Herdr is a terminal workspace manager designed for AI coding agents. It features a client-server architecture where a persistent background server manages terminal panes, workspaces, and tabs, while thin clients (TUI and CLI) connect via Unix sockets/named pipes. The system enables detaching and reattaching sessions without losing agent state, provides agent-native control through CLI/socket API for spawning and managing AI agents (Claude, Codex, Copilot, etc.), and maintains session persistence across restarts and network disruptions. It includes a plugin system for extensibility, comprehensive AI agent integrations, and a rich terminal interface built with Ratatui.
callx is a Node.js CLI tool that serves as a unified runtime switcher and configuration manager for AI coding assistants like Claude Code and OpenCode. It allows users to define multiple AI 'sprites' (agent configurations) with different providers, models, skills, and MCP servers, all managed through a single `~/.config/callx` directory. The system provides two entry points: the `call` command for quick invocation of sprites, and `callcli`, a full-featured TUI built with Ink and React for managing providers, sprites, skills, MCP servers, and git worktrees. The architecture is a monolith CLI application with a clear separation between runtime utilities (`lib/runtime.js`), git worktree helpers (`lib/worktree.js`), and the TUI frontend (`lib/callcli/app.mjs`). The runtime module handles all configuration file I/O, provider normalization, skill syncing via symlinks, and MCP server configuration generation for both Claude Code and OpenCode formats. The TUI provides a Monokai-themed interface with list-based navigation, field-level editing, and multi-select for skills and MCP servers. Key data flows include: reading `providers.json` for API keys and endpoints, reading sprite `config.json` for CLI type and model selection, syncing skill packs from `skillpacks/` to sprite-specific `skills/` directories via symlinks, and generating MCP configuration files from `mcpservers/` definitions. The tool also integrates with git worktrees, providing an interactive flow to create new branches and worktrees with suggested naming conventions. External dependencies are minimal: Node.js 18+, Ink and React for the TUI, and the target AI CLI tools (opencode-ai, @anthropic-ai/claude-code).
This repository provides a comprehensive CLI toolkit and programmatic interface (Python SDK and notebook clients) for creating, deploying, managing, monitoring, and evaluating AI agents on Amazon Bedrock AgentCore. The system supports multiple agent frameworks (Strands, LangChain/LangGraph, CrewAI, AutoGen, OpenAI Agents, Google ADK) and infrastructure-as-code options (CDK, Terraform). Key capabilities include project scaffolding via Jinja2 templates, runtime lifecycle management (configure, launch, destroy, invoke), memory management (short-term/long-term, semantic, user preference), observability (CloudWatch-based trace and log querying with OpenTelemetry span parsing), evaluation (LLM-as-a-judge with built-in and custom evaluators), policy engine CRUD, gateway management (MCP, API key, Cognito OAuth), and identity federation setup. The codebase is well-structured with a clear three-layer architecture (CLI -> operations -> services), extensive test coverage, and consistent error handling. However, the project is explicitly deprecated in favor of the @aws/agentcore NPM CLI, meaning future development is minimal and users are encouraged to migrate. Despite the deprecation, the reusable abstractions (e.g., CloudWatch query builder, trace processor, strategy models, configuration schemas) demonstrate solid software craftsmanship.