Add modes self and self_sliding_window for prompt caching (#9372)
* add self compaction method with proper caching (pass in tools, don't refresh sys prompt beforehand) + sliding fallback * updated prompts for self compaction * add tests for self, self_sliding_window modes and w/o refresh messages before compaction * add cache logging to summarization * better handling to prevent agent from continuing convo on self modes * if mode changes via summarize endpoint, will use default prompt for the new mode --------- Co-authored-by: Amy Guan <amy@letta.com>
This commit is contained in:
amysguan
@@ -15,17 +15,14 @@ import pytest
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from letta.agents.letta_agent_v3 import LettaAgentV3
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from letta.config import LettaConfig
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from letta.schemas.agent import CreateAgent, UpdateAgent
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from letta.schemas.block import BlockUpdate, CreateBlock
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from letta.schemas.agent import CreateAgent
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from letta.schemas.embedding_config import EmbeddingConfig
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from letta.schemas.enums import MessageRole
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from letta.schemas.letta_message import EventMessage, SummaryMessage
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from letta.schemas.letta_message_content import TextContent, ToolCallContent, ToolReturnContent
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from letta.schemas.llm_config import LLMConfig
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from letta.schemas.message import Message as PydanticMessage, MessageCreate
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from letta.schemas.run import Run as PydanticRun
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from letta.schemas.message import Message as PydanticMessage
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from letta.server.server import SyncServer
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from letta.services.run_manager import RunManager
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from letta.services.summarizer.summarizer import simple_summary
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from letta.settings import model_settings
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@@ -669,14 +666,24 @@ from unittest.mock import patch
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from letta.services.summarizer.summarizer_config import CompactionSettings
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# Test both summarizer modes: "all" summarizes entire history, "sliding_window" keeps recent messages
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SUMMARIZER_CONFIG_MODES: list[Literal["all", "sliding_window"]] = ["all", "sliding_window"]
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# Test all summarizer modes: "all" summarizes entire history, "sliding_window" keeps recent messages
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SUMMARIZER_CONFIG_MODES: list[Literal["all", "sliding_window", "self_compact_all", "self_compact_sliding_window"]] = [
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"all",
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"sliding_window",
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"self_compact_all",
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"self_compact_sliding_window",
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]
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@pytest.mark.asyncio
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@pytest.mark.parametrize("mode", SUMMARIZER_CONFIG_MODES, ids=SUMMARIZER_CONFIG_MODES)
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@pytest.mark.parametrize("llm_config", TESTED_LLM_CONFIGS, ids=[c.model for c in TESTED_LLM_CONFIGS])
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async def test_summarize_with_mode(server: SyncServer, actor, llm_config: LLMConfig, mode: Literal["all", "sliding_window"]):
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async def test_summarize_with_mode(
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server: SyncServer,
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actor,
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llm_config: LLMConfig,
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mode: Literal["all", "sliding_window", "self_compact_all", "self_compact_sliding_window"],
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):
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"""
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Test summarization with different CompactionSettings modes using LettaAgentV3.
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@@ -746,20 +753,20 @@ async def test_summarize_with_mode(server: SyncServer, actor, llm_config: LLMCon
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print()
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if mode == "all":
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# For "all" mode, V3 keeps:
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if mode == "all" or mode == "self_compact_all":
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# For "all" or "self" mode, V3 keeps:
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# 1. System prompt
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# 2. A single user summary message (system_alert JSON)
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# and no remaining historical messages.
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assert len(result) == 2, f"Expected 2 messages for 'all' mode (system + summary), got {len(result)}"
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assert len(result) == 2, f"Expected 2 messages for {mode} mode (system + summary), got {len(result)}"
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assert result[0].role == MessageRole.system
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assert result[1].role == MessageRole.user
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else:
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# For "sliding_window" mode, result should include:
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# For "sliding_window" or "self_compact_sliding_window" mode, result should include:
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# 1. System prompt
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# 2. User summary message
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# 3+. Recent user/assistant messages inside the window.
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assert len(result) > 2, f"Expected >2 messages for 'sliding_window' mode, got {len(result)}"
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assert len(result) > 2, f"Expected >2 messages for {mode} mode, got {len(result)}"
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assert result[0].role == MessageRole.system
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assert result[1].role == MessageRole.user
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@@ -1195,97 +1202,206 @@ async def test_sliding_window_cutoff_index_does_not_exceed_message_count(server:
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TESTED_LLM_CONFIGS,
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ids=[c.model for c in TESTED_LLM_CONFIGS],
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)
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async def test_large_system_prompt_summarization(server: SyncServer, actor, llm_config: LLMConfig):
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async def test_self_sliding_window_cutoff_index_does_not_exceed_message_count(server: SyncServer, actor, llm_config: LLMConfig):
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"""
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Test edge case of large system prompt / memory blocks.
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Test that the sliding window summarizer correctly calculates cutoff indices.
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This test verifies that summarization handles the case where the system prompt
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and memory blocks are very large, potentially consuming most of the context window.
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The summarizer should gracefully handle this scenario without errors.
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This test verifies the fix for a bug where the cutoff percentage was treated as
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a whole number (10) instead of a decimal (0.10), causing:
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message_cutoff_index = round(10 * 65) = 650
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when there were only 65 messages, resulting in an empty range loop and the error:
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"No assistant message found from indices 650 to 65"
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The fix changed:
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- max(..., 10) -> max(..., 0.10)
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- += 10 -> += 0.10
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- >= 100 -> >= 1.0
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This test uses the real token counter (via create_token_counter) to verify
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the sliding window logic works with actual token counting.
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"""
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from letta.llm_api.llm_client import LLMClient
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from letta.schemas.agent import AgentType
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from letta.services.summarizer.self_summarizer import self_summarize_sliding_window
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from letta.services.summarizer.summarizer_config import CompactionSettings
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from letta.services.telemetry_manager import TelemetryManager
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# Override context window to be small so we trigger summarization
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llm_config.context_window = 10000
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# Create a real summarizer config using the default factory
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# Override sliding_window_percentage to 0.3 for this test
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handle = llm_config.handle or f"{llm_config.model_endpoint_type}/{llm_config.model}"
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summarizer_config = CompactionSettings(model=handle)
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summarizer_config.sliding_window_percentage = 0.3
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# Create agent with large system prompt and memory blocks
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agent_name = f"test_agent_large_system_prompt_{llm_config.model}".replace(".", "_").replace("/", "_")
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agent_create = CreateAgent(
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name=agent_name,
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llm_config=llm_config,
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embedding_config=DEFAULT_EMBEDDING_CONFIG,
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system="SYSTEM PROMPT " * 10000, # Large system prompt
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memory_blocks=[
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CreateBlock(
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label="human",
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limit=200000,
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value="NAME " * 10000, # Large memory block
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# Create 65 messages (similar to the failing case in the bug report)
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# Pattern: system + alternating user/assistant messages
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messages = [
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PydanticMessage(
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role=MessageRole.system,
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content=[TextContent(type="text", text="You are a helpful assistant.")],
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)
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]
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# Add 64 more messages (32 user-assistant pairs)
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for i in range(32):
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messages.append(
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PydanticMessage(
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role=MessageRole.user,
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content=[TextContent(type="text", text=f"User message {i}")],
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)
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)
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messages.append(
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PydanticMessage(
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role=MessageRole.assistant,
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content=[TextContent(type="text", text=f"Assistant response {i}")],
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)
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],
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)
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agent_state = await server.agent_manager.create_agent_async(agent_create, actor=actor)
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# Create a run for the agent using RunManager
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run = PydanticRun(agent_id=agent_state.id)
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run = await RunManager().create_run(pydantic_run=run, actor=actor)
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# Create the agent loop using LettaAgentV3
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agent_loop = LettaAgentV3(agent_state=agent_state, actor=actor)
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# message the agent
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input_message = MessageCreate(role=MessageRole.user, content="Hello")
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# Call step on the agent - may trigger summarization due to large context
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from letta.errors import SystemPromptTokenExceededError
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with pytest.raises(SystemPromptTokenExceededError):
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response = await agent_loop.step(
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input_messages=[input_message],
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run_id=run.id,
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max_steps=3,
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)
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# Repair the agent by shortening the memory blocks and system prompt
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# Update system prompt to a shorter version
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short_system_prompt = "You are a helpful assistant."
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await server.agent_manager.update_agent_async(
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agent_id=agent_state.id,
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agent_update=UpdateAgent(system=short_system_prompt),
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actor=actor,
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)
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assert len(messages) == 65, f"Expected 65 messages, got {len(messages)}"
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# Update memory block to a shorter version
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short_memory_value = "The user's name is Alice."
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await server.agent_manager.modify_block_by_label_async(
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agent_id=agent_state.id,
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block_label="human",
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block_update=BlockUpdate(value=short_memory_value),
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actor=actor,
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)
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# This should NOT raise "No assistant message found from indices 650 to 65"
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# With the fix, message_count_cutoff_percent starts at max(0.7, 0.10) = 0.7
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# So message_cutoff_index = round(0.7 * 65) = 46, which is valid
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try:
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summary, remaining_messages = await self_summarize_sliding_window(
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actor=actor,
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agent_id="agent-test-self-sliding-window",
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agent_llm_config=llm_config,
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telemetry_manager=TelemetryManager(),
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llm_client=LLMClient.create(llm_config),
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agent_type=AgentType.letta_v1_agent,
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messages=messages,
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compaction_settings=summarizer_config,
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timezone="UTC",
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)
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# Reload agent state after repairs
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agent_state = await server.agent_manager.get_agent_by_id_async(agent_id=agent_state.id, actor=actor)
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print("REPAIRED AGENT STATE ======")
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print(agent_state.system)
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print(agent_state.blocks)
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# Verify the summary was generated (actual LLM response)
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assert summary is not None
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assert len(summary) > 0
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# Create a new run for the repaired agent
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run = PydanticRun(agent_id=agent_state.id)
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run = await RunManager().create_run(pydantic_run=run, actor=actor)
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# Verify remaining messages is a valid subset
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assert len(remaining_messages) < len(messages)
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assert len(remaining_messages) > 0
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# Create a new agent loop with the repaired agent state
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agent_loop = LettaAgentV3(agent_state=agent_state, actor=actor)
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print(f"Successfully summarized {len(messages)} messages to {len(remaining_messages)} remaining")
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print(f"Summary: {summary[:200]}..." if len(summary) > 200 else f"Summary: {summary}")
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print(f"Using {llm_config.model_endpoint_type} token counter for model {llm_config.model}")
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# Now the agent should be able to respond without context window errors
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response = await agent_loop.step(
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input_messages=[input_message],
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run_id=run.id,
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max_steps=3,
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)
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except ValueError as e:
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if "No assistant message found from indices" in str(e):
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# Extract the indices from the error message
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import re
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# Verify we got a valid response after repair
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assert response is not None
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assert response.messages is not None
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print(f"Agent successfully responded after repair with {len(response.messages)} messages")
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match = re.search(r"from indices (\d+) to (\d+)", str(e))
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if match:
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start_idx, end_idx = int(match.group(1)), int(match.group(2))
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pytest.fail(
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f"Bug detected: cutoff index ({start_idx}) exceeds message count ({end_idx}). "
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f"This indicates the percentage calculation bug where 10 was used instead of 0.10. "
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f"Error: {e}"
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)
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raise
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### NOTE: removing edge case test where sys prompt is huge for now
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### because we no longer refresh the system prompt before compaction
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### in order to leverage caching (for self compaction)
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# @pytest.mark.asyncio
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# @pytest.mark.parametrize(
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# "llm_config",
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# TESTED_LLM_CONFIGS,
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# ids=[c.model for c in TESTED_LLM_CONFIGS],
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# )
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# async def test_large_system_prompt_summarization(server: SyncServer, actor, llm_config: LLMConfig):
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# """
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# Test edge case of large system prompt / memory blocks.
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# This test verifies that summarization handles the case where the system prompt
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# and memory blocks are very large, potentially consuming most of the context window.
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# The summarizer should gracefully handle this scenario without errors.
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# """
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# # Override context window to be small so we trigger summarization
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# llm_config.context_window = 10000
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# # Create agent with large system prompt and memory blocks
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# agent_name = f"test_agent_large_system_prompt_{llm_config.model}".replace(".", "_").replace("/", "_")
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# agent_create = CreateAgent(
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# name=agent_name,
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# llm_config=llm_config,
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# embedding_config=DEFAULT_EMBEDDING_CONFIG,
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# system="SYSTEM PROMPT " * 10000, # Large system prompt
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# memory_blocks=[
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# CreateBlock(
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# label="human",
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# limit=200000,
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# value="NAME " * 10000, # Large memory block
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# )
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# ],
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# )
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# agent_state = await server.agent_manager.create_agent_async(agent_create, actor=actor)
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# # Create a run for the agent using RunManager
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# run = PydanticRun(agent_id=agent_state.id)
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# run = await RunManager().create_run(pydantic_run=run, actor=actor)
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# # Create the agent loop using LettaAgentV3
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# agent_loop = LettaAgentV3(agent_state=agent_state, actor=actor)
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# # message the agent
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# input_message = MessageCreate(role=MessageRole.user, content="Hello")
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# # Call step on the agent - may trigger summarization due to large context
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# from letta.errors import SystemPromptTokenExceededError
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# with pytest.raises(SystemPromptTokenExceededError):
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# response = await agent_loop.step(
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# input_messages=[input_message],
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# run_id=run.id,
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# max_steps=3,
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# )
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# # Repair the agent by shortening the memory blocks and system prompt
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# # Update system prompt to a shorter version
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# short_system_prompt = "You are a helpful assistant."
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# await server.agent_manager.update_agent_async(
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# agent_id=agent_state.id,
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# agent_update=UpdateAgent(system=short_system_prompt),
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# actor=actor,
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# )
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# # Update memory block to a shorter version
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# short_memory_value = "The user's name is Alice."
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# await server.agent_manager.modify_block_by_label_async(
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# agent_id=agent_state.id,
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# block_label="human",
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# block_update=BlockUpdate(value=short_memory_value),
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# actor=actor,
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# )
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# # Reload agent state after repairs
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# agent_state = await server.agent_manager.get_agent_by_id_async(agent_id=agent_state.id, actor=actor)
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# print("REPAIRED AGENT STATE ======")
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# print(agent_state.system)
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# print(agent_state.blocks)
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# # Create a new run for the repaired agent
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# run = PydanticRun(agent_id=agent_state.id)
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# run = await RunManager().create_run(pydantic_run=run, actor=actor)
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# # Create a new agent loop with the repaired agent state
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# agent_loop = LettaAgentV3(agent_state=agent_state, actor=actor)
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# # Now the agent should be able to respond without context window errors
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# response = await agent_loop.step(
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# input_messages=[input_message],
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# run_id=run.id,
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# max_steps=3,
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# )
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# # Verify we got a valid response after repair
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# assert response is not None
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# assert response.messages is not None
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# print(f"Agent successfully responded after repair with {len(response.messages)} messages")
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# @pytest.mark.asyncio
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@@ -1718,6 +1834,127 @@ async def test_summarize_all(server: SyncServer, actor, llm_config: LLMConfig):
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print(f"Using {llm_config.model_endpoint_type} for model {llm_config.model}")
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@pytest.mark.asyncio
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@pytest.mark.parametrize(
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"llm_config",
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TESTED_LLM_CONFIGS,
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ids=[c.model for c in TESTED_LLM_CONFIGS],
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)
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async def test_summarize_self(server: SyncServer, actor, llm_config: LLMConfig):
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"""
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Test the summarize_all function with real LLM calls.
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This test verifies that the 'all' summarization mode works correctly,
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summarizing the entire conversation into a single summary string.
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"""
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from letta.llm_api.llm_client import LLMClient
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from letta.schemas.agent import AgentType
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from letta.services.summarizer.self_summarizer import self_summarize_all
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from letta.services.summarizer.summarizer_config import CompactionSettings
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from letta.services.telemetry_manager import TelemetryManager
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# Create a summarizer config with "self" mode
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handle = llm_config.handle or f"{llm_config.model_endpoint_type}/{llm_config.model}"
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summarizer_config = CompactionSettings(model=handle)
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summarizer_config.mode = "self"
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# Create test messages - a simple conversation
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messages = [
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PydanticMessage(
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role=MessageRole.system,
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content=[TextContent(type="text", text="You are a helpful assistant.")],
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)
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]
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# Add 10 user-assistant pairs
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for i in range(10):
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messages.append(
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PydanticMessage(
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role=MessageRole.user,
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content=[TextContent(type="text", text=f"User message {i}: What is {i} + {i}?")],
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)
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)
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messages.append(
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PydanticMessage(
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role=MessageRole.assistant,
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content=[TextContent(type="text", text=f"Assistant response {i}: {i} + {i} = {i * 2}.")],
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)
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)
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assert len(messages) == 21, f"Expected 21 messages, got {len(messages)}"
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# Call summarize_all with real LLM
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summary, new_in_context_messages = await self_summarize_all(
|
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actor=actor,
|
||||
agent_id="agent-test-self-sliding-window",
|
||||
agent_llm_config=llm_config,
|
||||
telemetry_manager=TelemetryManager(),
|
||||
llm_client=LLMClient.create(llm_config),
|
||||
agent_type=AgentType.letta_v1_agent,
|
||||
messages=messages,
|
||||
compaction_settings=summarizer_config,
|
||||
timezone="UTC",
|
||||
)
|
||||
|
||||
# Verify the summary was generated
|
||||
assert len(new_in_context_messages) == 1
|
||||
assert summary is not None
|
||||
assert len(summary) > 0
|
||||
assert len(summary) <= 5000 # length should be less than 500 words, give some buffer in test
|
||||
|
||||
print(f"Successfully summarized {len(messages)} messages using 'self' mode")
|
||||
print(f"Summary: {summary[:200]}..." if len(summary) > 200 else f"Summary: {summary}")
|
||||
print(f"Using {llm_config.model_endpoint_type} for model {llm_config.model}")
|
||||
|
||||
|
||||
@pytest.mark.asyncio
|
||||
@pytest.mark.parametrize("llm_config", TESTED_LLM_CONFIGS, ids=[c.model for c in TESTED_LLM_CONFIGS])
|
||||
async def test_self_mode_fallback(server: SyncServer, actor, llm_config: LLMConfig):
|
||||
"""If self summarize fails, it should have proper fallback."""
|
||||
from unittest.mock import AsyncMock, patch
|
||||
|
||||
messages = [
|
||||
PydanticMessage(
|
||||
role=MessageRole.system,
|
||||
content=[TextContent(type="text", text="You are a helpful assistant.")],
|
||||
)
|
||||
]
|
||||
for i in range(10):
|
||||
messages.append(
|
||||
PydanticMessage(
|
||||
role=MessageRole.user,
|
||||
content=[TextContent(type="text", text=f"User message {i}: Test message {i}.")],
|
||||
)
|
||||
)
|
||||
messages.append(
|
||||
PydanticMessage(
|
||||
role=MessageRole.assistant,
|
||||
content=[TextContent(type="text", text=f"Assistant response {i}: Acknowledged message {i}.")],
|
||||
)
|
||||
)
|
||||
|
||||
agent_state, in_context_messages = await create_agent_with_messages(server, actor, llm_config, messages)
|
||||
|
||||
handle = llm_config.handle or f"{llm_config.model_endpoint_type}/{llm_config.model}"
|
||||
agent_state.compaction_settings = CompactionSettings(model=handle, mode="self_compact_all")
|
||||
|
||||
agent_loop = LettaAgentV3(agent_state=agent_state, actor=actor)
|
||||
|
||||
# Mock self_summarize_all to always fail
|
||||
with patch(
|
||||
"letta.services.summarizer.compact.self_summarize_all",
|
||||
new_callable=AsyncMock,
|
||||
side_effect=RuntimeError("Simulated self_summarize_all failure"),
|
||||
):
|
||||
summary_message, compacted_messages, summary_text = await agent_loop.compact(messages=in_context_messages)
|
||||
|
||||
assert summary_message is not None
|
||||
assert summary_text is not None
|
||||
assert len(summary_text) > 0
|
||||
assert len(compacted_messages) < len(in_context_messages)
|
||||
print(f"Fallback succeeded: {len(in_context_messages)} -> {len(compacted_messages)} messages")
|
||||
|
||||
|
||||
# =============================================================================
|
||||
# CompactionStats tests
|
||||
# =============================================================================
|
||||
@@ -2033,3 +2270,15 @@ async def test_compact_with_stats_params_embeds_stats(server: SyncServer, actor,
|
||||
assert stats.context_tokens_after is not None # Should be set by compact()
|
||||
assert stats.messages_count_after == len(compacted_messages) # final_messages already includes summary
|
||||
assert stats.context_window == llm_config.context_window
|
||||
|
||||
|
||||
### basic self summarization
|
||||
|
||||
|
||||
### fallback chain
|
||||
|
||||
### basic self sliding window summarization
|
||||
|
||||
### self sliding window preserves recent msgs
|
||||
|
||||
### self mode return compaction stats
|
||||
|
||||
@@ -209,7 +209,7 @@ class TestSummarizeSlidingWindowTelemetryContext:
|
||||
await summarizer_sliding_window.summarize_via_sliding_window(
|
||||
actor=mock_actor,
|
||||
llm_config=mock_llm_config,
|
||||
agent_llm_config=mock_llm_config, # case where agent and summarizer have same config
|
||||
agent_llm_config=mock_llm_config, # case where agent and summarizer have same config
|
||||
summarizer_config=mock_compaction_settings,
|
||||
in_context_messages=mock_messages,
|
||||
agent_id=agent_id,
|
||||
|
||||
Reference in New Issue
Block a user