mirror of
https://github.com/cpacker/MemGPT.git
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feat: Improve agent creation time (#1792)
This commit is contained in:
Matthew Zhou
GitHub
parent
e625cc67da
commit
4aca83df8e
@ -1,8 +1,10 @@
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from datetime import datetime
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from typing import Dict, List, Optional
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from typing import Dict, List, Optional, Set, Tuple
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import numpy as np
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from sqlalchemy import Select, and_, func, literal, or_, select, union_all
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import sqlalchemy as sa
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from sqlalchemy import Select, and_, func, insert, literal, or_, select, union_all
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from sqlalchemy.dialects.postgresql import insert as pg_insert
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from letta.constants import (
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BASE_MEMORY_TOOLS,
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@ -19,13 +21,17 @@ from letta.log import get_logger
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from letta.orm import Agent as AgentModel
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from letta.orm import AgentPassage, AgentsTags
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from letta.orm import Block as BlockModel
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from letta.orm import BlocksAgents
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from letta.orm import Group as GroupModel
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from letta.orm import IdentitiesAgents
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from letta.orm import Identity as IdentityModel
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from letta.orm import Source as SourceModel
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from letta.orm import SourcePassage, SourcesAgents
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from letta.orm import Tool as ToolModel
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from letta.orm import ToolsAgents
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from letta.orm.enums import ToolType
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from letta.orm.errors import NoResultFound
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from letta.orm.sandbox_config import AgentEnvironmentVariable
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from letta.orm.sandbox_config import AgentEnvironmentVariable as AgentEnvironmentVariableModel
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from letta.orm.sqlalchemy_base import AccessType
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from letta.orm.sqlite_functions import adapt_array
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@ -36,16 +42,14 @@ from letta.schemas.block import BlockUpdate
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from letta.schemas.embedding_config import EmbeddingConfig
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from letta.schemas.group import Group as PydanticGroup
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from letta.schemas.group import ManagerType
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from letta.schemas.llm_config import LLMConfig
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from letta.schemas.memory import Memory
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from letta.schemas.message import Message
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from letta.schemas.message import Message as PydanticMessage
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from letta.schemas.message import MessageCreate, MessageUpdate
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from letta.schemas.passage import Passage as PydanticPassage
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from letta.schemas.source import Source as PydanticSource
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from letta.schemas.tool import Tool as PydanticTool
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from letta.schemas.tool_rule import ContinueToolRule as PydanticContinueToolRule
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from letta.schemas.tool_rule import TerminalToolRule as PydanticTerminalToolRule
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from letta.schemas.tool_rule import ToolRule as PydanticToolRule
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from letta.schemas.tool_rule import ContinueToolRule, TerminalToolRule
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from letta.schemas.user import User as PydanticUser
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from letta.serialize_schemas import MarshmallowAgentSchema
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from letta.serialize_schemas.marshmallow_message import SerializedMessageSchema
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@ -77,7 +81,6 @@ from letta.utils import enforce_types, united_diff
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logger = get_logger(__name__)
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# Agent Manager Class
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class AgentManager:
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"""Manager class to handle business logic related to Agents."""
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@ -92,124 +95,206 @@ class AgentManager:
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self.passage_manager = PassageManager()
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self.identity_manager = IdentityManager()
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@staticmethod
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def _resolve_tools(session, names: Set[str], ids: Set[str], org_id: str) -> Tuple[Dict[str, str], Dict[str, str]]:
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"""
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Bulk‑fetch all ToolModel rows matching either name ∈ names or id ∈ ids
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(and scoped to this organization), and return two maps:
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name_to_id, id_to_name.
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Raises if any requested name or id was not found.
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"""
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stmt = select(ToolModel.id, ToolModel.name).where(
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ToolModel.organization_id == org_id,
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or_(
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ToolModel.name.in_(names),
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ToolModel.id.in_(ids),
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),
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)
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rows = session.execute(stmt).all()
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name_to_id = {name: tid for tid, name in rows}
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id_to_name = {tid: name for tid, name in rows}
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missing_names = names - set(name_to_id.keys())
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missing_ids = ids - set(id_to_name.keys())
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if missing_names:
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raise ValueError(f"Tools not found by name: {missing_names}")
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if missing_ids:
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raise ValueError(f"Tools not found by id: {missing_ids}")
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return name_to_id, id_to_name
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@staticmethod
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@trace_method
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def _bulk_insert_pivot(session, table, rows: list[dict]):
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if not rows:
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return
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dialect = session.bind.dialect.name
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if dialect == "postgresql":
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stmt = pg_insert(table).values(rows).on_conflict_do_nothing()
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elif dialect == "sqlite":
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stmt = sa.insert(table).values(rows).prefix_with("OR IGNORE")
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else:
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# fallback: filter out exact-duplicate dicts in Python
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seen = set()
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filtered = []
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for row in rows:
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key = tuple(sorted(row.items()))
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if key not in seen:
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seen.add(key)
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filtered.append(row)
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stmt = sa.insert(table).values(filtered)
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session.execute(stmt)
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# ======================================================================================================================
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# Basic CRUD operations
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# ======================================================================================================================
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@trace_method
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@enforce_types
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def create_agent(
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self,
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agent_create: CreateAgent,
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actor: PydanticUser,
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) -> PydanticAgentState:
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system = derive_system_message(
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agent_type=agent_create.agent_type,
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enable_sleeptime=agent_create.enable_sleeptime,
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system=agent_create.system,
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)
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def create_agent(self, agent_create: CreateAgent, actor: PydanticUser) -> PydanticAgentState:
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# validate required configs
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if not agent_create.llm_config or not agent_create.embedding_config:
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raise ValueError("llm_config and embedding_config are required")
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# create blocks (note: cannot be linked into the agent_id is created)
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block_ids = list(agent_create.block_ids or []) # Create a local copy to avoid modifying the original
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# blocks
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block_ids = list(agent_create.block_ids or [])
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if agent_create.memory_blocks:
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for create_block in agent_create.memory_blocks:
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block = self.block_manager.create_or_update_block(PydanticBlock(**create_block.model_dump(to_orm=True)), actor=actor)
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block_ids.append(block.id)
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# add passed in `tools`
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tool_names = agent_create.tools or []
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# add base tools
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if agent_create.include_base_tools:
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if agent_create.agent_type == AgentType.sleeptime_agent:
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tool_names.extend(BASE_SLEEPTIME_TOOLS)
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else:
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if agent_create.enable_sleeptime:
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tool_names.extend(BASE_SLEEPTIME_CHAT_TOOLS)
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else:
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tool_names.extend(BASE_TOOLS + BASE_MEMORY_TOOLS)
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if agent_create.include_multi_agent_tools:
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tool_names.extend(MULTI_AGENT_TOOLS)
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# remove duplicates
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tool_names = list(set(tool_names))
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# convert tool names to ids
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tool_ids = []
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for tool_name in tool_names:
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tool = self.tool_manager.get_tool_by_name(tool_name=tool_name, actor=actor)
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if not tool:
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raise ValueError(f"Tool {tool_name} not found")
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tool_ids.append(tool.id)
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# add passed in `tool_ids`
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for tool_id in agent_create.tool_ids or []:
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if tool_id not in tool_ids:
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tool = self.tool_manager.get_tool_by_id(tool_id=tool_id, actor=actor)
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if tool:
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tool_ids.append(tool.id)
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tool_names.append(tool.name)
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else:
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raise ValueError(f"Tool {tool_id} not found")
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# add default tool rules
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tool_rules = agent_create.tool_rules or []
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if agent_create.include_base_tool_rules:
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# apply default tool rules
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for tool_name in tool_names:
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if tool_name == "send_message" or tool_name == "send_message_to_agent_async" or tool_name == "memory_finish_edits":
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tool_rules.append(PydanticTerminalToolRule(tool_name=tool_name))
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elif tool_name in BASE_TOOLS + BASE_MEMORY_TOOLS + BASE_SLEEPTIME_TOOLS:
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tool_rules.append(PydanticContinueToolRule(tool_name=tool_name))
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# if custom rules, check tool rules are valid
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if agent_create.tool_rules:
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check_supports_structured_output(model=agent_create.llm_config.model, tool_rules=agent_create.tool_rules)
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# Create the agent
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agent_state = self._create_agent(
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name=agent_create.name,
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system=system,
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agent_type=agent_create.agent_type,
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llm_config=agent_create.llm_config,
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embedding_config=agent_create.embedding_config,
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block_ids=block_ids,
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tool_ids=tool_ids,
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source_ids=agent_create.source_ids or [],
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tags=agent_create.tags or [],
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identity_ids=agent_create.identity_ids or [],
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description=agent_create.description,
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metadata=agent_create.metadata,
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tool_rules=tool_rules,
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actor=actor,
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project_id=agent_create.project_id,
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template_id=agent_create.template_id,
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base_template_id=agent_create.base_template_id,
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message_buffer_autoclear=agent_create.message_buffer_autoclear,
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enable_sleeptime=agent_create.enable_sleeptime,
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)
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# If there are provided environment variables, add them in
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if agent_create.tool_exec_environment_variables:
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agent_state = self._set_environment_variables(
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agent_id=agent_state.id,
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env_vars=agent_create.tool_exec_environment_variables,
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pydantic_blocks = [PydanticBlock(**b.model_dump(to_orm=True)) for b in agent_create.memory_blocks]
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created_blocks = self.block_manager.batch_create_blocks(
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pydantic_blocks,
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actor=actor,
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)
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block_ids.extend([blk.id for blk in created_blocks])
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return self.append_initial_message_sequence_to_in_context_messages(actor, agent_state, agent_create.initial_message_sequence)
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# tools
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tool_names = set(agent_create.tools or [])
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if agent_create.include_base_tools:
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if agent_create.agent_type == AgentType.sleeptime_agent:
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tool_names |= set(BASE_SLEEPTIME_TOOLS)
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elif agent_create.enable_sleeptime:
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tool_names |= set(BASE_SLEEPTIME_CHAT_TOOLS)
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else:
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tool_names |= set(BASE_TOOLS + BASE_MEMORY_TOOLS)
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if agent_create.include_multi_agent_tools:
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tool_names |= set(MULTI_AGENT_TOOLS)
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supplied_ids = set(agent_create.tool_ids or [])
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source_ids = agent_create.source_ids or []
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identity_ids = agent_create.identity_ids or []
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tag_values = agent_create.tags or []
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with self.session_maker() as session:
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with session.begin():
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name_to_id, id_to_name = self._resolve_tools(
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session,
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tool_names,
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supplied_ids,
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actor.organization_id,
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)
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tool_ids = set(name_to_id.values()) | set(id_to_name.keys())
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tool_names = set(name_to_id.keys()) # now canonical
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tool_rules = list(agent_create.tool_rules or [])
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if agent_create.include_base_tool_rules:
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for tn in tool_names:
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if tn in {"send_message", "send_message_to_agent_async", "memory_finish_edits"}:
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tool_rules.append(TerminalToolRule(tool_name=tn))
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elif tn in (BASE_TOOLS + BASE_MEMORY_TOOLS + BASE_SLEEPTIME_TOOLS):
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tool_rules.append(ContinueToolRule(tool_name=tn))
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if tool_rules:
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check_supports_structured_output(model=agent_create.llm_config.model, tool_rules=tool_rules)
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new_agent = AgentModel(
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name=agent_create.name,
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system=derive_system_message(
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agent_type=agent_create.agent_type,
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enable_sleeptime=agent_create.enable_sleeptime,
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system=agent_create.system,
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),
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agent_type=agent_create.agent_type,
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llm_config=agent_create.llm_config,
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embedding_config=agent_create.embedding_config,
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organization_id=actor.organization_id,
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description=agent_create.description,
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metadata_=agent_create.metadata,
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tool_rules=tool_rules,
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project_id=agent_create.project_id,
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template_id=agent_create.template_id,
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base_template_id=agent_create.base_template_id,
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message_buffer_autoclear=agent_create.message_buffer_autoclear,
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enable_sleeptime=agent_create.enable_sleeptime,
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created_by_id=actor.id,
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last_updated_by_id=actor.id,
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)
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session.add(new_agent)
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session.flush()
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aid = new_agent.id
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self._bulk_insert_pivot(
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session,
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ToolsAgents.__table__,
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[{"agent_id": aid, "tool_id": tid} for tid in tool_ids],
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)
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if block_ids:
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rows = [
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{"agent_id": aid, "block_id": bid, "block_label": lbl}
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for bid, lbl in session.execute(select(BlockModel.id, BlockModel.label).where(BlockModel.id.in_(block_ids))).all()
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]
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self._bulk_insert_pivot(session, BlocksAgents.__table__, rows)
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self._bulk_insert_pivot(
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session,
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SourcesAgents.__table__,
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[{"agent_id": aid, "source_id": sid} for sid in source_ids],
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)
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self._bulk_insert_pivot(
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session,
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AgentsTags.__table__,
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[{"agent_id": aid, "tag": tag} for tag in tag_values],
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)
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self._bulk_insert_pivot(
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session,
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IdentitiesAgents.__table__,
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[{"agent_id": aid, "identity_id": iid} for iid in identity_ids],
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)
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if agent_create.tool_exec_environment_variables:
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env_rows = [
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{
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"agent_id": aid,
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"key": key,
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"value": val,
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"organization_id": actor.organization_id,
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}
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for key, val in agent_create.tool_exec_environment_variables.items()
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]
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session.execute(insert(AgentEnvironmentVariable).values(env_rows))
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# initial message sequence
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init_messages = self._generate_initial_message_sequence(
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actor,
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agent_state=new_agent.to_pydantic(include_relationships={"memory"}),
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supplied_initial_message_sequence=agent_create.initial_message_sequence,
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)
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new_agent.message_ids = [msg.id for msg in init_messages]
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session.refresh(new_agent)
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self.message_manager.create_many_messages(pydantic_msgs=init_messages, actor=actor)
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return new_agent.to_pydantic()
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@enforce_types
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def append_initial_message_sequence_to_in_context_messages(
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self, actor: PydanticUser, agent_state: PydanticAgentState, initial_message_sequence: Optional[List[MessageCreate]] = None
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) -> PydanticAgentState:
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def _generate_initial_message_sequence(
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self, actor: PydanticUser, agent_state: PydanticAgentState, supplied_initial_message_sequence: Optional[List[MessageCreate]] = None
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) -> List[Message]:
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init_messages = initialize_message_sequence(
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agent_state=agent_state, memory_edit_timestamp=get_utc_time(), include_initial_boot_message=True
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)
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if initial_message_sequence is not None:
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if supplied_initial_message_sequence is not None:
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# We always need the system prompt up front
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system_message_obj = PydanticMessage.dict_to_message(
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agent_id=agent_state.id,
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@ -219,7 +304,7 @@ class AgentManager:
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# Don't use anything else in the pregen sequence, instead use the provided sequence
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init_messages = [system_message_obj]
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init_messages.extend(
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package_initial_message_sequence(agent_state.id, initial_message_sequence, agent_state.llm_config.model, actor)
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package_initial_message_sequence(agent_state.id, supplied_initial_message_sequence, agent_state.llm_config.model, actor)
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)
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else:
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init_messages = [
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@ -227,63 +312,14 @@ class AgentManager:
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for msg in init_messages
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]
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return self.append_to_in_context_messages(init_messages, agent_id=agent_state.id, actor=actor)
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return init_messages
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@enforce_types
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def _create_agent(
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self,
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actor: PydanticUser,
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name: str,
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system: str,
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agent_type: AgentType,
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llm_config: LLMConfig,
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embedding_config: EmbeddingConfig,
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block_ids: List[str],
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tool_ids: List[str],
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source_ids: List[str],
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tags: List[str],
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identity_ids: List[str],
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description: Optional[str] = None,
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metadata: Optional[Dict] = None,
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tool_rules: Optional[List[PydanticToolRule]] = None,
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project_id: Optional[str] = None,
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template_id: Optional[str] = None,
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base_template_id: Optional[str] = None,
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message_buffer_autoclear: bool = False,
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enable_sleeptime: Optional[bool] = None,
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def append_initial_message_sequence_to_in_context_messages(
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self, actor: PydanticUser, agent_state: PydanticAgentState, initial_message_sequence: Optional[List[MessageCreate]] = None
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) -> PydanticAgentState:
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"""Create a new agent."""
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with self.session_maker() as session:
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# Prepare the agent data
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data = {
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"name": name,
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"system": system,
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"agent_type": agent_type,
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"llm_config": llm_config,
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"embedding_config": embedding_config,
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"organization_id": actor.organization_id,
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"description": description,
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"metadata_": metadata,
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"tool_rules": tool_rules,
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"project_id": project_id,
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"template_id": template_id,
|
||||
"base_template_id": base_template_id,
|
||||
"message_buffer_autoclear": message_buffer_autoclear,
|
||||
"enable_sleeptime": enable_sleeptime,
|
||||
}
|
||||
|
||||
# Create the new agent using SqlalchemyBase.create
|
||||
new_agent = AgentModel(**data)
|
||||
_process_relationship(session, new_agent, "tools", ToolModel, tool_ids, replace=True)
|
||||
_process_relationship(session, new_agent, "sources", SourceModel, source_ids, replace=True)
|
||||
_process_relationship(session, new_agent, "core_memory", BlockModel, block_ids, replace=True)
|
||||
_process_tags(new_agent, tags, replace=True)
|
||||
_process_relationship(session, new_agent, "identities", IdentityModel, identity_ids, replace=True)
|
||||
|
||||
new_agent.create(session, actor=actor)
|
||||
|
||||
# Convert to PydanticAgentState and return
|
||||
return new_agent.to_pydantic()
|
||||
init_messages = self._generate_initial_message_sequence(actor, agent_state, initial_message_sequence)
|
||||
return self.append_to_in_context_messages(init_messages, agent_id=agent_state.id, actor=actor)
|
||||
|
||||
@enforce_types
|
||||
def update_agent(self, agent_id: str, agent_update: UpdateAgent, actor: PydanticUser) -> PydanticAgentState:
|
||||
|
||||
@ -37,6 +37,29 @@ class BlockManager:
|
||||
block.create(session, actor=actor)
|
||||
return block.to_pydantic()
|
||||
|
||||
@enforce_types
|
||||
def batch_create_blocks(self, blocks: List[PydanticBlock], actor: PydanticUser) -> List[PydanticBlock]:
|
||||
"""
|
||||
Batch-create multiple Blocks in one transaction for better performance.
|
||||
Args:
|
||||
blocks: List of PydanticBlock schemas to create
|
||||
actor: The user performing the operation
|
||||
Returns:
|
||||
List of created PydanticBlock instances (with IDs, timestamps, etc.)
|
||||
"""
|
||||
if not blocks:
|
||||
return []
|
||||
|
||||
with self.session_maker() as session:
|
||||
block_models = [
|
||||
BlockModel(**block.model_dump(to_orm=True, exclude_none=True), organization_id=actor.organization_id) for block in blocks
|
||||
]
|
||||
|
||||
created_models = BlockModel.batch_create(items=block_models, db_session=session, actor=actor)
|
||||
|
||||
# Convert back to Pydantic
|
||||
return [m.to_pydantic() for m in created_models]
|
||||
|
||||
@enforce_types
|
||||
def update_block(self, block_id: str, block_update: BlockUpdate, actor: PydanticUser) -> PydanticBlock:
|
||||
"""Update a block by its ID with the given BlockUpdate object."""
|
||||
|
||||
@ -21,9 +21,11 @@ from letta.schemas.passage import Passage as PydanticPassage
|
||||
from letta.schemas.tool_rule import ToolRule
|
||||
from letta.schemas.user import User
|
||||
from letta.system import get_initial_boot_messages, get_login_event
|
||||
from letta.tracing import trace_method
|
||||
|
||||
|
||||
# Static methods
|
||||
@trace_method
|
||||
def _process_relationship(
|
||||
session, agent: AgentModel, relationship_name: str, model_class, item_ids: List[str], allow_partial=False, replace=True
|
||||
):
|
||||
|
||||
@ -5,6 +5,8 @@ import time
|
||||
import uuid
|
||||
from concurrent.futures import ThreadPoolExecutor, as_completed
|
||||
|
||||
import matplotlib.pyplot as plt
|
||||
import pandas as pd
|
||||
import pytest
|
||||
from dotenv import load_dotenv
|
||||
from letta_client import Letta
|
||||
@ -131,10 +133,9 @@ def agent_state(client, roll_dice_tool, weather_tool, rethink_tool):
|
||||
# --- Load Test --- #
|
||||
|
||||
|
||||
def create_agents_for_user(client, roll_dice_tool, rethink_tool, user_index: int) -> float:
|
||||
"""Create agents and return E2E latency in seconds."""
|
||||
start_time = time.time()
|
||||
|
||||
def create_agents_for_user(client, roll_dice_tool, rethink_tool, user_index: int) -> tuple:
|
||||
"""Create agents and return E2E latencies in seconds along with user index."""
|
||||
# Setup blocks first
|
||||
num_blocks = 10
|
||||
blocks = []
|
||||
for i in range(num_blocks):
|
||||
@ -148,8 +149,12 @@ def create_agents_for_user(client, roll_dice_tool, rethink_tool, user_index: int
|
||||
blocks.append(block)
|
||||
block_ids = [b.id for b in blocks]
|
||||
|
||||
# Now create agents and track individual latencies
|
||||
agent_latencies = []
|
||||
num_agents_per_user = 100
|
||||
for i in range(num_agents_per_user):
|
||||
start_time = time.time()
|
||||
|
||||
client.agents.create(
|
||||
name=f"user{user_index}_agent_{str(uuid.uuid4())[5:]}",
|
||||
tool_ids=[roll_dice_tool.id, rethink_tool.id],
|
||||
@ -163,16 +168,94 @@ def create_agents_for_user(client, roll_dice_tool, rethink_tool, user_index: int
|
||||
block_ids=block_ids,
|
||||
)
|
||||
|
||||
end_time = time.time()
|
||||
return end_time - start_time
|
||||
end_time = time.time()
|
||||
latency = end_time - start_time
|
||||
agent_latencies.append({"user_index": user_index, "agent_index": i, "latency": latency})
|
||||
|
||||
return user_index, agent_latencies
|
||||
|
||||
|
||||
def plot_agent_creation_latencies(latency_data):
|
||||
"""
|
||||
Plot the distribution of agent creation latencies.
|
||||
|
||||
Args:
|
||||
latency_data: List of dictionaries with latency information
|
||||
"""
|
||||
# Convert to DataFrame for easier analysis
|
||||
df = pd.DataFrame(latency_data)
|
||||
|
||||
# Overall latency distribution
|
||||
plt.figure(figsize=(12, 10))
|
||||
|
||||
# Plot 1: Overall latency histogram
|
||||
plt.subplot(2, 2, 1)
|
||||
plt.hist(df["latency"], bins=30, alpha=0.7, color="blue")
|
||||
plt.title(f"Agent Creation Latency Distribution (n={len(df)})")
|
||||
plt.xlabel("Latency (seconds)")
|
||||
plt.ylabel("Frequency")
|
||||
plt.grid(True, alpha=0.3)
|
||||
|
||||
# Plot 2: Latency by user (boxplot)
|
||||
plt.subplot(2, 2, 2)
|
||||
user_groups = df.groupby("user_index")
|
||||
plt.boxplot([group["latency"] for _, group in user_groups])
|
||||
plt.title("Latency Distribution by User")
|
||||
plt.xlabel("User Index")
|
||||
plt.ylabel("Latency (seconds)")
|
||||
plt.xticks(range(1, len(user_groups) + 1), sorted(df["user_index"].unique()))
|
||||
plt.grid(True, alpha=0.3)
|
||||
|
||||
# Plot 3: Time series of latencies
|
||||
plt.subplot(2, 1, 2)
|
||||
for user_idx in sorted(df["user_index"].unique()):
|
||||
user_data = df[df["user_index"] == user_idx]
|
||||
plt.plot(user_data["agent_index"], user_data["latency"], marker=".", linestyle="-", alpha=0.7, label=f"User {user_idx}")
|
||||
|
||||
plt.title("Agent Creation Latency Over Time")
|
||||
plt.xlabel("Agent Creation Sequence")
|
||||
plt.ylabel("Latency (seconds)")
|
||||
plt.legend(loc="upper right")
|
||||
plt.grid(True, alpha=0.3)
|
||||
|
||||
# Add statistics as text
|
||||
stats_text = (
|
||||
f"Mean: {df['latency'].mean():.2f}s\n"
|
||||
f"Median: {df['latency'].median():.2f}s\n"
|
||||
f"Min: {df['latency'].min():.2f}s\n"
|
||||
f"Max: {df['latency'].max():.2f}s\n"
|
||||
f"Std Dev: {df['latency'].std():.2f}s"
|
||||
)
|
||||
plt.figtext(0.02, 0.02, stats_text, fontsize=10, bbox=dict(facecolor="white", alpha=0.8))
|
||||
|
||||
plt.tight_layout()
|
||||
|
||||
# Save the plot
|
||||
plot_file = f"agent_creation_latency_plot_{time.strftime('%Y%m%d_%H%M%S')}.png"
|
||||
plt.savefig(plot_file)
|
||||
plt.close()
|
||||
|
||||
print(f"Latency plot saved to {plot_file}")
|
||||
|
||||
# Return statistics for reporting
|
||||
return {
|
||||
"mean": df["latency"].mean(),
|
||||
"median": df["latency"].median(),
|
||||
"min": df["latency"].min(),
|
||||
"max": df["latency"].max(),
|
||||
"std": df["latency"].std(),
|
||||
"count": len(df),
|
||||
"plot_file": plot_file,
|
||||
}
|
||||
|
||||
|
||||
@pytest.mark.slow
|
||||
def test_parallel_create_many_agents(client, roll_dice_tool, rethink_tool):
|
||||
num_users = 10
|
||||
num_users = 7
|
||||
max_workers = min(num_users, 20)
|
||||
|
||||
latencies = []
|
||||
# To collect all latency data across users
|
||||
all_latency_data = []
|
||||
|
||||
with ThreadPoolExecutor(max_workers=max_workers) as executor:
|
||||
futures = {
|
||||
@ -182,15 +265,30 @@ def test_parallel_create_many_agents(client, roll_dice_tool, rethink_tool):
|
||||
|
||||
with tqdm(total=num_users, desc="Creating agents") as pbar:
|
||||
for future in as_completed(futures):
|
||||
user_idx = futures[future]
|
||||
try:
|
||||
latency = future.result()
|
||||
latencies.append(latency)
|
||||
tqdm.write(f"[User {user_idx}] Agent creation latency: {latency:.2f} seconds")
|
||||
user_idx, user_latencies = future.result()
|
||||
all_latency_data.extend(user_latencies)
|
||||
|
||||
# Calculate and display per-user statistics
|
||||
latencies = [data["latency"] for data in user_latencies]
|
||||
avg_latency = sum(latencies) / len(latencies)
|
||||
tqdm.write(f"[User {user_idx}] Completed {len(latencies)} agents")
|
||||
tqdm.write(f"[User {user_idx}] Avg: {avg_latency:.2f}s, Min: {min(latencies):.2f}s, Max: {max(latencies):.2f}s")
|
||||
except Exception as e:
|
||||
user_idx = futures[future]
|
||||
tqdm.write(f"[User {user_idx}] Error during agent creation: {str(e)}")
|
||||
pbar.update(1)
|
||||
|
||||
if latencies:
|
||||
avg_latency = sum(latencies) / len(latencies)
|
||||
print(f"Average agent creation latency per user: {avg_latency:.2f} seconds")
|
||||
if all_latency_data:
|
||||
# Plot all collected latency data
|
||||
stats = plot_agent_creation_latencies(all_latency_data)
|
||||
|
||||
print("\n===== Agent Creation Latency Statistics =====")
|
||||
print(f"Total agents created: {stats['count']}")
|
||||
print(f"Mean latency: {stats['mean']:.2f} seconds")
|
||||
print(f"Median latency: {stats['median']:.2f} seconds")
|
||||
print(f"Min latency: {stats['min']:.2f} seconds")
|
||||
print(f"Max latency: {stats['max']:.2f} seconds")
|
||||
print(f"Standard deviation: {stats['std']:.2f} seconds")
|
||||
print(f"Latency plot saved to: {stats['plot_file']}")
|
||||
print("============================================")
|
||||
|
||||
326
poetry.lock
generated
326
poetry.lock
generated
@ -963,6 +963,82 @@ files = [
|
||||
test = ["PyYAML", "mock", "pytest"]
|
||||
yaml = ["PyYAML"]
|
||||
|
||||
[[package]]
|
||||
name = "contourpy"
|
||||
version = "1.3.2"
|
||||
description = "Python library for calculating contours of 2D quadrilateral grids"
|
||||
optional = false
|
||||
python-versions = ">=3.10"
|
||||
files = [
|
||||
{file = "contourpy-1.3.2-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:ba38e3f9f330af820c4b27ceb4b9c7feee5fe0493ea53a8720f4792667465934"},
|
||||
{file = "contourpy-1.3.2-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:dc41ba0714aa2968d1f8674ec97504a8f7e334f48eeacebcaa6256213acb0989"},
|
||||
{file = "contourpy-1.3.2-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:9be002b31c558d1ddf1b9b415b162c603405414bacd6932d031c5b5a8b757f0d"},
|
||||
{file = "contourpy-1.3.2-cp310-cp310-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:8d2e74acbcba3bfdb6d9d8384cdc4f9260cae86ed9beee8bd5f54fee49a430b9"},
|
||||
{file = "contourpy-1.3.2-cp310-cp310-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:e259bced5549ac64410162adc973c5e2fb77f04df4a439d00b478e57a0e65512"},
|
||||
{file = "contourpy-1.3.2-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:ad687a04bc802cbe8b9c399c07162a3c35e227e2daccf1668eb1f278cb698631"},
|
||||
{file = "contourpy-1.3.2-cp310-cp310-musllinux_1_2_aarch64.whl", hash = "sha256:cdd22595308f53ef2f891040ab2b93d79192513ffccbd7fe19be7aa773a5e09f"},
|
||||
{file = "contourpy-1.3.2-cp310-cp310-musllinux_1_2_x86_64.whl", hash = "sha256:b4f54d6a2defe9f257327b0f243612dd051cc43825587520b1bf74a31e2f6ef2"},
|
||||
{file = "contourpy-1.3.2-cp310-cp310-win32.whl", hash = "sha256:f939a054192ddc596e031e50bb13b657ce318cf13d264f095ce9db7dc6ae81c0"},
|
||||
{file = "contourpy-1.3.2-cp310-cp310-win_amd64.whl", hash = "sha256:c440093bbc8fc21c637c03bafcbef95ccd963bc6e0514ad887932c18ca2a759a"},
|
||||
{file = "contourpy-1.3.2-cp311-cp311-macosx_10_9_x86_64.whl", hash = "sha256:6a37a2fb93d4df3fc4c0e363ea4d16f83195fc09c891bc8ce072b9d084853445"},
|
||||
{file = "contourpy-1.3.2-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:b7cd50c38f500bbcc9b6a46643a40e0913673f869315d8e70de0438817cb7773"},
|
||||
{file = "contourpy-1.3.2-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:d6658ccc7251a4433eebd89ed2672c2ed96fba367fd25ca9512aa92a4b46c4f1"},
|
||||
{file = "contourpy-1.3.2-cp311-cp311-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:70771a461aaeb335df14deb6c97439973d253ae70660ca085eec25241137ef43"},
|
||||
{file = "contourpy-1.3.2-cp311-cp311-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:65a887a6e8c4cd0897507d814b14c54a8c2e2aa4ac9f7686292f9769fcf9a6ab"},
|
||||
{file = "contourpy-1.3.2-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:3859783aefa2b8355697f16642695a5b9792e7a46ab86da1118a4a23a51a33d7"},
|
||||
{file = "contourpy-1.3.2-cp311-cp311-musllinux_1_2_aarch64.whl", hash = "sha256:eab0f6db315fa4d70f1d8ab514e527f0366ec021ff853d7ed6a2d33605cf4b83"},
|
||||
{file = "contourpy-1.3.2-cp311-cp311-musllinux_1_2_x86_64.whl", hash = "sha256:d91a3ccc7fea94ca0acab82ceb77f396d50a1f67412efe4c526f5d20264e6ecd"},
|
||||
{file = "contourpy-1.3.2-cp311-cp311-win32.whl", hash = "sha256:1c48188778d4d2f3d48e4643fb15d8608b1d01e4b4d6b0548d9b336c28fc9b6f"},
|
||||
{file = "contourpy-1.3.2-cp311-cp311-win_amd64.whl", hash = "sha256:5ebac872ba09cb8f2131c46b8739a7ff71de28a24c869bcad554477eb089a878"},
|
||||
{file = "contourpy-1.3.2-cp312-cp312-macosx_10_13_x86_64.whl", hash = "sha256:4caf2bcd2969402bf77edc4cb6034c7dd7c0803213b3523f111eb7460a51b8d2"},
|
||||
{file = "contourpy-1.3.2-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:82199cb78276249796419fe36b7386bd8d2cc3f28b3bc19fe2454fe2e26c4c15"},
|
||||
{file = "contourpy-1.3.2-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:106fab697af11456fcba3e352ad50effe493a90f893fca6c2ca5c033820cea92"},
|
||||
{file = "contourpy-1.3.2-cp312-cp312-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:d14f12932a8d620e307f715857107b1d1845cc44fdb5da2bc8e850f5ceba9f87"},
|
||||
{file = "contourpy-1.3.2-cp312-cp312-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:532fd26e715560721bb0d5fc7610fce279b3699b018600ab999d1be895b09415"},
|
||||
{file = "contourpy-1.3.2-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:f26b383144cf2d2c29f01a1e8170f50dacf0eac02d64139dcd709a8ac4eb3cfe"},
|
||||
{file = "contourpy-1.3.2-cp312-cp312-musllinux_1_2_aarch64.whl", hash = "sha256:c49f73e61f1f774650a55d221803b101d966ca0c5a2d6d5e4320ec3997489441"},
|
||||
{file = "contourpy-1.3.2-cp312-cp312-musllinux_1_2_x86_64.whl", hash = "sha256:3d80b2c0300583228ac98d0a927a1ba6a2ba6b8a742463c564f1d419ee5b211e"},
|
||||
{file = "contourpy-1.3.2-cp312-cp312-win32.whl", hash = "sha256:90df94c89a91b7362e1142cbee7568f86514412ab8a2c0d0fca72d7e91b62912"},
|
||||
{file = "contourpy-1.3.2-cp312-cp312-win_amd64.whl", hash = "sha256:8c942a01d9163e2e5cfb05cb66110121b8d07ad438a17f9e766317bcb62abf73"},
|
||||
{file = "contourpy-1.3.2-cp313-cp313-macosx_10_13_x86_64.whl", hash = "sha256:de39db2604ae755316cb5967728f4bea92685884b1e767b7c24e983ef5f771cb"},
|
||||
{file = "contourpy-1.3.2-cp313-cp313-macosx_11_0_arm64.whl", hash = "sha256:3f9e896f447c5c8618f1edb2bafa9a4030f22a575ec418ad70611450720b5b08"},
|
||||
{file = "contourpy-1.3.2-cp313-cp313-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:71e2bd4a1c4188f5c2b8d274da78faab884b59df20df63c34f74aa1813c4427c"},
|
||||
{file = "contourpy-1.3.2-cp313-cp313-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:de425af81b6cea33101ae95ece1f696af39446db9682a0b56daaa48cfc29f38f"},
|
||||
{file = "contourpy-1.3.2-cp313-cp313-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:977e98a0e0480d3fe292246417239d2d45435904afd6d7332d8455981c408b85"},
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||||
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||||
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||||
|
||||
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|
||||
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||||
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||||
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||||
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||||
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||||
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||||
[[package]]
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
[[package]]
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||||
test = ["ipykernel", "pre-commit", "pytest (<8)", "pytest-cov", "pytest-timeout"]
|
||||
|
||||
[[package]]
|
||||
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|
||||
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|
||||
description = "A fast implementation of the Cassowary constraint solver"
|
||||
optional = false
|
||||
python-versions = ">=3.10"
|
||||
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||||
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|
||||
|
||||
[[package]]
|
||||
name = "langchain"
|
||||
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|
||||
@ -3242,6 +3495,63 @@ dev = ["marshmallow-sqlalchemy[tests]", "pre-commit (>=3.5,<5.0)", "tox"]
|
||||
docs = ["furo (==2024.8.6)", "sphinx (==8.2.3)", "sphinx-copybutton (==0.5.2)", "sphinx-design (==0.6.1)", "sphinx-issues (==5.0.0)", "sphinxext-opengraph (==0.10.0)"]
|
||||
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|
||||
|
||||
[[package]]
|
||||
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|
||||
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|
||||
description = "Python plotting package"
|
||||
optional = false
|
||||
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|
||||
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||||
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|
||||
{file = "matplotlib-3.10.1-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:0721a3fd3d5756ed593220a8b86808a36c5031fce489adb5b31ee6dbb47dd5b2"},
|
||||
{file = "matplotlib-3.10.1-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:d0673b4b8f131890eb3a1ad058d6e065fb3c6e71f160089b65f8515373394698"},
|
||||
{file = "matplotlib-3.10.1-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:8e875b95ac59a7908978fe307ecdbdd9a26af7fa0f33f474a27fcf8c99f64a19"},
|
||||
{file = "matplotlib-3.10.1-cp310-cp310-musllinux_1_2_x86_64.whl", hash = "sha256:2589659ea30726284c6c91037216f64a506a9822f8e50592d48ac16a2f29e044"},
|
||||
{file = "matplotlib-3.10.1-cp310-cp310-win_amd64.whl", hash = "sha256:a97ff127f295817bc34517255c9db6e71de8eddaab7f837b7d341dee9f2f587f"},
|
||||
{file = "matplotlib-3.10.1-cp311-cp311-macosx_10_12_x86_64.whl", hash = "sha256:057206ff2d6ab82ff3e94ebd94463d084760ca682ed5f150817b859372ec4401"},
|
||||
{file = "matplotlib-3.10.1-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:a144867dd6bf8ba8cb5fc81a158b645037e11b3e5cf8a50bd5f9917cb863adfe"},
|
||||
{file = "matplotlib-3.10.1-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:56c5d9fcd9879aa8040f196a235e2dcbdf7dd03ab5b07c0696f80bc6cf04bedd"},
|
||||
{file = "matplotlib-3.10.1-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:0f69dc9713e4ad2fb21a1c30e37bd445d496524257dfda40ff4a8efb3604ab5c"},
|
||||
{file = "matplotlib-3.10.1-cp311-cp311-musllinux_1_2_x86_64.whl", hash = "sha256:4c59af3e8aca75d7744b68e8e78a669e91ccbcf1ac35d0102a7b1b46883f1dd7"},
|
||||
{file = "matplotlib-3.10.1-cp311-cp311-win_amd64.whl", hash = "sha256:11b65088c6f3dae784bc72e8d039a2580186285f87448babb9ddb2ad0082993a"},
|
||||
{file = "matplotlib-3.10.1-cp312-cp312-macosx_10_13_x86_64.whl", hash = "sha256:66e907a06e68cb6cfd652c193311d61a12b54f56809cafbed9736ce5ad92f107"},
|
||||
{file = "matplotlib-3.10.1-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:e9b4bb156abb8fa5e5b2b460196f7db7264fc6d62678c03457979e7d5254b7be"},
|
||||
{file = "matplotlib-3.10.1-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:1985ad3d97f51307a2cbfc801a930f120def19ba22864182dacef55277102ba6"},
|
||||
{file = "matplotlib-3.10.1-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:c96f2c2f825d1257e437a1482c5a2cf4fee15db4261bd6fc0750f81ba2b4ba3d"},
|
||||
{file = "matplotlib-3.10.1-cp312-cp312-musllinux_1_2_x86_64.whl", hash = "sha256:35e87384ee9e488d8dd5a2dd7baf471178d38b90618d8ea147aced4ab59c9bea"},
|
||||
{file = "matplotlib-3.10.1-cp312-cp312-win_amd64.whl", hash = "sha256:cfd414bce89cc78a7e1d25202e979b3f1af799e416010a20ab2b5ebb3a02425c"},
|
||||
{file = "matplotlib-3.10.1-cp313-cp313-macosx_10_13_x86_64.whl", hash = "sha256:c42eee41e1b60fd83ee3292ed83a97a5f2a8239b10c26715d8a6172226988d7b"},
|
||||
{file = "matplotlib-3.10.1-cp313-cp313-macosx_11_0_arm64.whl", hash = "sha256:4f0647b17b667ae745c13721602b540f7aadb2a32c5b96e924cd4fea5dcb90f1"},
|
||||
{file = "matplotlib-3.10.1-cp313-cp313-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:aa3854b5f9473564ef40a41bc922be978fab217776e9ae1545c9b3a5cf2092a3"},
|
||||
{file = "matplotlib-3.10.1-cp313-cp313-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:7e496c01441be4c7d5f96d4e40f7fca06e20dcb40e44c8daa2e740e1757ad9e6"},
|
||||
{file = "matplotlib-3.10.1-cp313-cp313-musllinux_1_2_x86_64.whl", hash = "sha256:5d45d3f5245be5b469843450617dcad9af75ca50568acf59997bed9311131a0b"},
|
||||
{file = "matplotlib-3.10.1-cp313-cp313-win_amd64.whl", hash = "sha256:8e8e25b1209161d20dfe93037c8a7f7ca796ec9aa326e6e4588d8c4a5dd1e473"},
|
||||
{file = "matplotlib-3.10.1-cp313-cp313t-macosx_10_13_x86_64.whl", hash = "sha256:19b06241ad89c3ae9469e07d77efa87041eac65d78df4fcf9cac318028009b01"},
|
||||
{file = "matplotlib-3.10.1-cp313-cp313t-macosx_11_0_arm64.whl", hash = "sha256:01e63101ebb3014e6e9f80d9cf9ee361a8599ddca2c3e166c563628b39305dbb"},
|
||||
{file = "matplotlib-3.10.1-cp313-cp313t-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:3f06bad951eea6422ac4e8bdebcf3a70c59ea0a03338c5d2b109f57b64eb3972"},
|
||||
{file = "matplotlib-3.10.1-cp313-cp313t-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:a3dfb036f34873b46978f55e240cff7a239f6c4409eac62d8145bad3fc6ba5a3"},
|
||||
{file = "matplotlib-3.10.1-cp313-cp313t-musllinux_1_2_x86_64.whl", hash = "sha256:dc6ab14a7ab3b4d813b88ba957fc05c79493a037f54e246162033591e770de6f"},
|
||||
{file = "matplotlib-3.10.1-cp313-cp313t-win_amd64.whl", hash = "sha256:bc411ebd5889a78dabbc457b3fa153203e22248bfa6eedc6797be5df0164dbf9"},
|
||||
{file = "matplotlib-3.10.1-pp310-pypy310_pp73-macosx_10_15_x86_64.whl", hash = "sha256:648406f1899f9a818cef8c0231b44dcfc4ff36f167101c3fd1c9151f24220fdc"},
|
||||
{file = "matplotlib-3.10.1-pp310-pypy310_pp73-macosx_11_0_arm64.whl", hash = "sha256:02582304e352f40520727984a5a18f37e8187861f954fea9be7ef06569cf85b4"},
|
||||
{file = "matplotlib-3.10.1-pp310-pypy310_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:d3809916157ba871bcdd33d3493acd7fe3037db5daa917ca6e77975a94cef779"},
|
||||
{file = "matplotlib-3.10.1.tar.gz", hash = "sha256:e8d2d0e3881b129268585bf4765ad3ee73a4591d77b9a18c214ac7e3a79fb2ba"},
|
||||
]
|
||||
|
||||
[package.dependencies]
|
||||
contourpy = ">=1.0.1"
|
||||
cycler = ">=0.10"
|
||||
fonttools = ">=4.22.0"
|
||||
kiwisolver = ">=1.3.1"
|
||||
numpy = ">=1.23"
|
||||
packaging = ">=20.0"
|
||||
pillow = ">=8"
|
||||
pyparsing = ">=2.3.1"
|
||||
python-dateutil = ">=2.7"
|
||||
|
||||
[package.extras]
|
||||
dev = ["meson-python (>=0.13.1,<0.17.0)", "pybind11 (>=2.13.2,!=2.13.3)", "setuptools (>=64)", "setuptools_scm (>=7)"]
|
||||
|
||||
[[package]]
|
||||
name = "matplotlib-inline"
|
||||
version = "0.1.7"
|
||||
@ -4793,6 +5103,20 @@ cffi = ">=1.4.1"
|
||||
docs = ["sphinx (>=1.6.5)", "sphinx-rtd-theme"]
|
||||
tests = ["hypothesis (>=3.27.0)", "pytest (>=3.2.1,!=3.3.0)"]
|
||||
|
||||
[[package]]
|
||||
name = "pyparsing"
|
||||
version = "3.2.3"
|
||||
description = "pyparsing module - Classes and methods to define and execute parsing grammars"
|
||||
optional = false
|
||||
python-versions = ">=3.9"
|
||||
files = [
|
||||
{file = "pyparsing-3.2.3-py3-none-any.whl", hash = "sha256:a749938e02d6fd0b59b356ca504a24982314bb090c383e3cf201c95ef7e2bfcf"},
|
||||
{file = "pyparsing-3.2.3.tar.gz", hash = "sha256:b9c13f1ab8b3b542f72e28f634bad4de758ab3ce4546e4301970ad6fa77c38be"},
|
||||
]
|
||||
|
||||
[package.extras]
|
||||
diagrams = ["jinja2", "railroad-diagrams"]
|
||||
|
||||
[[package]]
|
||||
name = "pypdf"
|
||||
version = "5.4.0"
|
||||
@ -6846,4 +7170,4 @@ tests = ["wikipedia"]
|
||||
[metadata]
|
||||
lock-version = "2.0"
|
||||
python-versions = "<3.14,>=3.10"
|
||||
content-hash = "16f6a0c089d3eeca4107a9191201138340570cd40d52ca21a827ac0189fbf15d"
|
||||
content-hash = "5fd927f0519033fb00c3769ab81d682019e532697a374457c50faf4f75bdca02"
|
||||
|
||||
@ -90,6 +90,7 @@ mcp = "^1.3.0"
|
||||
firecrawl-py = "^1.15.0"
|
||||
apscheduler = "^3.11.0"
|
||||
aiomultiprocess = "^0.9.1"
|
||||
matplotlib = "^3.10.1"
|
||||
|
||||
|
||||
[tool.poetry.extras]
|
||||
|
||||
@ -435,7 +435,7 @@ def sarah_agent(server: SyncServer, default_user, default_organization):
|
||||
agent_create=CreateAgent(
|
||||
name="sarah_agent",
|
||||
memory_blocks=[],
|
||||
llm_config=LLMConfig.default_config("gpt-4"),
|
||||
llm_config=LLMConfig.default_config("gpt-4o-mini"),
|
||||
embedding_config=EmbeddingConfig.default_config(provider="openai"),
|
||||
include_base_tools=False,
|
||||
),
|
||||
@ -451,7 +451,7 @@ def charles_agent(server: SyncServer, default_user, default_organization):
|
||||
agent_create=CreateAgent(
|
||||
name="charles_agent",
|
||||
memory_blocks=[CreateBlock(label="human", value="Charles"), CreateBlock(label="persona", value="I am a helpful assistant")],
|
||||
llm_config=LLMConfig.default_config("gpt-4"),
|
||||
llm_config=LLMConfig.default_config("gpt-4o-mini"),
|
||||
embedding_config=EmbeddingConfig.default_config(provider="openai"),
|
||||
include_base_tools=False,
|
||||
),
|
||||
@ -466,7 +466,7 @@ def comprehensive_test_agent_fixture(server: SyncServer, default_user, print_too
|
||||
create_agent_request = CreateAgent(
|
||||
system="test system",
|
||||
memory_blocks=memory_blocks,
|
||||
llm_config=LLMConfig.default_config("gpt-4"),
|
||||
llm_config=LLMConfig.default_config("gpt-4o-mini"),
|
||||
embedding_config=EmbeddingConfig.default_config(provider="openai"),
|
||||
block_ids=[default_block.id],
|
||||
tool_ids=[print_tool.id],
|
||||
@ -586,7 +586,7 @@ def agent_with_tags(server: SyncServer, default_user):
|
||||
|
||||
@pytest.fixture
|
||||
def dummy_llm_config() -> LLMConfig:
|
||||
return LLMConfig.default_config("gpt-4")
|
||||
return LLMConfig.default_config("gpt-4o-mini")
|
||||
|
||||
|
||||
@pytest.fixture
|
||||
@ -655,7 +655,7 @@ def test_create_agent_passed_in_initial_messages(server: SyncServer, default_use
|
||||
create_agent_request = CreateAgent(
|
||||
system="test system",
|
||||
memory_blocks=memory_blocks,
|
||||
llm_config=LLMConfig.default_config("gpt-4"),
|
||||
llm_config=LLMConfig.default_config("gpt-4o-mini"),
|
||||
embedding_config=EmbeddingConfig.default_config(provider="openai"),
|
||||
block_ids=[default_block.id],
|
||||
tags=["a", "b"],
|
||||
@ -669,6 +669,7 @@ def test_create_agent_passed_in_initial_messages(server: SyncServer, default_use
|
||||
)
|
||||
assert server.message_manager.size(agent_id=agent_state.id, actor=default_user) == 2
|
||||
init_messages = server.agent_manager.get_in_context_messages(agent_id=agent_state.id, actor=default_user)
|
||||
|
||||
# Check that the system appears in the first initial message
|
||||
assert create_agent_request.system in init_messages[0].content[0].text
|
||||
assert create_agent_request.memory_blocks[0].value in init_messages[0].content[0].text
|
||||
@ -682,7 +683,7 @@ def test_create_agent_default_initial_message(server: SyncServer, default_user,
|
||||
create_agent_request = CreateAgent(
|
||||
system="test system",
|
||||
memory_blocks=memory_blocks,
|
||||
llm_config=LLMConfig.default_config("gpt-4"),
|
||||
llm_config=LLMConfig.default_config("gpt-4o-mini"),
|
||||
embedding_config=EmbeddingConfig.default_config(provider="openai"),
|
||||
block_ids=[default_block.id],
|
||||
tags=["a", "b"],
|
||||
@ -710,7 +711,7 @@ def test_create_agent_with_json_in_system_message(server: SyncServer, default_us
|
||||
)
|
||||
create_agent_request = CreateAgent(
|
||||
system=system_prompt,
|
||||
llm_config=LLMConfig.default_config("gpt-4"),
|
||||
llm_config=LLMConfig.default_config("gpt-4o-mini"),
|
||||
embedding_config=EmbeddingConfig.default_config(provider="openai"),
|
||||
block_ids=[default_block.id],
|
||||
tags=["a", "b"],
|
||||
@ -858,7 +859,7 @@ def test_list_agents_ascending(server: SyncServer, default_user):
|
||||
agent1 = server.agent_manager.create_agent(
|
||||
agent_create=CreateAgent(
|
||||
name="agent_oldest",
|
||||
llm_config=LLMConfig.default_config("gpt-4"),
|
||||
llm_config=LLMConfig.default_config("gpt-4o-mini"),
|
||||
embedding_config=EmbeddingConfig.default_config(provider="openai"),
|
||||
memory_blocks=[],
|
||||
include_base_tools=False,
|
||||
@ -872,7 +873,7 @@ def test_list_agents_ascending(server: SyncServer, default_user):
|
||||
agent2 = server.agent_manager.create_agent(
|
||||
agent_create=CreateAgent(
|
||||
name="agent_newest",
|
||||
llm_config=LLMConfig.default_config("gpt-4"),
|
||||
llm_config=LLMConfig.default_config("gpt-4o-mini"),
|
||||
embedding_config=EmbeddingConfig.default_config(provider="openai"),
|
||||
memory_blocks=[],
|
||||
include_base_tools=False,
|
||||
@ -890,7 +891,7 @@ def test_list_agents_descending(server: SyncServer, default_user):
|
||||
agent1 = server.agent_manager.create_agent(
|
||||
agent_create=CreateAgent(
|
||||
name="agent_oldest",
|
||||
llm_config=LLMConfig.default_config("gpt-4"),
|
||||
llm_config=LLMConfig.default_config("gpt-4o-mini"),
|
||||
embedding_config=EmbeddingConfig.default_config(provider="openai"),
|
||||
memory_blocks=[],
|
||||
include_base_tools=False,
|
||||
@ -904,7 +905,7 @@ def test_list_agents_descending(server: SyncServer, default_user):
|
||||
agent2 = server.agent_manager.create_agent(
|
||||
agent_create=CreateAgent(
|
||||
name="agent_newest",
|
||||
llm_config=LLMConfig.default_config("gpt-4"),
|
||||
llm_config=LLMConfig.default_config("gpt-4o-mini"),
|
||||
embedding_config=EmbeddingConfig.default_config(provider="openai"),
|
||||
memory_blocks=[],
|
||||
include_base_tools=False,
|
||||
@ -927,7 +928,7 @@ def test_list_agents_ordering_and_pagination(server: SyncServer, default_user):
|
||||
agent_create=CreateAgent(
|
||||
name=name,
|
||||
memory_blocks=[],
|
||||
llm_config=LLMConfig.default_config("gpt-4"),
|
||||
llm_config=LLMConfig.default_config("gpt-4o-mini"),
|
||||
embedding_config=EmbeddingConfig.default_config(provider="openai"),
|
||||
include_base_tools=False,
|
||||
),
|
||||
@ -1288,7 +1289,7 @@ def test_list_agents_by_tags_pagination(server: SyncServer, default_user, defaul
|
||||
agent_create=CreateAgent(
|
||||
name="agent1",
|
||||
tags=["pagination_test", "tag1"],
|
||||
llm_config=LLMConfig.default_config("gpt-4"),
|
||||
llm_config=LLMConfig.default_config("gpt-4o-mini"),
|
||||
embedding_config=EmbeddingConfig.default_config(provider="openai"),
|
||||
memory_blocks=[],
|
||||
include_base_tools=False,
|
||||
@ -1304,7 +1305,7 @@ def test_list_agents_by_tags_pagination(server: SyncServer, default_user, defaul
|
||||
agent_create=CreateAgent(
|
||||
name="agent2",
|
||||
tags=["pagination_test", "tag2"],
|
||||
llm_config=LLMConfig.default_config("gpt-4"),
|
||||
llm_config=LLMConfig.default_config("gpt-4o-mini"),
|
||||
embedding_config=EmbeddingConfig.default_config(provider="openai"),
|
||||
memory_blocks=[],
|
||||
include_base_tools=False,
|
||||
@ -1346,7 +1347,7 @@ def test_list_agents_query_text_pagination(server: SyncServer, default_user, def
|
||||
name="Search Agent One",
|
||||
memory_blocks=[],
|
||||
description="This is a search agent for testing",
|
||||
llm_config=LLMConfig.default_config("gpt-4"),
|
||||
llm_config=LLMConfig.default_config("gpt-4o-mini"),
|
||||
embedding_config=EmbeddingConfig.default_config(provider="openai"),
|
||||
include_base_tools=False,
|
||||
),
|
||||
@ -1358,7 +1359,7 @@ def test_list_agents_query_text_pagination(server: SyncServer, default_user, def
|
||||
name="Search Agent Two",
|
||||
memory_blocks=[],
|
||||
description="Another search agent for testing",
|
||||
llm_config=LLMConfig.default_config("gpt-4"),
|
||||
llm_config=LLMConfig.default_config("gpt-4o-mini"),
|
||||
embedding_config=EmbeddingConfig.default_config(provider="openai"),
|
||||
include_base_tools=False,
|
||||
),
|
||||
@ -1370,7 +1371,7 @@ def test_list_agents_query_text_pagination(server: SyncServer, default_user, def
|
||||
name="Different Agent",
|
||||
memory_blocks=[],
|
||||
description="This is a different agent",
|
||||
llm_config=LLMConfig.default_config("gpt-4"),
|
||||
llm_config=LLMConfig.default_config("gpt-4o-mini"),
|
||||
embedding_config=EmbeddingConfig.default_config(provider="openai"),
|
||||
include_base_tools=False,
|
||||
),
|
||||
@ -1700,7 +1701,7 @@ def test_refresh_memory(server: SyncServer, default_user):
|
||||
agent = server.agent_manager.create_agent(
|
||||
CreateAgent(
|
||||
name="test",
|
||||
llm_config=LLMConfig.default_config("gpt-4"),
|
||||
llm_config=LLMConfig.default_config("gpt-4o-mini"),
|
||||
embedding_config=EmbeddingConfig.default_config(provider="openai"),
|
||||
include_base_tools=False,
|
||||
),
|
||||
@ -2706,6 +2707,36 @@ def test_get_agents_for_block(server: SyncServer, sarah_agent, charles_agent, de
|
||||
assert charles_agent.id in agent_state_ids
|
||||
|
||||
|
||||
def test_batch_create_multiple_blocks(server: SyncServer, default_user):
|
||||
block_manager = BlockManager()
|
||||
num_blocks = 10
|
||||
|
||||
# Prepare distinct blocks
|
||||
blocks_to_create = [PydanticBlock(label=f"batch_label_{i}", value=f"batch_value_{i}") for i in range(num_blocks)]
|
||||
|
||||
# Create the blocks
|
||||
created_blocks = block_manager.batch_create_blocks(blocks_to_create, actor=default_user)
|
||||
assert len(created_blocks) == num_blocks
|
||||
|
||||
# Map created blocks by label for lookup
|
||||
created_by_label = {blk.label: blk for blk in created_blocks}
|
||||
|
||||
# Assert all blocks were created correctly
|
||||
for i in range(num_blocks):
|
||||
label = f"batch_label_{i}"
|
||||
value = f"batch_value_{i}"
|
||||
assert label in created_by_label, f"Missing label: {label}"
|
||||
blk = created_by_label[label]
|
||||
assert blk.value == value
|
||||
assert blk.organization_id == default_user.organization_id
|
||||
assert blk.id is not None
|
||||
|
||||
# Confirm all created blocks exist in the full list from get_blocks
|
||||
all_labels = {blk.label for blk in block_manager.get_blocks(actor=default_user)}
|
||||
expected_labels = {f"batch_label_{i}" for i in range(num_blocks)}
|
||||
assert expected_labels.issubset(all_labels)
|
||||
|
||||
|
||||
# ======================================================================================================================
|
||||
# Block Manager Tests - Checkpointing
|
||||
# ======================================================================================================================
|
||||
@ -3401,7 +3432,7 @@ def test_get_set_agents_for_identities(server: SyncServer, sarah_agent, charles_
|
||||
agent_with_identity = server.create_agent(
|
||||
CreateAgent(
|
||||
memory_blocks=[],
|
||||
llm_config=LLMConfig.default_config("gpt-4"),
|
||||
llm_config=LLMConfig.default_config("gpt-4o-mini"),
|
||||
embedding_config=EmbeddingConfig.default_config(provider="openai"),
|
||||
identity_ids=[identity.id],
|
||||
include_base_tools=False,
|
||||
@ -3411,7 +3442,7 @@ def test_get_set_agents_for_identities(server: SyncServer, sarah_agent, charles_
|
||||
agent_without_identity = server.create_agent(
|
||||
CreateAgent(
|
||||
memory_blocks=[],
|
||||
llm_config=LLMConfig.default_config("gpt-4"),
|
||||
llm_config=LLMConfig.default_config("gpt-4o-mini"),
|
||||
embedding_config=EmbeddingConfig.default_config(provider="openai"),
|
||||
include_base_tools=False,
|
||||
),
|
||||
@ -4618,7 +4649,7 @@ def test_job_usage_stats_add_and_get(server: SyncServer, sarah_agent, default_jo
|
||||
step_manager.log_step(
|
||||
agent_id=sarah_agent.id,
|
||||
provider_name="openai",
|
||||
model="gpt-4",
|
||||
model="gpt-4o-mini",
|
||||
model_endpoint="https://api.openai.com/v1",
|
||||
context_window_limit=8192,
|
||||
job_id=default_job.id,
|
||||
@ -4669,7 +4700,7 @@ def test_job_usage_stats_add_multiple(server: SyncServer, sarah_agent, default_j
|
||||
step_manager.log_step(
|
||||
agent_id=sarah_agent.id,
|
||||
provider_name="openai",
|
||||
model="gpt-4",
|
||||
model="gpt-4o-mini",
|
||||
model_endpoint="https://api.openai.com/v1",
|
||||
context_window_limit=8192,
|
||||
job_id=default_job.id,
|
||||
@ -4685,7 +4716,7 @@ def test_job_usage_stats_add_multiple(server: SyncServer, sarah_agent, default_j
|
||||
step_manager.log_step(
|
||||
agent_id=sarah_agent.id,
|
||||
provider_name="openai",
|
||||
model="gpt-4",
|
||||
model="gpt-4o-mini",
|
||||
model_endpoint="https://api.openai.com/v1",
|
||||
context_window_limit=8192,
|
||||
job_id=default_job.id,
|
||||
@ -4731,7 +4762,7 @@ def test_job_usage_stats_add_nonexistent_job(server: SyncServer, sarah_agent, de
|
||||
step_manager.log_step(
|
||||
agent_id=sarah_agent.id,
|
||||
provider_name="openai",
|
||||
model="gpt-4",
|
||||
model="gpt-4o-mini",
|
||||
model_endpoint="https://api.openai.com/v1",
|
||||
context_window_limit=8192,
|
||||
job_id="nonexistent_job",
|
||||
@ -4757,7 +4788,7 @@ def test_list_tags(server: SyncServer, default_user, default_organization):
|
||||
agent_create=CreateAgent(
|
||||
name="tag_agent_" + str(i),
|
||||
memory_blocks=[],
|
||||
llm_config=LLMConfig.default_config("gpt-4"),
|
||||
llm_config=LLMConfig.default_config("gpt-4o-mini"),
|
||||
embedding_config=EmbeddingConfig.default_config(provider="openai"),
|
||||
tags=tags[i : i + 3], # Each agent gets 3 consecutive tags
|
||||
include_base_tools=False,
|
||||
|
||||
Loading…
Reference in New Issue
Block a user