jif
90d · built 2026-08-09
90-day totals
- Commits
- 522
- Grow
- 48.2
- Maintenance
- 48.1
- Fixes
- 12.3
- Total ETV
- 108.5
Where this dev ranks
Percentile against the global top-100 leaderboard (all-time totals).
- By commits
- Top 7 %
- By Growth share
- Top 12 %
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).
↑+43.9 %
vs 132 prior
↑+6.1 pp
recent vs prior
↑+19.6 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
- 1.2ETVAdd 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
- 1.1ETVSimplify 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
- 1.0ETVResolve 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
- 1.0ETVRun compact hooks for remote compaction v2 (#22828) ## Why Remote compaction v2 is the `/responses` implementation of session-history compaction, but it still needs to preserve the observable contract of the legacy `/responses/compact` path. In particular, users and integrations that rely on `PreCompact` and `PostCompact` hooks should not see different behavior when `remote_compaction_v2` is enabled. ## What Changed - Runs `PreCompact` before issuing the remote compaction v2 request, including `Interrupted` analytics when a pre-hook stops execution. - Runs `PostCompact` after a successful v2 compaction and aborts the turn if the post-hook stops execution. - Adds `compact_remote_parity` coverage that compares legacy and v2 compaction across manual transcript shapes, automatic pre-turn compaction, automatic mid-turn compaction, hook payloads, replacement history, follow-up request payloads, and API-key `service_tier=fast` behavior. - Registers the new parity suite under `core/tests/suite`. Relevant code: - [`compact_remote_v2.rs`](https://github.com/openai/codex/blob/af63745cb502183a6fc447d0240f8150934d70b7/codex-rs/core/src/compact_remote_v2.rs) - [`compact_remote_parity.rs`](https://github.com/openai/codex/blob/af63745cb502183a6fc447d0240f8150934d70b7/codex-rs/core/tests/suite/compact_remote_parity.rs) ## Verification - Added `core/tests/suite/compact_remote_parity.rs` to assert parity between legacy remote compaction and remote compaction v2 for the affected request, hook, rollout-history, and follow-up paths. - Existing `compact_remote_v2` unit coverage still exercises v2 replacement-history retention and compaction-output collection.github.com-openai-codex · 5d30764f · 2026-05-15
- 1.0ETVSupport plaintext agent messages (#27830) ## Why Multi-agent v2 `send_message` deliveries already reach the receiving model as typed `agent_message` items with encrypted content. Child-completion notifications are generated by Codex itself, so their content is plaintext and previously fell back to a serialized JSON envelope inside an assistant message. With plaintext `input_text` supported for `agent_message`, both delivery paths can use the same model-visible type while preserving explicit author and recipient metadata. ## What changed - add plaintext `input_text` support to `AgentMessageInputContent` and regenerate the affected app-server schemas - preserve `InterAgentCommunication` as structured mailbox input instead of converting it to assistant text - record delivered communications as typed `agent_message` history items - persist a dedicated rollout item so local delivery metadata such as `trigger_turn` remains available without leaking into the Responses request - reconstruct typed agent messages on resume and preserve fork-turn truncation behavior - remove request-time assistant-content parsing - preserve plaintext and encrypted inter-agent deliveries in stage-one memory inputs - normalize and link plaintext and encrypted agent messages in rollout traces without treating inbound messages as child results - cover the real MultiAgent V2 child-completion path end to end with deterministic mailbox synchronization ## Verification - `just test -p codex-core plaintext_multi_agent_v2_completion_sends_agent_message` - `just test -p codex-core input_queue_drains_mailbox_in_delivery_order record_initial_history_reconstructs_typed_inter_agent_message fork_turn_positions_use_inter_agent_delivery_metadata` - `just test -p codex-memories-write serializes_inter_agent_communications_for_memory` - `just test -p codex-rollout-trace agent_messages_preserve_routing_and_content sub_agent_started_activity_creates_spawn_edge` - `just test -p codex-rollout-trace agent_result_edge_falls_back_to_child_thread_without_result_message` - `just test -p codex-protocol -p codex-rollout -p codex-app-server-protocol`github.com-openai-codex · 8f2d6416 · 2026-06-12
- 0.9ETVScope network approvals by environment (#28899) Stacked on #28766. ## Why Network approvals are environment-scoped: allowing a host in one execution environment should not allow the same host in another environment. #28766 adds the inert IDs and constructor plumbing. This PR applies the behavior on top. ## What changed - Route managed network traffic through per-environment HTTP and SOCKS proxy listeners. - Stamp HTTP, HTTPS CONNECT, SOCKS TCP, and SOCKS UDP policy requests with the source environment at the proxy boundary. - Carry the selected execution environment through shell, unified exec, zsh-fork, and sandbox transform paths. - Include the environment in pending, approved-for-session, and denied-for-session network approval cache keys. - Include the environment in approval IDs and approval prompts. - Preserve legacy fallback for unattributed requests, but deny when active-call attribution is ambiguous. - Fail closed if an environment-specific proxy endpoint cannot be prepared. ## Validation - just fmt - CI will run tests and clippygithub.com-openai-codex · bd5d31b2 · 2026-06-19
- 0.9ETVfix: attribut network requests to the exact exec on linux (#29697) ## Why Managed-network commands within one Codex conversation share the same HTTP and SOCKS proxy ingress. When several exec calls run concurrently, the proxy sees the requested destination but cannot tell which exec opened the connection. For example: ```text exec A: curl https://example.com/a ─┐ ├─> conversation proxy ─> Guardian exec B: curl https://example.com/b ─┘ host: example.com trigger: unknown ```. Three parallel network execs reached Guardian without their triggering call IDs or commands. Guardian denied the requests, but Codex could not safely associate those outcomes with the individual tool calls. ## What changes Keep the shared proxy ingress and tag each connection at the existing trusted Linux bridge: ```text exec A ─> existing Linux bridge ─> [token A][proxy bytes] ─┐ ├─> shared HTTP/SOCKS ingress exec B ─> existing Linux bridge ─> [token B][proxy bytes] ─┘ │ token A ─> exec A ─────┤ token B ─> exec B ─────┘ ``` The complete path is: ```text active exec registration │ ├─ registers its UUID as a short-lived attribution token ├─ passes the token to the Linux sandbox helper ├─ helper removes the token before launching the user command ├─ existing host bridge prepends the token to each proxy connection ├─ shared proxy consumes the bounded attribution frame └─ proxy attaches the matching execution-scoped state ├─ Guardian receives the exact call ID and command └─ a denial finishes/cancels the matching tool call ``` Dropping the active or deferred exec registration removes the token. Connections that were already accepted retain their resolved attribution; new connections using an expired token fail closed. ## Before and after Before, Guardian could receive only the network destination: ```json { "tool": "network_access", "host": "www.17track.net", "port": 443, "protocol": "https" } ``` After, the same request includes the action that caused it: ```json { "tool": "network_access", "host": "www.17track.net", "port": 443, "protocol": "https", "trigger": { "callId": "exec-network-first", "command": ["/bin/sh", "-c", "curl https://www.17track.net"] } } ``` ## Listener accounting This PR does **not** create proxy listeners per exec. ```text Existing topology: one conversation -> one HTTP listener + optional one SOCKS listener Discarded per-exec approach: one conversation -> existing listener pair + up to one additional listener pair per active exec This PR: one conversation -> existing listener pair only + one small token-map entry per active exec ``` The Linux sandbox already creates a trusted routing bridge for each sandboxed command. This PR adds a short frame write to that bridge rather than introducing another listener, task, or proxy process. The existing conversation-scoped listener pair remains. Making a single proxy service shared across multiple conversations would be a separate multi-tenant architecture change involving per-conversation policy, configuration, audit, and Guardian routing. ## Keeping the implementation small The attribution is bound once, when the TCP connection enters the proxy. The ingress installs an execution-scoped clone of the existing `NetworkProxyState`, so the established HTTP, SOCKS, MITM, policy, audit, and blocked-request paths continue using their existing state lookup. This avoids plumbing a new request-context type through every protocol handler. Outside the two ingress wrappers, protocol-specific request handling is unchanged. ## Security behavior - Tokens are generated from the existing random execution registration IDs. - The trusted Linux helper consumes and removes the token before executing user code. - Attribution frames have a fixed magic prefix, bounded token length, and bounded read timeout. - Unknown or expired tokens close the connection. - A token presented to a proxy for another environment closes the connection. - Existing unframed callers preserve the current conservative attribution behavior. ## Platform scope Exact bridge attribution is enabled on Linux. macOS and Windows retain their current shared-proxy behavior. ## Test coverage The concurrent end-to-end test starts two managed-network execs together and synchronizes them so both are active before either connects. It then inspects the two Guardian requests and compares the complete attribution pairs: ```text (exec-network-first, exact first command) (exec-network-second, exact second command) ``` Focused proxy coverage verifies the bounded frame and that a registered framed connection receives the matching execution and environment state. ## Scope This fixes the Linux network-to-exec attribution path and records a denial against the exact matching tool call. It intentionally does not change: - delivery of an entirely unattributed denial to the parent turn; - how parallel denials count toward the Guardian circuit breaker; - how the UI displays the rejection reason or completed-turn state. Those remain separate concerns from attribution. ## Relationship to #29456 and #29668 #29456 made the proxy environment and sandbox policy come from the same prepared network context. This PR adds the execution token to that prepared launch and consumes it at the shared ingress. This follows #29668's shared-ingress framing direction, but completes the production registration, Linux bridge, core call mapping, denial mapping, and concurrent end-to-end path. It also keeps attribution in the existing per-connection proxy state instead of introducing request-context plumbing through every HTTP, SOCKS, and MITM handler. This PR is intended to supersede #29668 for the Linux attribution fix. --------- Co-authored-by: viyatb-oai <viyatb@openai.com> Co-authored-by: Codex <noreply@openai.com>github.com-openai-codex · 1013295c · 2026-07-06