Basic Concepts
HyperFlow is a self-improving agent framework. Instead of manually tuning an AI agent, you let another AI agent do it automatically.
The core idea comes from evolutionary computation algorithms, which are optimization methods inspired by biological evolution. Instead of having a single AI agent try to optimize its logic once, the system maintains a "population" (or archive) of different agent versions. Over multiple generations, the system "selects" the best performing agents, applies "mutations" (having the MetaAgent rewrite their code to fix errors), and generates new offspring agents. Over time, the agents naturally evolve to achieve higher success rates on tasks without human intervention.
The Two Agents
TaskAgent — The Worker
The TaskAgent serves as the problem solver for domain-specific tasks. It receives a formatted prompt, optionally uses tools, and returns a prediction.
- Input: A task description.
- Output: A prediction.
- Tools: Domain-specific, optional.
MetaAgent — The Improver
The MetaAgent's job is to make the system better. It acts as the "mutation operator". Because HyperFlow defines an agent as a computable program, the MetaAgent can refine the entire codebase—including its own meta-logic, prompts, internal tools, and task-solving strategies. This is known as metacognitive self-modification.
- Input: Repo path + eval results path + parent score
- Output: Modified source code on disk (patches/diffs)
- Tools:
bash(run shell commands) +editor(view/edit files) — built-in
The Evolutionary Loop
This evolutionary loop ensures that even if the initial agent is weak, the system can eventually "discover" more robust task-solving machinery.
Next Steps:
The Archive
The archive is the central data structure stored as a JSON Lines (JSONL) file that records the history of all generations. It serves as a versioned record of evolutionary improvement, acting as a family tree where different generations can branch from different original parent versions.