head to head · open source
llama_index vs trnrt
llama_index has 52,875 GitHub stars, 8,541 forks, 770 open issues and last shipped yesterday. trnrt has 1,317 stars, 12 forks, 10 open issues and last shipped yesterday. llama_index leads on adoption by 3,915% (52,875 vs 1,317 stars). llama_index is written in Python under MIT; trnrt is written in Python under Apache-2.0. llama_index has attracted 16% as many forks as stars, trnrt 1%. llama_index was the more recently maintained of the two, and both are self-hostable with no licence fee. The two share 1 topic tag (llm), so they are genuine substitutes rather than adjacent tools.
Two open source projects, one decision. Both are free and self-hostable — the differences are community size, license terms, language stack and release pace.
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Side by side
| llama_index | trnrt | |
|---|---|---|
| GitHub stars | ★ 53K | ★ 1.3K |
| License | MIT | Apache-2.0 |
| Written in | Python | Python |
| Last push | 2026-11-11 | 2026-11-11 |
| Forks | ⑂ 8.5K | ⑂ 12 |
| Self-hosting | Yes | Yes |
| Data ownership | Your server | Your server |
pick llama_index if
- You weight community size — 53K stars and counting
- You want the MIT license terms
- Your stack matches Python
- You value the larger contributor base for long-term maintenance
pick trnrt if
- You want the trnrt feature set and don't need the biggest community
- You prefer the Apache-2.0 license terms
- Your stack matches Python
- You evaluated both and trnrt fits your workflow better
About llama_index
🗂️ LlamaIndex (OSS Framework) 🦙 [!NOTE] The current focus of LlamaIndex is to build the best AI powered engine for document parsing and extraction. LlamaParse is our enterprise platform for agentic OCR, parsing, extraction, indexing and more. LiteParse represents our efforts to build the best free, fast, cheap text parser in the market. ParseBench and ExtractBench represent our commitment towards open benchmarking for parsing and extraction. The company itself has undergone an evolution since when this OSS framework first launched 3 years ago in 2023. Since the early days, the framework has consisted of a…
read the full llama_index overview →
About trnrt
Quickstart · Benchmarks · Docs · Kernels · Blog post · runanywhere.ai TrnRT is an open inference engine for large language models on AWS Trainium. It serves an OpenAI compatible API from one trn2.48xlarge , runs decode as NKI megakernels (about one kernel per layer half), and keeps a prefix cache across device and host memory. GLM 5.3 Flash is the first supported model; more open models are on the way. How we built it, with the AgentX results: Serving GLM 5.3 Flash on one Trainium2 box with NKI layer megakernels. Highlights Quickstart You need Docker, a trn2.48xlarge to serve on, and any x86 64 host to compile…
read the full trnrt overview →
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Frequently asked questions
Is llama_index or trnrt more popular?
llama_index has 52,875 GitHub stars and trnrt has 1,317. llama_index has the larger community by that measure.
Are llama_index and trnrt free?
Both are open source. llama_index is licensed under MIT and trnrt under Apache-2.0. Neither carries a licence fee.
What is the difference between llama_index and trnrt?
llama_index is written in Python and trnrt in Python. The practical differences are community size, licence terms, language stack and release cadence — all compared in the table above.
Which should I choose, llama_index or trnrt?
Choose llama_index if you want the larger community (52,875 stars) or its MIT licence terms. Choose trnrt if its feature set, stack or Apache-2.0 licence fits better. Both are self-hostable.