Harness Streaming Feature

The harness must support streaming independently from the graph. Graph streaming can forward harness events, but direct harness users should also be able to consume streams.

Responsibilities

• Stream model token/message deltas.
• Stream tool progress.
• Stream usage and cost updates.
• Stream cache hit/miss events.
• Stream summary events.
• Stream final outputs.
• Forward events into the registry event bus.
• Merge provider chunks into a final assistant message.
• Preserve streamed tool-call chunks where providers support them.
• Support cancellation and backpressure.
• Support stream replay from event stores.

Source Inspiration

LangChain exposes model streams, runnable event streams, callback streaming, and tracer streams:

• chat model stream: https://github.com/langchain-ai/langchain/blob/master/libs/core/langchain_core/language_models/chat_model_stream.py
• runnable event streams: https://github.com/langchain-ai/langchain/tree/master/libs/core/langchain_core/runnables
• streaming tracers: https://github.com/langchain-ai/langchain/tree/master/libs/core/langchain_core/tracers
• provider streaming adapters such as OpenAI: https://github.com/langchain-ai/langchain/blob/master/libs/partners/openai/langchain_openai/chat_models/base.py

Stream Modes

• messages: model deltas and final messages
• tools: tool lifecycle and progress
• usage: token updates
• cost: price updates
• events: all harness events
• final: final result only

Every stream item should carry run ids and component ids so web UIs can merge harness streams with graph streams.

Stream Items

pub enum HarnessStreamItem {
    Event(HarnessEvent),
    MessageDelta(MessageDelta),
    ToolCallDelta(ToolCallDelta),
    ToolProgress(ToolProgress),
    Usage(UsageRecord),
    Cost(CostRecord),
    Final(AgentRun),
}

A stream consumer should be able to subscribe to a subset of modes without changing execution. Dropping a consumer must not cancel the run unless the consumer owns the run cancellation token.

Chunk Merging

Streaming adapters must merge chunks deterministically:

• text chunks preserve order
• reasoning/thinking chunks preserve order on a side channel
• content block indexes are respected
• tool-call chunks are correlated by id or index
• cumulative usage is converted into deltas or clearly marked cumulative
• final message equals the merged stream
• invalid partial tool calls are surfaced as repairable parse errors