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feat(asyncify): migrate batch sbox methods (#2396)

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cthomas
2025-05-23 18:40:30 -07:00
committed by GitHub
parent b9d79f5229
commit 2407cb13b9
3 changed files with 10 additions and 10 deletions
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16
letta/agents/letta_agent_batch.py
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@@ -248,7 +248,7 @@ class LettaAgentBatch(BaseAgent):
await self._mark_steps_complete_async(llm_batch_id, ctx.agent_ids)
log_event(name="prepare_next")
next_reqs, next_step_state = self._prepare_next_iteration(exec_results, ctx, msg_map)
next_reqs, next_step_state = await self._prepare_next_iteration_async(exec_results, ctx, msg_map)
if len(next_reqs) == 0:
await self.job_manager.update_job_by_id_async(
job_id=letta_batch_id, job_update=JobUpdate(status=JobStatus.completed), actor=self.actor
@@ -389,15 +389,15 @@ class LettaAgentBatch(BaseAgent):
if updates:
await self.batch_manager.bulk_update_llm_batch_items_request_status_by_agent_async(updates=updates)
def _build_sandbox(self) -> Tuple[SandboxConfig, Dict[str, Any]]:
async def _build_sandbox(self) -> Tuple[SandboxConfig, Dict[str, Any]]:
sbx_type = SandboxType.E2B if tool_settings.e2b_api_key else SandboxType.LOCAL
cfg = self.sandbox_config_manager.get_or_create_default_sandbox_config(sandbox_type=sbx_type, actor=self.actor)
env = self.sandbox_config_manager.get_sandbox_env_vars_as_dict(cfg.id, actor=self.actor, limit=100)
cfg = await self.sandbox_config_manager.get_or_create_default_sandbox_config_async(sandbox_type=sbx_type, actor=self.actor)
env = await self.sandbox_config_manager.get_sandbox_env_vars_as_dict_async(cfg.id, actor=self.actor, limit=100)
return cfg, env
@trace_method
async def _execute_tools(self, ctx: _ResumeContext) -> Sequence[tuple[str, ToolExecutionResult]]:
sbx_cfg, sbx_env = self._build_sandbox()
async def _execute_tools(self, ctx: _ResumeContext) -> Sequence[Tuple[str, Tuple[str, bool]]]:
sbx_cfg, sbx_env = await self._build_sandbox()
rethink_memory_tool_name = "rethink_memory"
tool_params = []
# TODO: This is a special case - we need to think about how to generalize this
@@ -484,7 +484,7 @@ class LettaAgentBatch(BaseAgent):
]
await self.batch_manager.bulk_update_llm_batch_items_step_status_by_agent_async(updates)
def _prepare_next_iteration(
async def _prepare_next_iteration_async(
self,
exec_results: Sequence[Tuple[str, "ToolExecutionResult"]],
ctx: _ResumeContext,
@@ -513,7 +513,7 @@ class LettaAgentBatch(BaseAgent):
for aid, new_msgs in msg_map.items():
ast = ctx.agent_state_map[aid]
if not ast.message_buffer_autoclear:
self.agent_manager.set_in_context_messages(
await self.agent_manager.set_in_context_messages_async(
agent_id=aid,
message_ids=ast.message_ids + [m.id for m in new_msgs],
actor=self.actor,

2
letta/server/rest_api/routers/v1/agents.py
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@@ -638,7 +638,7 @@ async def send_message(
actor = await server.user_manager.get_actor_or_default_async(actor_id=actor_id)
# TODO: This is redundant, remove soon
agent = await server.agent_manager.get_agent_by_id_async(agent_id, actor)
agent_eligible = agent.enable_sleeptime or not agent.multi_agent_group
agent_eligible = (agent.enable_sleeptime or not agent.multi_agent_group) or agent.agent_type == AgentType.sleeptime_agent
experimental_header = request_obj.headers.get("X-EXPERIMENTAL") or "false"
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"]

2
tests/mcp/mcp_config.json
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@@ -1 +1 @@
{}
{}