agentlytics

Add --json for machine-readable output:

deno run --allow-read --allow-env mod.ts --json

If you've cloned the repo, you can also use Deno tasks for the full dashboard:

deno task start       # Full dashboard (all permissions)
deno task scan        # Lightweight CLI scan
deno task scan:json   # JSON output

Node.js

$ npx agentlytics

(● ●) [● ●] Agentlytics
{● ●} <● ●> Unified analytics for your AI coding agents

Looking for AI coding agents...
   ✓ Cursor              498 sessions
   ✓ Windsurf             20 sessions
   ✓ Windsurf Next        56 sessions
   ✓ Claude Code           6 sessions
   ✓ VS Code              23 sessions
   ✓ Zed                   1 session
   ✓ Codex                 3 sessions
   ✓ Gemini CLI            2 sessions
   ...and 6 more

(● ●) [● ●] {● ●} <● ●> ✓ 691 analyzed, 360 cached (27.1s)
✓ Dashboard ready at http://localhost:4637

To only build the cache without starting the server:

npx agentlytics --collect
# or: pnpm dlx agentlytics --collect

Features

• Dashboard — KPIs, activity heatmap, editor breakdown, coding streaks, token economy, peak hours, top models & tools
• Sessions — Search, filter, and read full conversations with syntax highlighting. Open any chat in a slide-over sidebar.
• Costs — Estimate your AI spend broken down by model, editor, project, and month. Spot your most expensive sessions.
• Projects — Per-project analytics: sessions, messages, tokens, models, editor breakdown, and drill-down detail views
• Deep Analysis — Tool frequency heatmaps, model distribution, token breakdown, and filterable drill-down analytics
• Compare — Side-by-side editor comparison with efficiency ratios, token usage, and session patterns
• Subscriptions — Live view of your editor plans, usage quotas, remaining credits, and rate limits across Cursor, Windsurf, Claude Code, Copilot, Codex, and more
• Relay — Share AI session context across your team via MCP

Supported Editors

| Editor | Msgs | Tools | Models | Tokens | |--------|:----:|:-----:|:------:|:------:| | Cursor | ✅ | ✅ | ✅ | ✅ | | Windsurf | ✅ | ✅ | ✅ | ✅ | | Windsurf Next | ✅ | ✅ | ✅ | ✅ | | Antigravity | ✅ | ✅ | ✅ | ✅ | | Claude Code | ✅ | ✅ | ✅ | ✅ | | VS Code | ✅ | ✅ | ✅ | ✅ | | VS Code Insiders | ✅ | ✅ | ✅ | ✅ | | Zed | ✅ | ✅ | ✅ | ❌ | | OpenCode | ✅ | ✅ | ✅ | ✅ | | Codex | ✅ | ✅ | ✅ | ✅ | | Gemini CLI | ✅ | ✅ | ✅ | ✅ | | Copilot CLI | ✅ | ✅ | ✅ | ✅ | | Cursor Agent | ✅ | ❌ | ❌ | ❌ | | Command Code | ✅ | ✅ | ❌ | ❌ | | Goose | ✅ | ✅ | ✅ | ❌ | | Kiro | ✅ | ✅ | ✅ | ❌ |

Windsurf, Windsurf Next, and Antigravity must be running during scan.

Relay

Relay enables multi-user context sharing across a team. One person starts a relay server, others join and share selected project sessions. An MCP server is exposed so AI clients can query across everyone's coding history.

Start a relay

npx agentlytics --relay
# or: pnpm dlx agentlytics --relay

Optionally protect with a password:

RELAY_PASSWORD=secret npx agentlytics --relay

This starts a relay server on port 4638 and prints the join command and MCP endpoint:

  ⚡ Agentlytics Relay

  Share this command with your team:
    cd /path/to/project
    npx agentlytics --join 192.168.1.16:4638

  MCP server endpoint (add to your AI client):
    http://192.168.1.16:4638/mcp

Join a relay

cd /path/to/your-project
npx agentlytics --join <host:port>
# or: pnpm dlx agentlytics --join <host:port>

If the relay is password-protected:

RELAY_PASSWORD=secret npx agentlytics --join <host:port>

Username is auto-detected from git config user.email. You can override it with --username <name>.

You'll be prompted to select which projects to share. The client then syncs session data to the relay every 30 seconds.

MCP Tools

Connect your AI client to the relay's MCP endpoint (http://<host>:4638/mcp) to access these tools:

| Tool | Description | |------|-------------| | list_users | List all connected users and their shared projects | | search_sessions | Full-text search across all users' chat messages | | get_user_activity | Get recent sessions for a specific user | | get_session_detail | Get full conversation messages for a session |

Example query to your AI: "What did alice do in auth.js?"

Relay REST API

| Endpoint | Description | |----------|-------------| | GET /relay/health | Health check and user count | | GET /relay/users | List connected users | | GET /relay/search?q=<query> | Search messages across all users | | GET /relay/activity/:username | User's recent sessions | | GET /relay/session/:chatId | Full session detail | | POST /relay/sync | Receives data from join clients |

Relay is designed for trusted local networks. Set RELAY_PASSWORD env on both server and clients to enable password protection.

How It Works

Editor files/APIs → editors/*.js → cache.js (SQLite) → server.js (REST) → React SPA

Relay:  join clients → POST /relay/sync → relay.db (SQLite) → MCP server → AI clients

Deno:   Editor files → mod.ts (zero deps) → stdout (CLI/JSON)

All data is normalized into a local SQLite cache at ~/.agentlytics/cache.db. The Express server exposes read-only REST endpoints consumed by the React frontend. Relay data is stored separately in ~/.agentlytics/relay.db. The Deno sandboxed edition (mod.ts) bypasses SQLite entirely and reads editor files directly for a lightweight, permission-minimal CLI report.

API

| Endpoint | Description | |----------|-------------| | GET /api/overview | Dashboard KPIs, editors, modes, trends | | GET /api/daily-activity | Daily counts for heatmap | | GET /api/dashboard-stats | Hourly, weekday, streaks, tokens, velocity | | GET /api/chats | Paginated session list | | GET /api/chats/:id | Full chat with messages | | GET /api/projects | Project-level aggregations | | GET /api/deep-analytics | Tool/model/token breakdowns | | GET /api/tool-calls | Individual tool call instances | | GET /api/refetch | SSE: wipe cache and rescan |

All endpoints accept optional editor filter. See API.md for full request/response documentation.

Roadmap

• [ ] Offline Windsurf/Antigravity support — Read cascade data from local file structure instead of requiring the app to be running (see below)
• [ ] LLM-powered insights — Use an LLM to analyze session patterns, generate summaries, detect coding habits, and surface actionable recommendations
• [ ] Linux & Windows support — Adapt editor paths for non-macOS platforms
• [ ] Export & reports — PDF/CSV export of analytics and session data
• [x] Cost tracking — Estimate API costs per editor/model based on token usage

Contributions Needed

Windsurf / Windsurf Next / Antigravity offline reading — Currently these editors require their app to be running because data is fetched via ConnectRPC from the language server process. Unlike Cursor or Claude Code, there's no known local file structure to read cascade history from