# Show HN: NeuralBridge - Self-Healing SDK for LLM-Powered AI Agents

> Source: <https://dev.to/hhhfs9s7y9code/show-hn-neuralbridge-self-healing-sdk-for-llm-powered-ai-agents-309o>
> Published: 2026-06-13 09:21:26+00:00

After months of production experience running LLM calls at scale, we realized something uncomfortable: **every AI agent eventually crashes**. Not because the code is wrong, but because LLM APIs fail in ways you can't predict.

Timeouts. Rate limits. Empty responses. Schema violations. Drift. These aren't edge cases — they're the norm.

So we built NeuralBridge: an embedded SDK that makes LLM calls self-healing.

Try running 100,000 LLM calls through any single provider. You'll see:

Most teams solve this by building their own retry logic, circuit breakers, and fallback chains. It works — until it doesn't. Because the next failure is always the one you didn't anticipate.

Instead of a gateway (which adds latency and infrastructure), we embedded the reliability logic directly into the SDK:

``` python
from neuralbridge import SelfHealingEngine

engine = SelfHealingEngine()
result = engine.call("Write a Python function for binary search")

if result.recovered:
    print(f"Fault: {result.diagnosis}")
    print(f"Recovery: {result.recovery_action}")
```

When a call fails, the engine:

| Metric | Value |
|---|---|
| Auto-recovery rate | 84.1% of faults |
| Fault patterns recognized | 280+ |
| Recovery strategies | 30+ |
| Learned rules (flywheel) | 88+ |
| Diagnosis latency | 19us P50 |
| Install size | 375 KB |

We went Apache 2.0 because reliability infrastructure should be a commodity. The SDK is free and open. Pro features (enterprise SSO, audit logs, priority support) fund continued development.

```
pip install neuralbridge-sdk
python
import neuralbridge as nb

result = nb.run("Explain quantum computing in one sentence")
print(result.text)
pip install neuralbridge-sdk
```

We'd love your feedback, issues, and contributions. What failure patterns have you seen in production that we should handle?