CTOP: Terminal UI to Monitor Claude Code Sessions, Zero Deps
CTOP is a terminal UI that lets you monitor all your running Claude Code and Codex sessions from a single pane. It's built with pure Node.js, has zero dependencies, makes no network calls, and works on macOS, Linux, and Windows.
What it shows per session
- CPU, memory, uptime, status
- Context window saturation bar (input / cache / output / free out of 200k)
- Token breakdown: input, output, cache creation, cache read
- Model, branch, session ID, service tier
- Cost estimates per session and aggregate
Other features
- Vim-style navigation (
hjkl,g/G) - Two view modes: list (table) and pane (card grid)
- Kill individual or all sessions (SIGTERM or SIGKILL)
- Sort by CPU, memory, context%; filter by branch/model/directory
- Live log tailing and full-text search across conversations
- Desktop notifications when sessions complete
- 5 color themes, plugin system
The author runs 6–15+ Claude and Codex sessions across different repos and built CTOP to track context burn, idle sessions, and memory usage. It pairs well with Agent View — Agent View handles dispatching and input needs, while CTOP fills in cost tracking, CPU/memory, context window bars, and historical stats.
Installation
npm i -g ctop-claudeOr via Homebrew:
brew tap aakashadesara/ctop && brew install ctop-claudeGitHub: github.com/aakashadesara/ctop — MIT licensed. PRs welcome (a recent adopter added performance improvements for 70+ sessions).
📖 Read the full source: r/ClaudeAI
👀 See Also
Throttle Meter: Open-Source Claude Code Usage Meter for macOS
Open-source macOS menu bar app that reads local Claude Code logs to show real-time 5-hour and weekly usage, with threshold notifications and token-saving hooks. Also has a €19 commercial sibling with Exact mode (reads claude.ai's internal API via Safari).
Three Repositories for RAG and AI Agent Development
A Reddit post highlights three repositories for developers building with RAG and AI agents: memvid for agent memory, llama_index for RAG pipelines, and Continue for coding assistants. The author notes that pure RAG works best for knowledge retrieval, while memory systems are better for agents, with hybrid approaches being common in real tools.
LiteParse: Fast Open-Source Document Parser for AI Agents
LiteParse is an open-source document parser that provides spatial text parsing with bounding boxes, runs locally without GPUs, and supports PDFs, Office documents, and images. It can be installed as a skill for 40+ AI agents including Claude Code, Cursor, and OpenClaw.
cq: A Local-First Knowledge Sharing System for AI Coding Agents
Mozilla.ai's cq is an open-source tool that lets AI coding agents share 'knowledge units' about common gotchas via a local SQLite store, with optional team sharing through a Docker API. It installs as a Claude Code plugin or OpenCode MCP server.