TokenTelemetry — Observability for AI coding agents: see token usage, cost & reasoning (100% local, open source) #1258
VasiHemanth
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Full disclosure: I maintain Data Olympus, an adjacent governed project-memory MCP server. The observability angle resonates. One thing I would want in any memory benchmark or dashboard is a distinction between retrieved and allowed to govern. A memory can be relevant but still be stale, superseded, or only an unreviewed agent observation. In Data Olympus v0.4.0 we made that state explicit: proposal provenance, human promotion, validity windows, supersession edges, and a maintenance ledger. That has been useful for auditing why an agent saw a rule and whether it should have been allowed into the prompt at all. |
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Hey MemPalace community!
Building AI agents that use MemPalace for persistent memory is powerful — but one thing that's been missing is visibility into what those agents are actually doing and spending.
I built TokenTelemetry to fill that gap — a 100% local, open-source observability dashboard for AI coding agents.
What it tracks
Supported agents (zero config)
Claude Code, Codex, Gemini CLI, Cursor, GitHub Copilot, Qwen, Vibe, Antigravity, OpenCode — reads directly from local log files.
Why it's relevant here
If you're running agents with MemPalace memory backends, TokenTelemetry gives you the observability layer on top — so you can see not just what your agent remembered, but how much it cost and what reasoning it did to get there.
Install
curl -fsSL https://raw.githubusercontent.com/VasiHemanth/tokentelemetry/main/install.sh | bashMIT licensed · 100% local · no signup · Node 18+, Python 3.9+
GitHub: https://github.com/VasiHemanth/tokentelemetry
Website: https://tokentelemetry.com
Would love feedback, especially from folks running long agentic sessions with memory!
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