OpenHuman vs private-gpt
Side-by-side comparison of two AI agent tools
Short answer
- OpenHuman is growing faster: +3,180 GitHub stars in the last 30 days vs +57 for private-gpt.
- Pick OpenHuman for: openHuman is the fastest, cheapest, most efficient open-source agent harness. Pick private-gpt for: interact with your documents using the power of GPT, 100% privately, no data leaks.
From GitHub data refreshed daily.
O
OpenHumanopen-source
OpenHuman is the fastest, cheapest, most efficient open-source agent harness. Written in Rust
private-gptopen-source
Interact with your documents using the power of GPT, 100% privately, no data leaks
Metrics
| OpenHuman | private-gpt | |
|---|---|---|
| Stars | 40.4k | 57.6k |
| Star velocity /mo | 3.2k | 56.82539682539682 |
| Commits (90d) | 22.6k | 62 |
| Releases (6m) | 10 | 4 |
| Overall score | 0.9408550749378012 | 0.5500380972578883 |
Pros
- +Complete data privacy with 100% local processing and no external data transmission
- +Production-ready with comprehensive API following OpenAI standards and streaming support
- +Flexible architecture offering both high-level RAG pipeline and low-level API for custom implementations
Cons
- -Requires significant local compute resources to run LLMs effectively
- -Setup complexity may be challenging for non-technical users
- -Limited to documents that can be processed and stored locally
Use Cases
- •Enterprise document analysis for regulated industries requiring complete data privacy
- •Offline research and document querying in environments without internet connectivity
- •Building custom AI applications with contextual document understanding without cloud dependencies
FAQ
- Which is more popular, OpenHuman or private-gpt?
- private-gpt has more GitHub stars (57,562 vs 40,447).
- Which is more actively developed, OpenHuman or private-gpt?
- OpenHuman had more commits in the last 90 days (22,600 vs 62).
- Should I use OpenHuman or private-gpt?
- Compare their capabilities, limitations and "best for" notes above. Both are open source, so trying each on a small task is the fastest way to decide.