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6602a13645c5c167fb14dae5403b489ffe806542
24
Commits
| Author | SHA1 | Message | Date | |
|---|---|---|---|---|
 jif
|
723b23efd0 |
Reinject missing World State fragments on resume (#30152)
## Why World State restores its structured snapshot on resume so unchanged sections do not have to be rendered again. That is safe only when the model-visible fragment represented by the snapshot is still present in retained history. For selected executor skills, the failing selected-capability scenario exposed this state: ```text persisted World State: selected skill catalog is known retained model history: selected skill catalog message is missing next diff: unchanged, so emit nothing ``` The model resumes without being told about the selected skill catalog. ## What changed World State contributions may now optionally describe the concrete model-visible fragment that must remain in retained history. When a persisted snapshot is present: ```text matching retained fragment exists -> trust snapshot, emit nothing matching retained fragment missing -> treat section as absent, render current state once ``` The skills extension uses this for non-empty selected-environment catalogs by matching its exact rendered catalog body. Empty or hidden catalogs do not require a fragment. ## Scope This does not clear or rebuild the whole World State baseline. It does not change skill discovery, cache invalidation, environment availability, or MCP runtime behavior. It only keeps a persisted section snapshot and its retained model context consistent across resume/history reconstruction. ## Coverage A focused World State regression test verifies both sides: - a missing retained fragment is rendered again - a matching retained fragment avoids duplicate injection |
||
|
jif
|
3095ea9c3d |
Project selected plugin runtime by environment availability (#30093)
## Why
Selected plugin metadata is stable, but MCP processes are live runtime
state. They need different lifetimes:
- the MCP extension caches manifest, MCP, and connector declarations for
each stable selected root;
- each model step projects that cached metadata through the roots that
resolved as ready for that exact step;
- the MCP manager is rebuilt only when that availability projection
changes.
This matches executor skills: both features consume the same resolved
step roots instead of inferring readiness from the turn's selected
environments.
## Behavior
```text
E1 not ready for this step
-> no E1 MCP servers or connectors
-> cached plugin metadata stays in ext/mcp
E1 becomes ready
-> reuse cached metadata
-> publish one MCP runtime containing E1 capabilities
same ready roots on the next step
-> reuse the exact runtime; no rediscovery and no MCP restart
resume
-> create new extension thread state and a new MCP runtime
```
All model-facing consumers use the same step snapshot:
```text
resolved selected roots
|
v
extension MCP/connector projection
|
v
{ MCP config, connector snapshot, MCP manager }
|
+-> advertise model tools
+-> build app/connector tools
+-> execute MCP calls
```
## Cache contract
The existing MCP extension owns a cache keyed by the full
`SelectedCapabilityRoot`:
```rust
let state = thread_store.get_or_init(SelectedExecutorPluginMcpState::default);
```
The cache lives with extension thread state. Environment availability
filters projection but does not invalidate metadata. Resume creates new
thread state. There is no file watcher or executor generation because
contents behind a stable environment/root are assumed stable.
## What changes
- Keeps executor plugin discovery and cached metadata in `ext/mcp`.
- Caches MCP and connector declarations together per selected root.
- Uses the step's already-resolved capability roots, including lazy
environments that are not turn environments.
- Reuses the current MCP runtime when the ready-root projection is
unchanged.
- Uses the same step MCP manager and connector snapshot for
model-visible tools and execution.
- Resolves direct thread-scoped MCP requests from the current
selected-root projection.
## Deliberately out of scope
- `app/list` remains based on the latest global host-plugin state; this
PR does not make its response or notifications thread-specific.
- `required = true` startup semantics do not apply to delayed executor
MCP activation.
- No filesystem/content invalidation.
- No transport-disconnect watcher.
- No executor generations or environment replacement semantics.
- No client sharing across complete manager replacements.
## Stack
1. Extension-owned World State sections.
2. Project executor skills through World State.
3. Pin one MCP runtime to each model step.
4. **This PR:** project selected MCP and connector state from
extension-owned metadata.
5. Integration coverage for selected capability availability and resume.
## Verification
-
`selected_plugin_servers_use_managed_requirements_for_the_selected_root_id`
- The stacked integration PR covers unavailable to ready activation,
unchanged-runtime reuse, skills, MCP tools, connector attribution, and
cold resume.
|
||
|
jif
|
5eebeb8169 |
Project executor skills through World State (#30088)
## Why A selected executor environment can be unavailable in one model step and ready in the next. The model should see its skills only while that environment is ready, without rescanning stable files on every sample. The product assumption is simple: - an environment ID names one stable logical environment; - the selected root contents do not change during the thread. ## Behavior ```text E1 unavailable -> do not show E1 skills E1 ready -> discover once, cache, show through World State E1 unavailable -> hide skills, keep cache E1 ready again -> reuse cache, show skills again resume -> create a new thread cache and discover again ``` The cache key is the full `SelectedCapabilityRoot`. Availability does not invalidate it; dropping the extension's thread state does. The step supplies the ready selected roots directly. They do not have to be turn environments: ```text turn environment: laptop selected root: worker:/plugins/lint-fix worker ready -> lint-fix skills are visible ``` ## What changes - Keeps executor skill catalogs in the existing skills extension. - Passes the roots resolved as ready for the step into World State contributors. - Loads each ready selected root at most once per thread. - Contributes the executor catalog as the `skills` World State section. - Uses the exact step catalog for explicit skill selection and body reads. - Leaves host and orchestrator skill behavior where it already lives. Taking a step snapshot itself does not add an RPC. Executor filesystem calls happen only on the first discovery of a stable root for that thread. ## What does not change - No filesystem watcher or content-based invalidation. - No retry/generation framework. - No skill runtime migration into core. - No general rewrite of the skills extension. ## Stack 1. Extension-owned World State sections. 2. **This PR:** project cached executor skills through World State. 3. Pin one MCP runtime to each model step. 4. Project selected MCP/app/connector metadata by environment availability. 5. One end-to-end integration scenario. |
||
|
jif
|
c9e6d9783d |
Let extensions contribute World State sections (#30100)
## Why #29856 already owns the durable thread intent and exact environment binding. This PR adds only the small missing extension boundary: an extension can contribute one named World State section, while core still owns persistence, diffing, and model-visible fragment types. This lets skills stay in the skills extension instead of moving their runtime into core. ## Shape ```text extension-owned state | | contribute section id + JSON snapshot + renderer v core World State | | compare with the previous snapshot v no message, or one incremental model-visible update ``` The extension API is deliberately small: ```rust fn contribute_world_state(...) -> Vec<WorldStateSectionContribution> ``` Core adapts the rendered result to `ContextualUserFragment`, records the snapshot, and keeps the existing compaction/resume behavior. ## What changes - Adds extension-owned World State section contributions. - Calls those contributors from the existing per-step World State builder. - Restores durable selected capability roots into extension thread state on resume. - Keeps the actual model-context fragment and rollout machinery in core. ## What does not change - No skill or MCP implementation moves out of its extension. - No new file watcher, generation, or RPC. - No generic migration of existing World State sections. - No change to the stable environment-ID assumption from #29856. ## Example ```text step 1 snapshot: skills = [] step 2 snapshot: skills = [executor-demo:deploy] core asks the skills extension to render only that change. ``` ## Stack 1. **This PR:** let extensions contribute World State sections. 2. Project executor skills through the skills extension. 3. Pin one MCP runtime to each model step. 4. Project selected MCP/app/connector metadata by environment availability. 5. One end-to-end integration scenario. |
||
|
jif
|
9684ec25be |
Add turn-scoped context contributions (#28911)
## Summary - keep context injection on a single ContextContributor trait - split context injection into thread-scoped and turn-scoped contribution methods - wire turn-scoped fragments into initial context assembly so extensions can contribute context from turn-local state |
||
|
jif
|
6d9df687a5 |
skills: hide orchestrator skills with a local executor (#28333)
## Why App-server threads without a local executor need orchestrator-owned skills from the hosted `codex_apps` MCP server. Threads with the local executor already discover installed skills from the local filesystem. After the orchestrator skill provider was enabled for every app-server thread, local-executor threads also received the hosted skill catalog and the `skills.list` and `skills.read` tools. This changed the existing local behavior and could expose a second hosted copy of a skill that was already installed locally. ## What changed - Expose the thread's selected execution environments to extensions at thread startup. - Enable orchestrator skills only when the reserved local environment is not selected. - Apply that decision consistently to hosted skill catalog discovery, explicit skill injection, and the `skills.list` and `skills.read` tools. ## Verification - The existing no-executor app-server test continues to verify hosted skill discovery, invocation, and child-resource reads. - A new app-server test verifies that local-executor threads do not receive hosted skill context or `skills.*` tools. |
||
|
jif
|
c3a479620f |
Add selected-plugin precedence and attribution to the MCP catalog (#27884)
## Why **In short:** this PR resolves already-discovered MCP registrations. It does not read selected plugins or discover their MCP servers. The resolved MCP catalog currently builds config and auto-discovered plugin registrations before runtime contributors are applied. A thread-selected plugin needs a distinct precedence tier in that same initial resolution pass: otherwise a disabled lower-precedence winner can leave stale name-level state behind, and the winning MCP tools cannot be attributed to the selected package reliably. This PR adds that catalog boundary before executor discovery is connected. ## What changed - Added an explicit selected-plugin registration tier between auto-discovered plugins and explicit config. - Collected selected-plugin contributions before the initial catalog build, while leaving compatibility and generic extension overlays in their existing runtime phase. - Retained the winning plugin ID and display name directly on plugin-owned catalog registrations. - Derived MCP tool provenance from the winning catalog entry instead of joining against local-only plugin summaries. - Retained the winning selected server's tool approval policy in the running connection manager, so a selected registration cannot inherit approval behavior from a losing local plugin. - Kept remembered approval session-scoped for selected plugins until there is an authority-aware persistence contract; Codex will not write approval back to an unrelated local plugin. - Preserved existing name-level disabled vetoes for discovered plugins and config, while keeping a selected package's own disabled registration scoped to that registration. - Preserved deterministic selection order and existing config, compatibility, and extension precedence. The resulting order is: ```text auto-discovered plugin < selected plugin < explicit config < compatibility registration < extension overlay ``` ## Behavior and scope This is a catalog and provenance change only. No production host contributes selected-plugin MCP registrations yet, so existing local MCP behavior remains unchanged. The stacked follow-up, #27870, installs the executor plugin provider that produces these registrations. App-server activation remains a separate final step. ## Verification Focused tests cover precedence, deterministic selected-plugin conflicts, disabled-veto behavior across catalog phases, managed requirements before selected-plugin resolution, winning-server approval policy, and attribution when local and selected packages share an ID or server name. CI owns execution of the test suite. |
||
|
jif
|
693082f3c4 |
Make MCP server contributions thread-scoped (#27670)
## Why `selectedCapabilityRoots` belongs to one thread, but MCP contributors previously received only the global Codex config. That left no clean way for a selected executor capability to contribute MCP servers to its own thread. ## What this PR does - Gives MCP contributors a small context containing the config and, for a running thread, its frozen host-seeded inputs. - Uses the same thread inputs during startup, status queries, refreshes, and skill dependency checks. - Keeps threadless MCP operations and the existing hosted Apps behavior unchanged. - Adds coverage showing that two threads resolve independent registrations and that later lifecycle mutations do not change the frozen MCP inputs. This PR does not discover plugin manifests, add MCP servers, or launch anything new. It only establishes the thread-scoped registration boundary. ## Follow-ups - Resolve selected executor plugin roots through their owning environment filesystem. - Convert their stdio MCP declarations into environment-bound registrations and add an executor MCP end-to-end test. ## Verification - `just fmt` - `cargo check --tests -p codex-protocol -p codex-extension-api -p codex-mcp-extension -p codex-core -p codex-app-server` Tests and Clippy were not run. |
||
|
jif
|
9cd11e9e62 |
Use plugin-service MCP as the hosted plugin runtime (#27198)
## Stack - Base: #27191 - This PR is the third vertical and should be reviewed against `jif/external-plugins-2`, not `main`. ## Why #27191 moves the host-owned Apps MCP registration behind an extension contributor, but deliberately preserves the existing endpoint-selection feature while that contribution contract lands. App-server can therefore resolve the server through extensions, yet the hosted plugin endpoint is still selected through temporary `apps_mcp_path_override` plumbing. That is not the long-term plugin model. A plugin can bundle skills, connectors, MCP servers, and hooks, and those components do not all need the same source or execution environment. In particular, an authenticated HTTP MCP server can expose plugin capabilities directly from a backend without an executor or an orchestrator filesystem. This PR completes that hosted vertical. App-server's MCP extension now owns the aggregate hosted plugin runtime at `/ps/mcp`. Connector actions continue to arrive as MCP tools, while backend-provided skills arrive as MCP resources and use Codex's existing resource list/read paths. No second backend client, skill filesystem, or generic plugin activation framework is introduced. The backend route remains the hosted implementation. This change replaces Codex's temporary endpoint-selection mechanism, not the service behind the endpoint. ## What changed ### Hosted plugin runtime The MCP extension now contributes `codex_apps` as the hosted plugin runtime rather than as a configurable Apps endpoint: - `https://chatgpt.com` resolves to `https://chatgpt.com/backend-api/ps/mcp`; - a bare custom ChatGPT base resolves to `/api/codex/ps/mcp`; - the existing product-SKU header and ChatGPT authentication behavior are preserved; - executor availability is never consulted for this streamable HTTP transport. The same MCP connection carries both component shapes supported by the hosted endpoint: - connector actions are discovered and invoked as MCP tools; - hosted skills are enumerated and read as MCP resources through the existing `list_mcp_resources` and `read_mcp_resource` paths. This keeps component access in the subsystem that already owns the protocol instead of downloading backend skills into an orchestrator filesystem or inventing a parallel hosted-skill client. ### Explicit runtime ordering `McpManager` now resolves the reserved `codex_apps` entry in three ordered phases: 1. install the legacy Apps fallback for compatibility; 2. apply ordered extension `Set` or `Remove` overlays; 3. apply the final ChatGPT-auth gate without synthesizing the server again. This ordering is important: - an ordinary configured or plugin MCP server cannot claim the auth-bearing `codex_apps` name; - an extension-contributed hosted runtime wins over the fallback; - an extension `Remove` remains authoritative; - a host without the MCP extension retains the legacy Apps endpoint and current local-only behavior. The temporary `legacy_apps_mcp_loader_enabled` coordination flag is no longer needed. ### Remove the path override The `apps_mcp_path_override` feature and its runtime plumbing are removed, including: - the feature registry entry and structured feature config; - `Config` and `McpConfig` fields; - config schema output; - config-lock materialization; - URL override handling in `codex-mcp`. Existing boolean and structured forms still deserialize as ignored compatibility input. They are omitted from new serialized config, and config-lock comparison normalizes the removed input so older locks remain replayable. ### App-server coverage App-server MCP fixtures now serve the hosted route at `/api/codex/ps/mcp`. Existing resource-read and tool/elicitation flows therefore exercise the extension-owned endpoint rather than succeeding through the legacy fallback. The stack also adds the missing `codex_chatgpt::connectors` re-export for the manager-backed connector helper introduced in #27191. ## Compatibility - App-server installs the extension and uses `/ps/mcp` for the hosted runtime. - CLI and other hosts that do not install the extension retain the legacy Apps endpoint. - Apps disabled or non-ChatGPT authentication removes `codex_apps` from the effective runtime view. - Existing local plugins, local skills, executor-selected skills, configured MCP servers, and MCP OAuth behavior are otherwise unchanged. - Backend plugin enablement remains account/workspace state owned by the hosted endpoint; this PR does not add thread-local backend plugin selection. ## Architectural fit The stack now proves two independent runtime shapes: 1. #27184 resolves filesystem-backed skills through the executor that owns a selected root. 2. #27191 and this PR resolve a backend-hosted HTTP MCP through an extension with no executor. Together they preserve the intended separation: - selection identifies a plugin/root when explicit selection is needed; - each component's owning extension resolves its concrete access mechanism; - execution stays with the runtime required by that component; - existing skills, MCP, connector, and hook subsystems remain the downstream consumers. ## Planned follow-ups 1. **Executor stdio MCP:** selecting an executor plugin registers a manifest-declared stdio MCP server and executes it in the environment that owns the plugin. 2. **Optional backend selection:** only if CCA needs thread-local selection distinct from backend account/workspace enablement, add a concrete backend-owned capability location and surface those selected skills through the skills catalog. 3. **Connector metadata and hooks:** activate those plugin components through their existing owning subsystems, with executor hooks remaining environment-bound. 4. **Propagation and persistence:** define explicit resume, fork, subagent, refresh, and environment-removal semantics once selected roots have multiple real consumers. 5. **Local convergence:** migrate legacy local skill, MCP, connector, and hook paths behind their owning extensions one vertical at a time, then remove duplicate core managers and compatibility plumbing after parity. ## Verification Coverage in this change exercises: - extension-owned `/backend-api/ps/mcp` registration without an executor; - preservation of the legacy endpoint in hosts without the extension; - extension `Set` and `Remove` precedence over the legacy fallback; - ChatGPT-auth gating for the reserved server; - hosted MCP resource reads with and without an active thread; - connector tool invocation and MCP elicitation through the hosted route; - ignored boolean and structured forms of the removed path override; - config-lock replay compatibility for the removed feature. `cargo check -p codex-features -p codex-mcp-extension -p codex-app-server` passes. Tests and Clippy were not run locally under the current development instruction; CI provides the full validation pass. |
||
|
jif
|
4ec3b8eeea |
Route hosted Apps MCP through extensions (#27191)
## Stack - Base: #27184 - This PR is the second vertical and should be reviewed against `jif/external-plugins-1`, not `main`. ## Why CCA is moving toward a split runtime where the orchestrator may have no filesystem or executor, but it still needs to activate remotely hosted plugin components. HTTP MCP servers are the simplest complete example: they need configuration and host authentication, but they do not need an executor process. The Apps MCP endpoint is currently synthesized by a special-purpose loader inside the MCP runtime. That works locally, but it leaves hosted MCP activation outside the extension model being established in #27184. It also makes the Apps path a poor foundation for plugins whose skills, MCP servers, connectors, and hooks may come from different sources or execute in different places. This PR moves that one behavior behind an extension-owned contribution while preserving the existing local fallback. It deliberately does not introduce a generic plugin activation framework. ## What changed ### MCP extension contribution `codex-extension-api` gains an ordered `McpServerContributor` contract. A contributor returns typed `Set` or `Remove` overlays for MCP server configuration; later contributors win for the names they own. The contract stays at the existing MCP configuration boundary. Extensions do not create a second connection manager or transport abstraction. ### Hosted Apps MCP extension A new `codex-mcp-extension` contributes the reserved `codex_apps` server from the existing Apps feature, ChatGPT base URL, path override, and product SKU configuration. When `apps_mcp_path_override` is enabled for `https://chatgpt.com`, the resulting streamable HTTP endpoint is `https://chatgpt.com/backend-api/ps/mcp`. The existing ChatGPT-auth gate remains authoritative, so this server can run in an orchestrator-only process without being exposed for API-key sessions. ### One resolved runtime view `McpManager` now distinguishes three views: - **configured:** config- and plugin-backed servers before extension overlays; - **runtime:** configured servers plus host-installed extension contributions; - **effective:** runtime servers after auth gating and compatibility built-ins. App-server installs the hosted MCP extension and uses the runtime view for thread startup, refresh, status, threadless resource reads, connector discovery, and MCP OAuth lookup. This keeps `mcpServer/oauth/login` consistent with the servers exposed by the other MCP APIs. The hosted Apps server itself continues to use existing ChatGPT host authentication rather than MCP OAuth. ## Compatibility Hosts that do not install the MCP extension retain the existing Apps MCP synthesis path. This preserves current local-only, CLI, and standalone-host behavior while app-server exercises the extension path. Disabling Apps removes the reserved `codex_apps` entry, and losing ChatGPT auth removes it from the effective runtime view. Executor availability is not consulted for this HTTP transport. ## Follow-ups The next vertical will resolve a manifest-declared stdio MCP server from an executor-selected plugin root and execute it in the environment that owns that root. Later verticals can add backend-owned skills, connector metadata, hooks, durable selection semantics, and incremental local convergence without changing the component-specific runtime boundaries introduced here. ## Verification Focused coverage was added for: - contributing the hosted Apps MCP at `/backend-api/ps/mcp` without an executor; - requiring ChatGPT auth in the effective runtime view; - removing a reserved configured Apps server when the Apps feature is disabled. `cargo check -p codex-app-server -p codex-mcp-extension -p codex-extension-api -p codex-mcp` passed. Tests and Clippy were not run locally under the current development instruction; CI provides the full validation pass. |
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jif
|
271d5cecf2 |
feat: add extension turn-input contributors (#25959)
## Disclaimer Do not use for now ## Why Extensions can already contribute prompt fragments and request same-turn item injection, but there was no host-owned hook for contributing structured `ResponseItem`s while Codex is assembling a new turn's initial model input. This change adds that seam so extensions can attach turn-local input that depends on the submitted user input and resolved turn environments without routing through prompt text or late injection. ## What changed - add `TurnInputContributor` to `codex_extension_api` and export the new `TurnInputContext` / `TurnInputEnvironment` types it receives - teach `ExtensionRegistry` to register and expose turn-input contributors alongside the existing extension hooks - call registered turn-input contributors from `core/src/session/turn.rs` while building the initial injected input for a turn, then append their returned `ResponseItem`s after the skill and plugin injections |
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jif-oai
|
e7d156eb08 |
Add turn error lifecycle contributor (#24916)
Summary - Add TurnErrorInput and TurnLifecycleContributor::on_turn_error to the extension API. - Emit the turn-error lifecycle from core turn error paths, including usage limit failures. - Add direct lifecycle coverage for the emitted error facts and stores. Tests - just fmt - git diff --check - Not run: full tests or clippy (per instructions) |
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jif-oai
|
ec803fe6c7 |
Add thread start contributor facts (#24915)
Summary: add session source and persistent-state availability to ThreadStartInput; populate them from session init; update existing goal test harness constructors. Tests: just fmt; git diff --check. No full tests or clippy run per request. |
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jif-oai
|
d2ebb8d8ca |
feat: add thread idle lifecycle hook (#24744)
## Why Extensions can currently observe thread start, resume, and stop, but they do not have a lifecycle point for the host to say that immediately pending thread work has drained. That makes idle follow-up behavior harder to express as extension-owned logic instead of host-specific plumbing. This adds an explicit idle lifecycle hook so an extension can react when a thread becomes idle while the host keeps ownership of whether any submitted follow-up input starts a turn, is queued, or is ignored. ## What changed - Added `ThreadIdleInput` with access to the session-scoped and thread-scoped extension stores. - Added a default `on_thread_idle` method to `ThreadLifecycleContributor`. - Re-exported `ThreadIdleInput` from the extension API surface. ## Testing Not run; this only extends the extension API trait surface with a default hook and exported input type. |
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jif-oai
|
59507b8491 |
feat: expose turn-start metadata to extensions (#23688)
## Why The goal extension needs more context when a turn starts than `turn_store` alone provides. In particular, goal accounting needs the stable turn id, the effective collaboration mode, and the cumulative token-usage baseline captured at turn start so it can: - suppress goal accounting for plan-mode turns - compute exact per-turn deltas from cumulative `total_token_usage` snapshots instead of relying on the most recent usage event alone - keep the extension-owned accounting path aligned with the host turn lifecycle ## What - extend `codex_extension_api::TurnStartInput` to expose `turn_id`, `collaboration_mode`, and `token_usage_at_turn_start` - pass the full `TurnContext` plus the captured token-usage baseline through the turn-start lifecycle emission path - initialize goal turn accounting from the turn-start baseline and collaboration mode - switch goal token accounting to compute deltas from cumulative `total_token_usage` snapshots - add coverage for the new turn-start lifecycle fields and for goal-accounting baseline behavior ## Testing - added `turn_start_lifecycle_exposes_turn_metadata_and_token_baseline` in `codex-rs/core/src/session/tests.rs` - added `ext/goal/tests/accounting.rs` coverage for baseline-aware goal accounting and plan-mode suppression |
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jif-oai
|
c69cde3547 |
Add tool lifecycle extension contributor (#23309)
## Why Extensions that need to track runtime progress currently have no typed host signal for tool execution. The goal extension in particular needs to observe tool attempts without inspecting tool payloads, owning tool implementations, or staying coupled to core-only runtime plumbing. This adds a narrow lifecycle contributor API for host-owned tool execution: extensions can observe when an accepted tool call starts and how it finishes, while policy hooks and tool handlers continue to own payload rewriting, blocking, and execution. Relevant code: - [`ToolLifecycleContributor`](https://github.com/openai/codex/blob/3ad2850ffc7d8a1da19c65a92425637a59098f1b/codex-rs/ext/extension-api/src/contributors.rs#L119) defines the extension-facing observer contract. - [`tool_lifecycle.rs`](https://github.com/openai/codex/blob/3ad2850ffc7d8a1da19c65a92425637a59098f1b/codex-rs/ext/extension-api/src/contributors/tool_lifecycle.rs) defines the typed start/finish inputs, source, and outcome enums. - [`notify_tool_start` / `notify_tool_finish`](https://github.com/openai/codex/blob/3ad2850ffc7d8a1da19c65a92425637a59098f1b/codex-rs/core/src/tools/lifecycle.rs) bridges core tool dispatch into the extension registry. ## What Changed - Added `ToolLifecycleContributor` to `codex-extension-api`, including: - `ToolStartInput` - `ToolFinishInput` - `ToolCallSource` - `ToolCallOutcome` - Added registration and lookup support on `ExtensionRegistryBuilder` / `ExtensionRegistry`. - Wired core tool dispatch to notify lifecycle contributors for: - accepted tool starts - completed tool calls, including the tool output success marker - pre-tool-use blocks - failures before or after the handler runs - cancellation/abort in the parallel tool path - Registered the goal extension as a lifecycle contributor and added the outcome filter it will use for goal progress accounting. ## Test Coverage - Added `dispatch_notifies_tool_lifecycle_contributors` to cover lifecycle notification ordering and outcomes for successful and handler-failed tool calls. |
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jif-oai
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6f1a01fbdd |
Simplify 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. |
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jif-oai
|
6d65686313 |
feat: make ToolExecutor an async trait (#22560)
## Why `codex_tools::ToolExecutor` keeps a tool spec attached to its runtime handler, but extension tools still carried a parallel `ExtensionToolFuture` / `ExtensionToolExecutor` shape. That made extension-owned tools look different from host tools even though routing, registration, and execution need the same abstraction. This PR makes the shared executor contract directly async and lets extension tools implement it too, so host tools and extension tools can move through the same registration path. ## What changed - Changed `ToolExecutor::handle` to an `async fn` using `async-trait`, and updated built-in tool handlers to implement the async trait directly. - Replaced the bespoke `ExtensionToolFuture` contract with a marker `ExtensionToolExecutor` over `ToolExecutor<ToolCall, Output = JsonToolOutput>`, re-exporting `ToolExecutor` from `codex-extension-api`. - Updated the memories extension tools to implement the shared executor trait. - Split tool-router construction into collected executors plus hosted model specs, keeping hosted tools like web search and image generation separate from executable handlers. - Updated spec/router tests and extension-tool stubs for the new executor shape. ## Verification - Not run locally. |
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jif-oai
|
1dcc89f1d4 |
feat: move extension scope ids into ExtensionData (#22490)
## Summary - add a scoped level_id to ExtensionData and expose it through level_id() - remove thread_id/turn_id parameters from extension contributor inputs where the scoped ExtensionData already carries that identity - move turn-scoped extension data onto TurnContext so token usage and lifecycle contributors can share the same turn store ## Testing - cargo check -p codex-extension-api -p codex-core --tests - cargo test -p codex-extension-api - cargo test -p codex-guardian - cargo test -p codex-core --lib record_token_usage_info_notifies_extension_contributors - cargo test -p codex-core --lib submission_loop_channel_close_emits_thread_stop_lifecycle - cargo test -p codex-core --lib submission_loop_channel_close_aborts_active_turn_before_thread_stop_lifecycle - just fix -p codex-extension-api - just fix -p codex-guardian - just fix -p codex-core - just fmt ## Note - Attempted cargo test -p codex-core; it aborted in agent::control::tests::spawn_agent_fork_last_n_turns_keeps_only_recent_turns with the existing stack overflow before the full suite completed. |
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jif-oai
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27e67a8c2a |
feat: add turn lifecycle contributors (#22480)
## Why Extensions can already contribute prompt, tool, turn-item, and thread-lifecycle behavior, but there was no explicit host-owned hook for per-turn setup and cleanup. That makes extension-private turn state awkward: an extension either has to stash it outside the turn lifecycle or depend on core runtime objects. This adds a small turn lifecycle boundary. Extensions receive stable identifiers plus the existing session, thread, and turn `ExtensionData` stores, while core keeps owning task scheduling, cancellation, and turn teardown. ## What Changed - Added `TurnLifecycleContributor` with `on_turn_start`, `on_turn_stop`, and `on_turn_abort` callbacks in `codex-rs/ext/extension-api`. - Added typed `TurnStartInput`, `TurnStopInput`, and `TurnAbortInput` payloads that expose `thread_id`, `turn_id`, `session_store`, `thread_store`, and `turn_store`. - Registered and re-exported turn lifecycle contributors through `ExtensionRegistry` and `ExtensionRegistryBuilder`. - Wired `Session` to emit turn start, stop, and abort callbacks from the existing turn/task lifecycle paths. - Carried the turn-scoped `ExtensionData` through `RunningTask` and `RemovedTask` so stop/abort callbacks receive the same turn store created at turn start. ## Verification - Not run locally. |
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jif-oai
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5ab7e6b4c6 |
feat: add thread lifecycle contributor hooks (#22476)
## Why Extensions that need thread-scoped state currently only get a start-time callback. That is enough for seeding stores, but it leaves the host without a shared extension seam for later thread rehydrate and flush work as thread ownership evolves. This PR turns that start-only seam into a host-owned thread lifecycle contributor contract so extension-private state can stay behind the extension API instead of leaking extra orchestration through core. ## What changed - Replaced `ThreadStartContributor` with `ThreadLifecycleContributor` and added typed lifecycle inputs for thread start, resume, and stop. The contract lives in [`contributors/thread_lifecycle.rs`](https://github.com/openai/codex/blob/d0e9211f70e58d6b07ef07e84f359d1b9aa25955/codex-rs/ext/extension-api/src/contributors/thread_lifecycle.rs#L1-L64). - Kept the existing start-time behavior intact by routing session construction through `on_thread_start`. - Invoked `on_thread_stop` during session shutdown before thread-scoped extension state is dropped, while isolating contributor failures behind warning logs. - Migrated `git-attribution` and `guardian` onto the lifecycle registration path. - Renamed the extension registry plumbing from start-specific contributors to lifecycle-specific contributors. ## Notes `on_thread_resume` is introduced at the API boundary here so extensions can target the final lifecycle shape; host resume dispatch can be wired where that runtime path is finalized. |
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jif-oai
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9c5dfa7b1a |
Refactor extension tools onto shared ToolExecutor (#22369)
## Why Extension tools were split across two public runtime contracts: `codex-tool-api` exposed `ToolBundle` plus its own call/spec/error types, while core native tools used `codex_tools::ToolExecutor`. That made contributed tool specs and execution behavior easy to drift apart and added another crate boundary for what should be one executable-tool seam. This PR makes `ToolExecutor` the single runtime contract and keeps extension-specific pinning in `codex-extension-api`. ## Remaining todo https://github.com/openai/codex/pull/22369/changes#diff-b935ea8245c3ce568a30cff660175fa6390b66b872ae409e1e2e965738250741R5 Either generic `Invocation` or sub-extract the `ToolCall` and clean `ToolInvocation` ## What changed - Removed the `codex-tool-api` workspace crate and its dependencies from core and `codex-extension-api`. - Made `codex_tools::ToolExecutor` object-safe with `async_trait` so extension contributors can return a dyn executor. - Added the extension-facing aliases under `ext/extension-api/src/contributors/tools.rs`, including `ExtensionToolExecutor = dyn ToolExecutor<ToolCall, Output = ExtensionToolOutput>`. - Changed `ToolContributor::tools` to return extension executors directly instead of `ToolBundle`s. - Updated core’s extension tool handler/registry/router path to adapt those extension executors into the existing native `ToolInvocation` runtime path. - Added focused coverage for extension tools being registered, model-visible, dispatchable, and not replacing built-in tools. ## Verification - `cargo test -p codex-tools` - `cargo test -p codex-extension-api` |
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jif-oai
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672cc1f669 |
feat: 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 #22138 |
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jif-oai
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d2c3ebac1f |
extension: add initial typed extension API (#21736)
## Why `codex-core` still owns a growing amount of product-specific behavior. This PR starts the extraction path by introducing a small, typed first-party extension seam: features can install the contribution families they actually own, while the host keeps lifecycle and state ownership instead of pushing a broad service locator into the API. See the `examples/` for illustration ## Known limitations * Tool contract definition will be shared with core * Fragments must be extracted * Missing some contributors |