Eval-driven development for a local-LLM agent: how I shipped Lore 0.2.0 with confidence
The author discusses the challenges of building Lore, an open-source app that manages personal memory using local LLMs. To ensure prompt changes don't introduce regressions, they built an evaluation harness to thoroughly test the agent's behavior.
Why it matters
This article provides a detailed look at the challenges of building a robust, local-LLM-powered agent and the author's innovative approach to ensuring reliable performance.
Key Points
- 1Lore is a multi-stage agent that classifies user input, executes actions, and composes replies
- 2The author built a custom scenario provider and viewer to capture detailed pipeline traces for debugging
- 3Scenarios are treated as policy, not just test cases, to ensure the agent behaves correctly at every stage
Details
Lore is an open-source app that manages personal memory using local large language models (LLMs). The author explains that the biggest challenge is not the technical implementation, but ensuring that prompt changes don't silently introduce regressions. To address this, they built an evaluation harness around the agent, with the rule that no prompt change ships without a fresh evaluation run, and no evaluation failure gets fixed by special-casing the test. The harness includes a custom scenario provider that spins up a clean database, drives the agent loop, and captures a structured pipeline trace for every assistant turn. This allows the author to debug issues by inspecting the trace, rather than just the final output. The scenarios are treated as policy, not just test cases, with each one exercising a specific aspect of the agent's behavior. This eval-driven development approach has enabled the author to ship Lore 0.2.0 with confidence.
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