How to get picked for search by coding agents
Postgres Full-Text Search took 17% of 459 judged search sessions. What the numbers say a vendor in this category should do.
If you sell search engines, this page is the part of the market no dashboard shows you: what a coding agent does when a developer asks for search and never compares vendors.
The numbers come from 459 judged sessions with Claude Code, Codex and Cursor, spread across 11 realistic codebases, with every session read by a judge.
What coding agents choose for search
Across 459 judged sessions, Postgres Full-Text Search was chosen most often, in 17% of runs. OpenSearch was second with 13%. In 23% of runs the agent wrote the code itself and adopted no product at all.
| # | Product | Runs won | Share |
|---|---|---|---|
| 1 | Built in-house (no product adopted) | 106 | 23% |
| 2 | Postgres Full-Text Search | 79 | 17% |
| 3 | OpenSearch | 59 | 13% |
| 4 | Typesense | 42 | 9% |
| 5 | Meilisearch | 40 | 9% |
| 6 | Oracle Indexed Search | 23 | 5% |
| 7 | Algolia | 23 | 5% |
| 8 | Elasticsearch | 22 | 5% |
| 9 | Atlas Search | 17 | 4% |
| 10 | Redis Query Engine | 9 | 2% |
Full board, every run replayable: the search leaderboard.
What the shape of this category means
The leader takes only 17% of runs. This category is genuinely open and the ordering can be moved.
With the top product at 17%, search is decided in the moment, from what the agent reads and what it finds in the repository. Nothing is locked in, which is the best situation a vendor can be in and the one where the work pays fastest.
The order here is set by the quality of what an agent can read and by whether your product is already present in the codebase. Both are things you can change.
The agents do not agree with each other
In this category the three agents we ran put different products first.
| Agent | Runs | Picked most often |
|---|---|---|
| Claude Code | 152 | Postgres Full-Text Search (34) |
| Codex | 155 | OpenSearch (27) |
| Cursor | 152 | Meilisearch (26) |
That split decides where a vendor spends. Codex ran a web search in 53% of decision runs and Claude Code in 1.6%, so the pages you publish are live in half of Codex's search sessions and almost none of Claude Code's. Taking OpenSearch's position with Codex is a content problem. Taking Postgres Full-Text Search's with Claude Code is a repository problem.
Who is asking changes the answer
Every request in this experiment was written as a specific kind of person. In this category the leader changes with the person.
| Who is asking | Runs | Picked most often |
|---|---|---|
| Vibe coder | 65 | Postgres Full-Text Search |
| Junior developer | 193 | OpenSearch |
| Senior engineer | 106 | Postgres Full-Text Search |
| Enterprise team | 95 | OpenSearch |
That is 2 different products winning search for 4 kinds of buyer, out of the same 459 sessions. Nobody here is winning search. They are each winning one kind of buyer.
If you sell to more than one of them, you need pages for each. See how to win the enterprise persona.
What you are really competing against
In 23% of runs, the agent wrote the code itself. That makes hand-written code the largest single competitor in this category, larger than most vendors in the table above.
This changes the job of your content. Before you argue that you are better than another vendor, you have to argue that the problem is harder than it looks. What breaks at volume. Which edge cases cost a weekend. What the maintenance actually costs after six months.
That argument has to exist as a page an agent can read, with specifics and numbers. "It is harder than you think" is not an argument. "Here are the four failure modes and what each one costs" is.
Considered, and never chosen
Because the judge records every product an agent raised and not only the one it picked, this board also shows who kept reaching the shortlist and losing. In search the clearest case is Solr: on the table in 62 sessions, chosen in none.
| Product | Raised in | Chosen in |
|---|---|---|
| Solr | 62 sessions | 0 |
Being rejected is a better position than being unknown, and a cheaper one to fix. The product is already in the agent's head and on the list. Whatever ended those 62 sessions is recorded in each transcript, one reason at a time.
What to do about it in search
- Argue that the problem is harder than it looks, before you argue that you are better than a rival. 23% of search sessions ended in hand-written code, so in roughly one session in 4 no vendor was in the running at all. Write the failure modes and the year-two maintenance cost, with numbers, in the documentation rather than the blog.
- Treat the ordering as movable. The leader holds 17%, so agents are deliberating rather than defaulting, and the inputs they use can change the answer. This is the most winnable shape a category comes in.
- Pick which buyer you are for. The same search need written as a vibe coder landed on Postgres Full-Text Search, and written as an enterprise team landed on OpenSearch. Those are two markets, and the enterprise one needs pages containing the constraint words: audit log, data residency, retention, single sign-on. See how to win the enterprise persona.
- Measure per agent. Claude Code put Postgres Full-Text Search first, Codex put OpenSearch first, Cursor put Meilisearch first. A blended number for search describes a market that does not exist.
The work that applies to every category rather than to this one is written up separately: audit your documentation, write a quickstart an agent can follow, and how to measure install share.
Every search engine on this board
One page per product, with its install share, the per-agent split, and how often it was raised without being chosen.
- Do coding agents recommend Postgres Full-Text Search? — chosen in 17% of sessions
- Do coding agents recommend OpenSearch? — chosen in 13% of sessions
- Do coding agents recommend Typesense? — chosen in 9% of sessions
- Do coding agents recommend Meilisearch? — chosen in 9% of sessions
- Do coding agents recommend Oracle Indexed Search? — chosen in 5% of sessions
- Do coding agents recommend Algolia? — chosen in 5% of sessions
- Do coding agents recommend Elasticsearch? — chosen in 5% of sessions
- Do coding agents recommend Atlas Search? — chosen in 4% of sessions
- Do coding agents recommend Redis Query Engine? — chosen in 2% of sessions
- Do coding agents recommend MySQL FULLTEXT? — chosen in 2% of sessions
- Do coding agents recommend MongoDB Text Search? — chosen in 2% of sessions
- Do coding agents recommend Solr? — raised in 62 sessions, chosen in none
Where these numbers come from
459 judged sessions in search across 11 codebases, part of a published set of 5,292. Real coding agents at pinned versions, in sandboxes, inside realistic codebases, with a simulated project owner in the loop and a blind judge on every session. The full method is on one page: how we measured this.
Every search run can be replayed on the board.
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Common questions
How many codebases is this based on?
459 judged sessions across 11 realistic codebases. A category only runs on repositories where its seam is open, so coverage differs: some categories ran on more than ten codebases and some on two.
What search engine do coding agents choose?
Across 459 judged sessions, Postgres Full-Text Search was chosen most often, in 17% of runs. OpenSearch was second with 13%. The result changes by agent and by who is asking.
Do Claude Code and Codex pick the same search engine?
No. Claude Code picked Postgres Full-Text Search, Codex picked OpenSearch, Cursor picked Meilisearch. Measuring one agent tells you about part of the market only.
How often do agents build search themselves instead of installing something?
In 23% of runs the agent wrote the code itself rather than adopting a product. That makes hand-written code one of the strongest competitors in the category.
How can a vendor improve its position here?
Make the quickstart run when pasted, state the current version on the documentation page, use one name across product, package and import, write pages for the symptoms users describe rather than only the category name, and get into the repository through templates and framework integrations.
Which search engines do agents consider but never choose?
Solr (raised in 62 sessions, chosen in none). Being considered and not chosen is a different problem from being unknown, and it is usually fixable.
Where this comes from
Armature ran 5,292 judged sessions with Claude Code, Codex and Cursor inside 51 realistic codebases, and published every run. The numbers on this page come from that work.