{"categories":[{"label":"Artificial Intelligence","url":"https://skillfed.io/packages/category/scientific-engineering-artificial-intelligence/11"}],"enrichment":{"capability":"Hindsight-api-slim provides a persistent memory system for AI agents that stores facts, tracks entities and relationships, and enables temporal and semantic reasoning using PostgreSQL with pgvector.","skillfed_tags":["agent-memory","temporal-reasoning","vector-search"],"use_cases":["Build a conversational AI assistant that remembers user preferences and past interactions across sessions without re-prompting.","Create a multi-turn agent that tracks entities (people, projects, dates) and reasons about temporal relationships (\"what happened last spring?\").","Implement a code review agent that learns from feedback, stores coding style preferences, and applies them to future reviews.","Develop a customer support bot that maintains a knowledge graph of customer issues, resolutions, and patterns to improve future responses.","Run an autonomous research agent that accumulates findings, tracks source relationships, and synthesizes insights over time."],"what_it_does":"Hindsight-api-slim is a memory backend for AI agents that mimics human memory by storing facts, tracking entities and their relationships, and reasoning about time and context. It runs as a FastAPI server (default port 8888) backed by PostgreSQL with pgvector for semantic search, and exposes both a REST API and an MCP server interface for tool integration. The system supports three memory types (world facts, experience facts, and observations), combines multiple retrieval strategies (semantic, keyword, graph, and temporal), and allows agents to form opinions based on configurable disposition traits like skepticism and empathy.\n\nThe package is designed for AI agent frameworks and LLM applications that need persistent, queryable context across conversations. It integrates with multiple LLM providers (OpenAI, Anthropic, Gemini, Groq, Ollama, LMStudio) and includes Python SDK methods for creating memory banks, storing facts, recalling relevant memories, and reflecting on queries with reasoning. The dependency footprint is substantial\u201454 runtime packages including aiohttp, asyncpg, langchain-core, litellm, and observability tools\u2014reflecting its role as infrastructure for complex agent systems.","worth_installing":"Yes, if you are building AI agents or LLM applications that require persistent, queryable memory with temporal and semantic reasoning. The low install friction, active maintenance, permissive license, and zero known vulnerabilities make it a solid choice. The substantial dependency footprint and requirement for Python 3.11+ and external LLM credentials are expected trade-offs for agent infrastructure. Not suitable if you need a lightweight memory layer or are working with Python versions below 3.11."},"id":"hindsight-api-slim","links":{"html":"https://skillfed.io/packages/hindsight-api-slim","md":"https://skillfed.io/packages/hindsight-api-slim.md","pypi":"https://pypi.org/project/hindsight-api-slim/"},"maintenance":{"status":"active"},"meta":{"latest_release":"2026-08-14","license_spdx":"MIT","license_treatment":"permissive","name":"hindsight-api-slim","python_support":"supports_current","summary":"Hindsight: Agent Memory That Works Like Human Memory"},"popularity":{"monthly_downloads":80552,"position":14286,"tier":"top_15000"},"security":{"n_vulnerabilities":0},"version":"0.9.1"}