Reflect FAQs

Question 1

Which programming languages and evaluators does Reflect support?

Accepted Answer

Reflect supports a variety of evaluators including `cargo test` (Rust), `pytest` (Python), `eslint` (JavaScript), and `tsc` (TypeScript). It's also highly configurable, allowing users to define custom evaluators for other languages or testing frameworks.

Question 2

Is Reflect designed for efficient and cost-effective operation?

Accepted Answer

Yes, Reflect is built for efficiency. Its core error pattern matching is deterministic and has zero LLM cost. It uses SQLite with FTS5 for persistent storage, requiring no external dependencies and ensuring instant startup and high performance, making it a very cost-effective solution for AI agent memory.

Question 3

What is Reflect and how does it help AI coding agents?

Accepted Answer

Reflect is a self-correction engine for AI coding agents that implements the Reflexion pattern. It enables agents to learn from their failures, store structured lessons, and prevent the same mistakes from recurring across different coding sessions, significantly improving agent reliability and efficiency.

Question 4

How does Reflect prevent AI agents from repeating past errors?

Accepted Answer

Reflect uses a hybrid approach: it deterministically extracts error patterns (e.g., regex-based) from test outputs, stores agent-provided critiques and lessons with deduplication and confidence scoring, and leverages persistent cross-session memory (SQLite FTS5) for efficient recall, ensuring smarter retries.

Question 5

What are the key differences between Reflect and traditional AI memory solutions?

Accepted Answer

Unlike plain memory or LLM self-reflection, Reflect provides structured error signals, tracks pattern occurrences and trends, includes confidence scoring and deduplication, and ensures persistent cross-session recall. Its deterministic pattern matching layer operates with zero LLM cost, making it fast and efficient.

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