Self-Evolving AI Agent SDK - Make any AI agent 20-50% smarter automatically
Project description
Zubbl
Self-Evolving AI Agent SDK — Make any AI agent 20-50% smarter, automatically.
Zubbl combines trajectory learning (cross-task pattern extraction) with reflexion (per-task verbal self-correction) to create a dual-loop improvement system. Your agent fails, Zubbl captures it, reflects on what went wrong, learns a reusable policy, and improves next time. No code changes, no redeployment.
Eval results: Baseline 82% → Post-Zubbl 88% (+6 points) on HotPotQA hard reasoning tasks. 3 of 9 failures recovered via reflexion.
Install
pip install zubbl
Quick Start
from zubbl import ZubblClient
zubbl = ZubblClient(api_key="your-api-key")
agent = zubbl.wrap(your_agent) # That's it.
How It Works — Dual Learning Loop
Agent fails a task
│
├──► Trajectory Learning (cross-task)
│ Captures execution trajectory → extracts patterns →
│ scores policies → serves recommendations for similar tasks
│
└──► Reflexion (per-task, NeurIPS 2023)
LLM generates verbal self-reflection on failure →
agent retries with reflection in-context →
successful retry becomes a new trajectory for cross-task learning
Based on: Reflexion (NeurIPS 2023), ExpeL (AAAI 2024), ACE (ICLR 2026).
Project Structure
src/
zubbl/ SDK package (core client, LLM wrapper, guardrails, interceptor)
api/ FastAPI backend with migrations
db/ Database client
worker/ Background jobs (validator, reflector, RL worker)
edge/ Cloudflare Workers edge function
eval/ Benchmark eval harness (HotPotQA, MBPP)
k8s/ Kubernetes deployment configs
tests/ Test suite (pytest)
docs/ Documentation
scripts/ Deployment scripts
examples/ Usage examples
Development
pip install -e ".[dev]"
pytest tests/
Deployment
bash scripts/deploy.sh
See docs/Deployment-Guide.md for full instructions.
Links
- Docs: https://docs.zubbl.ai
- Website: https://zubbl.ai
License
MIT
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