In Q1 2026, Claude status page recorded 48 incidents — more than one every two days. OpenAI went down for 21 hours total last year. 72% of enterprises rely on a single AI provider.
When OpenAI goes down at 3am, your product goes down with it. Your users see Internal Error. Your PagerDuty fires. You wake up, manually switch models, and lose 30+ minutes.
At $300K+ per hour of downtime in financial services, passive retry is not enough.
We built NeuralBridge — an embedded self-healing SDK for AI API calls.
How It Works
NeuralBridge sits between your code and the AI API. When a call fails, it automatically:
- Diagnoses the error (rate limit? timeout? model not found? server error?)
- Executes a recovery strategy (retry with backoff, fallback to another model, degrade gracefully)
- Recovers to the primary provider when it comes back online
All in 0.0025ms. Your users never notice.
Three Lines of Code
from neuralbridge import register, can_proceed, heal
register("openai_timeout", strategy="fallback")
if can_proceed():
result = heal(call_openai, model_ref={"model": "gpt-4"})
No config files. No dashboards. No infrastructure. Just code.
Benchmarks (v1.2.1)
| Metric | Value |
|---|---|
| Auto-heal rate | 95.19% |
| Diagnosis latency | 0.0025ms |
| Throughput | 333K ops/sec |
| Package size | 110KB |
| Dependencies | Zero |
| InvalidModel recovery | 100% |
Why Not Just Use a Gateway?
Gateways (Portkey, Helicone, etc.) sit outside your app. They add latency, become a single point of failure, and route your data through their servers.
NeuralBridge is embedded — it lives in your code, adds 110KB, and your data never leaves your infrastructure.
| NeuralBridge | Gateway | |
|---|---|---|
| Deployment | pip install | External service |
| Latency overhead | 0.0025ms | 50-200ms |
| Data routing | None (embedded) | Through gateway |
| Package size | 110KB | N/A (external) |
| Single point of failure | No | Yes |
Supply Chain Security
LiteLLM, the most popular open-source LLM gateway (41K stars, 95M+ downloads), had a TeamPCP dependency poisoning incident and multiple CVEs. At 16.5MB with deep dependency trees, auditing it is nearly impossible.
NeuralBridge is 110KB with zero dependencies. You can audit the entire codebase in an afternoon.
Real-World Scenarios
OpenAI goes down globally (happened April 20, 2026):
- Without NeuralBridge: Product shows errors. Wake up, manually switch to Claude.
- With NeuralBridge: Auto-diagnosed as server error. Fallback to Claude triggered in 0.0025ms.
Rate limited on GPT-4 (happens daily):
- Without NeuralBridge: Request fails. Implement exponential backoff yourself.
- With NeuralBridge: Auto-detected as rate limit. Retry with backoff + fallback model.
Model deprecated (DeepSeek V4 migration, May 2026):
- Without NeuralBridge: model_not_found error. Update code, redeploy.
- With NeuralBridge: 100% InvalidModel recovery. Auto-maps to new model name.
Get Started
pip install neuralbridge-sdk
- Landing Page: https://hhhfs9s7y9-code.github.io/neuralbridge-sdk/
- PyPI: https://pypi.org/project/neuralbridge-sdk/
- GitHub: https://github.com/hhhfs9s7y9-code/neuralbridge-sdk
The AI API reliability problem is only getting worse. As more companies build on LLMs, the blast radius of each outage grows. We think self-healing at the SDK layer — not external monitoring, not manual intervention — is the answer.
Would love to hear your thoughts.
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