jif
90d · built 2026-07-24
90-day totals
- Commits
- 482
- Grow
- 49.0
- Maintenance
- 50.8
- Fixes
- 6.1
- Total ETV
- 106
Where this dev ranks
Percentile against the global top-100 leaderboard (all-time totals).
- By commits
- Top 8 %
- By Growth share
- Top 10 %
30-day trajectory
Last 30 days vs. the 30 days before. Up arrows on Growth and ETV mean improvement; up arrow on Fixes share means more time on fixes (worse).
↓-42.3 %
vs 208 prior
↑+1.6 pp
recent vs prior
↑+9.0 pp
recent vs prior
Daily performance
Daily ETV, stacked by Growth, Maintenance and Fixes.
Work-mix over time
Share of Growth / Maintenance / Fixes over a rolling 7-day window. Reads as 'where is effort flowing right now'.
Repository spread
Where this developer's commits land. Concentrated work (top1 > 80%) vs polymath spread (top1 < 30%).
Most impactful commits
Top 20 by ETV in the 90-day window.
- 1.8ETVfeat: add layered --profile-v2 config files (#17141) ## Why `--profile-v2 <name>` gives launchers and runtime entry points a named profile config without making each profile duplicate the base user config. The base `$CODEX_HOME/config.toml` still loads first, then `$CODEX_HOME/<name>.config.toml` layers above it and becomes the active writable user config for that session. That keeps shared defaults, plugin/MCP setup, and managed/user constraints in one place while letting a named profile override only the pieces that need to differ. ## What Changed - Added the shared `--profile-v2 <name>` runtime option with validated plain names, now represented by `ProfileV2Name`. - Extended config layer state so the base user config and selected profile config are both `User` layers; APIs expose the active user layer and merged effective user config. - Threaded profile selection through runtime entry points: `codex`, `codex exec`, `codex review`, `codex resume`, `codex fork`, and `codex debug prompt-input`. - Made user-facing config writes go to the selected profile file when active, including TUI/settings persistence, app-server config writes, and MCP/app tool approval persistence. - Made plugin, marketplace, MCP, hooks, and config reload paths read from the merged user config so base and profile layers both participate. - Updated app-server config layer schemas to mark profile-backed user layers. ## Limits `--profile-v2` is still rejected for config-management subcommands such as feature, MCP, and marketplace edits. Those paths remain tied to the base `config.toml` until they have explicit profile-selection semantics. Some adjacent background writes may still update base or global state rather than the selected profile: - marketplace auto-upgrade metadata - automatic MCP dependency installs from skills - remote plugin sync or uninstall config edits - personality migration marker/default writes ## Verification Added targeted coverage for profile name validation, layer ordering/merging, selected-profile writes, app-server config writes, session hot reload, plugin config merging, hooks/config fixture updates, and MCP/app approval persistence. --------- Co-authored-by: Codex <noreply@openai.com>github.com-openai-codex · deedf3b2 · 2026-05-14
- 1.8ETVReuse MCP connections across runtime refreshes (#34952) ## Why Refreshing MCP runtime state should not restart an unchanged, ready server or relist its tools. ## What changed - Reconcile refreshed MCP configuration against the published connection set and reuse connections whose transport, environment, authentication, OAuth credentials, and client capabilities are unchanged. - Keep tool filters, timeouts, metadata, and plugin provenance in the published server view so those settings can change without reconnecting. - Update elicitation authority in place for reused connections, and reconnect when connection-defining inputs or live OAuth credentials change. ## Testing Added coverage for unchanged-server reuse, view-only and elicitation-policy updates, connection identity changes, OAuth credential changes, and avoiding redundant tool listing. GitOrigin-RevId: f04f5db5fbdde127b6a14f6aa9673112c0b557e7github.com-openai-codex · e19e6531 · 2026-07-23
- 1.7ETVCentralize thread MCP state in `McpRuntime` (#34930) ## What changed - Make `McpRuntime` own the published MCP configuration, connections, elicitation routing, and selected capability roots for a thread. - Capture immutable MCP bindings for model steps and tool calls so in-flight work keeps a consistent connection set and approval authority while refreshed state is published atomically. - Mark MCP state dirty when relevant configuration, plugins, environments, authentication, or elicitation capabilities change, then rebuild it before the next sampling request or out-of-band MCP operation. - Separate config reloads from server invalidation: `ReloadMcpConfig` applies resolved MCP inputs, while `RefreshMcpServers` requests reinitialization from the thread's latest state. ## Testing - Cover refreshed state visibility for existing turns, stable step bindings, current approval authority, cancelled refresh retries, resource-client reconciliation, and Apps recovery between sampling requests. GitOrigin-RevId: 59eabb1aa8dc083426bd18ef4d3630508f376401github.com-openai-codex · e497325a · 2026-07-23
- 1.5ETVmulti-agent: add path-based v2 activity tracking (#27007) ## Why Multi-agent v2 identifies agents by canonical paths, but its tool handlers still emitted the larger legacy collaboration begin/end events built around nickname and role metadata. App-server, rollout-trace, analytics, and TUI consumers therefore lacked one compact path-based completion signal that behaved consistently across live events and replay. The TUI also needs a bounded `/agent` status surface for v2 agents. It should use recent local activity for previews, refresh liveness without loading full histories, and keep the legacy picker available when no path-backed v2 agent is known. ## What changed - Replace the v2 `spawn_agent`, `send_message`, `followup_task`, and `interrupt_agent` legacy lifecycle emissions with a success-only `SubAgentActivity` event. The event records the tool call ID, occurrence time, affected thread, canonical agent path, and `started`, `interacted`, or `interrupted` kind. - Expose the activity as a completion-only app-server v2 `subAgentActivity` thread item in live notifications and reconstructed history, regenerate the protocol schemas, and count it in sub-agent tool analytics. - Track canonical paths from live activity and loaded-thread metadata in the TUI, and render the activity in live and replayed transcripts. - Make `/agent` list running path-backed agents with summaries from bounded local event buffers. Each summary is capped at 240 graphemes, the scan is capped at six recent items, only the last three wrapped lines are shown, and command output is omitted. Liveness falls back to metadata-only `thread/read` when local turn state is unavailable. - Persist the activity as a terminal rollout-trace runtime payload and reduce it to the corresponding spawn, send, follow-up, or close interaction edge. `interrupt_agent` is classified as a close-edge operation. - Preserve the legacy picker when no path-backed v2 agent is known. ## Compatibility App-server v2 clients that consumed `collabAgentToolCall` begin/end pairs for these tools must handle the new completion-only `subAgentActivity` item. Legacy v1 collaboration behavior is unchanged. ## Screenshot <img width="684" height="288" alt="Screenshot 2026-06-08 at 15 40 47" src="https://github.com/user-attachments/assets/194b3cd0-619d-45fb-b587-cf3e2b1b8a1d" /> ## Testing - `just test -p codex-app-server-protocol` - `just test -p codex-rollout-trace` - Added focused coverage for activity analytics, terminal trace serialization, spawn-edge reduction, `interrupt_agent` classification, TUI status rendering without aggregated command output, and clearing stale running state after a completed turn.github.com-openai-codex · fae27093 · 2026-06-09
- 1.4ETVRead compressed rollouts and materialize before append (#25087) ## Why Local rollout compression needs a cold `.jsonl.zst` representation without letting compressed physical paths leak into append-mode writers. The unsafe case is resume or metadata update code successfully reading a compressed rollout and then appending raw JSONL bytes to the zstd file. This PR folds the former #25088 materialization slice into the read-support PR so the reader changes and append-safety invariant land together. ## What Changed - Teach rollout readers, discovery, listing, search, and ID lookup to understand compressed `.jsonl.zst` rollouts. - Keep `.jsonl` as the logical/stored rollout path while allowing read paths to open either plain or compressed storage. - Materialize compressed rollouts back to plain `.jsonl` before append-mode writes, including resume and direct metadata append paths. - Preserve compressed-file permissions when materializing back to plain JSONL. - Refresh thread-store resolved rollout paths after compatibility metadata writes so reconciliation follows the materialized file. - Avoid treating transient compression temp files as real rollout lookup results. ## Remaining Stack #25089 remains the separate worker PR. It is based directly on this PR and stays behind the disabled `local_thread_store_compression` feature flag. The worker still has a broader coordination question: a resume or metadata update can race with background compression while a plain file is being replaced by `.jsonl.zst`. This PR handles the read and materialize-before-append primitives; it does not make the worker production-ready. ## Validation - `just test -p codex-rollout` - `just test -p codex-thread-store` - `just fix -p codex-rollout` - `just fix -p codex-thread-store` - `just bazel-lock-check`github.com-openai-codex · a8a60712 · 2026-06-01
- 1.3ETVLoad selected executor skills through extensions (#27184) ## Why CCA is moving toward a split runtime where the orchestrator may not have a filesystem, while executors can expose preinstalled plugins and skills. A thread therefore needs to select capabilities without asking app-server or core to interpret executor-owned paths through the orchestrator's filesystem. The longer-term model is broader than executor skills: - A plugin is a bundle of skills, MCP servers, connectors/apps, and hooks. - A plugin root can be local, executor-owned, or hosted by a backend. - Components inside one plugin can use different access and execution mechanisms. A skill may be read from a filesystem or through backend tools; an HTTP MCP server can run without an executor; a stdio MCP server or hook needs an execution environment. - Core should carry generic extension initialization data. The extension that owns a component should discover it, expose it to the model, and invoke it through the appropriate runtime. This PR establishes that architecture through one complete vertical: selecting a root on an executor, discovering the skills beneath it, exposing those skills to the model, and reading an explicitly invoked `SKILL.md` through the same executor. ## Contract `thread/start` gains an experimental `selectedCapabilityRoots` field: ```json { "selectedCapabilityRoots": [ { "id": "deploy-plugin@1", "location": { "type": "environment", "environmentId": "workspace", "path": "/opt/codex/plugins/deploy" } } ] } ``` The root is intentionally not classified as a "plugin" or "skill" in the API. It can point at a standalone skill, a directory containing several skills, or a plugin containing skills and other components. This PR only teaches the skills extension how to consume it; later extensions can resolve MCP, connector, and hook components from the same selection. The platform-supplied `id` is stable selection identity. The location says which runtime owns the root and gives that runtime an opaque path. App-server does not inspect or canonicalize the path. ## What changed ### Generic thread extension initialization App-server converts selected roots into `ExtensionDataInit`. Core carries that generic initialization value until the final thread ID is known, then creates thread-scoped `ExtensionData` before lifecycle contributors run. This keeps `Session` and core independent of the capability-selection contract. The initialization value is consumed during construction; it is not retained as another long-lived `Session` field. ### Executor-backed skills The skills extension now owns an `ExecutorSkillProvider` that: - resolves the selected environment through `EnvironmentManager` - discovers, canonicalizes, and reads skills through that environment's `ExecutorFileSystem` - contributes the bounded selected-skill catalog as stable developer context - reads an explicitly invoked skill body through the authority that listed it - warns when an environment or root is unavailable - never falls back to the orchestrator filesystem for an executor-owned root Skill catalog and instruction fragments have hard byte bounds, which also bound them below the 10K-token per-item context limit. If a selected executor skill has the same name as a legacy local skill, the executor selection owns that invocation and the local body is not injected a second time. Existing local and bundled skill loading remains in place. Omitting `selectedCapabilityRoots` therefore preserves current local-only behavior. ## Current semantics - Only environment-owned locations are represented in this first contract. - Roots are resolved by the destination extension, not by app-server or core. - An unavailable executor or invalid root produces a warning and no capabilities from that root; it does not trigger a local-filesystem fallback. - Selection applies to a newly started active thread. - MCP servers, connectors, and hooks beneath a selected plugin root are not activated yet. - Selection is not yet persisted or inherited across resume, fork, or subagent creation. Existing local capabilities continue to behave as they do today in those flows. ## Planned vertical follow-ups 1. **Hosted HTTP MCP:** add an extension-backed HTTP MCP source that works without an executor, then replace the special-purpose MCP plugins loader with that implementation. 2. **Executor MCP:** register and execute stdio MCP servers through the environment that owns the selected plugin root. 3. **Backend skills:** add a hosted skill source whose catalog and bodies are accessed through extension tools rather than a filesystem. 4. **Connectors and hooks:** activate those components through their owning extensions, using the same selected-root boundary and component-specific runtime. 5. **Durable selection:** define the desired-selection lifecycle, persist it, and make resume, fork, and subagent inheritance explicit rather than accidental. 6. **Local convergence:** incrementally route existing local plugin, skill, and MCP loading through the same extension model while preserving current local behavior. Each follow-up remains reviewable as an end-to-end capability. The platform selects roots, generic thread extension data carries the selection, and the owning extension resolves and operates its component. ## Verification Coverage added for: - app-server end-to-end discovery and explicit invocation of a skill inside an executor-selected plugin root - exclusive invocation when a selected executor skill collides with a local skill name - executor filesystem authority for discovery, canonicalization, and reads - thread extension initialization before lifecycle contributors run - stable executor catalog context, explicit invocation, context rebuilding, hidden skills, and preserved host/remote catalog behavior Targeted protocol, core-skills, skills-extension, core lifecycle, and app-server executor-skill tests were run during development.github.com-openai-codex · 89ac3ec2 · 2026-06-09
- 1.3ETVMove memory state to a dedicated SQLite DB (#24591) ## Summary Generated memory rows and their stage-one/stage-two job state currently live in `state_5.sqlite` alongside thread metadata. That makes memory cleanup and regeneration share the main state schema even though those rows are memory-pipeline data and can be rebuilt independently from the durable thread records. This PR moves the memory-owned tables into a dedicated `memories_1.sqlite` runtime database while keeping thread metadata in `state_5.sqlite`. ## Changes - Adds a separate memories DB runtime, migrator, path helpers, telemetry kind, and Bazel compile data for `state/memory_migrations`. - Introduces `MemoryStore` behind `StateRuntime::memories()` and moves memory table/job operations onto that store. - Drops the old memory tables from the state DB and recreates their schema in `state/memory_migrations/0001_memories.sql`. - Updates memory startup, citation usage tracking, rollout pollution handling, `debug clear-memories`, and app-server `memory/reset` to operate through the memories DB. - Preserves cross-DB behavior by hydrating thread metadata from the state DB when selecting visible memory outputs and checking stage-one staleness. ## Verification - Added/updated `codex-state` tests for deleted-thread memory visibility and already-polluted phase-two enqueue behavior. - Updated `debug clear-memories`, app-server `memory/reset`, and memories startup tests to seed and assert memory rows through `memories_1.sqlite`.github.com-openai-codex · aad59a09 · 2026-05-26
- 1.3ETVResume exec-server sessions after disconnect (#28512) Supersedes #28288 (closed). ## Why A short WebSocket interruption currently ends every client-side process handle, even though exec-server keeps the server session and its processes alive for a short time. This is especially visible for executor-backed stdio MCP servers: a temporary connection loss becomes a permanent `Transport closed` error. The server already has the information needed to resume the session, but the client opens a fresh session instead of using it. This change reconnects below the process and MCP layers. Existing process handles stay valid, missed output is recovered, and the same server-side processes continue running. ## State machine One logical `ExecServerClient` stays alive while its underlying RPC connection changes generations. ```text transport closes +------------------------------------------------+ | v +-------------+ +-------------+ | Connected | | Recovering | +-------------+ +-------------+ ^ | | session resumed, processes caught up | retryable error +------------------------------------------------+ loops until deadline | | deadline or permanent error v +-------------+ | Failed | +-------------+ ``` ### `Connected` - New RPC calls use the current connection. - Process notifications are published in sequence order. - A disconnect only starts recovery if it came from the current connection generation. Late events from older generations cannot replace the active connection. ### `Recovering` - New calls wait instead of choosing a half-connected RPC client. - Existing process handles, wake subscriptions, and event subscriptions stay open. - Streaming HTTP response bodies fail immediately because their byte streams cannot be resumed safely. - Recovery first waits for process starts that were already in flight. A start whose result became ambiguous is cleaned up after reconnection instead of being silently adopted. - The client reconnects with the learned `session_id`. The server may briefly report that the old connection is still attached, so that error is retried until the detach finishes. - The notification consumer starts before the resume handshake completes. This prevents a busy process from filling the notification queue and blocking the initialize response. - Before installing the new connection, the client catches up every recoverable process with `process/read`. ### `Failed` - Recovery stops after 25 seconds or after a permanent error. - Waiting calls are released with one stable disconnect error. - Existing process sessions receive a terminal failure instead of waiting forever. ## Recovering process events Output, exit, and close events share one sequence. During normal operation, the client buffers early events until every lower sequence has been published. After reconnection, the client reads each process starting after its last published sequence: 1. Retained output chunks are inserted by sequence number. 2. Exit and close state are reconstructed in their sequence positions. 3. Events already received as live notifications are ignored as duplicates. 4. Newly contiguous events are published in order. 5. If the server no longer retains enough output to fill a sequence gap, only that process is terminated and failed. The recovered connection remains usable for other processes. The server reports its full next event sequence for unbounded reads, including exit and close events. Closed processes remain readable for the same 30-second window used to retain detached sessions. ## Other details - Detached server sessions are retained for 30 seconds, leaving margin around the client's 25-second recovery deadline. - Session attach and detach update the active notification sender under the same attachment lock, so an old connection cannot clear a newly attached sender. - A dedicated error code distinguishes the temporary "session is still attached" race from permanent initialization errors. - Process starts are identity-checked on both client and server. Cleanup from an older start cannot remove a newer process that reused the same ID. - Mutating requests that were already in flight when the transport closed are not replayed, because the client cannot know whether the server applied them. Requests started after recovery is known wait for the replacement connection. - We assume the server/client version stays in sync (on the before/after this PR) ## User impact Long-running commands and stdio MCP servers can survive a temporary exec-server WebSocket interruption without changing process IDs or losing output produced during the outage.github.com-openai-codex · cf17e1bc · 2026-06-17
- 1.2ETVRemove the network proxy config wrapper (#31767) ## Why `NetworkProxyConfig` only wrapped `NetworkProxySettings` in a single `network` field. That extra level made runtime callers repeat `.network` everywhere without representing a real boundary. ## What changed - move the managed-network fields directly onto `NetworkProxyConfig` - collapse the matching partial-config wrapper - update runtime callers and tests to use the direct fields - keep the user-facing permissions/profile TOML layout unchanged The internal serialized shape now matches the runtime type itself. This does not change managed-network behavior or the `config.toml` shape.github.com-openai-codex · cf6d8ad9 · 2026-07-09
- 1.2ETVMake turn diff tracking operation backed (#21180) ## Summary - replace filesystem-based turn diff tracking with an operation-backed accumulator - preserve enough verified apply_patch state to render move-overwrite cases correctly - keep the turn/diff/updated contract intact while removing remote-only turn-diff test skips This takes the assumption that no 3P services rely on the output format of `apply_patch` ## Why For the CCA file system isolation push --------- Co-authored-by: Codex <noreply@openai.com>github.com-openai-codex · f7e8ff8e · 2026-05-07
- 1.2ETVAdd shadow metrics for lexical skill selection (#32761) ## What changed - Add an opt-in `skill_search` feature that ranks prompt-visible skills against each turn's user input with a bounded weighted lexical selector. - Keep the ranked selection out of model-visible context and record metrics for selection cost, catalog reduction, and whether later implicit or `skills.read` invocations matched the ranked candidates. - Include host-provided skills in the experiment catalog without changing the rendered skill catalog. ## Testing - Add selector unit tests covering ranking, limits, truncation, stop words, and deterministic tie-breaking. - Add extension tests covering turn-local invocation recording and host-skill shadow selection. GitOrigin-RevId: 4d00a1c805ea8b391d6c6ac6a8450afa88ca3e25github.com-openai-codex · c1001092 · 2026-07-13
- 1.2ETVfeat: use git-backed workspace diffs for memory consolidation (#18982) ## Why This PR make the `morpheus` agent (memory phase 2) use a git diff to start it's consolidation. The workflow is the following: 1. The agent acquire a lock 2. If `.codex/memories` does not exist or is not a git root, initialize everything (and make a first empty commit) 3. Update `raw_memories.md` and `rollout_summaries/` as before. Basically we select max N phase 1 memories based on a given policy 4. We use git (`gix`) to get a diff between the current state of `.codex/memories` and the last commit. 5. Dump the diff in `phase2_workspace_diff.md` 6. Spawn `morpheus` and point it to `phase2_workspace_diff.md` 7. Wait for `morpheus` to be done 8. Re-create a new `.git` and make one single commit on it. We do this because we don't want to preserve history through `.git` and this is cheap anyway 9. We release the lock On top of this, we keep the retry policies etc etc The goals of this new workflow are: * Better support of any memory extensions such as `chronicle` * Allow the user to manually edit memories and this will be considered by the phase 2 agent As a follow-up we will need to add support for user's edition while `morpheus` is running ## What Changed - Added memory workspace helpers that prepare the git baseline, compute the diff, write `phase2_workspace_diff.md`, and reset the baseline after successful consolidation. - Updated Phase 2 to sync current inputs into `raw_memories.md` and `rollout_summaries/`, prune old extension resources, skip clean workspaces, and run the consolidation subagent only when the workspace has changes. - Tightened Phase 2 job ownership around long-running consolidation with heartbeats and an ownership check before resetting the baseline. - Simplified the prompt and state APIs so DB watermarks are bookkeeping, while workspace dirtiness decides whether consolidation work exists. - Updated the memory pipeline README and tests for workspace diffs, extension-resource cleanup, pollution-driven forgetting, selection ranking, and baseline persistence. ## Verification - Added/updated coverage in `core/src/memories/tests.rs`, `core/src/memories/workspace_tests.rs`, `state/src/runtime/memories.rs`, and `core/tests/suite/memories.rs`. --------- Co-authored-by: Codex <noreply@openai.com>github.com-openai-codex · 01ab25db · 2026-04-27
- 1.1ETVSimplify tool executor and registry plumbing (#22636) ## Why The tool runtime path still had a typed output associated type on `ToolExecutor`, plus a core-only `RegisteredTool` adapter and extension-only executor aliases. That made every new shared tool runtime carry extra adapter plumbing before it could participate in core dispatch, extension tools, hook payloads, telemetry, and model-visible spec generation. This PR moves output erasure to the shared executor boundary so core and extension tools can use the same execution contract directly. ## What Changed - Changed `codex_tools::ToolExecutor` to return `Box<dyn ToolOutput>` instead of an associated `Output` type. - Removed the extension-specific `ExtensionToolExecutor` / `ExtensionToolOutput` aliases and exposed `ToolExecutor<ToolCall>` plus `ToolOutput` through `codex-extension-api`. - Reworked core tool registration around `CoreToolRuntime` and `ToolRegistry::from_tools`, removing the extra `RegisteredTool` / `ToolRegistryBuilder` layer. - Consolidated model-visible spec planning and registry construction in `core/src/tools/spec_plan.rs`, including deferred tool search and code-mode-only filtering. - Added `ToolOutput` helpers for post-tool-use hook ids and inputs so MCP, unified exec, extension, and other boxed outputs preserve the same hook payload behavior. - Updated core handlers, memories tools, and the related registry/spec/router tests to use the simplified contract. ## Test Coverage - Updated coverage for tool spec planning, registry lookup, deferred tool search registration, extension tool routing, post-tool-use hook payloads, dispatch tracing, guardian output extraction, and memories extension tool execution.github.com-openai-codex · 6f1a01fb · 2026-05-15
- 1.1ETVBind MCP calls to captured catalog revisions (#34588) ## Why An MCP connection or tool catalog can change after a model step captures its available tools. Calls from that step must not be rerouted to a replacement client or run against a catalog revision the model did not see. ## What changed - Add `McpBinding` to capture the ready clients, visible tools, resources, and server metadata for one sampling request. - Prepare calls against the captured client and reject them if the tool catalog changes before call preparation and execution finish. - Keep resource access and cache identity tied to the binding's exact connection set, and omit servers that do not have a ready client. - Preserve each client's unfiltered tool catalog so bindings can apply the current filter consistently. ## Testing Add coverage for connection replacement, stale catalog rejection, serialized call preparation and refresh, ready-client capture, and binding-scoped resource identity. GitOrigin-RevId: a25da4d2c7f5ed06c41caa0c6d15c464c1698314github.com-openai-codex · 65f8bf68 · 2026-07-21
- 1.1ETVfeat: split memories part 2 (#19860) Keep extracting memories out of core and moving the write trigger in the app-server This is temporary and it should move at the client level as a follow-up This makes core fully independant from `codex-memories-write` --------- Co-authored-by: Codex <noreply@openai.com>github.com-openai-codex · 431ebeae · 2026-04-28
- 1.0ETVResolve per-thread multi-agent runtime (#25722) Stack split from #25708. Original PR intentionally left open. This third PR resolves the effective per-thread multi-agent runtime from persisted metadata, inherited runtime, and current model selection.github.com-openai-codex · bf9fd885 · 2026-06-02
- 1.0ETVAdd process-scoped SQLite telemetry (#22154) ## Summary - add SQLite init, backfill-gate, and fallback telemetry without introducing a cross-cutting state-db access wrapper - install one process-scoped telemetry sink after OTEL startup and let low-level state/rollout paths emit through it directly - add process-start metrics for the process owners that initialize SQLite --------- Co-authored-by: Owen Lin <owen@openai.com>github.com-openai-codex · b401666c · 2026-05-11
- 1.0ETVImplement v1 skills extension prompt injection (#26167) ## Why The skills extension needs a real turn-time path before host, executor, or remote skills can be routed through it. The previous code was mostly a placeholder catalog/provider sketch, so there was no bounded available-skills fragment, no source-owned `SKILL.md` read, and no place for warnings or per-turn selection state to live. This PR makes `ext/skills` the authority-preserving flow for listing candidate skills and injecting only explicitly selected main prompts, without adding more of that logic to `codex-core`. ## What changed - Expands catalog entries with `main_prompt`, display path, short description, dependency metadata, enabled/prompt visibility flags, and authority/package-aware read requests. - Replaces the placeholder `providers/*` modules with `SkillProviderSource` and `SkillProviders`, routing list/read/search calls by source kind and surfacing provider failures as warnings. - Adds bounded available-skills rendering and `SKILL.md` main-prompt truncation before the fragments enter model context. - Resolves explicit skill selections from structured `UserInput::Skill`, skill-file mentions, `skill://...` paths, and plain `$skill` text mentions, then reads selected prompts through their owning provider. - Stores mutable per-thread skills config and per-turn catalog/selection/warning state. - Adds `install_with_providers` so tests and future host wiring can supply concrete providers. ## Testing - Not run locally. - Added `codex-rs/ext/skills/tests/skills_extension.rs` coverage for available-catalog injection, selected prompt injection through the owning provider, and prompt-hidden skills that remain invokable.github.com-openai-codex · 96d2d2f6 · 2026-06-03
- 1.0ETVOrder unified exec lifecycle events reliably (#34713) ## What changed - Treat output-task closure as the signal that trailing output is complete, drain any remaining chunks before publishing command completion, and retain the grace period as a fallback. - Wait for deferred network-denial classification before emitting the final command result. - Serialize `write_stdin` interaction and completion events so an interaction that exits a process is published first, and avoid pruning processes while their terminal events are being finalized. ## Testing - Add coverage for late output, missing output-close signals, late network denials, interaction/completion ordering, cross-platform aggregated output, and pruning during finalization. GitOrigin-RevId: 46a118552bb1a4658b78aa5a5ca76eb8c5fba571github.com-openai-codex · 9fc715c0 · 2026-07-22
- 1.0ETVfeat: wire extension tool bundles into core (#22147) ## Why This is the next narrow step toward moving concrete tool families out of core. After #22138 introduced `codex-tool-api`, we still needed a real end-to-end seam that lets an extension own an executable tool definition once and have core install it without the temporary `extension-api` wrapper or a dependency on `codex-tools`. `codex-tool-api` is the small extension-facing execution contract, while `codex-tools` still has a different job: host-side shared tool metadata and planning logic that is not “run this contributed tool”, like spec shaping, namespaces, discovery, code-mode augmentation, and MCP/dynamic-to-Responses API conversion ## What changed - Moved the shared leaf tool-spec and JSON Schema types into `codex-tool-api`, so the executable contract now lives with [`ToolBundle`](https://github.com/openai/codex/blob/c538758095337d4fe0a52a172363ccede4066bda/codex-rs/tool-api/src/bundle.rs#L19-L70). - Replaced the temporary extension-side tool wrapper with direct `ToolBundle` use in `codex-extension-api`. - Taught core to collect contributed bundles, include them in spec planning, register them through [`ToolRegistryBuilder::register_tool_bundle`](https://github.com/openai/codex/blob/c538758095337d4fe0a52a172363ccede4066bda/codex-rs/core/src/tools/registry.rs#L653-L667), and dispatch them through the existing router/runtime path. - Added focused coverage for contributed tools becoming model-visible and dispatchable, plus spec-planning coverage for contributed function and freeform tools. ## Verification - Added `extension_tool_bundles_are_model_visible_and_dispatchable` in `core/src/tools/router_tests.rs`. - Added spec-plan coverage in `core/src/tools/spec_plan_tests.rs` for contributed extension bundles. ## Related - Follow-up to #22138github.com-openai-codex · 672cc1f6 · 2026-05-11