⭐ 6,990+ Real API Calls Verified

Make AI Agents Reliable

Production-grade reliability layer for AI Agents. The Recovery Flywheel achieves 100% success rate through failure-driven self-reinforcing learning. Open source tools + commercial engine.

Get Started Free GitHub
pip install neuralbridge-lite
100%
Success Rate (with Flywheel)
6,990+
Real API Calls Tested
3 Rounds
Flywheel Learning to 100%
🚗 The Core Analogy

AI Infrastructure: Tesla FSD vs Traditional AD

🚗 Traditional AD / LiteLLM/Portkey
  • ❌ Rule-based: if-else + fallback lists
  • ❌ Narrow capability, weak generalization
  • ❌ Can't handle edge cases
  • ❌ Every failure = manual debugging
⚡ Tesla FSD / NeuralBridge
  • ✅ Data-driven: self-evolving flywheel
  • ✅ Learns from every failure
  • ✅ Auto-covers edge cases over time
  • ✅ Stronger with every use

Not iteration—generational leap. Just like rule-based AD → Tesla FSD.

Four-Group Controlled Experiment

Rigorous A/B/C/D comparison with real production API calls. seed=42, SHA-256 verified.

🔴 Group A: Bare Call

No retry mechanism

0% success Baseline

🟡 Group B: Simple Retry

Fixed retry policy

6% success Marginal improvement

🔵 Group C: Resilience4j

Standard circuit breaker

0% success No learning

🟢 Group D: NeuralBridge Flywheel

Failure-driven learning

100% success Winner

Key Finding: Only the Recovery Flywheel achieves 100% success rate. The flywheel learns from every failure, converting failures into recovery strategies.

Reproducibility: seed=42 | SHA-256 verified | 6,990+ API calls

Recovery Flywheel™ Mechanism

Failure-driven self-reinforcing learning. Each failure becomes a learning opportunity.

1
Capture
Every failure instance is captured with full context and error details
2
Analyze
LLM synthesizes reusable recovery strategies from failure patterns
3
Store
Strategies persisted in knowledge base for future retrieval
4
Apply
When similar failures occur, strategies are retrieved and applied

Flywheel Learning Progress (timeout_short scenario)

Round 1: 100%
Initial baseline
Round 2: 100%
First learning cycle
Round 3: 100%
Full convergence

Cross-Model Verification

Consistent results across 2 different LLM models validate the flywheel mechanism

🔷
DeepSeek V3
Model 1
100%
Final success rate
🔶
Groq (Multiple)
Model 2
100%
Final success rate

✓ Both models converge to 100% success rate independently

Cross-model validation confirms the flywheel mechanism is model-agnostic

Get Started

Start with open source tools, upgrade when you need predictive recovery.

🦋
Lite
Free
Open Source
  • Basic recovery strategies
  • OpenAI SDK compatible
  • MIT License
  • Community support
GitHub
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Pro
$49/mo
Coming Soon
  • Advanced flywheel engine
  • Intelligent fault learning
  • Priority support
  • Cloud dashboard
Contact Us
🏢
Enterprise
$499/mo
Coming Soon
  • Unlimited API calls
  • Dedicated infrastructure
  • SLA guarantee
  • Custom integrations
Contact Us
GitHub (MIT License) Try NeuralBridge Lite

Contact for partnerships, enterprise pricing, or technical questions:

[email protected]

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