This product was not featured by Product Hunt yet. It will not be visible on their landing page and won't be ranked (cannot win product of the day regardless of upvotes).
reads the session logs your AI coding tools already write to disk — Claude Code, Codex, Gemini, Cursor, opencode & more — and turns them into analytics: tokens, tool calls, context pressure, retries. Native macOS, private, free & open source.
I build my own product, and these days most of the work runs through AI coding agents — Claude Code, Codex, Cursor — plus a pile of custom MCP servers I've wired up. Which is great, until a session goes sideways and you're left wondering: which tool ate all the context? Why did this run balloon past 600K tokens? Is that MCP actually pulling its weight, or just poisoning every prompt?
With a lot of custom MCPs it's genuinely not trivial to see what's happening, and reading raw JSONL logs to find out is miserable. So I built AGANAL — a native macOS app that reads the session logs your agents already leave on disk and turns them into analytics. One shared model across every provider, so the same dashboard works whether the run came from Claude Code, Codex, or opencode.
Where it's earned its keep: finding the MCPs and MCP functions that quietly poison a session — the tools that fire on every turn, dump huge results into context, or never get used at all. Once you can see tool usage, token cost, and context-by-category per run, it's obvious what to cut. Trim a noisy MCP (or a couple of its functions) and the agent gets faster, cheaper, and sharper.
What's inside: - 📊 Tokens over time, tool-call breakdowns, context-window pressure, retries - 🔎 A filterable event stream — with the raw JSONL underneath - 🤖 A CLI and an "Analyse with Agent" tab that hands any session to an LLM to summarize / find errors / review cost - 🔒 100% local — it only reads files already on your disk. No account, no upload, no telemetry.
It's free and open source (Swift/SwiftUI). Fun bit: the app's own agent-analysis feature caught a real token-counting bug *in the app* while I was testing it 🙂
Would love your feedback — especially which signals you'd want for spotting context bloat, and which agents to add next.
Finally a way to see what claude code and cursor are actually doing under the hood. Token spikes and context pressure graphs were eye opening on my own sessions. Runs smooth on mac.
About AGANAL on Product Hunt
“See what your AI coding agents actually did”
AGANAL was submitted on Product Hunt and earned 0 upvotes and 2 comments, placing #130 on the daily leaderboard. reads the session logs your AI coding tools already write to disk — Claude Code, Codex, Gemini, Cursor, opencode & more — and turns them into analytics: tokens, tool calls, context pressure, retries. Native macOS, private, free & open source.
AGANAL was featured in Open Source (68.6k followers), Developer Tools (515.4k followers), Artificial Intelligence (473.1k followers) and GitHub (41.3k followers) on Product Hunt. Together, these topics include over 218k products, making this a competitive space to launch in.
Who hunted AGANAL?
AGANAL was hunted by Ihor Herasymovych. A “hunter” on Product Hunt is the community member who submits a product to the platform — uploading the images, the link, and tagging the makers behind it. Hunters typically write the first comment explaining why a product is worth attention, and their followers are notified the moment they post. Around 79% of featured launches on Product Hunt are self-hunted by their makers, but a well-known hunter still acts as a signal of quality to the rest of the community. See the full all-time top hunters leaderboard to discover who is shaping the Product Hunt ecosystem.
Want to see how AGANAL stacked up against nearby launches in real time? Check out the live launch dashboard for upvote speed charts, proximity comparisons, and more analytics.
Ihor Herasymovych
Hey Product Hunt 👋
I build my own product, and these days most of the work runs through AI coding
agents — Claude Code, Codex, Cursor — plus a pile of custom MCP servers I've
wired up. Which is great, until a session goes sideways and you're left
wondering: which tool ate all the context? Why did this run balloon past 600K
tokens? Is that MCP actually pulling its weight, or just poisoning every prompt?
With a lot of custom MCPs it's genuinely not trivial to see what's happening, and
reading raw JSONL logs to find out is miserable. So I built AGANAL — a native
macOS app that reads the session logs your agents already leave on disk and turns
them into analytics. One shared model across every provider, so the same
dashboard works whether the run came from Claude Code, Codex, or opencode.
Where it's earned its keep: finding the MCPs and MCP functions that quietly
poison a session — the tools that fire on every turn, dump huge results into
context, or never get used at all. Once you can see tool usage, token cost, and
context-by-category per run, it's obvious what to cut. Trim a noisy MCP (or a
couple of its functions) and the agent gets faster, cheaper, and sharper.
What's inside:
- 📊 Tokens over time, tool-call breakdowns, context-window pressure, retries
- 🔎 A filterable event stream — with the raw JSONL underneath
- 🤖 A CLI and an "Analyse with Agent" tab that hands any session to an LLM
to summarize / find errors / review cost
- 🔒 100% local — it only reads files already on your disk. No account, no
upload, no telemetry.
It's free and open source (Swift/SwiftUI). Fun bit: the app's own
agent-analysis feature caught a real token-counting bug *in the app* while I was
testing it 🙂
Would love your feedback — especially which signals you'd want for spotting
context bloat, and which agents to add next.
→ Free download + source: https://aganal.the-ihor.com