I built a CLI for uploading documents and querying them with an LLM agent that uses search tools rather than stuffing everything into the context window. I recorded a demo using the CrossFit 2025 rulebook that shows how this approach compares to traditional RAG and direct context injection[1].
The core insight is that LLMs running in loops with tool access are unreasonably effective at this kind of knowledge retrieval task[2]. Instead of hoping the right chunks make it into your context, the agent can iteratively search, refine queries, and reason about what it finds.
The CLI handles the full workflow:
```bash trieve upload ./document.pdf trieve ask "What are the key findings?"
```
You can customize the RAG behavior, check upload status, and the responses stream back with expandable source references. I really enjoy having this workflow available in the terminal and I'm curious if others find this paradigm as compelling as I do.
Considering adding more commands and customization options if there's interest. The tool is free for up to 1k document chunks.
Source code is on GitHub[3] and available via npm[4].
Would love any feedback on the approach or CLI design!
[1]: https://www.youtube.com/watch?v=SAV-esDsRUk [2]: https://news.ycombinator.com/item?id=43998472 [3]: https://github.com/devflowinc/trieve/blob/main/clients/cli/i... [4]: https://www.npmjs.com/package/trieve-cli