feat(agent): add streaming groundwork for future TUI
Preserve the provider and agent-loop streaming primitives plus the CLI experiment scaffolding so this work can be resumed later without blocking urgent bug fixes on main. Made-with: Cursor
This commit is contained in:
Xubin Ren
@@ -3,6 +3,7 @@
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import asyncio
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import json
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from abc import ABC, abstractmethod
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from collections.abc import Awaitable, Callable
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from dataclasses import dataclass, field
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from typing import Any
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@@ -223,6 +224,90 @@ class LLMProvider(ABC):
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except Exception as exc:
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return LLMResponse(content=f"Error calling LLM: {exc}", finish_reason="error")
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async def chat_stream(
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self,
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messages: list[dict[str, Any]],
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tools: list[dict[str, Any]] | None = None,
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model: str | None = None,
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max_tokens: int = 4096,
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temperature: float = 0.7,
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reasoning_effort: str | None = None,
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tool_choice: str | dict[str, Any] | None = None,
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on_content_delta: Callable[[str], Awaitable[None]] | None = None,
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) -> LLMResponse:
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"""Stream a chat completion, calling *on_content_delta* for each text chunk.
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Returns the same ``LLMResponse`` as :meth:`chat`. The default
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implementation falls back to a non-streaming call and delivers the
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full content as a single delta. Providers that support native
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streaming should override this method.
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"""
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response = await self.chat(
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messages=messages, tools=tools, model=model,
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max_tokens=max_tokens, temperature=temperature,
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reasoning_effort=reasoning_effort, tool_choice=tool_choice,
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)
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if on_content_delta and response.content:
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await on_content_delta(response.content)
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return response
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async def _safe_chat_stream(self, **kwargs: Any) -> LLMResponse:
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"""Call chat_stream() and convert unexpected exceptions to error responses."""
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try:
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return await self.chat_stream(**kwargs)
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except asyncio.CancelledError:
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raise
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except Exception as exc:
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return LLMResponse(content=f"Error calling LLM: {exc}", finish_reason="error")
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async def chat_stream_with_retry(
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self,
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messages: list[dict[str, Any]],
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tools: list[dict[str, Any]] | None = None,
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model: str | None = None,
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max_tokens: object = _SENTINEL,
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temperature: object = _SENTINEL,
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reasoning_effort: object = _SENTINEL,
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tool_choice: str | dict[str, Any] | None = None,
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on_content_delta: Callable[[str], Awaitable[None]] | None = None,
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) -> LLMResponse:
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"""Call chat_stream() with retry on transient provider failures."""
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if max_tokens is self._SENTINEL:
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max_tokens = self.generation.max_tokens
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if temperature is self._SENTINEL:
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temperature = self.generation.temperature
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if reasoning_effort is self._SENTINEL:
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reasoning_effort = self.generation.reasoning_effort
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kw: dict[str, Any] = dict(
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messages=messages, tools=tools, model=model,
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max_tokens=max_tokens, temperature=temperature,
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reasoning_effort=reasoning_effort, tool_choice=tool_choice,
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on_content_delta=on_content_delta,
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)
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for attempt, delay in enumerate(self._CHAT_RETRY_DELAYS, start=1):
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response = await self._safe_chat_stream(**kw)
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if response.finish_reason != "error":
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return response
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if not self._is_transient_error(response.content):
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stripped = self._strip_image_content(messages)
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if stripped is not None:
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logger.warning("Non-transient LLM error with image content, retrying without images")
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return await self._safe_chat_stream(**{**kw, "messages": stripped})
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return response
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logger.warning(
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"LLM transient error (attempt {}/{}), retrying in {}s: {}",
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attempt, len(self._CHAT_RETRY_DELAYS), delay,
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(response.content or "")[:120].lower(),
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)
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await asyncio.sleep(delay)
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return await self._safe_chat_stream(**kw)
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async def chat_with_retry(
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self,
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messages: list[dict[str, Any]],
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@@ -4,6 +4,7 @@ import hashlib
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import os
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import secrets
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import string
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from collections.abc import Awaitable, Callable
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from typing import Any
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import json_repair
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@@ -223,6 +224,64 @@ class LiteLLMProvider(LLMProvider):
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clean["tool_call_id"] = map_id(clean["tool_call_id"])
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return sanitized
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def _build_chat_kwargs(
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self,
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messages: list[dict[str, Any]],
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tools: list[dict[str, Any]] | None,
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model: str | None,
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max_tokens: int,
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temperature: float,
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reasoning_effort: str | None,
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tool_choice: str | dict[str, Any] | None,
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) -> tuple[dict[str, Any], str]:
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"""Build the kwargs dict for ``acompletion``.
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Returns ``(kwargs, original_model)`` so callers can reuse the
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original model string for downstream logic.
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"""
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original_model = model or self.default_model
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resolved = self._resolve_model(original_model)
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extra_msg_keys = self._extra_msg_keys(original_model, resolved)
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if self._supports_cache_control(original_model):
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messages, tools = self._apply_cache_control(messages, tools)
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max_tokens = max(1, max_tokens)
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kwargs: dict[str, Any] = {
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"model": resolved,
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"messages": self._sanitize_messages(
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self._sanitize_empty_content(messages), extra_keys=extra_msg_keys,
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),
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"max_tokens": max_tokens,
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"temperature": temperature,
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}
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if self._gateway:
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kwargs.update(self._gateway.litellm_kwargs)
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self._apply_model_overrides(resolved, kwargs)
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if self._langsmith_enabled:
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kwargs.setdefault("callbacks", []).append("langsmith")
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if self.api_key:
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kwargs["api_key"] = self.api_key
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if self.api_base:
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kwargs["api_base"] = self.api_base
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if self.extra_headers:
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kwargs["extra_headers"] = self.extra_headers
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if reasoning_effort:
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kwargs["reasoning_effort"] = reasoning_effort
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kwargs["drop_params"] = True
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if tools:
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kwargs["tools"] = tools
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kwargs["tool_choice"] = tool_choice or "auto"
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return kwargs, original_model
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async def chat(
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self,
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messages: list[dict[str, Any]],
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@@ -233,71 +292,54 @@ class LiteLLMProvider(LLMProvider):
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reasoning_effort: str | None = None,
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tool_choice: str | dict[str, Any] | None = None,
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) -> LLMResponse:
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"""
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Send a chat completion request via LiteLLM.
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Args:
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messages: List of message dicts with 'role' and 'content'.
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tools: Optional list of tool definitions in OpenAI format.
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model: Model identifier (e.g., 'anthropic/claude-sonnet-4-5').
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max_tokens: Maximum tokens in response.
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temperature: Sampling temperature.
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Returns:
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LLMResponse with content and/or tool calls.
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"""
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original_model = model or self.default_model
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model = self._resolve_model(original_model)
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extra_msg_keys = self._extra_msg_keys(original_model, model)
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if self._supports_cache_control(original_model):
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messages, tools = self._apply_cache_control(messages, tools)
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# Clamp max_tokens to at least 1 — negative or zero values cause
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# LiteLLM to reject the request with "max_tokens must be at least 1".
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max_tokens = max(1, max_tokens)
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kwargs: dict[str, Any] = {
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"model": model,
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"messages": self._sanitize_messages(self._sanitize_empty_content(messages), extra_keys=extra_msg_keys),
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"max_tokens": max_tokens,
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"temperature": temperature,
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}
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if self._gateway:
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kwargs.update(self._gateway.litellm_kwargs)
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# Apply model-specific overrides (e.g. kimi-k2.5 temperature)
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self._apply_model_overrides(model, kwargs)
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if self._langsmith_enabled:
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kwargs.setdefault("callbacks", []).append("langsmith")
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# Pass api_key directly — more reliable than env vars alone
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if self.api_key:
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kwargs["api_key"] = self.api_key
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# Pass api_base for custom endpoints
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if self.api_base:
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kwargs["api_base"] = self.api_base
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# Pass extra headers (e.g. APP-Code for AiHubMix)
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if self.extra_headers:
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kwargs["extra_headers"] = self.extra_headers
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if reasoning_effort:
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kwargs["reasoning_effort"] = reasoning_effort
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kwargs["drop_params"] = True
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if tools:
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kwargs["tools"] = tools
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kwargs["tool_choice"] = tool_choice or "auto"
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"""Send a chat completion request via LiteLLM."""
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kwargs, _ = self._build_chat_kwargs(
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messages, tools, model, max_tokens, temperature,
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reasoning_effort, tool_choice,
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)
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try:
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response = await acompletion(**kwargs)
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return self._parse_response(response)
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except Exception as e:
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# Return error as content for graceful handling
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return LLMResponse(
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content=f"Error calling LLM: {str(e)}",
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finish_reason="error",
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)
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async def chat_stream(
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self,
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messages: list[dict[str, Any]],
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tools: list[dict[str, Any]] | None = None,
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model: str | None = None,
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max_tokens: int = 4096,
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temperature: float = 0.7,
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reasoning_effort: str | None = None,
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tool_choice: str | dict[str, Any] | None = None,
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on_content_delta: Callable[[str], Awaitable[None]] | None = None,
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) -> LLMResponse:
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"""Stream a chat completion via LiteLLM, forwarding text deltas."""
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kwargs, _ = self._build_chat_kwargs(
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messages, tools, model, max_tokens, temperature,
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reasoning_effort, tool_choice,
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)
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kwargs["stream"] = True
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try:
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stream = await acompletion(**kwargs)
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chunks: list[Any] = []
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async for chunk in stream:
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chunks.append(chunk)
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if on_content_delta:
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delta = chunk.choices[0].delta if chunk.choices else None
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text = getattr(delta, "content", None) if delta else None
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if text:
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await on_content_delta(text)
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full_response = litellm.stream_chunk_builder(
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chunks, messages=kwargs["messages"],
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)
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return self._parse_response(full_response)
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except Exception as e:
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return LLMResponse(
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content=f"Error calling LLM: {str(e)}",
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finish_reason="error",
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