Best Open-Source Agent Frameworks: LangGraph vs PydanticAI
A data-backed comparison of the top two agent frameworks on HVTracker, built from public trust signals rather than stars alone.
Short answer: LangGraph currently leads PydanticAI on HVTracker's evidence-weighted trust score: 91.9 vs 90.7/100. This is not a popularity ranking; it combines supply-chain safety, identity/provenance, transparency, maintenance, and adoption signals.
LangGraph
Build resilient agents.
PydanticAI
AI Agent Framework, the Pydantic way
LangGraph vs PydanticAI: trust signal breakdown
Both projects are tracked in the Agent Frameworks category, but they do not expose the same evidence. The table below compares the public signals that feed HVTrust.
| Signal | LangGraph | PydanticAI |
|---|---|---|
| HVTrust score | 91.9 | 90.7 |
| Safety / Integrity | 25.2/30 | 24.6/30 |
| Identity / Provenance | 20.0/20 | 20.0/20 |
| Transparency | 16.8/20 | 16.4/20 |
| Maintenance | 19.9/20 | 20.0/20 |
| Adoption | 10.0/10 | 9.7/10 |
| OSSF Scorecard | 6.8 | 6.4 |
| Signed commits | 100% | 100% |
| Package provenance | Verified | Verified |
Which one should you evaluate first?
If your priority is the most verifiable trust profile today, start with LangGraph. It has the stronger current HVTrust score and ranks higher in Agent Frameworks. If your use case depends on a specific runtime, language, license, or integration model, use the individual profiles rather than the headline score alone.
For production use, the practical checklist is: inspect the security policy, confirm package provenance or release signing where available, review recent maintenance cadence, and compare the exact trust breakdown. HVTracker is meant to reduce the first-pass research burden, not replace your own risk review.