feat: inject today's date into system prompt and add extended thinking support

Fixes age validation errors caused by the LLM not knowing the current date.

Changes:
- prompts.py: inject date.today() at the top of both system prompts so the
  LLM can accurately calculate a child's age from their date of birth
- llm.py: add optional thinking_budget parameter to complete(); when set,
  passes thinking={"type": "enabled", "budget_tokens": N} to litellm and
  raises max_tokens to thinking_budget + 4096 (Anthropic models only)
- config.py: add thinking_budget field, read from THINKING_BUDGET env var
- .env.example: document the THINKING_BUDGET option
- core.py: pass thinking_budget through to llm.complete()
- main.py: pass thinking_budget when constructing EmailAgent
- chat_app.py: switch from stream_complete to asyncio.to_thread(complete)
  so extended thinking works and so only the reply field is shown to
  the parent (not the raw JSON wrapper)

To enable extended thinking set THINKING_BUDGET=8000 in .env.

https://claude.ai/code/session_01SUWzMzFvSfWiHXA2p6rPg9
This commit is contained in:
Claude
2026-02-22 19:57:32 +00:00
parent 13cb35111d
commit 1bd11e6acd
7 changed files with 76 additions and 16 deletions
+33 -4
View File
@@ -5,7 +5,12 @@ from collections.abc import Generator
import litellm
async def acomplete(model: str, system: str, messages: list) -> str:
async def acomplete(
model: str,
system: str,
messages: list,
thinking_budget: int | None = None,
) -> str:
"""Call any LLM asynchronously and return the response text.
This is the async equivalent of ``complete()`` — use this from async
@@ -16,17 +21,30 @@ async def acomplete(model: str, system: str, messages: list) -> str:
model: litellm model string, e.g. "anthropic/claude-opus-4-6".
system: System prompt text.
messages: List of objects with .role and .content attributes.
thinking_budget: When set, enables extended thinking (Anthropic models
only). See ``complete()`` for details.
Returns:
The model's reply as a plain string.
"""
api_messages = [{"role": "system", "content": system}]
api_messages += [{"role": m.role, "content": m.content} for m in messages]
response = await litellm.acompletion(model=model, messages=api_messages, max_tokens=2048)
kwargs: dict = {"model": model, "messages": api_messages, "max_tokens": 2048}
if thinking_budget is not None:
kwargs["thinking"] = {"type": "enabled", "budget_tokens": thinking_budget}
kwargs["max_tokens"] = thinking_budget + 4096
response = await litellm.acompletion(**kwargs)
return response.choices[0].message.content
def complete(model: str, system: str, messages: list) -> str:
def complete(
model: str,
system: str,
messages: list,
thinking_budget: int | None = None,
) -> str:
"""Call any LLM and return the response text.
Args:
@@ -35,13 +53,24 @@ def complete(model: str, system: str, messages: list) -> str:
environment variable (ANTHROPIC_API_KEY, OPENAI_API_KEY, …).
system: System prompt text.
messages: List of objects with .role and .content attributes.
thinking_budget: When set, enables extended thinking (Anthropic models
only). The value is the token budget for the thinking phase; the
final ``max_tokens`` is set to ``thinking_budget + 4096`` so the
model has enough room to both think and reply.
Returns:
The model's reply as a plain string.
"""
api_messages = [{"role": "system", "content": system}]
api_messages += [{"role": m.role, "content": m.content} for m in messages]
response = litellm.completion(model=model, messages=api_messages, max_tokens=2048)
kwargs: dict = {"model": model, "messages": api_messages, "max_tokens": 2048}
if thinking_budget is not None:
kwargs["thinking"] = {"type": "enabled", "budget_tokens": thinking_budget}
# max_tokens must exceed budget_tokens or the API returns an error.
kwargs["max_tokens"] = thinking_budget + 4096
response = litellm.completion(**kwargs)
return response.choices[0].message.content