oh-my-openagent (omo) is a massive, actively-developed TypeScript monorepo that ships as a multi-harness AI-agent orchestration plugin. Its production function is to instrument coding-agent runtimes (OpenCode, OpenAI Codex CLI, and adapters for Claude Code and other harnesses) with a rich layer of lifecycle hooks, background agents, multi-model routing, LSP/AST tooling, team-mode parallelism, and a curated prompt system — all installable via a single `bunx oh-my-openagent install` or `npx lazycodex-ai install` command. The architecture is a workspace of 20+ pure-TypeScript core packages (model-core, rules-engine, skills-loader-core, team-core, tmux-core, delegate-core, hashline-core, lsp-core, mcp-client-core, etc.) that are consumed by two main adapter packages: `omo-opencode` (the full-featured OpenCode plugin with 11 agents, 54+ hooks, team mode, ultrawork/ULW loop, background agents, Codegraph MCP, etc.) and `omo-codex` (a lightweight Codex CLI plugin that vendories a subset of components — rules, comment-checker, LSP, git-bash, ultrawork, ulw-loop, telemetry — as isolated Codex components). A large shared-skills layer provides cross-harness SKILL.md files for programming languages, frontend design, ultimate browsing, debugging, and more. The codebase is notable for its aggressive package layering refactor (documented in ROADMAP.md), its strict no-excuse TypeScript policy, extensive automated QA evidence (.omo/evidence/), and its depth of agent-facing features like hash-anchored edits, prompt-async-gate, runtime fallback with model-suggestion retry, and the start-work/ULW-loop goal orchestration systems. With 64K stars, daily commits, 790 open issues, and a sprawling CI matrix, it is one of the most ambitious open-source agent-orchestration projects in maintenance today.
This repository implements a plugin for OpenCode, an AI coding assistant, providing persistent, self-editable memory blocks and an optional journal feature. The plugin injects structured memory instructions into the agent's system prompt and exposes dedicated tools (memory_list, memory_get, memory_set, memory_replace, memory_oversized) for the agent to manage its own long-term memory. Memory blocks are stored as markdown files with YAML frontmatter on disk, split into global and project scopes. An optional journal system offers append-only entries with semantic search via local embeddings (all-MiniLM-L6-v2), tags, and metadata tracking. The architecture is cleanly modular: the plugin entry point (src/plugin.ts) orchestrates a configurable memory store (src/memory.ts), a journal store (src/journal.ts), a snapshot caching layer (src/snapshot.ts), and tool definitions (src/tools.ts). The snapshot store is particularly notable for its careful handling of concurrent renders, invalidation, and persistence across process restarts, making it a reusable caching pattern. The codebase is well-tested, uses modern TypeScript with strict mode, and follows OpenCode v2 plugin API conventions. While some components are coupled to the OpenCode ecosystem, the frontmatter utilities, embedding wrapper, and snapshot store are self-contained and could be extracted for use in other projects.
This is an OpenCode plugin that provides isolated branch workspaces using devcontainers or git worktrees. It enables developers to work on multiple branches simultaneously with full container isolation (devcontainers) or lightweight filesystem isolation (worktrees). The plugin manages the entire lifecycle: creating clones or worktrees, allocating unique ports from a configurable range, copying gitignored secrets, and intercepting bash commands to run inside the target container or worktree directory. It also offers session state persistence, background job tracking for non-blocking container starts, and unified workspace management with cleanup of stale workspaces. The codebase is well-architected with clean separation into core modules (clones, config, devcontainer, git, jobs, paths, ports, worktree, workspaces) and a main plugin entry point that registers tools and bash interception hooks. It uses Node.js built-in test runner with comprehensive unit and integration tests. The plugin demonstrates strong engineering practices including file-based locking for concurrency, atomic writes, security-conscious command construction (shell quoting, Docker filter sanitization), and support for both Docker and Podman runtimes.
This repository is an OpenCode plugin that provides interactive pseudo-terminal (PTY) management for AI coding agents. It allows agents to spawn, write to, read from, and kill multiple background terminal sessions, enabling long-running processes like dev servers, watch modes, and interactive programs. The plugin exposes five tools (pty_spawn, pty_write, pty_read, pty_list, pty_kill) and two slash commands for opening a web UI. The architecture is modular: a core PTYManager orchestrates session lifecycle, output buffering (via a RingBuffer), and exit notifications, while adapters decouple the plugin from OpenCode V1 and V2 APIs. A Bun-based web server serves a React frontend with real-time WebSocket streaming and a REST API for session management. The codebase demonstrates strong TypeScript patterns, clean abstractions, and comprehensive testing (unit, integration, and Playwright E2E).
OpenCode-Mem is a plugin for OpenCode, an AI coding assistant, that provides persistent memory using vector embeddings and Turso/libSQL. It stores project-level technical memories (decisions, code facts, patterns) and learns user preferences/configs across sessions. The system uses local ONNX embeddings (or remote API) and supports multiple LLM providers (Anthropic, OpenAI, Google Gemini, MiniMax). It features auto-capture of session content, user profile learning with confidence decay, deduplication, and migration services. A full React web UI allows browsing, editing, and managing memories and user profiles. The architecture is modular: plugin entry, AI provider abstraction, Turso shard management, embedding service, web server, and shared API schemas. While primarily an application, it includes robust data pipeline patterns for vector search, ETL (migration), and user modeling.
Autoresearch-opencode is an OpenCode plugin and skill that enables autonomous code optimization experiments. It provides a slash command (`/autoresearch`) and a TypeScript plugin that injects context into the AI agent's system prompt to guide it through an iterative experiment loop. The system defines a protocol for the agent: it reads/writes a JSONL state file (`autoresearch.jsonl`), generates experiment scripts (`autoresearch.sh`), and maintains a worklog (`experiments/worklog.md`). The core logic is documented in markdown files (SKILL.md, commands/autoresearch.md) rather than executable code, making it a hybrid of documentation and configuration. The repository includes a well-crafted bash backup utility (`scripts/backup-state.sh`) and example optimization targets (bogo_sort.py). The system is designed to be used within the OpenCode environment and does not function as a standalone application.