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One free scan finds every outdated or missing driver and matches the right update for your exact hardware.Free scan · exact hardware matchTo test whether an AI coding agent’s memory is useful, save a specific working asset with one agent, switch to a fresh project and a different agent, and ask for that same asset by name. Then verify that it retrieved the saved version—not just generated something that looks similar. This is a proposed test, not a report that current agents have passed it.
What switching agents can reveal
An agent may retain context within a conversation or project without making that context available elsewhere. The practical question for developers using multiple tools is narrower: can a different agent find and reuse a particular piece of work saved by the first one?
A useful test focuses on an identifiable asset, such as a component, animation, helper function, or prompt. Ask the new agent for the same piece by name. Did it retrieve the saved code, or did it generate something that merely looks similar? A plausible reconstruction is not proof that the original asset, its version, or its behavior survived the handoff.
How to run the test
- Choose an asset with meaningful details. Start with a working project and select code or another reusable item specific enough that a fresh prompt could easily omit an important behavior, parameter, or implementation choice.
- Save it with agent A. Use the agent’s actual save or library workflow, and give the asset a distinct name you can request later. Record the version or other identifying details needed to check what comes back.
- Close that project. Move to a fresh project so the second agent cannot simply rely on the original project’s files or conversation context.
- Ask agent B for the asset by name. Make the request direct. Avoid supplying the code or detailed implementation hints that would let the agent recreate it instead of retrieving it.
- Inspect the result. Compare it with the saved source. Check identity and version as well as behavior and details; if the new project requires adaptation, distinguish those changes from what was actually stored.
- Try a third agent. Ask an agent that was not involved in the original save to retrieve the same item. Reuse limited to the saving tool does not demonstrate portability across agents.
What to compare between systems
Apply the same test asset and procedure to each workflow. These are evaluation questions, not published benchmark results.
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- Source fidelity: Does the result match the saved asset, or is it a newly generated substitute?
- Identity and version: Can you tell which named asset and version the agent retrieved?
- Behavior and detail: Do important behaviors and implementation details survive, including when the asset is adapted for the new project?
- Cross-project retrieval: Can the agent find the asset without access to the project where it was created?
- Cross-agent compatibility: Can a different, previously uninvolved agent retrieve and use it?
- Saving model: Must a user deliberately save each asset, or does the workflow also capture broader project context? A library of explicitly saved items and an automatic archive answer different needs.
- Access and onboarding: Can users connect the agents and complete the save-and-retrieve workflow without authentication or setup problems?
Where Sirro fits—and what is not yet established
Sirro founder Jonathan Berg describes the goal this way: “That is the test I’m interested in now: make it once, switch agents, and see if the next one can really pick it up.” His article presents this as a test he wants to run, not evidence that coding agents already pass it. It also reports authentication problems during Sirro’s beta and time spent fixing onboarding before seeking feedback on its library. Access and setup therefore matter alongside retrieval.
Sirro’s official product page describes an external library for deliberately saved, named reusable assets, including code, components, animations, prompts, patterns, and links. Connected coding agents can retrieve items by name across projects through MCP. Its documentation describes tools for saving, retrieving, listing, composing, and updating assets, along with connection instructions for supported agents. This is Sirro’s own description of its product; it does not establish that cross-agent retrieval succeeds in practice. The product page labels the service closed beta, and beta access and onboarding have been reported as issues, so check current availability before relying on it.
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The described library is not presented as a complete chat-history archive or automatic repository index. That distinction matters: a test of deliberately saved assets evaluates a different kind of continuity from a tool that attempts to capture conversations or scan a project automatically.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What a successful result would—and would not—show
If a different agent retrieves the right saved version from a fresh project and preserves its important behavior, that is evidence for portability in that particular workflow. It does not, by itself, establish that the agent remembers an entire project, that every asset type works, or that the result will be consistent across other agents and setups. Repeat the test with other assets and agents before drawing broader conclusions.
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No pass rate or independent performance comparison is established here. The useful outcome is a concrete answer to a bounded question: can another agent retrieve this named asset, preserve what matters about it, and make that handoff work outside the original project?
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