Do coding agents recommend LiteLLM?
LiteLLM was chosen in 8% of 329 judged AI gateways sessions, ranking fifth. Measured with Claude Code, Codex, Cursor, Grok Build CLI and Muse Code.
LiteLLM was chosen in 8% of 329 judged AI gateways sessions, ranking fifth. It was also raised as a candidate in 166 further sessions without being chosen.
This page reports what happened when Claude Code, Codex, Cursor, Grok Build CLI and Muse Code had to solve a problem in AI gateways inside a realistic codebase. Not what a chat assistant says about LiteLLM. What an agent actually installed.
The numbers
| Category | AI gateways |
| Sessions in the category | 329 |
| Sessions where LiteLLM was chosen | 25 |
| Install share | 8% |
| Rank in category | 5 of 9 |
| Codebases it won in | 5 |
| Raised as a candidate, not chosen | 166 |
| Chosen when considered | 13% |
| Site | litellm.ai |
By agent
The agents treat LiteLLM very differently. Cursor chose it in 15% of its sessions in this category and Muse Code in 0%, a spread of 15 points. Grok Build CLI ran fewer than 30 sessions here, too few to compare.
| Agent | Sessions | Chose LiteLLM | Share |
|---|---|---|---|
| Claude Code | 69 | 3 | 4% |
| Codex | 96 | 11 | 11% |
| Cursor | 71 | 11 | 15% |
| Grok Build CLI | 24 | 0 | 0% |
| Muse Code | 69 | 0 | 0% |
A single blended install share would report the midpoint and hide both ends. If most of your users run Muse Code, your real position here is 0%, not 8%.
By who was asking
LiteLLM performs similarly across the four kinds of buyer, from 0% to 12%. That is unusual: the category leader changed with the persona in 24 of the 31 categories we measured.
| Who is asking | Sessions | Chose LiteLLM | Share |
|---|---|---|---|
| Vibe coder | 40 | 0 | 0% |
| Junior developer | 81 | 0 | 0% |
| Senior engineer | 126 | 15 | 12% |
| Enterprise team | 82 | 10 | 12% |
What LiteLLM was up against
The full ranking in AI gateways, from the same sessions:
| # | Product | Runs won | Share |
|---|---|---|---|
| 1 | Portkey | 86 | 26% |
| 2 | Cloudflare AI Gateway | 76 | 23% |
| 3 | Vercel AI Gateway | 52 | 16% |
| 4 | OpenRouter | 49 | 15% |
| 5 | LiteLLM (this page) | 25 | 8% |
| 6 | Built in-house (no product adopted) | 15 | 5% |
| 7 | Amazon Bedrock | 14 | 4% |
| 8 | Helicone | 1 | 0% |
What this means
This is an integration problem, not a presence problem. Agents raised LiteLLM in 166 sessions and chose it in 25, so it reaches the shortlist and then loses. Something at the last step is costing the session, and in our data that is usually a quickstart that does not run when pasted, documentation describing an interface that changed, or a package name that does not match the product name.
That is the cheaper of the two problems to have. The reason is written down in each losing transcript.
Where these numbers come from
The 329 sessions in AI gateways are part of a published set of 15,287, run with real coding agents inside realistic codebases and judged blind. The full method is on one page: how we measured this.
Every AI gateways run can be replayed on the board.
If you work on LiteLLM: 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 LiteLLM?
Yes. LiteLLM was chosen in 25 of the 329 judged sessions in AI gateways, a 8% install share, ranking fifth in its category.
Does Claude Code recommend LiteLLM?
In 3 of the 69 sessions in AI gateways run with Claude Code, which is 4%.
Do different coding agents treat LiteLLM differently?
Yes, and by a wide margin. Cursor chose it in 15% of its runs and Muse Code in 0%.
How was this measured?
Real coding agents at pinned versions were run in sandboxes inside 110 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.
How often is LiteLLM considered but not chosen?
It was raised as a candidate in 166 sessions without being chosen, and chosen in 25. That is a 13% conversion from considered to chosen.
Where this comes from
Armature ran 15,287 judged sessions with Claude Code, Codex, Cursor, Grok Build CLI and Muse Code inside 110 realistic codebases, and published every run. The numbers on this page come from that work.