Thunderbolt is a full-stack, cross-platform AI chat and agent client built by Mozilla Thunderbird. It ships as a React/Vite web app, a Tauri desktop shell (macOS/Windows/Linux), native iOS/Android apps, and a Bun-based CLI. The system provides chat with multiple LLM providers (OpenAI-compatible, Anthropic, custom Tinfoil enclave), a skills system for user-defined slash-command instructions, voice mode with STT/TTS, MCP tool integration, ACP (Agent Client Protocol) remote agent support, and a widget system for rich chat outputs (weather, maps, citations, document previews, link previews). The backend is an Elysia/Bun server handling OIDC/SAML/OTP auth, an inference usage ledger with signed receipts, a CORS-bypassing proxy with streaming, and PowerSync sync coordination. Data flows from local SQLite (wa-sqlite/bun-sqlite) through an E2E-encrypted PowerSync pipeline to PostgreSQL, with device registration, envelope key storage, and canary proof-of-possession for recovery. The CLI provides a separate agent harness with iroh P2P transport, device-grant auth, and a workspace jail for safe file operations. The repo also contains a substantial AI evaluation framework (scenario-based scoring with Wilson confidence intervals), a Pulumi/K8s/Docker deployment stack, and a marketing/docs site in Astro. While the dominant character is clearly an application, it has a notable data-engineering flavor in its inference usage tracking, eval harness, and telemetry pipelines.
acpx is a headless CLI client and runtime for the Agent Client Protocol (ACP), enabling orchestration of AI agent sessions with persistent state, multi-step flow execution, permission policies, and a replay viewer for auditing. The architecture combines a CLI entry point, an embeddable runtime (AcpRuntime), a queue-based IPC system for shared session access, and a separate React-based replay viewer for inspecting flow run bundles. It supports 20+ built-in agents (Claude, Gemini, Cursor, etc.) via ACP, manages session persistence as JSON files with event logs, and provides output formatting in text, JSON, or quiet modes. The codebase is predominantly TypeScript with extensive test coverage (85%+), conformance testing against the ACP specification, and platform-specific process management for Windows and POSIX systems.
A production-grade AI coding agent (the Codex CLI) that operates as a terminal-based assistant. It provides a multi-modal, extensible platform for thread-based conversations with language models (OpenAI, Bedrock, Ollama), including sandboxed code execution, MCP tool integration, plugin/skills/hooks extension system, and state persistence via SQLite. The architecture is a Rust monorepo with a client-server split: a high-level TUI or CLI sending JSON-RPC messages to an app-server that manages session state, model routing, and execution policies. The system supports cross-platform sandboxing (Linux bubblewrap, Windows managed sandbox, macOS seatbelt) and includes comprehensive telemetry, rollout tracing, and guardian approval pipelines. It is a fork of OpenAI's Codex CLI, with significant Rust rewrite and enhanced extensibility. While the codebase exhibits strong engineering practices (extensive testing, layered configuration, well-defined protocol types, and platform-agnostic abstractions), it is a large monorepo with many interdependent crates, some legacy modules, and platform-specific workarounds that reduce overall cohesion. Key reusable components include the protocol type definition crate (with clever no-op macro optimizations for build speed), a RAII OpenTelemetry timer, a RedactedString wrapper for secret handling, and token-aware text truncation utilities.
Goose is an open-source AI agent platform that provides a desktop application (Electron/React), CLI (Rust), and SDK (Python, Kotlin) for interacting with large language models through a unified Agent Client Protocol (ACP). In production, it enables users to define sessions with multiple LLM providers (Anthropic, OpenAI, Google, Azure, Bedrock, local models via MLX/GGUF, and 40+ declarative providers), extend capabilities via MCP servers and built-in platform extensions (shell, image, file editing, code analysis, apps creation), and automate workflows through parameterized recipes. The architecture is a hybrid: a Rust backend handles agent state machine orchestration, tool execution, permission management, and provider abstraction, while an Electron frontend provides a rich chat interface with settings, extension management, scheduling, and MCP apps display. Key entry points include the CLI binary (crates/goose-cli/src/main.rs), the ACP server (crates/goose/src/acp/server/), and the desktop renderer (ui/desktop/src/renderer.tsx). Data flows involve user input → state machine processing → LLM provider calls → tool execution → response streaming back to the UI, with telemetry and cost tracking throughout. External dependencies include OpenAI/Anthropic/Google APIs, MCP servers, Whisper for dictation, and optional services like PostHog and Langfuse for observability.
This repository is an open-source terminal-based AI coding assistant built in Rust, clean-room reimplementing Anthropic's Claude Code. It provides an interactive TUI (ratatui) for multi-provider LLM conversation (Anthropic, OpenAI, Google, and 30+ OpenAI-compatible) with a rich tool ecosystem for file editing, bash execution, MCP integration, code analysis, web fetching, and agent orchestration. The architecture is modular: a provider abstraction layer (`LlmProvider` trait), plugin system, slash commands, session management, and an Agent Client Protocol (ACP) for external IDE integration. The codebase is organized into 12+ workspace crates with clear separation of concerns, but many modules are still in active refactoring (issue #232) with stubs, TODOs, and incomplete feature gates (36 experimental features). The permission system, tool budget enforcement, and streaming decoders demonstrate strong engineering, while dual feature flag systems and global mutable state introduce maintenance risk. Overall, it is a feature-rich, well-architected application with some rough edges from ongoing migration.