Test AI models autonomously
SIA is a self-improving AI framework developed to autonomously improve the performance of AI models and agents on specific benchmark tasks. This Python-based system enables artificial intelligence systems to optimize their processes by analyzing their own outputs.
What you get
- It autonomously improves the task performance of artificial intelligence models.
- Meta provides cyclic refinement between target and feedback agents.
- It offers high accuracy and processing speed efficiency in benchmark tasks.
Installation
python3 -m venv .venv && source .venv/bin/activate
pip install 'sia-agent[claude]'
export ANTHROPIC_API_KEY="..."python3 -m venv .venv && source .venv/bin/activate
pip install 'sia-agent[openhands]'
# Export the key(s) for the provider(s) you'll use:
export ANTHROPIC_API_KEY="..." # for anthropic/* models
export GEMINI_API_KEY="..." # for gemini/* models (or GOOGLE_API_KEY)
export OPENAI_API_KEY="..." # for openai/* modelsRunning it
sia run --task gpqa --max_gen 5 --run_id 1sia webIf you don't write code
I want to improve the performance of an AI agent using the SIA framework. After completing the installation, which command should I use to start the self-improvement cycle by selecting one of the available tasks (e.g. gpqa) and how should I interpret the outputs at the end of the process (target_agent.py, agent_execution.json, improvement.md)? Also, how can I include my own custom task directory in the system?
Related dictionary terms
Links
TreScout did not build this tool · we found it in GitHub trends and wrote it up. This page describes the repository as of 2026-06-12: The star count and our text belong to that day, the repository may have changed since. Check the repository link for the current state. This page was machine-translated from the Turkish original · the Turkish version prevails.