters (like IDs, tokens), and finally generates Python code that can reproduce the action by making the necessary HTTP requests in the correct order. The architecture uses LangGraph for workflow orchestration, with specialized LLM-powered agents handling different aspects of the analysis (URL identification, dynamic parameter extraction, dependency resolution).

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.

This repository serves as a landing page for the Letta project, directing users to the current implementation in the `letta-ai/letta-code` repository. It contains policy files (contributing, privacy, security, terms), an AI usage policy, citation metadata, and GitHub workflows to enforce contribution guidelines. The main branch holds static markdown files, while the retired Letta V1 server code is preserved in the `archive` branch. The repository does not contain runnable application code but serves as an informational hub.

Solace Agent Mesh is a production-grade, full-stack platform for building, deploying, and managing event-driven multi-agent AI systems. It comprises a Python backend (FastAPI, Google ADK, SAC), a React/TypeScript web UI (chat, projects, workflows, prompts, scheduled tasks), a Click-based CLI, and a Docusaurus documentation site. The system uses a microservices-like architecture with separate gateway (HTTP/SSE), platform service (model configuration, storage), and agent components, all communicating via the Solace event broker and the A2A (Agent-to-Agent) protocol. Key entry points include the CLI (`solace-agent-mesh`), the web UI (`/chat`, `/projects`, `/workflows`), and the REST API (`/api/v1`). Data flows involve user requests via SSE, agent orchestration with workflow DAGs, tool execution (built-in, MCP, OpenAPI), artifact storage (S3/GCS/Azure/filesystem), and session persistence in SQL databases (SQLite/PostgreSQL/MySQL). External dependencies include a Solace broker, LLM endpoints (LiteLLM), optional databases, and cloud storage services. The repository is marked as deprecated (Development Status 7 – Inactive), with the new version being a desktop application.

This repository is a curated collection of 280+ n8n workflow templates, organized into 18 categories covering email automation, AI agents, RAG pipelines, social media, DevOps, document processing, and more. It is not a software application itself but a library of importable JSON workflow definitions for the n8n automation platform. The templates demonstrate practical automation patterns such as webhook-triggered AI agents, scheduled data enrichment, human-in-the-loop approvals, and vector-store-based retrieval-augmented generation. While the collection is extensive and popular (25k+ stars), individual templates vary in quality, with many containing hardcoded placeholders, deprecated model references, and no automated testing. The repository serves as a valuable reference for n8n users seeking ready-made automation blueprints, but users must adapt credentials and verify compatibility with their n8n version.

This is the umbrella monorepo for Evo CRM Community, an open-source AI-powered customer support platform. The repository itself does not contain application source code; it orchestrates seven independent microservices (auth, CRM, frontend, AI processor, core service, bot runtime, and event flow) via Git submodules and Docker Compose/Swarm configurations. The primary function of this repo is to provide a single entry point for cloning, building, seeding, and deploying the full stack. It includes a nginx API gateway for path-based routing to backend services, CI/CD workflows for building and publishing Docker images, migration version collision guards, and extensive release documentation. The actual business logic, AI agent execution, conversation management, and user interface reside in the submodules.

This is an educational repository of Python scripts, mini-projects, and markdown guides for building LLM-based AI agents applied to cybersecurity. The code is organized into five progressive parts: foundational LLM/embeddings usage, prompt templates, prompt chaining, RAG over security data, and agentic workflows. It demonstrates multiple agent frameworks (LangChain, LangGraph, CrewAI, DeepAgents) and MCP server integrations for tools like nmap and Shodan. The dominant character is application/tutorial code rather than data research, although it bundles small datasets (CVE embeddings, sample RAG corpora) and commits Chroma vector stores for offline exercises. The repo is not a single deployable service; it is a collection of entry-point scripts and small frameworks. Architecture is a set of standalone examples with shared patterns: state graphs for orchestration, YAML-configured crews, MCP adapters, and RAG ingestion/query pipelines. Most scripts require OpenAI-compatible API keys and/or local tools such as nmap/Ollama. Simulated data and placeholder responses in some examples make it a learning environment rather than production security tooling. Maintenance is current but drift is moderate: some examples use deprecated langchain vectorstore imports, documentation is duplicated across subdirectories, several referenced files are missing, and hardcoded model strings like gpt-5.4-mini appear dated or fictional. There is no root-level automated test suite; only the Shodan MCP subproject contains tests. The strongest reusable patterns are the MCP server wrapping, LangGraph conditional-state orchestration, CrewAI declarative crew configuration, and the hierarchical SOC agent supervisor.

AgentHub (also known as Maestro) is an Electron desktop application that orchestrates a virtual software development team of 46 AI agents powered by Claude Code CLI. The system enforces a 'Harness Engineering' methodology: Skills (standardized slash commands), Hooks (pre/post tool interceptors), Gates (quality checkpoints G0–G6), and FileWatchers (auto-sync markdown files to database). The GUI is intentionally limited to monitoring and three core operations—most workflow is driven by slash commands within Claude Code sessions. Architecture follows a three-layer pattern: Vue 3 + TypeScript renderer communicates via Electron IPC (contextBridge) to a main process with ~16 singleton services (session-manager, task-manager, gate-keeper, hook-manager, etc.). Persistence uses sql.js (SQLite in-memory) with a debounced disk save and 13 migrations. The source of truth is markdown files in `.knowledge/` and `.tasks/` directories, which are watched by chokidar and synced to the database. The app supports bilingual UI (zh-TW/en) and runs on Windows, macOS, and Linux. Key features include: sprint-based project management with task dependency tracking, interactive terminal sessions (xterm.js + node-pty), cost tracking per session, Git integration, a gate pipeline with review workflows, and a skill/hook management system with hash-based idempotent deployment. The codebase is well-structured with reusable components and services, though it shows some technical drift (alpha dependencies, brand name mismatch, Windows-specific paths) and high complexity from the extensive agent definitions and knowledge base.

This repository is an educational course on harness engineering for AI coding agents. It is a collection of lecture materials, code examples, and project templates that teach how to build environments, state management, verification, and control mechanisms for AI coding agents. The course is structured as 14 lectures and 8 projects, with a sample Electron knowledge-base app used as a running example. The repository is primarily documentation and educational content, with some reusable templates and scripts for harness creation and auditing.

This repository is a curated list (awesome list) of AutoResearch use cases, implementations, benchmarks, and forks. It does not contain any source code, data, or executable components. The sole content is a README.md file that organizes links to external projects, each with descriptions and links to optimization traces. The repository serves as a directory for the AutoResearch community, not as a software application or data research pipeline. It is a static documentation resource with no runtime behavior, architecture, or data flows.

Vizra ADK is a retired Laravel package (final release v0.0.48) that provides a comprehensive framework for building AI agents with LLM orchestration, tool systems, memory, workflows, evaluations, tracing, and MCP integration. It is not a standalone application but a library designed to be installed into Laravel projects. The architecture follows Laravel package conventions: service providers, facades (Agent, Workflow), Eloquent models, Artisan commands, Livewire components, and database migrations. Key entry points include the Agent facade for running agents, the Workflow facade for orchestrating multi-agent workflows, and a set of CLI generators for scaffolding agents, tools, and evaluations. The system supports multiple LLM providers via Prism PHP, persistent memory (session, user, vector), tool chaining, sub-agent delegation, and an evaluation framework with LLM-as-a-Judge assertions. The codebase is well-structured with strong test coverage (Pest + Mockery), but the package is officially retired with no further development, leading to high technical drift as dependencies age out. Despite retirement, many components (e.g., ToolChain, VersionablePrompts, AgentVectorProxy, HasLogging) are self-contained and demonstrate reusable patterns suitable for extraction into new projects.

This repository is an educational tutorial for learning LangChain, LangGraph, and Deep Agents frameworks. It contains two tracks: 101 (fundamentals) and 201 (production patterns) with Jupyter notebooks and standalone agents runnable via `langgraph dev`. The code demonstrates multi-agent supervision, human-in-the-loop interrupts, middleware hooks, MCP tool integration, and a deep research agent with skills and memory. All agents use mock or simulated data (Chinook SQLite, Tavily search) and are designed for learning rather than production. The architecture centers on a centralized model configuration (`utils/models.py`) that supports multiple LLM providers, and reusable patterns like subgraph composition, dynamic prompts, and composite backends. While the code is educational and lacks production hardening (error handling, testing, security), it provides clear, well-commented examples of advanced agent patterns.

Agentspan is a comprehensive, multi-language agent orchestration platform built on top of Orkes Conductor. It provides SDKs in Python, TypeScript, Java, and C# for defining, running, and deploying AI agents with rich features: tools, guardrails, multi-agent strategies (handoff, sequential, parallel, router, swarm, plan-execute), streaming, human-in-the-loop, code execution in sandboxes, credential management, and scheduling. The architecture follows a server/client pattern where agent definitions compile to Conductor workflows, with a Java/Spring Boot server, a Go CLI with a Bubble Tea TUI, and a React/Material-UI web interface for visual workflow editing and execution monitoring. The repository is archived (read-only) as of August 2026, with its active development having merged into the Orkes Conductor repository. Despite being archived, the codebase represents a mature, well-crafted multi-agent runtime with extensive examples, comprehensive testing, and clean abstractions across all supported languages. The SDKs demonstrate consistent design patterns—agent composition via strategies, tool registration via decorators/annotations, guardrail chains with composable termination conditions, and thread-safe credential injection—making the codebase a valuable reference for building agent orchestration systems.

The repo is a monorepo with the TypeScript library in the root and the autoreview skill duplicated across `.agents`, `.claude`, `skills`, and `$AGENTS_HOME`. Entry points include `test/protocol.test.ts`, `.agents/skills/autoreview/SKILL.md`, and `scripts/check-pack.mjs`. External dependencies include Node.js, pnpm, git, and external AI CLIs. The codebase has strong security hardening but also a monolithic autoreview script (~80k tokens), tight coupling between tests and internal helper names, and platform-specific skips that create drift risk.