Commit Graph

11 Commits

  • feat(app-server, core): allow text + image content items for dynamic tool outputs (#10567)
    Took over the work that @aaronl-openai started here:
    https://github.com/openai/codex/pull/10397
    
    Now that app-server clients are able to set up custom tools (called
    `dynamic_tools` in app-server), we should expose a way for clients to
    pass in not just text, but also image outputs. This is something the
    Responses API already supports for function call outputs, where you can
    pass in either a string or an array of content outputs (text, image,
    file):
    https://platform.openai.com/docs/api-reference/responses/create#responses_create-input-input_item_list-item-function_tool_call_output-output-array-input_image
    
    So let's just plumb it through in Codex (with the caveat that we only
    support text and image for now). This is implemented end-to-end across
    app-server v2 protocol types and core tool handling.
    
    ## Breaking API change
    NOTE: This introduces a breaking change with dynamic tools, but I think
    it's ok since this concept was only recently introduced
    (https://github.com/openai/codex/pull/9539) and it's better to get the
    API contract correct. I don't think there are any real consumers of this
    yet (not even the Codex App).
    
    Old shape:
    `{ "output": "dynamic-ok", "success": true }`
    
    New shape:
    ```
    {
        "contentItems": [
          { "type": "inputText", "text": "dynamic-ok" },
          { "type": "inputImage", "imageUrl": "data:image/png;base64,AAA" }
        ]
      "success": true
    }
    ```
  • feat: replace custom mcp-types crate with equivalents from rmcp (#10349)
    We started working with MCP in Codex before
    https://crates.io/crates/rmcp was mature, so we had our own crate for
    MCP types that was generated from the MCP schema:
    
    
    https://github.com/openai/codex/blob/8b95d3e082376f4cb23e92641705a22afb28a9da/codex-rs/mcp-types/README.md
    
    Now that `rmcp` is more mature, it makes more sense to use their MCP
    types in Rust, as they handle details (like the `_meta` field) that our
    custom version ignored. Though one advantage that our custom types had
    is that our generated types implemented `JsonSchema` and `ts_rs::TS`,
    whereas the types in `rmcp` do not. As such, part of the work of this PR
    is leveraging the adapters between `rmcp` types and the serializable
    types that are API for us (app server and MCP) introduced in #10356.
    
    Note this PR results in a number of changes to
    `codex-rs/app-server-protocol/schema`, which merit special attention
    during review. We must ensure that these changes are still
    backwards-compatible, which is possible because we have:
    
    ```diff
    - export type CallToolResult = { content: Array<ContentBlock>, isError?: boolean, structuredContent?: JsonValue, };
    + export type CallToolResult = { content: Array<JsonValue>, structuredContent?: JsonValue, isError?: boolean, _meta?: JsonValue, };
    ```
    
    so `ContentBlock` has been replaced with the more general `JsonValue`.
    Note that `ContentBlock` was defined as:
    
    ```typescript
    export type ContentBlock = TextContent | ImageContent | AudioContent | ResourceLink | EmbeddedResource;
    ```
    
    so the deletion of those individual variants should not be a cause of
    great concern.
    
    Similarly, we have the following change in
    `codex-rs/app-server-protocol/schema/typescript/Tool.ts`:
    
    ```
    - export type Tool = { annotations?: ToolAnnotations, description?: string, inputSchema: ToolInputSchema, name: string, outputSchema?: ToolOutputSchema, title?: string, };
    + export type Tool = { name: string, title?: string, description?: string, inputSchema: JsonValue, outputSchema?: JsonValue, annotations?: JsonValue, icons?: Array<JsonValue>, _meta?: JsonValue, };
    ```
    
    so:
    
    - `annotations?: ToolAnnotations` ➡️ `JsonValue`
    - `inputSchema: ToolInputSchema` ➡️ `JsonValue`
    - `outputSchema?: ToolOutputSchema` ➡️ `JsonValue`
    
    and two new fields: `icons?: Array<JsonValue>, _meta?: JsonValue`
    
    ---
    [//]: # (BEGIN SAPLING FOOTER)
    Stack created with [Sapling](https://sapling-scm.com). Best reviewed
    with [ReviewStack](https://reviewstack.dev/openai/codex/pull/10349).
    * #10357
    * __->__ #10349
    * #10356
  • feat: better UI for unified_exec (#6515)
    <img width="376" height="132" alt="Screenshot 2025-11-12 at 17 36 22"
    src="https://github.com/user-attachments/assets/ce693f0d-5ca0-462e-b170-c20811dcc8d5"
    />
  • feature: Add "!cmd" user shell execution (#2471)
    feature: Add "!cmd" user shell execution
    
    This change lets users run local shell commands directly from the TUI by
    prefixing their input with ! (e.g. !ls). Output is truncated to keep the
    exec cell usable, and Ctrl-C cleanly
      interrupts long-running commands (e.g. !sleep 10000).
    
    **Summary of changes**
    
    - Route Op::RunUserShellCommand through a dedicated UserShellCommandTask
    (core/src/tasks/user_shell.rs), keeping the task logic out of codex.rs.
    - Reuse the existing tool router: the task constructs a ToolCall for the
    local_shell tool and relies on ShellHandler, so no manual MCP tool
    lookup is required.
    - Emit exec lifecycle events (ExecCommandBegin/ExecCommandEnd) so the
    TUI can show command metadata, live output, and exit status.
    
    **End-to-end flow**
    
      **TUI handling**
    
    1. ChatWidget::submit_user_message (TUI) intercepts messages starting
    with !.
    2. Non-empty commands dispatch Op::RunUserShellCommand { command };
    empty commands surface a help hint.
    3. No UserInput items are created, so nothing is enqueued for the model.
    
      **Core submission loop**
    4. The submission loop routes the op to handlers::run_user_shell_command
    (core/src/codex.rs).
    5. A fresh TurnContext is created and Session::spawn_user_shell_command
    enqueues UserShellCommandTask.
    
      **Task execution**
    6. UserShellCommandTask::run emits TaskStartedEvent, formats the
    command, and prepares a ToolCall targeting local_shell.
      7. ToolCallRuntime::handle_tool_call dispatches to ShellHandler.
    
      **Shell tool runtime**
    8. ShellHandler::run_exec_like launches the process via the unified exec
    runtime, honoring sandbox and shell policies, and emits
    ExecCommandBegin/End.
    9. Stdout/stderr are captured for the UI, but the task does not turn the
    resulting ToolOutput into a model response.
    
      **Completion**
    10. After ExecCommandEnd, the task finishes without an assistant
    message; the session marks it complete and the exec cell displays the
    final output.
    
      **Conversation context**
    
    - The command and its output never enter the conversation history or the
    model prompt; the flow is local-only.
      - Only exec/task events are emitted for UI rendering.
    
    **Demo video**
    
    
    https://github.com/user-attachments/assets/fcd114b0-4304-4448-a367-a04c43e0b996
  • [MCP] Render MCP tool call result images to the model (#5600)
    It's pretty amazing we have gotten here without the ability for the
    model to see image content from MCP tool calls.
    
    This PR builds off of 4391 and fixes #4819. I would like @KKcorps to get
    adequete credit here but I also want to get this fix in ASAP so I gave
    him a week to update it and haven't gotten a response so I'm going to
    take it across the finish line.
    
    
    This test highlights how absured the current situation is. I asked the
    model to read this image using the Chrome MCP
    <img width="2378" height="674" alt="image"
    src="https://github.com/user-attachments/assets/9ef52608-72a2-4423-9f5e-7ae36b2b56e0"
    />
    
    After this change, it correctly outputs:
    > Captured the page: image dhows a dark terminal-style UI labeled
    `OpenAI Codex (v0.0.0)` with prompt `model: gpt-5-codex medium` and
    working directory `/codex/codex-rs`
    (and more)  
    
    Before this change, it said:
    > Took the full-page screenshot you asked for. It shows a long,
    horizontally repeating pattern of stylized people in orange, light-blue,
    and mustard clothing, holding hands in alternating poses against a white
    background. No text or other graphics-just rows of flat illustration
    stretching off to the right.
    
    Without this change, the Figma, Playwright, Chrome, and other visual MCP
    servers are pretty much entirely useless.
    
    I tested this change with the openai respones api as well as a third
    party completions api
  • Pass TurnContext around instead of sub_id (#5421)
    Today `sub_id` is an ID of a single incoming Codex Op submition. We then
    associate all events triggered by this operation using the same
    `sub_id`.
    
    At the same time we are also creating a TurnContext per submission and
    we'd like to start associating some events (item added/item completed)
    with an entire turn instead of just the operation that started it.
    
    Using turn context when sending events give us flexibility to change
    notification scheme.
  • chore: rework tools execution workflow (#5278)
    Re-work the tool execution flow. Read `orchestrator.rs` to understand
    the structure
  • feat: parallel tool calls (#4663)
    Add parallel tool calls. This is configurable at model level and tool
    level
  • chore: refactor tool handling (#4510)
    # Tool System Refactor
    
    - Centralizes tool definitions and execution in `core/src/tools/*`:
    specs (`spec.rs`), handlers (`handlers/*`), router (`router.rs`),
    registry/dispatch (`registry.rs`), and shared context (`context.rs`).
    One registry now builds the model-visible tool list and binds handlers.
    - Router converts model responses to tool calls; Registry dispatches
    with consistent telemetry via `codex-rs/otel` and unified error
    handling. Function, Local Shell, MCP, and experimental `unified_exec`
    all flow through this path; legacy shell aliases still work.
    - Rationale: reduce per‑tool boilerplate, keep spec/handler in sync, and
    make adding tools predictable and testable.
    
    Example: `read_file`
    - Spec: `core/src/tools/spec.rs` (see `create_read_file_tool`,
    registered by `build_specs`).
    - Handler: `core/src/tools/handlers/read_file.rs` (absolute `file_path`,
    1‑indexed `offset`, `limit`, `L#: ` prefixes, safe truncation).
    - E2E test: `core/tests/suite/read_file.rs` validates the tool returns
    the requested lines.
    
    ## Next steps:
    - Decompose `handle_container_exec_with_params` 
    - Add parallel tool calls