GPT-6 Astra’s experimental Codex context management lets eligible users keep notes across context windows and search earlier messages and tool results from the same task. It is off by default, requires a new task, and does not guarantee that every note is current or that a long session will use fewer tokens. Treat it as a feature to verify with a disposable facts fixture, not as a promise of perfect memory.
This guide explains the current setup, the distinction between standard compaction and experimental context management, and a practical test for exact retrieval and stale-note correction. It complements DMT’s broader GPT-6 Astra prompting guide and the older AI-agent harness context-compaction article.
What the experimental feature does
OpenAI’s current Codex model documentation says that supported Codex clients let ChatGPT Plus and Pro users opt in. Astra can keep notes across context windows and search earlier messages and tool results from the same task. The experiment is off by default and, at launch, is unavailable with Business, Enterprise or API-key sign-in.
The open-source Codex implementation change describes the setting as under development. It enables token-budget context, history notes and a model-controlled new_context tool for eligible sessions. That implementation detail explains the design direction; it is not a guarantee that the model will write a useful note, retrieve it at the right time or resolve a conflict with a changed file.
| Question | Documented answer | What remains to test |
|---|---|---|
| Is it enabled automatically? | No. The experiment is off by default. | Whether your current client and account actually activate it. |
| How do I enable it? | Set features.context_management.experimental_mode = true in config.toml, then start a new task. | Whether the new task reports the expected context tools/behavior. |
| What can it search? | Earlier messages and tool results from the same task. | Whether the model retrieves the exact source needed at the point of use. |
| Does it make stale notes impossible? | No such guarantee is documented. | Whether a changed source file supersedes an older note. |
| Does it guarantee lower cost? | No. | Actual usage, latency, retries and recovery work on your task. |
Enable it without mistaking visibility for activation
Configuration correction, 10 September 2026: use the nested table below, or merge the relative dotted key into an existing [features] table. A root dotted assignment followed later by [features] can redeclare that table and fail parsing. Putting the dotted line before the first table is therefore not sufficient advice for every existing configuration.
[features.context_management]
experimental_mode = trueIf [features.context_management] already exists, edit its current value. Alternatively, inside an existing [features] table, add context_management.experimental_mode = true. Choose one form; do not create duplicate tables or place the key inside an unrelated table.
- Confirm a supported client and eligible ChatGPT sign-in. The Models page lists Plus/Pro; the current configuration reference also names Pro Lite. Verify your actual client/account while that wording differs.
- Back up and edit the configuration used by that client. The usual file is
~/.codex/config.toml; check a customCODEX_HOMEand more-specific project or CLI overrides. - Validate the file and confirm that the actual client recognizes the feature.
codex --versionidentifies the CLI;codex features listreports its feature state. - Start a new task, as the official documentation requires. Record client, date, model, effort and config state.
- Use a disposable facts fixture to test a real transition and a changed source before assuming useful retention or lower usage.
In the September 10 checks, the desktop runtime 0.153.4 accepted the feature while an older PATH CLI 0.140.0 rejected its nested map. Align the command with a supported runtime, or put the experiment in a separate supported-client profile and remove it from the shared base config. An override of false will not cure an old client’s inability to parse the table. Current profile files are separate profile-name.config.toml files, as described in OpenAI’s profile documentation.
Four local fixtures were executed with Python’s tomllib: a root dotted flag followed by [features] failed; a relative key inside [features] and a dedicated nested table resolved correctly; a misplaced key under another table could parse at the wrong scope. These checks establish syntax and placement only. See the usage case study and setup-check screenshot for the measured context traces and the client-recognition distinction.
Seeing Astra in a picker does not prove that experimental context management is active. The Models documentation excludes Business, Enterprise and API-key sign-in at launch. Recheck current feature eligibility and configuration precedence as rollout changes.
A safe facts fixture for a real transition test
A useful test needs a fact that is easy to verify, a source that can change, and a clear rule for which source wins. The following fixture is an original proposal for a local disposable folder. It has not been run by DMT.
# facts.md — proposed test fixture Release channel: staging Approval owner: reviewer@example.test Rule version: 1 Source of truth: this file Last changed: 2026-09-07
Use a task that reads the file, records the exact values and performs a harmless local action such as creating a report. Continue long enough that a genuine context-window transition occurs. Record the transition rather than assuming it happened because the task became long. After the transition, ask for the exact release channel, approval owner, rule version and source path. The grader should compare the answer with the file, character for character where appropriate.
Then edit only the named source fields—Release channel, Rule version and Last changed. Preserve Approval owner and Source of truth so the test changes one fact and one version rather than silently replacing the whole fixture:
Release channel: production Rule version: 2 Last changed: 2026-09-07
Ask the agent to report the current values and explain what happened to the old note. A passing freshness behavior would use the current file, identify the old version as superseded and avoid silently repeating “staging.” An old note is not itself a failure if the agent re-reads the source and explicitly supersedes it. The failure is using the old value as current after the source changed.
The result sheet
Use one row per task run. Do not report a percentage until the denominator and the failed examples are preserved.
| Field | Record |
|---|---|
| Run identity | Run ID, task fixture version, client, date and fresh/new-task status. |
| Model conditions | Exact model, reasoning setting, tools, experimental flag and plan/client surface. |
| Transition evidence | Whether a context-window transition or standard compaction was actually observed, with available log evidence. |
| Exact recovery | Each requested fact, expected value, returned value and pass/fail. |
| Supersession | Whether the changed source won over the older note, and whether the model explained the change. |
| Operational cost | Exposed usage, elapsed time, tool calls, retries and human correction time; mark unavailable fields as unavailable. |
| Failure class | Missing retrieval, stale source, wrong task, compaction hang, tool error, authority conflict or unknown. |
Run a baseline with the same fixture and task under the normal context behavior if you need a comparison. Keep model, effort, tool permissions and grader fixed. A two-by-two design can cross inherited versus cleaned instructions with default versus experimental context management, but do not change both treatments and then attribute the result to one of them.
What early users are reporting
The current public evidence is mixed and too early for a success rate. It is useful for deciding what to test:
| Source | Evidence class | Report | Limit |
|---|---|---|---|
| r/codex experimental-setting thread | First-hand post | The author says the new behavior feels better, loses fewer details and repeats less after transitions; existing sessions initially had problems, while new sessions had not shown the same issue. | One author, no task count, no controlled baseline, no usage log. |
| r/codex starts-from-scratch report | First-hand post and comment | Users describe Astra changing direction after compaction and disregarding a staged audit workflow; one comment says an external .md reduced the worst effects for other models. | It does not establish that experimental mode was enabled or isolate Astra from standard compaction. |
| r/codex auto-compact report | First-hand post | The author reports indefinite compaction across multiple chats on Astra High with default settings; comments compare slower Astra compaction with faster Sol compaction. | No controlled run or denominator; comments are separate users and may reflect a bug rather than the experiment. |
| OpenAI Codex issue #22219 | Repository issue | A reporter describes stale task-state after repeated work and says no compaction was observed during the incident. | Stale state is self-reported and has no minimal deterministic reproduction. |
| Ether_eagle1 reply | Open question | Asks whether notes become stale after a refactor and can be superseded. | Question, not a first-hand result; it identifies the freshness test this guide adds. |
Several negative reports refer to compaction but do not show the experimental flag, while the positive report is one user’s early experience. Treat them as failure modes and test ideas. Do not call this a community consensus.
Design notes so stale memory can be detected
The core risk is not only forgetting. An agent can remember an old fact with enough confidence to make it look current. Codex Discussion #42703 proposes source pointers and explicit update/delete/no-op operations so a note resolves to current evidence instead of becoming a hidden copy. The open-source coldstart project uses content hashes and marks notes when the anchored file changes. These are design artifacts, not proof that Astra implements either approach, but they suggest concrete checks:
- Store the source path, version or content hash with a durable note.
- Make the source of truth explicit for every fact that can change.
- Require a fresh read when the source marker changes.
- Keep current task state separate from permanent project rules.
- Record corrections instead of appending another contradictory note.
Do not infer that the new context feature fixes ordinary stale-state bugs. The Codex issue above describes a stale task narrative without an observed compaction. Keep retrieval, source freshness and turn-state correctness as separate failure classes.
Troubleshoot by isolating the layer
| Symptom | First check | Safe next action |
|---|---|---|
| The setting appears to do nothing | Plan, client, new-task requirement and exact config path. | Start a fresh supported task and record the activation conditions; do not edit production prompts to compensate. |
| An old fact returns after a refactor | Read the current source file and compare its version/hash with the note. | Mark the note stale, re-read the source and add a freshness check to the fixture. |
| Auto-compaction hangs | Whether the run was using experimental mode, model/effort, client version and visible context state. | Save the log and report the failure; do not claim a manual workaround is a product fix. |
| Usage rises | Context transitions, tool output, retries, reasoning setting and total input/output usage. | Compare a fixed baseline; subscription percentages alone cannot identify task-level cost. |
| The agent repeats a previous task | Latest user message, current files and task state, even if no compaction occurred. | Separate stale-state handling from context-retrieval handling and preserve the failing trace. |
Questions readers usually ask
Does experimental context management increase Astra’s context window?
It changes how Codex carries and retrieves context across windows. It does not establish that every client exposes the model’s full API context specification in one session, and it does not remove the need to manage tool output and task state.
Will notes survive in a new task?
The official description refers to earlier messages and tool results from the same task. Do not assume that a new task inherits another task’s notes. Test the boundary if your workflow depends on it.
Can I turn this on for a Business, Enterprise or API-key account?
The current launch documentation says the experiment is not available for those sign-in modes. Product eligibility can change, so check the current official page for your client and account.
Does it make external checkpoint files unnecessary?
No. An explicit source-of-truth file remains useful for facts that need inspection, versioning or human review. Use the feature as another retrieval path and validate it against the file.
Can I compare experimental notes with a cleaned AGENTS.md file in one test?
You can, but cross the treatments deliberately. Compare inherited versus cleaned instructions under both default and experimental context behavior, or keep the context mode fixed while testing instructions. Changing both at once makes the result ambiguous.
Method and limits
Author: Tayeeb Khan, Digital Marketer Tayeeb. Product facts were checked against the current OpenAI Codex documentation and open-source Codex implementation on 7 September 2026. Community evidence includes first-hand Reddit reports, a Codex issue and a public design discussion; it is an early purposive sample, not a sentiment survey. X replies identify questions about stale notes and compatibility but do not establish outcomes. The fixture, result sheet and expected passes in this article are proposed examples. DMT has not run the described Astra experiment, so no retention rate, cost saving or quality lift is claimed.
For general task contracts and evaluation, use the existing Astra prompting guide.
For model availability and links to the other focused guides, see the GPT-6 Astra overview.
