Do coding agents recommend LlamaParse?
Coding agents raised LlamaParse in 118 of 360 judged document processing sessions and chose it in none of them. Measured with Claude Code, Codex and Cursor.
Coding agents raised LlamaParse in 118 of 360 judged document processing sessions and chose it in none of them.
This page reports what happened when Claude Code, Codex and Cursor had to solve a problem in document processing inside a realistic codebase. Not what a chat assistant says about LlamaParse. What an agent actually installed.
The numbers
| Category | Document processing |
| Sessions in the category | 360 |
| Sessions where LlamaParse was chosen | 0 |
| Install share | 0% |
| Raised as a candidate, not chosen | 118 |
| Chosen when considered | 0% |
| Site | llamaindex.ai |
Which agents raised LlamaParse
Every agent considered it and none adopted it. Cursor raised it most often, in 55% of its document processing sessions.
| Agent | Sessions | Raised LlamaParse | Chose it |
|---|---|---|---|
| Claude Code | 120 | 35 (29%) | 0 |
| Codex | 120 | 17 (14%) | 0 |
| Cursor | 120 | 66 (55%) | 0 |
Which buyers it came up for
It surfaced most for requests written as senior engineer, in 60% of those sessions. Knowing which buyer already has the product in mind tells you which pages to fix first.
| Who is asking | Sessions | Raised it |
|---|---|---|
| Vibe coder | 30 | 0 (0%) |
| Junior developer | 180 | 33 (18%) |
| Senior engineer | 120 | 72 (60%) |
| Enterprise team | 30 | 13 (43%) |
What LlamaParse was up against
The full ranking in document processing, from the same sessions:
| # | Product | Runs won | Share |
|---|---|---|---|
| 1 | Anthropic Claude | 87 | 24% |
| 2 | Azure AI Document Intelligence | 76 | 21% |
| 3 | Amazon Textract | 37 | 10% |
| 4 | Google Cloud Document AI | 31 | 9% |
| 5 | Google Gemini | 17 | 5% |
| 6 | Ocrolus | 15 | 4% |
| 7 | Reducto | 13 | 4% |
| 8 | Azure AI Content Understanding | 11 | 3% |
What this means
Raised 118 times and chosen none is a specific, diagnosable result, and a better starting position than being unknown. The agent has the product in mind. It reached the shortlist 118 times. Something then lost every one of those sessions.
In our data the causes, in the order they occur:
- The quickstart does not run when pasted, so the agent abandoned it mid-integration.
- The documentation describes an interface that changed, so the generated code failed.
- The product name and the package name differ, so the install step went wrong.
- The fit was genuinely wrong for the repository, which is fine and worth knowing.
All but the last are fixable in days, and the reason is written down in the session transcript.
Where these numbers come from
The 360 sessions in document processing are part of a published set of 7,385, run with real coding agents inside realistic codebases and judged blind. The full method is on one page: how we measured this.
Every document processing run can be replayed on the board.
If you work on LlamaParse: the judge recorded a reason for every session where it was raised and passed over. Those reasons are in the transcripts.
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Common questions
Do coding agents recommend LlamaParse?
They raise it but do not choose it. Across 118 sessions where LlamaParse came up as a candidate, agents chose something else every time.
Does Claude Code recommend LlamaParse?
In 0 of the 120 sessions in document processing run with Claude Code, which is 0%.
Do different coding agents treat LlamaParse differently?
Not much. The three agents chose it at similar rates, between 0% and 0% of their runs.
How was this measured?
Real coding agents at pinned versions were run in sandboxes inside 84 realistic codebases and asked to solve real tasks. A simulated project owner approved or questioned each recommendation before any code was written, and a judge from a model family that builds none of the agents read every session blind.
Where this comes from
Armature ran 7,385 judged sessions with Claude Code, Codex and Cursor inside 84 realistic codebases, and published every run. The numbers on this page come from that work.