
HyperAgents
Self-referential self-improving agents that can optimize for any computable task
The Lens
A 'meta agent' modifies the 'task agent' and can also modify itself. It's agents all the way down.
The architecture combines a task agent (solves the problem) and a meta agent (optimizes both the task agent and its own optimization strategy) into a single editable Python repository. The meta agent can modify any file, including its own source code. In benchmarks, this self-referential loop outperforms agents without self-improvement, and the meta-level improvements transfer across domains.
This is from the paper 'Hyperagents' (arxiv 2603.19461). The catch: this is a research project, not a production tool. The license says 'Other' which likely means Meta's research license. Check before using commercially. Running self-modifying agents requires serious compute (the paper uses multiple LLM calls per iteration) and serious trust in the guardrails. Fascinating research. Not something you deploy to production tomorrow.
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Free vs Self-Hosted vs Paid
fully freeResearch code released under Meta's license (check LICENSE.md, likely non-commercial research use). No paid tier, no hosted version. You need your own GPU compute and LLM API access to run experiments.
Free for research. Check license for commercial use. You pay for compute and LLM APIs.
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License: MIT License
Review license manually.
Commercial use: ✗ Restricted
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