chore: bump to 0.7.27 with bugfixes (#2660)

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Sarah Wooders 2025-05-24 21:14:01 -07:00 committed by GitHub
commit 2716d422da
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12 changed files with 124 additions and 78 deletions

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@ -1,4 +1,4 @@
__version__ = "0.7.26"
__version__ = "0.7.27"
# import clients
from letta.client.client import RESTClient

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@ -58,9 +58,10 @@ class OpenAIStreamingInterface:
def get_tool_call_object(self) -> ToolCall:
"""Useful for agent loop"""
function_name = self.last_flushed_function_name if self.last_flushed_function_name else self.function_name_buffer
return ToolCall(
id=self.letta_tool_message_id,
function=FunctionCall(arguments=self.current_function_arguments, name=self.last_flushed_function_name),
function=FunctionCall(arguments=self.current_function_arguments, name=function_name),
)
async def process(self, stream: AsyncStream[ChatCompletionChunk]) -> AsyncGenerator[LettaMessage, None]:

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@ -55,6 +55,18 @@ BASE_URL = "https://api.anthropic.com/v1"
# https://docs.anthropic.com/claude/docs/models-overview
# Sadly hardcoded
MODEL_LIST = [
{
"name": "claude-opus-4-20250514",
"context_window": 200000,
},
{
"name": "claude-sonnet-4-20250514",
"context_window": 200000,
},
{
"name": "claude-3-5-haiku-20241022",
"context_window": 200000,
},
## Opus
{
"name": "claude-3-opus-20240229",

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@ -56,6 +56,7 @@ def openai_check_valid_api_key(base_url: str, api_key: Union[str, None]) -> None
else:
raise ValueError("No API key provided")
def openai_get_model_list(url: str, api_key: Optional[str] = None, fix_url: bool = False, extra_params: Optional[dict] = None) -> dict:
"""https://platform.openai.com/docs/api-reference/models/list"""
from letta.utils import printd

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@ -41,7 +41,7 @@ def is_openai_reasoning_model(model: str) -> bool:
"""Utility function to check if the model is a 'reasoner'"""
# NOTE: needs to be updated with new model releases
is_reasoning = model.startswith("o1") or model.startswith("o3")
is_reasoning = model.startswith("o1") or model.startswith("o3") or model.startswith("o4")
return is_reasoning
@ -187,7 +187,8 @@ class OpenAIClient(LLMClientBase):
tool_choice=tool_choice,
user=str(),
max_completion_tokens=llm_config.max_tokens,
temperature=llm_config.temperature if supports_temperature_param(model) else None,
# NOTE: the reasoners that don't support temperature require 1.0, not None
temperature=llm_config.temperature if supports_temperature_param(model) else 1.0,
)
# always set user id for openai requests

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@ -289,7 +289,7 @@ class OpenAIProvider(Provider):
# for openai, filter models
if self.base_url == "https://api.openai.com/v1":
allowed_types = ["gpt-4", "o1", "o3"]
allowed_types = ["gpt-4", "o1", "o3", "o4"]
# NOTE: o1-mini and o1-preview do not support tool calling
# NOTE: o1-pro is only available in Responses API
disallowed_types = ["transcribe", "search", "realtime", "tts", "audio", "computer", "o1-mini", "o1-preview", "o1-pro"]

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@ -120,18 +120,21 @@ class DatabaseRegistry:
async_pg_uri = async_pg_uri.replace("sslmode=", "ssl=")
async_engine = create_async_engine(async_pg_uri, **self._build_sqlalchemy_engine_args(is_async=True))
self._async_engines["default"] = async_engine
# Create async session factory
self._async_session_factories["default"] = async_sessionmaker(
autocommit=False, autoflush=False, bind=self._async_engines["default"], class_=AsyncSession
)
self._initialized["async"] = True
else:
self.logger.warning("Async SQLite is currently not supported. Please use PostgreSQL for async database operations.")
# TODO (cliandy): unclear around async sqlite support in sqlalchemy, we will not currently support this
self._initialized["async"] = False
self._initialized["async"] = True
engine_path = "sqlite+aiosqlite:///" + os.path.join(self.config.recall_storage_path, "sqlite.db")
self.logger.info("Creating sqlite engine " + engine_path)
async_engine = create_async_engine(engine_path, **self._build_sqlalchemy_engine_args(is_async=True))
# Create async session factory
self._async_engines["default"] = async_engine
self._async_session_factories["default"] = async_sessionmaker(
autocommit=False, autoflush=False, bind=self._async_engines["default"], class_=AsyncSession
)
self._initialized["async"] = True
def _build_sqlalchemy_engine_args(self, *, is_async: bool) -> dict:
"""Prepare keyword arguments for create_engine / create_async_engine."""

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@ -716,6 +716,7 @@ async def send_message_streaming(
feature_enabled = settings.use_experimental or experimental_header.lower() == "true"
model_compatible = agent.llm_config.model_endpoint_type in ["anthropic", "openai", "together", "google_ai", "google_vertex"]
model_compatible_token_streaming = agent.llm_config.model_endpoint_type in ["anthropic", "openai"]
not_letta_endpoint = not ("letta" in agent.llm_config.model_endpoint)
if agent_eligible and feature_enabled and model_compatible:
if agent.enable_sleeptime:
@ -745,7 +746,7 @@ async def send_message_streaming(
)
from letta.server.rest_api.streaming_response import StreamingResponseWithStatusCode
if request.stream_tokens and model_compatible_token_streaming:
if request.stream_tokens and model_compatible_token_streaming and not_letta_endpoint:
result = StreamingResponseWithStatusCode(
experimental_agent.step_stream(
input_messages=request.messages,

147
poetry.lock generated
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@ -1,4 +1,4 @@
# This file is automatically @generated by Poetry 2.1.2 and should not be changed by hand.
# This file is automatically @generated by Poetry 2.1.3 and should not be changed by hand.
[[package]]
name = "aiohappyeyeballs"
@ -147,6 +147,25 @@ files = [
[package.dependencies]
frozenlist = ">=1.1.0"
[[package]]
name = "aiosqlite"
version = "0.21.0"
description = "asyncio bridge to the standard sqlite3 module"
optional = false
python-versions = ">=3.9"
groups = ["main"]
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]
[package.dependencies]
typing_extensions = ">=4.0"
[package.extras]
dev = ["attribution (==1.7.1)", "black (==24.3.0)", "build (>=1.2)", "coverage[toml] (==7.6.10)", "flake8 (==7.0.0)", "flake8-bugbear (==24.12.12)", "flit (==3.10.1)", "mypy (==1.14.1)", "ufmt (==2.5.1)", "usort (==1.0.8.post1)"]
docs = ["sphinx (==8.1.3)", "sphinx-mdinclude (==0.6.1)"]
[[package]]
name = "alembic"
version = "1.15.2"
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@ -2168,7 +2193,7 @@ description = "Lightweight in-process concurrent programming"
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groups = ["main"]
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markers = "platform_machine == \"aarch64\" or platform_machine == \"ppc64le\" or platform_machine == \"x86_64\" or platform_machine == \"amd64\" or platform_machine == \"AMD64\" or platform_machine == \"win32\" or platform_machine == \"WIN32\" or (extra == \"dev\" or extra == \"desktop\" or extra == \"all\") and platform_python_implementation == \"CPython\""
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@ -6430,69 +6455,69 @@ files = [
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name = "sqlalchemy"
version = "2.0.40"
version = "2.0.41"
description = "Database Abstraction Library"
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]
[package.dependencies]
@ -7587,4 +7612,4 @@ tests = ["wikipedia"]
[metadata]
lock-version = "2.1"
python-versions = "<3.14,>=3.10"
content-hash = "837f6a25033a01cca117f4c61bcf973bc6ccfcda442615bbf4af038061bf88ce"
content-hash = "911953a472c1d904113530807858c47e249931dc311093a7276aeae895b53f8e"

View File

@ -1,6 +1,6 @@
[tool.poetry]
name = "letta"
version = "0.7.26"
version = "0.7.27"
packages = [
{include = "letta"},
]
@ -36,7 +36,7 @@ fastapi = { version = "^0.115.6", optional = true}
uvicorn = {version = "^0.24.0.post1", optional = true}
pydantic = "^2.10.6"
html2text = "^2020.1.16"
sqlalchemy = "^2.0.25"
sqlalchemy = {extras = ["asyncio"], version = "^2.0.41"}
pexpect = {version = "^4.9.0", optional = true}
pyright = {version = "^1.1.347", optional = true}
qdrant-client = {version="^1.9.1", optional = true}
@ -92,6 +92,7 @@ aiomultiprocess = "^0.9.1"
matplotlib = "^3.10.1"
asyncpg = "^0.30.0"
tavily-python = "^0.7.2"
aiosqlite = "^0.21.0"
[tool.poetry.extras]

View File

@ -586,6 +586,7 @@ def _modify(group_id, server, actor, max_val, min_val):
actor=actor,
)
def test_valid_buffer_lengths_above_four(group_id, server, actor):
# both > 4 and max > min
updated = _modify(group_id, server, actor, max_val=10, min_val=5)

View File

@ -151,7 +151,7 @@ def test_archival(agent_obj):
def test_recall(server, agent_obj, default_user):
"""Test that an agent can recall messages using a keyword via conversation search."""
keyword = "banana"
keyword_backwards = "".join(reversed(keyword))
"".join(reversed(keyword))
# Send messages
for msg in ["hello", keyword, "tell me a fun fact"]: