The original promise of an inference API was wonderfully simple: send some input, receive some output. If you kept both, you had the conversation. You could inspect it, archive it, replay it, or give it to a different model.
That abstraction was never completely true. For instance prompt caches live on somebody else’s GPUs, tokenization differs between models, and sampling is not reproducible (and quite intentionally so). But the semantic record of a session in the form of a transcript could still belong to the user. A transcript should contain the instructions, messages, tool calls and tool results. Another sufficiently capable model might not continue identically, but it could understand what happened and take over.
Inference APIs are frustratingly moving away from that property, at least somewhat. They increasingly return a mixture of text and provider-bound state that is very intentionally non-portable.
reasoning tokens that are billed to the user but returned only as opaque, encrypted blobs, with useless summaries at best
web searches where the model sees source material the client never sees
compacted context that only the original provider can decrypt
subagent instructions and messages hidden from the application running the agents in the form of encrypted payloads
file, vector-store, container, and cache references that cannot be resolved anywhere else.
response and conversation state that is entirely keyed by IDs that are stored fully on the provider’s servers
Each feature comes with a basic justification that’s trivial for a provider to come up with, along with good arguments for why this is good for the user. Together all of these things change the ownership reality of an AI session: the transcript on your machine is no longer your session but a partial view of a session whose operational state belongs to an inference provider and not you.
We are not fans of this direction, and we want to talk a bit about what it means to you, as a user, and what it means to us, as people developing tools in this space.
A Practical Test for Session Ownership
By a portable session we do not mean that switching from one model to another must produce the same next token. That’s a given because models have different capabilities, trained personalities, context windows, and ways of working with tools. And well, it’s all quite nondeterministic anyway. Portability means something more modest:
const transcript = session.export(); revokeCredentials(oldProvider); session = newProvider.continueFrom(transcript);
The archive should contain enough intelligible information for another model to continue the work. It should not require the old provider to dereference an ID, decrypt a blob, remember a search result, or reconstruct a summary.
This gives us five useful tests:
Inspection: Can the user see what the model saw, what tools did, and what agents told each other?
Export: Is the session self-contained, apart from ordinary artifacts that can also be downloaded?
Replay: Can another implementation reconstruct a semantically equivalent context?
Audit: Can a human explain why the system took an action after the fact?
Deletion: Can the user identify and remove every server-side copy on which the session depends?
A response ID is not a transcript (as the data is stored on the server), a ciphertext is not user-controlled stated (as the user cannot decrypt it), a list of citations is not the evidence that was placed in the model’s context by a search result (as you cannot typically fetch the same data as the model did).
Encryption for Whom?
The naming and marketing around these features can be misleading. encrypted_content sounds like a privacy feature under the user’s control. Usually it is a capsule that the client cannot read and only the provider can open. The provider chooses the keys, decrypts the content for its own models, and defines where the data can be replayed.
A better term is provider-sealed state.
Provider sealing can have a real privacy benefit. OpenAI, for example, can return encrypted reasoning to a client using store: false, then decrypt it in memory on the next request without persisting the intermediate state. That is better than requiring server-side conversation storage, particularly for Zero Data Retention customers. But, remember, there is not really anything that needs encryption to begin with!
This encryption does not hide the data from the inference provider but it hides it from you.
Stored Conversations Turn a Transcript into a Pointer
OpenAI’s Responses API stores responses by default. Its documentation says response objects are retained for at least 30 days by default. store: false is available and should be used, as it makes it work more like completions: the data is not stored on OpenAI’s servers.
The new Gemini Interactions API has made a similar choice. It defaults to store: true. On the paid tier interactions are retained for 55 days, and on the free tier for one day.
And obviously, the idea of storing state on the server is quite attractive:
const first = responses.create({ model: “frontier-model”, input: “Investigate this production failure”, store: true, });
const second = responses.create({ model: “frontier-model”, previousResponseId: first.id, input: “Now implement the fix”, store: true, });
The application sends less data, the provider can preserve hidden reasoning and tool state, and cache routing becomes easier. But if the local application only records the user messages and final text, first.id is now a foreign key into a database it does not control.
No Reasoning For You
All major labs claim to have legitimate reasons not to expose raw chain of thought. As a result, on non-open-weights models we typically do not see these tokens.
Raw reasoning is not visible via the API. With stored responses, prior reasoning can be recovered through previous_response_id. With store: false, the API returns encrypted_content, which the client must preserve and replay. Persisted reasoning remains opaque even when reasoning.context: “all_turns” lets a later sample use it.
Anthropic returns the encrypted full thinking in a signature field. The readable thinking text, when enabled, is a summary produced by another model, not the raw chain of thought. Thinking blocks must be passed back unchanged during tool-use turns. Anthropic’s documentation also says thinking blocks are tied to the model that produced them and should be stripped when switching models. So these reasoning traces do not attempt to be portable within Anthropic.
The same story repeats with all closed-weights models.
These encryption mechanisms permit continuity inside an ecosystem but they do not create a portable transcript that can be taken to another provider’s model. A session archive can contain the blob, but another model cannot use its meaning:
{“type”: “reasoning”, “encrypted_content”: “gAAAAAB…“} {“type”: “thinking”, “thinking”: “”, “signature”: “EqQBCg…“} {“type”: “thought”, “summary”: [], “signature”: “EpoGCp…“}
Hidden Searches
Server-side web search is one of the clearest examples of a transcript having holes in it hidden from the user. A client-side search tool behaves like any other tool:
const result = search(query); record({ query, retrievedAt: now(), results: result.map((item) => ({ url: item.url, title: item.title, passages: item.passages, })), }); model.send({ toolResult: result });
The user can inspect the ranking and passages, refetch the pages, cache a copy, or provide the same evidence to another model.
With hosted search, the provider performs a private tool loop. OpenAI, Google and Anthropic expose search actions, citations, and optionally a list of source URLs, but not the complete text context used to produce an answer. A URL is not a stable replay, instead its contents can change or have been reduced to a much shorter snippet before the model saw it.
The final answer may be perfectly good. The problem appears on the next turn:
Compare the third source with the first one, re-check the disputed number, and continue this research using another model.
Compare the third source with the first one, re-check the disputed number, and continue this research using another model.
The new model receives an answer and a few URLs. It does not receive the result ranking, extracted passages, filtered-out material, or exact evidence the first model used. The old provider is still part of the session even if the next request goes elsewhere. Even if you have the citations and you were to re-fetch you cannot reproduce the precise data.
Hosted search should have a full-fidelity export mode containing queries, result metadata, retrieved passages, timestamps and retained contents. Concise citations can remain the user interface but they should not be the only record.
Opaque Compaction
Long agent sessions eventually need compaction. A visible, client-controlled summary is lossy, but it is at least inspectable and transferable. The user can review it, edit it, or ask a different model to produce another one.
OpenAI’s server-side compaction instead emits an encrypted compaction item. The documentation describes it as “opaque and not intended to be human-interpretable.” The standalone /responses/compact endpoint returns a “canonical next context window” that clients are instructed to pass on as-is.
Conceptually, the transition looks like this:
// Before: expensive but portable let history = [ userMessage, assistantMessage, toolCall, fullToolResult, // … 200,000 more tokens of intelligible history ];
// After: cheap to continue only with the original provider history = [ { type: “compaction”, encryptedContent: “enc_provider_only_state…”, }, …recentItems, ];
OpenAI can continue from the compressed meaning, but a different provider sees an unreadable string plus a recent suffix (well, would see it, we never pass this sort of information to another provider).
This is not technically necessary. Anthropic’s server-side compaction returns a compaction block with a readable content field. It lets the client provide custom summarization instructions, and the resulting summary can be inspected and passed to another model. Client-side compaction is also possible with any provider.
OpenAI’s sealed artifact may preserve more model-specific state than a plain summary and may perform better on the original model. That is a reasonable optional optimization but it should be accompanied by a readable handoff summary, not replace one. But again, a lot of this has the added benefit of further locking you into one ecosystem.
Subagents Come With Hidden Instructions
Multi-agent systems compound the problem because there is no longer one transcript. There is a tree of sessions and a stream of messages between them. Usually they are prompts as if a human wrote them, just now authored by a machine for another machine.
OpenAI’s hosted Responses Multi-agent beta returns three new item types: multi_agent_call, multi_agent_call_output, and agent_message. The example for spawn_agent contains an encrypted message argument, and inter-agent messages contain only encrypted_content. Automatic server-side compaction is implicitly enabled for every agent when Multi-agent is enabled, even if the client did not request it. Reasoning summaries are not supported and the API also injects root and subagent instructions that the developer cannot edit or remove.
This is a bundle of non-transferable state: sealed delegation, sealed agent messages, separate automatically compacted contexts, hidden reasoning, and provider-hosted orchestration.
A related change landed in the open-source Codex client in June 2026. The commit, titled “Encrypt multi-agent v2 message payloads”, explains the flow directly:
// Parent model’s tool call, as persisted by Codex { “name”: “spawn_agent”, “arguments”: { “task_name”: “worker”, “message”: “<ciphertext>” } }
// Child model’s input { “type”: “agent_message”, “author”: “/root”, “recipient”: “/root/worker”, “content”: [{ “type”: “encrypted_content”, “encrypted_content”: “<ciphertext>” }] }
The Responses API encrypts the tool argument emitted by the parent, Codex forwards it, and the API decrypts it internally for the child. Codex’s own InterAgentCommunication.content is empty. The exact task is absent from its readable rollout and history.
Presumably this is not merely an abstract model-switching concern. One could imagine if the child changes the wrong file, leaks a secret, duplicates another agent’s work, or follows a bad assumption, the user cannot answer the simple question of what was that agent asked to do?
An open Codex issue asks for the encrypted delivery to retain a separate readable audit copy. That is the minimum acceptable design. Better still, plaintext inter-agent messages should remain the norm.
“Most People Do Not Switch Models Mid-Session”
Probably not. Most people do not switch their operating system or phone provider every week either. But even if you do not utilize that freedom, it matters because it changes the relationship you have with the provider and the provider has with you.
As a user you also may need to move a session because a model is retired, a service is down, a price changes, a policy blocks the next request (hello fable), a confidential phase must run locally, or an auditor needs to reconstruct what happened. Agents are also making sessions much longer. A coding or research session can accumulate days of decisions and evidence and a personal assistant may accumulate session transcripts going back years (presumably as we don’t have them for that long yet).
The option to leave also creates discipline. If a provider knows that a user can continue elsewhere, it has to compete on model quality, price, reliability, and trust. If the user’s accumulated context can only be interpreted by one provider, it sets very unfortunate incentives.
What a Portable Inference API Should Promise
We would like inference providers and agent builders to adopt a small set of rules.
The local event log is canonical. Server storage may mirror or accelerate it, but the client can reconstruct the session without dereferencing server IDs.
Storage is explicit. store: false should be easy, documented, and preferably the default. Features that require retention should say so at the point of use.
No opaque item is the sole carrier of meaning. Encrypted reasoning, compaction, and tool signatures may be included for same-provider quality, but each has a readable, provider-neutral handoff representation.
Hosted tools have full-fidelity logs. Record exact inputs, outputs, evidence, filtering, provenance, timestamps, and content hashes — not only a polished answer and citations.
Subagent communication is auditable. Persist the exact readable task, messages, results, lineage, model, and tool permissions for every agent.
Compaction is inspectable. Return a readable summary, the instructions used to create it, and enough lineage to understand what was discarded.
Artifacts are exportable. Files, container outputs, search snapshots, and generated media can be downloaded into a content-addressed local archive.
Distillation Is Great Actually
There is a related form of lock-in at the model layer.
Some of the largest closed-weight US labs are increasingly hostile to outside distillation. Anthropic’s February 2026 post about alleged campaigns by DeepSeek, Moonshot, and MiniMax calls them “distillation attacks”. Its commercial terms say customers own their outputs, but prohibit using the service to train a competing AI model. At the same time, Anthropic’s own post acknowledges that “distillation is a widely used and legitimate training method” when frontier labs use it on their own models.
Anthropic uses robots to gather data from the public web for model development and they famously cut up books to scan them. OpenAI similarly says it trains on freely accessible public internet content and has argued that training on publicly available internet materials is fair use. Both companies describe distillation as a normal way to produce smaller models when it happens inside their own walls. OpenAI has also offered an explicit first-party API distillation workflow for using outputs from a stronger OpenAI model to fine-tune a smaller OpenAI model.
The moral asymmetry is still hard to miss. The labs ask society to accept that machines may learn from the enormous body of work humans placed on the internet — often without advance, individual permission — while insisting that other machines must not learn from outputs the labs generate. The broadest version of that principle conveniently allows learning to flow into closed models but not back out of them.
We think the default attitude toward distillation should move from hostility to support. Distillation can turn expensive frontier capability into smaller, cheaper, faster models that can run locally, offline, on constrained hardware, or under the user’s control. It can increase competition, preserve capability when an API disappears, and reduce the compute and energy required for common tasks.
The Minimum Freedom
A user should be able to close an account, keep a session, and hand it to another model. The new model may disagree, ask questions, or perform worse. It should not be staring at ciphertext where the old model saw the user’s history, evidence, plans, and delegated work.
We do not object to providers building better stateful APIs. We object to better performance being coupled to less user control. Stateful storage should be optional, hosted tools should be observable, compaction should be readable, agent communication should be auditable and ideally opaque reasoning is not opaque or at least should have a portable handoff. Distillation should be a path by which capability becomes more available, not a taboo used to justify ever higher walls.