"""LangGraph tool definitions for Hindsight memory operations. Provides factory functions that create LangGraph-compatible tool functions backed by Hindsight's retain/recall/reflect APIs. These tools can be bound to a ChatModel via `model.bind_tools()` or used in a ToolNode. """ import logging from typing import Any, Optional from hindsight_client import Hindsight from langchain_core.tools import tool from ._client import resolve_client from .config import get_config from .errors import HindsightError logger = logging.getLogger(__name__) def create_hindsight_tools( *, bank_id: str, client: Optional[Hindsight] = None, hindsight_api_url: Optional[str] = None, api_key: Optional[str] = None, budget: Optional[str] = None, max_tokens: Optional[int] = None, tags: Optional[list[str]] = None, recall_tags: Optional[list[str]] = None, recall_tags_match: Optional[str] = None, # Retain options retain_metadata: Optional[dict[str, str]] = None, retain_document_id: Optional[str] = None, # Recall options recall_types: Optional[list[str]] = None, recall_include_entities: bool = False, # Reflect options reflect_context: Optional[str] = None, reflect_max_tokens: Optional[int] = None, reflect_response_schema: Optional[dict[str, Any]] = None, reflect_tags: Optional[list[str]] = None, reflect_tags_match: Optional[str] = None, include_retain: bool = True, include_recall: bool = True, include_reflect: bool = True, ) -> list: """Create Hindsight memory tools for a LangGraph agent. Returns a list of LangChain tool instances compatible with LangGraph's ToolNode and ChatModel.bind_tools(). Args: bank_id: The Hindsight memory bank to operate on. client: Pre-configured Hindsight client (preferred). hindsight_api_url: API URL (used if no client provided). api_key: API key (used if no client provided). budget: Recall/reflect budget level (low/mid/high). max_tokens: Maximum tokens for recall results. tags: Tags applied when storing memories via retain. recall_tags: Tags to filter when searching memories. recall_tags_match: Tag matching mode (any/all/any_strict/all_strict). retain_metadata: Default metadata dict for retain operations. retain_document_id: Default document_id for retain (groups/upserts memories). recall_types: Fact types to filter (world, experience, opinion, observation). recall_include_entities: Include entity information in recall results. reflect_context: Additional context for reflect operations. reflect_max_tokens: Max tokens for reflect results (defaults to max_tokens). reflect_response_schema: JSON schema to constrain reflect output format. reflect_tags: Tags to filter memories used in reflect (defaults to recall_tags). reflect_tags_match: Tag matching for reflect (defaults to recall_tags_match). include_retain: Include the retain (store) tool. include_recall: Include the recall (search) tool. include_reflect: Include the reflect (synthesize) tool. Returns: List of LangChain tool instances. Raises: HindsightError: If no client or API URL can be resolved. """ resolved_client = resolve_client(client, hindsight_api_url, api_key) config = get_config() effective_tags = tags if tags is not None else (config.tags if config else None) effective_recall_tags = ( recall_tags if recall_tags is not None else (config.recall_tags if config else None) ) effective_recall_tags_match = ( recall_tags_match if recall_tags_match is not None else (config.recall_tags_match if config else "any") ) effective_budget = ( budget if budget is not None else (config.budget if config else "mid") ) effective_max_tokens = ( max_tokens if max_tokens is not None else (config.max_tokens if config else 4096) ) tools: list = [] if include_retain: @tool async def hindsight_retain(content: str) -> str: """Store information to long-term memory for later retrieval. Use this to save important facts, user preferences, decisions, or any information that should be remembered across conversations. Args: content: The information to store in memory. """ try: retain_kwargs: dict[str, Any] = {"bank_id": bank_id, "content": content} if effective_tags: retain_kwargs["tags"] = effective_tags if retain_metadata: retain_kwargs["metadata"] = retain_metadata if retain_document_id: retain_kwargs["document_id"] = retain_document_id await resolved_client.aretain(**retain_kwargs) return "Memory stored successfully." except Exception as e: logger.error(f"Retain failed: {e}") raise HindsightError(f"Retain failed: {e}") from e tools.append(hindsight_retain) if include_recall: @tool async def hindsight_recall(query: str) -> str: """Search long-term memory for relevant information. Use this to find previously stored facts, preferences, or context. Returns a numbered list of matching memories. Args: query: What to search for in memory. """ try: recall_kwargs: dict[str, Any] = { "bank_id": bank_id, "query": query, "budget": effective_budget, "max_tokens": effective_max_tokens, } if effective_recall_tags: recall_kwargs["tags"] = effective_recall_tags recall_kwargs["tags_match"] = effective_recall_tags_match if recall_types: recall_kwargs["types"] = recall_types if recall_include_entities: recall_kwargs["include_entities"] = True response = await resolved_client.arecall(**recall_kwargs) if not response.results: return "No relevant memories found." lines = [] for i, result in enumerate(response.results, 1): lines.append(f"{i}. {result.text}") return "\n".join(lines) except Exception as e: logger.error(f"Recall failed: {e}") raise HindsightError(f"Recall failed: {e}") from e tools.append(hindsight_recall) if include_reflect: @tool async def hindsight_reflect(query: str) -> str: """Synthesize a thoughtful answer from long-term memories. Use this when you need a coherent summary or reasoned response about what you know, rather than raw memory facts. Args: query: The question to reflect on using stored memories. """ try: reflect_kwargs: dict[str, Any] = { "bank_id": bank_id, "query": query, "budget": effective_budget, } if reflect_context: reflect_kwargs["context"] = reflect_context effective_reflect_max = reflect_max_tokens or effective_max_tokens if effective_reflect_max: reflect_kwargs["max_tokens"] = effective_reflect_max if reflect_response_schema: reflect_kwargs["response_schema"] = reflect_response_schema # Reflect tags: use reflect-specific or fall back to recall tags effective_reflect_tags = ( reflect_tags if reflect_tags is not None else effective_recall_tags ) effective_reflect_tags_match = ( reflect_tags_match or effective_recall_tags_match ) if effective_reflect_tags: reflect_kwargs["tags"] = effective_reflect_tags reflect_kwargs["tags_match"] = effective_reflect_tags_match response = await resolved_client.areflect(**reflect_kwargs) return response.text or "No relevant memories found." except Exception as e: logger.error(f"Reflect failed: {e}") raise HindsightError(f"Reflect failed: {e}") from e tools.append(hindsight_reflect) return tools