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chore: Deprecate O1 Agent (#573)

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This commit is contained in:
Matthew Zhou
2025-01-09 13:40:31 -10:00
committed by GitHub
parent dfa8c0091d
commit e89aff7b8b
8 changed files with 1 additions and 130 deletions
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1
.github/workflows/integration_tests.yml vendored
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@@ -29,7 +29,6 @@ jobs:
- "integration_test_tool_execution_sandbox.py"
- "integration_test_offline_memory_agent.py"
- "integration_test_agent_tool_graph.py"
- "integration_test_o1_agent.py"
services:
qdrant:
image: qdrant/qdrant

3
letta/agent.py
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@@ -16,7 +16,6 @@ from letta.constants import (
MESSAGE_SUMMARY_TRUNC_KEEP_N_LAST,
MESSAGE_SUMMARY_TRUNC_TOKEN_FRAC,
MESSAGE_SUMMARY_WARNING_FRAC,
O1_BASE_TOOLS,
REQ_HEARTBEAT_MESSAGE,
)
from letta.errors import ContextWindowExceededError
@@ -212,7 +211,7 @@ class Agent(BaseAgent):
# TODO: This is NO BUENO
# TODO: Matching purely by names is extremely problematic, users can create tools with these names and run them in the agent loop
# TODO: We will have probably have to match the function strings exactly for safety
if function_name in BASE_TOOLS or function_name in O1_BASE_TOOLS:
if function_name in BASE_TOOLS:
# base tools are allowed to access the `Agent` object and run on the database
function_args["self"] = self # need to attach self to arg since it's dynamically linked
function_response = callable_func(**function_args)

1
letta/constants.py
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@@ -42,7 +42,6 @@ DEFAULT_PRESET = "memgpt_chat"
# Base tools that cannot be edited, as they access agent state directly
# Note that we don't include "conversation_search_date" for now
BASE_TOOLS = ["send_message", "conversation_search", "archival_memory_insert", "archival_memory_search"]
O1_BASE_TOOLS = ["send_thinking_message", "send_final_message"]
# Base memory tools CAN be edited, and are added by default by the server
BASE_MEMORY_TOOLS = ["core_memory_append", "core_memory_replace"]

86
letta/o1_agent.py
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@@ -1,86 +0,0 @@
from typing import List, Optional, Union
from letta.agent import Agent, save_agent
from letta.interface import AgentInterface
from letta.schemas.agent import AgentState
from letta.schemas.message import Message
from letta.schemas.openai.chat_completion_response import UsageStatistics
from letta.schemas.usage import LettaUsageStatistics
from letta.schemas.user import User
def send_thinking_message(self: "Agent", message: str) -> Optional[str]:
"""
Sends a thinking message so that the model can reason out loud before responding.
Args:
message (str): Message contents. All unicode (including emojis) are supported.
Returns:
Optional[str]: None is always returned as this function does not produce a response.
"""
self.interface.internal_monologue(message)
return None
def send_final_message(self: "Agent", message: str) -> Optional[str]:
"""
Sends a final message to the human user after thinking for a while.
Args:
message (str): Message contents. All unicode (including emojis) are supported.
Returns:
Optional[str]: None is always returned as this function does not produce a response.
"""
self.interface.internal_monologue(message)
return None
class O1Agent(Agent):
def __init__(
self,
interface: AgentInterface,
agent_state: AgentState,
user: User,
max_thinking_steps: int = 10,
first_message_verify_mono: bool = False,
):
super().__init__(interface, agent_state, user)
self.max_thinking_steps = max_thinking_steps
self.first_message_verify_mono = first_message_verify_mono
def step(
self,
messages: Union[Message, List[Message]],
chaining: bool = True,
max_chaining_steps: Optional[int] = None,
**kwargs,
) -> LettaUsageStatistics:
"""Run Agent.inner_step in a loop, terminate when final thinking message is sent or max_thinking_steps is reached"""
# assert ms is not None, "MetadataStore is required"
next_input_message = messages if isinstance(messages, list) else [messages]
counter = 0
total_usage = UsageStatistics()
step_count = 0
while step_count < self.max_thinking_steps:
if counter > 0:
next_input_message = []
kwargs["first_message"] = False
step_response = self.inner_step(
messages=next_input_message,
**kwargs,
)
usage = step_response.usage
step_count += 1
total_usage += usage
counter += 1
self.interface.step_complete()
# check if it is final thinking message
if step_response.messages[-1].name == "send_final_message":
break
save_agent(self)
return LettaUsageStatistics(**total_usage.model_dump(), step_count=step_count)

1
letta/schemas/agent.py
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@@ -25,7 +25,6 @@ class AgentType(str, Enum):
memgpt_agent = "memgpt_agent"
split_thread_agent = "split_thread_agent"
o1_agent = "o1_agent"
offline_memory_agent = "offline_memory_agent"
chat_only_agent = "chat_only_agent"

3
letta/server/server.py
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@@ -22,7 +22,6 @@ from letta.data_sources.connectors import DataConnector, load_data
from letta.interface import AgentInterface # abstract
from letta.interface import CLIInterface # for printing to terminal
from letta.log import get_logger
from letta.o1_agent import O1Agent
from letta.offline_memory_agent import OfflineMemoryAgent
from letta.orm import Base
from letta.orm.errors import NoResultFound
@@ -390,8 +389,6 @@ class SyncServer(Server):
interface = interface or self.default_interface_factory()
if agent_state.agent_type == AgentType.memgpt_agent:
agent = Agent(agent_state=agent_state, interface=interface, user=actor)
elif agent_state.agent_type == AgentType.o1_agent:
agent = O1Agent(agent_state=agent_state, interface=interface, user=actor)
elif agent_state.agent_type == AgentType.offline_memory_agent:
agent = OfflineMemoryAgent(agent_state=agent_state, interface=interface, user=actor)
elif agent_state.agent_type == AgentType.chat_only_agent:

2
letta/services/helpers/agent_manager_helper.py
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@@ -89,8 +89,6 @@ def derive_system_message(agent_type: AgentType, system: Optional[str] = None):
# TODO: don't hardcode
if agent_type == AgentType.memgpt_agent:
system = gpt_system.get_system_text("memgpt_chat")
elif agent_type == AgentType.o1_agent:
system = gpt_system.get_system_text("memgpt_modified_o1")
elif agent_type == AgentType.offline_memory_agent:
system = gpt_system.get_system_text("memgpt_offline_memory")
elif agent_type == AgentType.chat_only_agent:

34
tests/integration_test_o1_agent.py
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@@ -1,34 +0,0 @@
from letta.client.client import create_client
from letta.constants import DEFAULT_HUMAN
from letta.o1_agent import send_final_message, send_thinking_message
from letta.schemas.agent import AgentType
from letta.schemas.embedding_config import EmbeddingConfig
from letta.schemas.llm_config import LLMConfig
from letta.schemas.memory import ChatMemory
from letta.utils import get_human_text, get_persona_text
def test_o1_agent():
client = create_client()
assert client is not None
thinking_tool = client.create_or_update_tool(send_thinking_message)
final_tool = client.create_or_update_tool(send_final_message)
agent_state = client.create_agent(
agent_type=AgentType.o1_agent,
tool_ids=[thinking_tool.id, final_tool.id],
llm_config=LLMConfig.default_config("gpt-4"),
embedding_config=EmbeddingConfig.default_config("text-embedding-ada-002"),
memory=ChatMemory(human=get_human_text(DEFAULT_HUMAN), persona=get_persona_text("o1_persona")),
)
agent = client.get_agent(agent_id=agent_state.id)
assert agent is not None
response = client.user_message(agent_id=agent_state.id, message="9.9 or 9.11, which is a larger number?")
assert response is not None
assert len(response.messages) > 3
if __name__ == "__main__":
test_o1_agent()