Is Orca Agent Search 2.0 actually fast?

For selective multi-keyword searches on the vendor's 400-session synthetic benchmark, it returned results in 1.2 to 3.4 milliseconds versus roughly 146 to 147 milliseconds for ripgrep. On a common-keyword test, ripgrep was faster at 11 ms versus 40 ms. All figures are vendor-reported medians of 15 warm runs.

Does it work across different AI agents?

The Orca team says yes: past conversations from Claude Code, OpenCode, Grok and more are searchable in one place. This claim comes from the vendor announcement and covers conversations those tools have already stored locally.

Can my AI agent use the search itself?

According to the Orca team, the search is available through the CLI, and an agent can run the command to look through past conversations on its own. That makes stored history available as retrievable context for the agent.

Do I need the command line to use it?

No. The feature works inside the Orca app, where results show the work tree name and first prompt of each session. The CLI is the second option, invoked with orca search plus your keywords.

Were the benchmark numbers independently verified?

No. The 105 MB, 400-session benchmark was run by the Orca team on synthetic data, and the timings exclude index building and CLI startup. Treat the figures as vendor-reported until independent tests appear.

Orca Agent Search 2.0 Finds Old AI Chats Fast

Orca Agent Search 2.0 is a keyword search feature for your past AI agent conversations, launched by the Orca team on October 6, 2026. It indexes sessions from tools like Claude Code and Grok, ranks matches with BM25, and runs from an app or the command line.

What Orca Agent Search 2.0 Does

Orca Agent Search 2.0 is a search feature for past AI agent conversations that the Orca team announced on October 6, 2026. You type keywords, it returns matching sessions ranked by relevance, with the first prompt of each session shown so you can tell which chat is which. Every speed and capability claim below comes from the vendor's own announcement, not from independent testing, and no public canonical source page for Orca could be verified at publication time, so treat the numbers as vendor-reported.

The pitch matters because agent sessions hold decisions. A fix you worked out in a terminal session last week is knowledge you paid for, and the transcript's framing is accurate: the usual alternative is scrolling through one tool's history after another. Search turns that history into something you query instead of scroll.

Which Agents It Searches Across

According to the Orca team, one search covers past conversations from multiple agents, including Claude Code, Anthropic agentic coding tool that runs in the terminal; OpenCode, an open-source terminal coding agent; and Grok, xAI's assistant, with more said to be supported.

Each agent keeps its own history in its own format, which is why finding an old session normally means remembering which tool you used first. Cross-agent search collapses that step. Note the scope: this searches conversations the tools have stored on your machine. It is a search layer over existing history files, not a backup or sync service, and the vendor does not describe any cloud storage component.

How to Use It: App and CLI

There are two entry points, and the vendor says both run the same search.

In the Orca app, a list of recent chats and terminals shows the work tree name and the first prompt of each session. The first prompt usually summarizes what the session was about, so you can identify the right one without opening ten candidates.
From the command line, the vendor says you type orca search followed by your keywords. Same search, typed instead of clicked.
Your agent can run the command too. The vendor says an agent can invoke the search itself and look through past conversations, which means the stored history becomes retrievable context for the agent, not only for you.

The third point is the one with real workflow consequences. A coding agent that can query its own past sessions can reuse a previous fix without you re-explaining it.

Why It Is Fast: Indexing and BM25

The vendor attributes the speed to two components. First, the search runs against a pre-built index, so queries look up terms instead of rereading every conversation. Second, results are ranked with BM25, a standard keyword-scoring function that orders matches so the closest ones appear first.

The split matters in practice. The index supplies speed; BM25 supplies order. With 50 matches, a raw grep-style hit list helps less than a ranked list with snippets, and the vendor says results include a short snippet of the matching text so you can verify relevance before opening a session. The index build itself happens ahead of time and its duration is excluded from the published timings, which is a caveat worth remembering before extrapolating the speed numbers to cold-start use.

The Vendor Benchmark Against Ripgrep

The Orca team ran a benchmark on 105 MB of synthetic agent history covering 400 sessions, comparing its search against ripgrep, the widely used command-line text search tool. The data was synthetic, meaning generated for the test rather than real user chats, and the figures are vendor-reported medians of 15 warm runs that exclude indexing time and CLI startup.

Test	Keywords	Orca search	Ripgrep	Faster
Selective search	2	1.2 ms	147 ms	Ripgrep 122x slower
Selective search	4	3.4 ms	146 ms	Ripgrep ~43x slower
Common search	2	40 ms	11 ms	Orca ~3.6x slower

The arithmetic checks out: 147 / 1.2 is about 122, and 146 / 3.4 is about 43, matching the vendor's stated 42 to 122x range for selective multi-keyword searches. Orca also returned ranked results and snippets where ripgrep returns raw matches. Because this is a vendor benchmark on synthetic data with a specific workload, it proves that configuration only; it is not evidence that Orca is faster on every search or every dataset.

Where Plain Text Search Still Wins

The third test cuts against the headline. When the two keywords were common, meaning they appeared in many conversations, Orca took 40 milliseconds against ripgrep's 11. Ripgrep was faster by roughly 3.6x on that workload.

This is a fair result to publish and a useful decision rule. Selective keywords, the kind that match only a handful of sessions, favor the indexed, ranked search. Common keywords favor plain text search. A 40-millisecond search is still quick in absolute terms, so the practical difference is small for a single query, but the shape of the benchmark shows exactly where the advantage lives. The vendor publishing the losing number is also a small credibility signal worth noting, since it frames the honest scope of the claim.

FAQ

Is Orca Agent Search 2.0 actually fast? For selective multi-keyword searches on the vendor's 400-session synthetic benchmark, it returned results in 1.2 to 3.4 milliseconds versus roughly 146 to 147 milliseconds for ripgrep. On a common-keyword test, ripgrep was faster at 11 ms versus 40 ms. All figures are vendor-reported medians of 15 warm runs.

Does it work across different AI agents? The Orca team says yes: past conversations from Claude Code, OpenCode, Grok and more are searchable in one place. This claim comes from the vendor announcement and covers conversations those tools have already stored locally.

Can my AI agent use the search itself? According to the Orca team, the search is available through the CLI, and an agent can run the command to look through past conversations on its own. That makes stored history available as retrievable context for the agent.

Do I need the command line to use it? No. The feature works inside the Orca app, where results show the work tree name and first prompt of each session. The CLI is the second option, invoked with orca search plus your keywords.

Were the benchmark numbers independently verified? No. The 105 MB, 400-session benchmark was run by the Orca team on synthetic data, and the timings exclude index building and CLI startup. Treat the figures as vendor-reported until independent tests appear.

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