IdeaSearch Documentation

IdeaSearch is an open-source Python framework for constructing iterative LLM-agent workflows with user-defined evaluation, persistent candidate memory, and multi-island search. It is intended for tasks where the generation–evaluation loop itself must be configured, recorded, or compared.

The framework produces evaluated candidates. Domain interpretation and validation remain separate steps and should use task-appropriate evidence, held-out data, or independent checks.

Project Components

• IdeaSearch Framework: Configures candidate generation, evaluation, memory, parallel islands, migration, budgets, and run artifacts.
• IdeaSearch-fit: Applies the framework to symbolic regression by combining candidate-expression generation with numerical parameter fitting.

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Framework Controls

• Task and measurement: User-defined candidate evaluation and optional database-level assessment.
• Initial conditions and memory: Starting candidates, prompts, historical examples, and evaluator feedback.
• Exploration and topology: Model sampling, mutation, crossover, parallel islands, and migration.
• Budget and records: Explicit interaction budgets, candidate databases, scores, logs, and backups.
• Result access: Retrieval of the current highest-scoring candidate and its evaluator score.

Documentation Pages