Your agents should learn from experience, not just follow instructions.

DeltaLoop is the open-source continuous fine-tuning layer that automatically converts your AI agent logs into training data and creates specialized LoRA adapters.

The endless loop ends here

Traditional Approach

Agent fails
Check logs
Rewrite prompt
Deploy & test
Repeat forever
Manual labor: 100+ hours
Prompt bloat: 1,500+ tokens
Model never learns

DeltaLoop

Agent runs
Auto-collect logs
Fine-tune model
Deploy adapter
Compounds over time
Fully automated
Minimal prompts: 120 tokens
Continuous improvement

How it works

Four steps. Fully automated. Continuously improving.

01

Capture

One-line callback automatically logs every agent execution

callbacks=[DeltaLoopCallback()]
02

Distill

Transform raw logs into high-quality training datasets

Filter → Dedupe → Format
03

Train

Fine-tune with LoRA adapters (only 17MB!)

2x faster, 50% less memory
04

Deploy

Load adapter into production. Model now knows your domain.

Zero downtime

The numbers speak for themselves

Real performance improvements. Measured results.

+31%
Task Success Rate
65% → 85%
+41%
Tool Use Accuracy
58% → 82%
-90%
Prompt Tokens
1,250 → 120
-66%
Response Latency
3.2s → 1.1s

Cost Savings Calculator

Prompt Engineering
$1,250/month
DeltaLoop
$250/month

Save 80% on inference costs. Plus eliminate manual prompt engineering labor.

Ready to stop rewriting prompts?

DeltaLoop is open source, production-ready, and works with any agent framework.

Framework agnostic (LangChain, AutoGen, CrewAI, custom)
Lightweight LoRA adapters (17MB vs 14GB full models)
Apache 2.0 license - no vendor lock-in
Three commands: distill, train, evaluate
pip install deltaloop