5b52a84fff
This commit renames the terminology across the entire codebase: - "mental models" (fact_type='mental_model' in memory_units) → "observations" - "reflections" table (stored reflect responses) → "mental_models" Changes include: - Database migration to rename tables, indexes, and constraints - API endpoints: /reflections → /mental-models, /mental-models → /observations - Config: ENABLE_MENTAL_MODELS → ENABLE_OBSERVATIONS - Response models and Pydantic classes - Reflect agent tools and prompts - Control plane UI and routes - Documentation and examples - Regenerated OpenAPI spec and client SDKs (Python, TypeScript) - Rust CLI: reflection commands → mental-model commands - LiteLLM: updated fact_types documentation
849 lines
33 KiB
Python
849 lines
33 KiB
Python
"""
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Reflect agent - agentic loop for reflection with native tool calling.
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Uses hierarchical retrieval:
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1. search_mental_models - User-curated summaries (highest quality)
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2. search_observations - Consolidated knowledge with freshness
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3. recall - Raw facts as ground truth
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"""
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import asyncio
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import json
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import logging
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import re
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import time
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from typing import TYPE_CHECKING, Any, Awaitable, Callable
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from .models import DirectiveInfo, LLMCall, ReflectAgentResult, TokenUsageSummary, ToolCall
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from .prompts import FINAL_SYSTEM_PROMPT, _extract_directive_rules, build_final_prompt, build_system_prompt_for_tools
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from .tools_schema import get_reflect_tools
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def _build_directives_applied(directives: list[dict[str, Any]] | None) -> list[DirectiveInfo]:
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"""Build list of DirectiveInfo from directive mental models."""
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if not directives:
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return []
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result = []
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for directive in directives:
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directive_id = directive.get("id", "")
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directive_name = directive.get("name", "")
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observations = directive.get("observations", [])
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rules = []
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for obs in observations:
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# Support both Pydantic Observation objects and dicts
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if hasattr(obs, "content"):
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rules.append(obs.content)
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elif isinstance(obs, dict) and obs.get("content"):
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rules.append(obs["content"])
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result.append(DirectiveInfo(id=directive_id, name=directive_name, rules=rules))
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return result
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if TYPE_CHECKING:
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from ..llm_wrapper import LLMProvider
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from ..response_models import LLMToolCall
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logger = logging.getLogger(__name__)
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DEFAULT_MAX_ITERATIONS = 10
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def _normalize_tool_name(name: str) -> str:
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"""Normalize tool name from various LLM output formats.
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Some LLMs output tool names in non-standard formats:
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- 'functions.done' (OpenAI-style prefix)
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- 'call=functions.done' (some models)
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- 'call=done' (some models)
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Returns the normalized tool name (e.g., 'done', 'recall', etc.)
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"""
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# Handle 'call=functions.name' or 'call=name' format
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if name.startswith("call="):
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name = name[len("call=") :]
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# Handle 'functions.name' format
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if name.startswith("functions."):
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name = name[len("functions.") :]
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return name
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def _is_done_tool(name: str) -> bool:
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"""Check if the tool name represents the 'done' tool."""
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return _normalize_tool_name(name) == "done"
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# Pattern to match done() call as text - handles done({...}) with nested JSON
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_DONE_CALL_PATTERN = re.compile(r"done\s*\(\s*\{.*$", re.DOTALL)
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def _clean_answer_text(text: str) -> str:
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"""Clean up answer text by removing any done() tool call syntax.
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Some LLMs output the done() call as text instead of a proper tool call.
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This strips out patterns like: done({"answer": "...", ...})
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"""
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# Remove done() call pattern from the end of the text
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cleaned = _DONE_CALL_PATTERN.sub("", text).strip()
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return cleaned if cleaned else text
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async def _generate_structured_output(
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answer: str,
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response_schema: dict,
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llm_config: "LLMProvider",
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reflect_id: str,
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) -> tuple[dict[str, Any] | None, int, int]:
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"""Generate structured output from an answer using the provided JSON schema.
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Args:
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answer: The text answer to extract structured data from
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response_schema: JSON Schema for the expected output structure
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llm_config: LLM provider for making the extraction call
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reflect_id: Reflect ID for logging
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Returns:
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Tuple of (structured_output, input_tokens, output_tokens).
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structured_output is None if generation fails.
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"""
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try:
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from typing import Any as TypingAny
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from pydantic import create_model
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def _json_schema_type_to_python(field_schema: dict) -> type:
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"""Map JSON schema type to Python type for better LLM guidance."""
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json_type = field_schema.get("type", "string")
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if json_type == "array":
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return list
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elif json_type == "object":
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return dict
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elif json_type == "integer":
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return int
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elif json_type == "number":
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return float
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elif json_type == "boolean":
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return bool
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else:
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return str
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# Build fields from JSON schema properties
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schema_props = response_schema.get("properties", {})
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required_fields = set(response_schema.get("required", []))
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fields: dict[str, TypingAny] = {}
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for field_name, field_schema in schema_props.items():
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field_type = _json_schema_type_to_python(field_schema)
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default = ... if field_name in required_fields else None
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fields[field_name] = (field_type, default)
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if not fields:
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return None
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DynamicModel = create_model("StructuredResponse", **fields)
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# Include the full schema in the prompt for better LLM guidance
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schema_str = json.dumps(response_schema, indent=2)
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# Call LLM with the answer to extract structured data
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structured_prompt = f"""Based on this answer, extract the information into the requested structured format.
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Answer: {answer}
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JSON Schema to follow:
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```json
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{schema_str}
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```
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Return ONLY a valid JSON object that matches this exact schema. Pay special attention to field types:
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- "type": "array" means the value must be a JSON array/list, NOT a string
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- "type": "string" means the value must be a string
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- "type": "object" means the value must be a JSON object
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Do not include any explanation, only the JSON object."""
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structured_result, usage = await llm_config.call(
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messages=[
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{
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"role": "system",
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"content": "Extract structured data from the given answer. Return only valid JSON matching the provided schema exactly.",
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},
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{"role": "user", "content": structured_prompt},
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],
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response_format=DynamicModel,
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scope="reflect_structured",
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skip_validation=True, # We'll handle the dict ourselves
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return_usage=True,
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)
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# Convert to dict
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if hasattr(structured_result, "model_dump"):
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structured_output = structured_result.model_dump()
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elif isinstance(structured_result, dict):
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structured_output = structured_result
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else:
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# Try to parse as JSON
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structured_output = json.loads(str(structured_result))
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logger.info(f"[REFLECT {reflect_id}] Generated structured output with {len(structured_output)} fields")
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return structured_output, usage.input_tokens, usage.output_tokens
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except Exception as e:
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logger.warning(f"[REFLECT {reflect_id}] Failed to generate structured output: {e}")
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return None, 0, 0
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async def run_reflect_agent(
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llm_config: "LLMProvider",
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bank_id: str,
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query: str,
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bank_profile: dict[str, Any],
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search_mental_models_fn: Callable[[str, int], Awaitable[dict[str, Any]]],
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search_observations_fn: Callable[[str, int], Awaitable[dict[str, Any]]],
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recall_fn: Callable[[str, int], Awaitable[dict[str, Any]]],
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expand_fn: Callable[[list[str], str], Awaitable[dict[str, Any]]],
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context: str | None = None,
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max_iterations: int = DEFAULT_MAX_ITERATIONS,
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max_tokens: int | None = None,
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response_schema: dict | None = None,
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directives: list[dict[str, Any]] | None = None,
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) -> ReflectAgentResult:
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"""
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Execute the reflect agent loop using native tool calling.
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The agent uses hierarchical retrieval:
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1. search_mental_models - User-curated summaries (try first)
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2. search_observations - Consolidated knowledge with freshness
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3. recall - Raw facts as ground truth
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Args:
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llm_config: LLM provider for agent calls
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bank_id: Bank identifier
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query: Question to answer
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bank_profile: Bank profile with name and mission
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search_mental_models_fn: Tool callback for searching mental models (query, max_results) -> result
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search_observations_fn: Tool callback for searching observations (query, max_results) -> result
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recall_fn: Tool callback for recall (query, max_tokens) -> result
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expand_fn: Tool callback for expand (memory_ids, depth) -> result
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context: Optional additional context
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max_iterations: Maximum number of iterations before forcing response
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max_tokens: Maximum tokens for the final response
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response_schema: Optional JSON Schema for structured output in final response
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directives: Optional list of directive mental models to inject as hard rules
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Returns:
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ReflectAgentResult with final answer and metadata
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"""
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reflect_id = f"{bank_id[:8]}-{int(time.time() * 1000) % 100000}"
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start_time = time.time()
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# Build directives_applied for the trace
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directives_applied = _build_directives_applied(directives)
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# Extract directive rules for tool schema (if any)
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directive_rules = _extract_directive_rules(directives) if directives else None
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# Get tools for this agent (with directive compliance field if directives exist)
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tools = get_reflect_tools(directive_rules=directive_rules)
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# Build initial messages (directives are injected into system prompt at START and END)
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system_prompt = build_system_prompt_for_tools(bank_profile, context, directives=directives)
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messages: list[dict[str, Any]] = [
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{"role": "system", "content": system_prompt},
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{"role": "user", "content": query},
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]
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# Tracking
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total_tools_called = 0
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tool_trace: list[ToolCall] = []
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tool_trace_summary: list[dict[str, Any]] = []
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llm_trace: list[dict[str, Any]] = []
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context_history: list[dict[str, Any]] = [] # For final prompt fallback
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# Token usage tracking - accumulate across all LLM calls
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total_input_tokens = 0
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total_output_tokens = 0
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# Track available IDs for validation (prevents hallucinated citations)
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available_memory_ids: set[str] = set()
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available_mental_model_ids: set[str] = set()
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available_observation_ids: set[str] = set()
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def _get_llm_trace() -> list[LLMCall]:
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return [
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LLMCall(
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scope=c["scope"],
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duration_ms=c["duration_ms"],
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input_tokens=c.get("input_tokens", 0),
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output_tokens=c.get("output_tokens", 0),
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)
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for c in llm_trace
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]
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def _get_usage() -> TokenUsageSummary:
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return TokenUsageSummary(
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input_tokens=total_input_tokens,
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output_tokens=total_output_tokens,
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total_tokens=total_input_tokens + total_output_tokens,
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)
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def _log_completion(answer: str, iterations: int, forced: bool = False):
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elapsed_ms = int((time.time() - start_time) * 1000)
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tools_summary = (
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", ".join(
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f"{t['tool']}({t['input_summary']})={t['duration_ms']}ms/{t.get('output_chars', 0)}c"
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for t in tool_trace_summary
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)
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or "none"
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)
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llm_summary = ", ".join(f"{c['scope']}={c['duration_ms']}ms" for c in llm_trace) or "none"
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total_llm_ms = sum(c["duration_ms"] for c in llm_trace)
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total_tools_ms = sum(t["duration_ms"] for t in tool_trace_summary)
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answer_preview = answer[:100] + "..." if len(answer) > 100 else answer
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mode = "forced" if forced else "done"
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logger.info(
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f"[REFLECT {reflect_id}] {mode} | "
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f"query='{query[:50]}...' | "
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f"iterations={iterations} | "
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f"llm=[{llm_summary}] ({total_llm_ms}ms) | "
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f"tools=[{tools_summary}] ({total_tools_ms}ms) | "
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f"answer='{answer_preview}' | "
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f"total={elapsed_ms}ms"
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)
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for iteration in range(max_iterations):
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is_last = iteration == max_iterations - 1
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if is_last:
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# Force text response on last iteration - no tools
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prompt = build_final_prompt(query, context_history, bank_profile, context)
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llm_start = time.time()
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response, usage = await llm_config.call(
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messages=[
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{"role": "system", "content": FINAL_SYSTEM_PROMPT},
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{"role": "user", "content": prompt},
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],
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scope="reflect_agent_final",
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max_completion_tokens=max_tokens,
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return_usage=True,
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)
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llm_duration = int((time.time() - llm_start) * 1000)
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total_input_tokens += usage.input_tokens
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total_output_tokens += usage.output_tokens
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llm_trace.append(
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{
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"scope": "final",
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"duration_ms": llm_duration,
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"input_tokens": usage.input_tokens,
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"output_tokens": usage.output_tokens,
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}
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)
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answer = _clean_answer_text(response.strip())
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# Generate structured output if schema provided
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structured_output = None
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if response_schema and answer:
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structured_output, struct_in, struct_out = await _generate_structured_output(
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answer, response_schema, llm_config, reflect_id
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)
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total_input_tokens += struct_in
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total_output_tokens += struct_out
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_log_completion(answer, iteration + 1, forced=True)
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return ReflectAgentResult(
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text=answer,
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structured_output=structured_output,
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iterations=iteration + 1,
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tools_called=total_tools_called,
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tool_trace=tool_trace,
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llm_trace=_get_llm_trace(),
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usage=_get_usage(),
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directives_applied=directives_applied,
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)
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# Call LLM with tools
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llm_start = time.time()
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try:
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result = await llm_config.call_with_tools(
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messages=messages,
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tools=tools,
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scope="reflect_agent",
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tool_choice="required" if iteration == 0 else "auto", # Force tool use on first iteration
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)
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llm_duration = int((time.time() - llm_start) * 1000)
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total_input_tokens += result.input_tokens
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total_output_tokens += result.output_tokens
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llm_trace.append(
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{
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"scope": f"agent_{iteration + 1}",
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"duration_ms": llm_duration,
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"input_tokens": result.input_tokens,
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"output_tokens": result.output_tokens,
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}
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)
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except Exception as e:
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err_duration = int((time.time() - llm_start) * 1000)
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logger.warning(f"[REFLECT {reflect_id}] LLM error on iteration {iteration + 1}: {e} ({err_duration}ms)")
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llm_trace.append({"scope": f"agent_{iteration + 1}_err", "duration_ms": err_duration})
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# Guardrail: If no evidence gathered yet, retry
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has_gathered_evidence = (
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bool(available_memory_ids) or bool(available_mental_model_ids) or bool(available_observation_ids)
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)
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if not has_gathered_evidence and iteration < max_iterations - 1:
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continue
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prompt = build_final_prompt(query, context_history, bank_profile, context)
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llm_start = time.time()
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response, usage = await llm_config.call(
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messages=[
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{"role": "system", "content": FINAL_SYSTEM_PROMPT},
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{"role": "user", "content": prompt},
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],
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scope="reflect_agent_final",
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max_completion_tokens=max_tokens,
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return_usage=True,
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)
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llm_duration = int((time.time() - llm_start) * 1000)
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total_input_tokens += usage.input_tokens
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total_output_tokens += usage.output_tokens
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llm_trace.append(
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{
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"scope": "final",
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"duration_ms": llm_duration,
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"input_tokens": usage.input_tokens,
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"output_tokens": usage.output_tokens,
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}
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)
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answer = _clean_answer_text(response.strip())
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|
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# Generate structured output if schema provided
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structured_output = None
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if response_schema and answer:
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structured_output, struct_in, struct_out = await _generate_structured_output(
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answer, response_schema, llm_config, reflect_id
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)
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total_input_tokens += struct_in
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total_output_tokens += struct_out
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|
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_log_completion(answer, iteration + 1, forced=True)
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return ReflectAgentResult(
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text=answer,
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structured_output=structured_output,
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iterations=iteration + 1,
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tools_called=total_tools_called,
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tool_trace=tool_trace,
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|
llm_trace=_get_llm_trace(),
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usage=_get_usage(),
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directives_applied=directives_applied,
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)
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# No tool calls - LLM wants to respond with text
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if not result.tool_calls:
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if result.content:
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answer = _clean_answer_text(result.content.strip())
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# Generate structured output if schema provided
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|
structured_output = None
|
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if response_schema and answer:
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structured_output, struct_in, struct_out = await _generate_structured_output(
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answer, response_schema, llm_config, reflect_id
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)
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total_input_tokens += struct_in
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|
total_output_tokens += struct_out
|
|
|
|
_log_completion(answer, iteration + 1)
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|
return ReflectAgentResult(
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text=answer,
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|
structured_output=structured_output,
|
|
iterations=iteration + 1,
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|
tools_called=total_tools_called,
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|
tool_trace=tool_trace,
|
|
llm_trace=_get_llm_trace(),
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usage=_get_usage(),
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directives_applied=directives_applied,
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)
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# Empty response, force final
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prompt = build_final_prompt(query, context_history, bank_profile, context)
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llm_start = time.time()
|
|
response, usage = await llm_config.call(
|
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messages=[
|
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{"role": "system", "content": FINAL_SYSTEM_PROMPT},
|
|
{"role": "user", "content": prompt},
|
|
],
|
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scope="reflect_agent_final",
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max_completion_tokens=max_tokens,
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return_usage=True,
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)
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llm_duration = int((time.time() - llm_start) * 1000)
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total_input_tokens += usage.input_tokens
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total_output_tokens += usage.output_tokens
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llm_trace.append(
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{
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"scope": "final",
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"duration_ms": llm_duration,
|
|
"input_tokens": usage.input_tokens,
|
|
"output_tokens": usage.output_tokens,
|
|
}
|
|
)
|
|
answer = _clean_answer_text(response.strip())
|
|
|
|
# Generate structured output if schema provided
|
|
structured_output = None
|
|
if response_schema and answer:
|
|
structured_output, struct_in, struct_out = await _generate_structured_output(
|
|
answer, response_schema, llm_config, reflect_id
|
|
)
|
|
total_input_tokens += struct_in
|
|
total_output_tokens += struct_out
|
|
|
|
_log_completion(answer, iteration + 1, forced=True)
|
|
return ReflectAgentResult(
|
|
text=answer,
|
|
structured_output=structured_output,
|
|
iterations=iteration + 1,
|
|
tools_called=total_tools_called,
|
|
tool_trace=tool_trace,
|
|
llm_trace=_get_llm_trace(),
|
|
usage=_get_usage(),
|
|
directives_applied=directives_applied,
|
|
)
|
|
|
|
# Check for done tool call (handle various LLM output formats)
|
|
done_call = next((tc for tc in result.tool_calls if _is_done_tool(tc.name)), None)
|
|
if done_call:
|
|
# Guardrail: Require evidence before done
|
|
has_gathered_evidence = (
|
|
bool(available_memory_ids) or bool(available_mental_model_ids) or bool(available_observation_ids)
|
|
)
|
|
if not has_gathered_evidence and iteration < max_iterations - 1:
|
|
# Add assistant message and fake tool result asking for evidence
|
|
messages.append(
|
|
{
|
|
"role": "assistant",
|
|
"tool_calls": [_tool_call_to_dict(done_call)],
|
|
}
|
|
)
|
|
messages.append(
|
|
{
|
|
"role": "tool",
|
|
"tool_call_id": done_call.id,
|
|
"name": done_call.name, # Required by Gemini
|
|
"content": json.dumps(
|
|
{
|
|
"error": "You must search for information first. Use search_mental_models(), search_observations(), or recall() before providing your final answer."
|
|
}
|
|
),
|
|
}
|
|
)
|
|
continue
|
|
|
|
# Process done tool
|
|
return await _process_done_tool(
|
|
done_call,
|
|
available_memory_ids,
|
|
available_mental_model_ids,
|
|
available_observation_ids,
|
|
iteration + 1,
|
|
total_tools_called,
|
|
tool_trace,
|
|
_get_llm_trace(),
|
|
_get_usage(),
|
|
_log_completion,
|
|
reflect_id,
|
|
directives_applied=directives_applied,
|
|
llm_config=llm_config,
|
|
response_schema=response_schema,
|
|
)
|
|
|
|
# Execute other tools in parallel (exclude done tool in all its format variants)
|
|
other_tools = [tc for tc in result.tool_calls if not _is_done_tool(tc.name)]
|
|
if other_tools:
|
|
# Add assistant message with tool calls
|
|
messages.append(
|
|
{
|
|
"role": "assistant",
|
|
"tool_calls": [_tool_call_to_dict(tc) for tc in other_tools],
|
|
}
|
|
)
|
|
|
|
# Execute tools in parallel
|
|
tool_tasks = [
|
|
_execute_tool_with_timing(
|
|
tc,
|
|
search_mental_models_fn,
|
|
search_observations_fn,
|
|
recall_fn,
|
|
expand_fn,
|
|
)
|
|
for tc in other_tools
|
|
]
|
|
tool_results = await asyncio.gather(*tool_tasks, return_exceptions=True)
|
|
total_tools_called += len(other_tools)
|
|
|
|
# Process results and add to messages
|
|
for tc, result_data in zip(other_tools, tool_results):
|
|
if isinstance(result_data, Exception):
|
|
# Tool execution failed - send error back to LLM so it can try again
|
|
logger.warning(f"[REFLECT {reflect_id}] Tool {tc.name} failed with exception: {result_data}")
|
|
output = {"error": f"Tool execution failed: {result_data}"}
|
|
duration_ms = 0
|
|
else:
|
|
output, duration_ms = result_data
|
|
|
|
# Normalize tool name for consistent tracking
|
|
normalized_tool_name = _normalize_tool_name(tc.name)
|
|
|
|
# Check if tool returned an error response - log but continue (LLM will see the error)
|
|
if isinstance(output, dict) and "error" in output:
|
|
logger.warning(
|
|
f"[REFLECT {reflect_id}] Tool {normalized_tool_name} returned error: {output['error']}"
|
|
)
|
|
|
|
# Track available IDs from tool results (only for successful responses)
|
|
if (
|
|
normalized_tool_name == "search_mental_models"
|
|
and isinstance(output, dict)
|
|
and "mental_models" in output
|
|
):
|
|
for mm in output["mental_models"]:
|
|
if "id" in mm:
|
|
available_mental_model_ids.add(mm["id"])
|
|
|
|
if (
|
|
normalized_tool_name == "search_observations"
|
|
and isinstance(output, dict)
|
|
and "observations" in output
|
|
):
|
|
for obs in output["observations"]:
|
|
if "id" in obs:
|
|
available_observation_ids.add(obs["id"])
|
|
|
|
if normalized_tool_name == "recall" and isinstance(output, dict) and "memories" in output:
|
|
for memory in output["memories"]:
|
|
if "id" in memory:
|
|
available_memory_ids.add(memory["id"])
|
|
|
|
# Add tool result message
|
|
messages.append(
|
|
{
|
|
"role": "tool",
|
|
"tool_call_id": tc.id,
|
|
"name": tc.name, # Required by Gemini
|
|
"content": json.dumps(output, default=str),
|
|
}
|
|
)
|
|
|
|
# Track for logging and context history
|
|
input_dict = {"tool": tc.name, **tc.arguments}
|
|
input_summary = _summarize_input(tc.name, tc.arguments)
|
|
|
|
tool_trace.append(
|
|
ToolCall(
|
|
tool=tc.name, input=input_dict, output=output, duration_ms=duration_ms, iteration=iteration + 1
|
|
)
|
|
)
|
|
|
|
try:
|
|
output_chars = len(json.dumps(output))
|
|
except (TypeError, ValueError):
|
|
output_chars = len(str(output))
|
|
|
|
tool_trace_summary.append(
|
|
{
|
|
"tool": tc.name,
|
|
"input_summary": input_summary,
|
|
"duration_ms": duration_ms,
|
|
"output_chars": output_chars,
|
|
}
|
|
)
|
|
|
|
# Keep context history for fallback final prompt
|
|
context_history.append({"tool": tc.name, "input": input_dict, "output": output})
|
|
|
|
# Should not reach here
|
|
answer = "I was unable to formulate a complete answer within the iteration limit."
|
|
_log_completion(answer, max_iterations, forced=True)
|
|
return ReflectAgentResult(
|
|
text=answer,
|
|
iterations=max_iterations,
|
|
tools_called=total_tools_called,
|
|
tool_trace=tool_trace,
|
|
llm_trace=_get_llm_trace(),
|
|
usage=_get_usage(),
|
|
directives_applied=directives_applied,
|
|
)
|
|
|
|
|
|
def _tool_call_to_dict(tc: "LLMToolCall") -> dict[str, Any]:
|
|
"""Convert LLMToolCall to OpenAI message format."""
|
|
return {
|
|
"id": tc.id,
|
|
"type": "function",
|
|
"function": {
|
|
"name": tc.name,
|
|
"arguments": json.dumps(tc.arguments),
|
|
},
|
|
}
|
|
|
|
|
|
async def _process_done_tool(
|
|
done_call: "LLMToolCall",
|
|
available_memory_ids: set[str],
|
|
available_mental_model_ids: set[str],
|
|
available_observation_ids: set[str],
|
|
iterations: int,
|
|
total_tools_called: int,
|
|
tool_trace: list[ToolCall],
|
|
llm_trace: list[LLMCall],
|
|
usage: TokenUsageSummary,
|
|
log_completion: Callable,
|
|
reflect_id: str,
|
|
directives_applied: list[DirectiveInfo],
|
|
llm_config: "LLMProvider | None" = None,
|
|
response_schema: dict | None = None,
|
|
) -> ReflectAgentResult:
|
|
"""Process the done tool call and return the result."""
|
|
args = done_call.arguments
|
|
|
|
answer = args.get("answer", "").strip()
|
|
if not answer:
|
|
answer = "No answer provided."
|
|
|
|
# Validate IDs (only include IDs that were actually retrieved)
|
|
used_memory_ids = [mid for mid in args.get("memory_ids", []) if mid in available_memory_ids]
|
|
used_mental_model_ids = [mid for mid in args.get("mental_model_ids", []) if mid in available_mental_model_ids]
|
|
used_observation_ids = [oid for oid in args.get("observation_ids", []) if oid in available_observation_ids]
|
|
|
|
# Generate structured output if schema provided
|
|
structured_output = None
|
|
final_usage = usage
|
|
if response_schema and llm_config and answer:
|
|
structured_output, struct_in, struct_out = await _generate_structured_output(
|
|
answer, response_schema, llm_config, reflect_id
|
|
)
|
|
# Add structured output tokens to usage
|
|
final_usage = TokenUsageSummary(
|
|
input_tokens=usage.input_tokens + struct_in,
|
|
output_tokens=usage.output_tokens + struct_out,
|
|
total_tokens=usage.total_tokens + struct_in + struct_out,
|
|
)
|
|
|
|
log_completion(answer, iterations)
|
|
return ReflectAgentResult(
|
|
text=answer,
|
|
structured_output=structured_output,
|
|
iterations=iterations,
|
|
tools_called=total_tools_called,
|
|
tool_trace=tool_trace,
|
|
llm_trace=llm_trace,
|
|
usage=final_usage,
|
|
used_memory_ids=used_memory_ids,
|
|
used_mental_model_ids=used_mental_model_ids,
|
|
used_observation_ids=used_observation_ids,
|
|
directives_applied=directives_applied,
|
|
)
|
|
|
|
|
|
async def _execute_tool_with_timing(
|
|
tc: "LLMToolCall",
|
|
search_mental_models_fn: Callable[[str, int], Awaitable[dict[str, Any]]],
|
|
search_observations_fn: Callable[[str, int], Awaitable[dict[str, Any]]],
|
|
recall_fn: Callable[[str, int], Awaitable[dict[str, Any]]],
|
|
expand_fn: Callable[[list[str], str], Awaitable[dict[str, Any]]],
|
|
) -> tuple[dict[str, Any], int]:
|
|
"""Execute a tool call and return result with timing."""
|
|
start = time.time()
|
|
result = await _execute_tool(
|
|
tc.name,
|
|
tc.arguments,
|
|
search_mental_models_fn,
|
|
search_observations_fn,
|
|
recall_fn,
|
|
expand_fn,
|
|
)
|
|
duration_ms = int((time.time() - start) * 1000)
|
|
return result, duration_ms
|
|
|
|
|
|
async def _execute_tool(
|
|
tool_name: str,
|
|
args: dict[str, Any],
|
|
search_mental_models_fn: Callable[[str, int], Awaitable[dict[str, Any]]],
|
|
search_observations_fn: Callable[[str, int], Awaitable[dict[str, Any]]],
|
|
recall_fn: Callable[[str, int], Awaitable[dict[str, Any]]],
|
|
expand_fn: Callable[[list[str], str], Awaitable[dict[str, Any]]],
|
|
) -> dict[str, Any]:
|
|
"""Execute a single tool by name."""
|
|
# Normalize tool name for various LLM output formats
|
|
tool_name = _normalize_tool_name(tool_name)
|
|
|
|
if tool_name == "search_mental_models":
|
|
query = args.get("query")
|
|
if not query:
|
|
return {"error": "search_mental_models requires a query parameter"}
|
|
max_results = args.get("max_results") or 5
|
|
return await search_mental_models_fn(query, max_results)
|
|
|
|
elif tool_name == "search_observations":
|
|
query = args.get("query")
|
|
if not query:
|
|
return {"error": "search_observations requires a query parameter"}
|
|
max_tokens = max(args.get("max_tokens") or 5000, 1000) # Default 5000, min 1000
|
|
return await search_observations_fn(query, max_tokens)
|
|
|
|
elif tool_name == "recall":
|
|
query = args.get("query")
|
|
if not query:
|
|
return {"error": "recall requires a query parameter"}
|
|
max_tokens = max(args.get("max_tokens") or 2048, 1000) # Default 2048, min 1000
|
|
return await recall_fn(query, max_tokens)
|
|
|
|
elif tool_name == "expand":
|
|
memory_ids = args.get("memory_ids", [])
|
|
if not memory_ids:
|
|
return {"error": "expand requires memory_ids"}
|
|
depth = args.get("depth", "chunk")
|
|
return await expand_fn(memory_ids, depth)
|
|
|
|
else:
|
|
return {"error": f"Unknown tool: {tool_name}"}
|
|
|
|
|
|
def _summarize_input(tool_name: str, args: dict[str, Any]) -> str:
|
|
"""Create a summary of tool input for logging, showing all params."""
|
|
if tool_name == "search_mental_models":
|
|
query = args.get("query", "")
|
|
query_preview = f"'{query[:30]}...'" if len(query) > 30 else f"'{query}'"
|
|
max_results = args.get("max_results") or 5
|
|
return f"(query={query_preview}, max_results={max_results})"
|
|
elif tool_name == "search_observations":
|
|
query = args.get("query", "")
|
|
query_preview = f"'{query[:30]}...'" if len(query) > 30 else f"'{query}'"
|
|
max_tokens = max(args.get("max_tokens") or 5000, 1000)
|
|
return f"(query={query_preview}, max_tokens={max_tokens})"
|
|
elif tool_name == "recall":
|
|
query = args.get("query", "")
|
|
query_preview = f"'{query[:30]}...'" if len(query) > 30 else f"'{query}'"
|
|
# Show actual value used (default 2048, min 1000)
|
|
max_tokens = max(args.get("max_tokens") or 2048, 1000)
|
|
return f"(query={query_preview}, max_tokens={max_tokens})"
|
|
elif tool_name == "expand":
|
|
memory_ids = args.get("memory_ids", [])
|
|
depth = args.get("depth", "chunk")
|
|
return f"(memory_ids=[{len(memory_ids)} ids], depth={depth})"
|
|
elif tool_name == "done":
|
|
answer = args.get("answer", "")
|
|
answer_preview = f"'{answer[:30]}...'" if len(answer) > 30 else f"'{answer}'"
|
|
memory_ids = args.get("memory_ids", [])
|
|
mental_model_ids = args.get("mental_model_ids", [])
|
|
observation_ids = args.get("observation_ids", [])
|
|
return (
|
|
f"(answer={answer_preview}, mem={len(memory_ids)}, mm={len(mental_model_ids)}, obs={len(observation_ids)})"
|
|
)
|
|
return str(args)
|