RFD 005: First-Class Inquiry Events

• Status: Implemented
• Category: Design
• Authors: Jean Mertz git@jeanmertz.com
• Date: 2026-02-17

Summary

This RFD proposes recording InquiryRequest and InquiryResponse events in the persisted ConversationStream, and introducing a centralized filtering mechanism so that these internal events are never sent to LLM providers. This gives us a complete audit trail of what happened during tool execution while preserving the provider contract that requires tool call requests to be immediately followed by tool call responses.

Motivation

The stateful tool inquiry system (see architecture doc) resolves tool questions by making structured output calls to the LLM. Today, these inquiries happen on a cloned conversation stream that is discarded after the answer is extracted. The real ConversationStream, the one persisted to disk, contains no record that an inquiry occurred. It looks like this:

ToolCallRequest(call_123, fs_modify_file, {path, patterns})
ToolCallResponse(call_123, "File modified successfully")

A reader of the conversation history cannot tell that between those two events, the tool paused, asked the LLM a question ("Create backup files?"), received an answer (true), and then completed. That context is lost.

With first-class inquiry events, the stream would look like:

ToolCallRequest(call_123, fs_modify_file, {path, patterns})
InquiryRequest(inq_1, tool:fs_modify_file, "Create backup files?", Boolean)
InquiryResponse(inq_1, true)
ToolCallResponse(call_123, "File modified successfully")

This matters for:

• Debugging: When a tool produces unexpected results, knowing what questions it asked and what answers it received is essential.
• Conversation replay: Tooling that replays or analyzes conversations can reconstruct the full execution flow.
• Future UI: A conversation viewer could display inquiry events as a collapsible sub-flow within a tool call, giving users visibility into the system's decision-making.

If we do nothing, inquiry state remains ephemeral and invisible. The persisted conversation is an incomplete record of what the system did.

Design

Overview

Three changes, in order of dependency:

1. EventKind::is_provider_visible() — A method on the event enum that declares whether an event type should be included in provider message streams. Structural and type-level.
2. Thread::into_parts() — A new decomposition method on Thread that renders system content and filters events in a single place. Providers consume ThreadParts instead of destructuring Thread manually.
3. Recording inquiry events — The ToolCoordinator pushes InquiryRequest and InquiryResponse into the real conversation stream at the right points during tool execution.

Design Goals

Goal	Description
Complete audit trail	Every inquiry is recorded in the persisted
	stream.
Provider contract intact	Providers never see inquiry events. The
	ToolCallRequest → ToolCallResponse pairing
	is preserved in the provider-facing stream.
Single filtering point	Event visibility is determined once, in one
	place. Providers don't need defensive
	catch-all arms.
No provider regression	Adding new internal event types in the future
	requires updating one method, not six
	providers.

Event Visibility

A new method on EventKind:

impl EventKind {
    /// Whether this event should be included when building provider messages.
    ///
    /// Internal events (turn markers, inquiry exchanges) are filtered out
    /// before the conversation stream reaches any provider's message
    /// conversion logic. Uses an allowlist so new event types are invisible
    /// to providers by default.
    pub const fn is_provider_visible(&self) -> bool {
        matches!(
            self,
            Self::ChatRequest(_)
                | Self::ChatResponse(_)
                | Self::ToolCallRequest(_)
                | Self::ToolCallResponse(_)
        )
    }
}

This is a positive list (allowlist), not a denylist. New event types are invisible to providers by default — you have to opt them in. This is safer than a denylist where forgetting to exclude a new type sends garbage to every provider.

Thread Decomposition

Thread has four public fields. Three providers use Thread::into_messages() (Llamacpp, Ollama, OpenAI), which renders system content and delegates event conversion to a closure. Three providers (Anthropic, Google, OpenRouter) destructure Thread manually because their APIs need system content and conversation messages as separate values — into_messages concatenates them into a single list.

This means event filtering has no single enforcement point. Each provider independently skips non-provider events in its own convert_events function, or via _ => None catch-all match arms. Every new internal event type requires touching every provider.

We introduce Thread::into_parts() as the universal decomposition method. System parts are returned as tagged SystemPart values rather than plain Strings, so that providers needing per-part cache control (Anthropic, OpenRouter) can match on the variant to decide cache placement:

/// A rendered piece of system content, tagged by origin.
pub enum SystemPart {
    /// A prompt or section string (system prompt, instructions, context).
    Prompt(String),

    /// Rendered attachment XML.
    Attachment(String),
}

pub struct ThreadParts {
    /// Rendered system content, tagged by origin.
    pub system_parts: Vec<SystemPart>,

    /// Conversation events filtered to provider-visible events only.
    pub events: ConversationStream,
}

impl Thread {
    /// Decompose the thread into rendered system parts and filtered events.
    pub fn into_parts(self) -> Result<ThreadParts> { /* ... */ }
}

into_messages delegates to into_parts() and flattens the tags to plain Strings for providers that don't need them:

pub fn into_messages<T, U, M, S>(
    self,
    to_system_messages: M,
    convert_stream: S,
) -> Result<Vec<T>> {
    let parts = self.into_parts()?;
    let strings: Vec<String> = parts
        .system_parts
        .into_iter()
        .map(SystemPart::into_inner)
        .collect();

    let mut items = vec![];
    items.extend(to_system_messages(strings));
    items.extend(convert_stream(parts.events));
    Ok(items)
}

The three providers using into_messages (Llamacpp, Ollama, OpenAI) get filtering for free — no code changes. The three that destructure Thread manually (Anthropic, Google, OpenRouter) migrate to into_parts().

Recording Inquiry Events

The ToolCoordinator records inquiry events at two points:

1. On inquiry spawn (handle_tool_result, when a question is routed through the inquiry backend): Push an InquiryRequest into the conversation stream.

2. On inquiry result (InquiryResult handler in the event loop): Push an InquiryResponse into the conversation stream.

Inquiry events are recorded for any question routed through the InquiryBackend, not only those with QuestionTarget::Assistant. In non-interactive environments (no TTY), user-targeted questions also fall through to the inquiry backend and are recorded.

Questions answered via interactive user prompts (TTY + QuestionTarget::User) are not recorded as inquiry events — they go through the ToolPrompter path, which doesn't touch the conversation stream.

execute_with_prompting receives events: &mut ConversationStream so the coordinator has write access.

The events sit between the ToolCallRequest and ToolCallResponse for the tool in question:

ToolCallRequest(call_123, fs_modify_file, {...})
  InquiryRequest(inq_1, tool:fs_modify_file, "Create backup?", Boolean)
  InquiryResponse(inq_1, true)
ToolCallResponse(call_123, "File modified successfully")

For multi-question tools, multiple inquiry pairs appear:

ToolCallRequest(call_123, fs_modify_file, {...})
  InquiryRequest(inq_1, tool:fs_modify_file, "Create backup?", Boolean)
  InquiryResponse(inq_1, true)
  InquiryRequest(inq_2, tool:fs_modify_file, "Overwrite existing?", Boolean)
  InquiryResponse(inq_2, false)
ToolCallResponse(call_123, "File modified successfully")

For parallel tool calls, inquiry events for different tools may interleave, but each InquiryRequest/InquiryResponse pair is matched by id, so ordering between tools doesn't affect correctness.

Schema Extraction Edge Case

Several providers check events.last().as_chat_request().schema to detect structured output requests. If inquiry events were at the tail of the stream, this check could break. In practice, inquiry events always sit between a ToolCallRequest and ToolCallResponse — never at the tail — so this is not an issue. The into_parts() filtering makes it impossible regardless, since the provider receives a stream where inquiry events don't exist.

Serialization

InquiryRequest and InquiryResponse are already fully integrated into the serialization system. EventKind includes both variants, InternalEventFlattened (the optimized deserializer) handles them, and ConversationStream has add_inquiry_request() / add_inquiry_response() helpers. No serialization work is needed.

Drawbacks

• Stream size: Inquiry events add to the persisted conversation. Each inquiry pair is small (the question text + a JSON answer value), but tools with many questions over many turns will accumulate. This is a minor cost compared to the tool arguments and responses already stored.

• Mutable stream reference: execute_with_prompting and handle_tool_result take &mut ConversationStream. The change is contained within the tool execution module.

Alternatives

Per-event metadata flag (provider_hidden: bool)

Instead of type-level visibility, add a provider_hidden field to ConversationEvent that any event instance can set.

Rejected because: for inquiry events, the flag would always be true — it's a property of the event type, not individual instances. A runtime flag adds serialization overhead to every event and makes the filtering intent implicit rather than structural. If a future need arises for per-instance visibility control (e.g. hiding a specific ChatRequest), this field can be added later in a backward-compatible way.

Filter in each provider (status quo)

Keep the current pattern where each provider's convert_events function skips non-provider events via pattern matching.

Rejected because: it's fragile. Every new internal event type requires touching every provider. The three providers that don't use into_messages each have their own filtering logic (Anthropic and OpenRouter explicitly match InquiryRequest / InquiryResponse; Google, Llamacpp, and Ollama use _ => None). This is the pattern we're trying to eliminate.

Filter via ConversationStream::provider_events() method

Add a method that returns a filtered iterator, and have each provider call it instead of into_iter().

This is better than the status quo but still requires each provider to remember to call the right method. A new provider that calls into_iter() instead of provider_events() silently gets unfiltered events. into_parts() makes the filtering mandatory — you can't get the events without going through it.

Non-Goals

• Rendering inquiry events in conversation show: Display formatting for inquiry events in the CLI is deferred. The events are stored and visible in the raw JSON. A future change can add pretty-printing.
• Exposing inquiry events to the LLM: Inquiry events are always provider-hidden. The inquiry itself is already represented by a ChatRequest (with structured schema) and ChatResponse::Structured in the inquiry backend's cloned stream — those are the provider-visible artifacts. The InquiryRequest/InquiryResponse events are our internal bookkeeping.
• Batching multiple inquiries: Combining questions from multiple tools into a single structured request. Noted as a future enhancement in the inquiry architecture doc.

Risks and Open Questions

• Parallel tool calls and stream ordering: When multiple tools run in parallel and both trigger inquiries, their InquiryRequest/InquiryResponse events may interleave in the stream. Each pair is correlated by id, so this is correct, but it may look confusing in the raw JSON. We could buffer inquiry events and insert them in tool-order, but this adds complexity for marginal readability benefit.

Implementation Plan

Phase 1: Event visibility and into_parts()

Add EventKind::is_provider_visible() to jp_conversation. Add Thread::into_parts() with the SystemPart enum and refactor into_messages() to delegate to it.

No provider changes. No behavioral changes. Can be merged independently.

Phase 2: Record inquiry events in the stream

In ToolCoordinator, push InquiryRequest on inquiry spawn and InquiryResponse on inquiry result. Change execute_with_prompting to take events: &mut ConversationStream.

Depends on Phase 1 (filtering must be in place before events are recorded).

Phase 3: Migrate OpenRouter to into_parts()

Replace manual Thread destructuring with into_parts(). The explicit InquiryRequest | InquiryResponse | TurnStart => vec![] arm in convert_events becomes a _ => vec![] catch-all (still needed for exhaustive matching, but internal events never reach it).

Independent of Phase 2.

Phase 4: Migrate Google to into_parts()

Replace manual Thread destructuring. System parts go to system_instruction, filtered events go to convert_events.

Independent of Phase 3.

Phase 5: Migrate Anthropic to into_parts()

Replace manual Thread destructuring. Anthropic extracts attachments from the thread before calling into_parts() to send them as native document blocks rather than XML. Cache control budget distribution across system_parts and events uses the SystemPart tags — no complex counter threading is needed.

Independent of Phase 4.

References

• Stateful Tool Inquiries Architecture — the inquiry system this RFD builds on.
• Query Stream Pipeline — turn loop and streaming architecture.
• jp_conversation::event::inquiry — existing InquiryRequest and InquiryResponse event types.
• jp_conversation::thread::Thread — the thread type being extended with into_parts().