The boring half: tracing, evals and cost control before you launch an agent
Before an AI agent goes to production it needs three things the demo did not: tracing of every model call and tool result, an evaluation suite that catches regressions before users do, and per-run cost tracking. Langfuse is the leading open-source, self-hostable option covering all three, and Anthropic's claude-cookbooks provides runnable patterns for tool use, retrieval and prompt caching. Both are free.
Free. 2 repositories, no payment. One email address opens the list.
The demo is the easy twenty per cent. I say that to every client and roughly none of them believe me, until the first week of real traffic.
That's when an agent that behaved perfectly in testing starts doing something inexplicable for about one customer in forty, and nobody can reconstruct what happened, because nothing was recorded.
So: tracing first, second agent later. The first thing we add to any agent project is a trace of every model call, every tool result and every retry, with the inputs attached. Not because it's good practice in the abstract, but because the alternative is debugging a non-deterministic system from a screenshot a customer sent you.
Then evals. You don't need an academic benchmark. You need maybe thirty examples of things your agent must get right, run automatically on every change, with a human reading the diffs. Teams skip this because it feels slow, then ship a prompt tweak that quietly breaks a case they fixed a month ago. A prompt change is a code change. Treat it like one.
And cost, which has a way of arriving as a surprise. Agents loop. A bug that adds two extra tool calls per run doesn't look like much until you multiply it by your user base. Track spend per run, alert on the outliers, and cap the loop. A hard iteration limit is unglamorous and has saved more than one of our projects from a memorable invoice.
This week: pick your last confusing agent failure and try to reconstruct it from your logs alone. Whatever you reach for and can't find is the first thing to start recording.
Where to send the 2 repositories
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Production: the boring half nobody posts about
The demo is the easy 20%. These two are what stand between a working prototype and something you would let a client near.
2 repositories, with the notes on each. Enter your email above to read them.
The master list: all 100 repositories
The free list above is the short version, and every repository on it is in here too. The Gods Repo Library is the whole catalogue: 100 repositories across 12 categories, each with a written note on what it is for and when to reach for it, and it carries the AI optimisation half, inference, serving, quantisation and evaluation, that the free list barely touches. Every figure in it is read from the GitHub API rather than typed by hand, and nothing archived or idle survives the build. Verified 2026-09-23.
- Orchestration: where the agent lives19
- Coding agents you can point at your own repo6
- MCP, tools and the integration layer10
- Browser and computer control5
- Retrieval: giving the model your data9
- Memory and vector stores7
- Evaluation, observability and guardrails11
- Prompt and output optimisation5
- Inference, serving and routing10
- Fine-tuning, quantisation and compression7
- Getting the data in5
- Courses, cookbooks and reference6
- All 100 repositories with a written note on each, emailed to you
- 88 of them are beyond the free list above
- A permanent link to the live version, refreshed against the GitHub API
- No subscription and no account, just the library
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