cc-agent-tree: Live Terminal Dashboard for Claude Code Agents

Four developers independently released open-source terminal dashboards that read Claude Code's existing JSONL session transcripts at ~/.claude/projects/<project>/<session>.jsonl and subagent files to render a live tree of running agents, after at least six GitHub issues filed against the anthropics/claude-code repository between May and June 2026 flagged that Anthropic's native /agents view shows only a flat list with no per-agent cost breakdown, timeline, or model switching. One developer reported that 98% of a 2.4-billion-token multi-agent run was re-sent context because a subagent looped undetected. The implementations include niemes/cc-agent-tree (Python 3.10+, no dependencies, --demo flag), MikeGT4/agent-tree (a Claude Code plugin opened with /agent-tree that allows mid-session model switching and stopping individual agents), and SeanoChang/agent-tree (post-run Tree, Timeline and Trace views).

Claude Code’s multi-agent mode is powerful and opaque in equal measure. You launch an orchestrator, it spawns subagents, and you stare at scrolling terminal output with no clear picture of which agent is burning your budget, which one stalled, or which one quietly looped itself into a multi-billion-token bill. This week, four developers independently shipped the same answer: an open-source terminal dashboard that reads the transcripts Claude already writes and draws a live tree of everything running. The Cost of Flying Blind Claude Code has a native /agents view. It shows a flat list of running agents. That is the entire observability story Anthropic ships today. No tree structure, no per-agent cost breakdown, no timeline, no way to switch a stalled subagent’s model without stopping everything. The community has been vocal about this. At least six GitHub issues filed against the anthropics/claude-code repository between May and June 2026 describe the same structural gap: the subagent surface lacks the observability and control primitives the main agent surface has. One developer reported that 98% of a 2.4-billion-token multi-agent run was re-sent context https://github.com/anthropics/claude-code/issues/106025 because a subagent looped undetected. No alert. No dashboard. Just a bill. Blind multi-agent runs are not just inconvenient — they are a budget risk that scales with task complexity. As agents get longer-running tasks, the gap between what you can see and what is actually happening becomes expensive. What cc-agent-tree Does The core insight behind all four tools is the same: Claude Code already writes detailed JSONL transcripts. Every session gets a file at ~/.claude/projects/<project /<session .jsonl . Every subagent gets its own file at <session /subagents/agent-<id .jsonl with a .meta.json beside it. These files exist regardless of what third-party tooling you install. The agent-tree tools tail them, parse them, and draw a live terminal UI. No daemon. No hooks. No additional Claude configuration. Nothing touches your running session. That read-only, zero-footprint approach is the right architectural call. Observability tooling that can interfere with the thing it observes is a liability. These tools watch; they do not touch. Four Tools, Four Use Cases Four separate implementations shipped within days of each other. They share the JSONL-reading approach but diverge meaningfully in what they do with it. niemes/cc-agent-tree https://github.com/niemes/cc-agent-tree is the closest to a pure monitor. Python 3.10+, no dependencies beyond a true-colour terminal and a monospace font at minimum 84×40 cells. Run python3 agent tree.py , pick a session, and get a live TUI showing the main session, most-used tools, and a card per open subagent. An optional bundled plugin writes richer event data to ~/.local/state/agent-tree/events/ for deeper subagent detail. The --demo flag lets you try it without a live session: git clone https://github.com/niemes/cc-agent-tree cd cc-agent-tree python3 agent tree.py --demo MikeGT4/agent-tree https://github.com/MikeGT4/agent-tree is the interactive version. Installed as a Claude Code plugin and opened with /agent-tree , it draws a side pane with agents as a tree of boxes: main session, subagents, cost. Clicking an agent shows its task, tool calls, and latest answer. Clicking the model lets you switch to a different model mid-session. You can also set effort level — Low, Medium, High, XHigh, or Max — and stop a specific agent without killing the whole session. Each box carries a timeline bar; the main box shows total session cost. SeanoChang/agent-tree https://github.com/SeanoChang/agent-tree focuses on post-run analysis. Three views: Tree nested agents with status and token counts , Timeline one horizontal bar per agent , and Trace every tool call, edit, and hand-back in chronological order per agent lane . It stores the last 150 runs across sessions per repository. Install with /plugin install agent-tree --marketplace SeanoChang/agent-tree . lifrary/agent-tree https://github.com/lifrary/agent-tree takes a different angle: session navigation and resumption. It renders any Claude Code session as a numbered file-tree in the terminal, organized into phases user prompts and sub-actions file edits, tool calls . Pick a numbered node and get resume markdown you can paste into a new session to continue or fork from that exact point. Available as both a CLI tool and MCP plugin. Four Tools in One Week Is a Signal When four developers independently ship variations of the same tool in a single week, that is not coincidence — it is a demand signal. The pattern is familiar: lazygit https://github.com/jesseduffield/lazygit emerged when terminal Git users found existing GUIs inadequate. k9s https://github.com/derailed/k9s appeared when kubectl alone stopped being sufficient for running Kubernetes in production. Simultaneous invention confirms the native tooling has left a real gap. Anthropic is aware. GitHub issue 24537 https://github.com/anthropics/claude-code/issues/24537 asks for a native Agent Hierarchy Dashboard with a unified real-time visualization for multi-agent workflows. That issue is open. The community tools are shipping now. Which One to Use - niemes/cc-agent-tree — zero installation overhead, just need to see what is running - MikeGT4/agent-tree — interactive control, model switching, per-agent stopping, cost tracking - SeanoChang/agent-tree — post-run analysis with timeline and trace history - lifrary/agent-tree — long sessions that break and need clean resumption points The common thread: your agents are already writing the data. You just needed a tool that would read it back to you.