Haystack vs PydanticAI
Haystack leads on trust: 96.4/100 (Grade A) against 89.3/100 (Grade A), a 7.1-point gap. Haystack leads on supply-chain integrity and transparency; PydanticAI leads on adoption and rests on broader evidence. Note: Haystack's evidence coverage is thinner (coverage B vs A) — its score rests on fewer independent signal types.
Open-source AI orchestration framework for building context-engineered, production-ready LLM applications. Design modular pipelines and agent workflows with explicit control over retrieval, routing, memory, and generation. Built for scalable agents, RAG, multimodal applications, semantic search…
Choose Haystack if supply-chain integrity and transparency matter most.
- +3.5Safety / Integrity: OSSF Scorecard 9.3 against 6.5
- +2.4Transparency: OSSF Scorecard 9.3 against 6.5
How Python does AI. Agents, realtime voice, image generation, embeddings. Every model, every interface, typed end to end.
Choose PydanticAI if adoption matters most.
- +1.0Adoption: 1.3M weekly downloads against 152k
- A vs BEvidence coverage: 4 of 5 independent signal types, against 3
Where they differ
1 dimension identical: Identity 18.0 · Full evidence table
An independent, evidence-based trust comparison of Haystack and PydanticAI, two Agent Frameworks projects in the HVTracker registry. Scores come from public, checkable signals — supply-chain provenance, OSSF Scorecard, maintenance, and adoption — not popularity.
Full evidence
| Signal | Haystackdeepset-ai/haystack | PydanticAIpydantic/pydantic-ai |
|---|---|---|
| HVTrust score | 96.4 | 89.3 |
| Evidence grade | A | A |
| Coverage grade | B | A |
| Overall rank | #1 | #25 |
| Rank in Agent Frameworks | #1 | #8 |
| GitHub stars | 26.6k | 20.4k |
| Last updated | 1d ago | today |
| Build provenance | Yes | Yes |
| OSSF Scorecard | 9.3 / 10 | 6.5 / 10 |
| License | Apache-2.0 | MIT |
| Downloads | 152k/wk | 1.3M/wk |
| Trust dimensions (points earned) | ||
| Safety / integrity / 25 | 24.1 | 20.6 |
| Identity & provenance / 18 | 18.0 | 18.0 |
| Transparency / 17 | 16.4 | 14.0 |
| Maintenance / 20 | 19.9 | 20.0 |
| Adoption / 20 | 17.5 | 18.5 |
| Runtime capability surface (full matrix) | ||
| MCP server | Declared | Declared |
| External providers | 1 — OpenAI | 8 — Amazon Bedrock, Anthropic, Cohere, … |
| Requires API keys | No | No |
| Plugin surface | plugins | plugins |
| Provenance drift | Match | Match |
How to read this: HVTrust (0–100) weighs supply-chain signals (provenance, OSSF Scorecard, signed commits, open license) alongside real-world adoption, scaled by an evidence-confidence factor. Grade bands: A ≥ 80, B ≥ 65, C ≥ 50, D < 50. Signals refresh daily. Full methodology v4.4 →