"Information about Adobe After Effects or its uses" matching MCP servers:

Enables persistent, portable memory for AI agents across sessions, devices, and providers with token-efficient 5-level lazy loading and automatic session capture.

Enables Claude Code to search, browse, and read its own past conversation history across all projects using BM25 keyword search.

Flatten Claude Code sessions: keep every prompt and event verbatim, resume at a lower token count. An MCP server.

An improved implementation of persistent memory using a local knowledge graph with a customizable --memory-path. This lets Claude remember information about the user across chats.

Your AI forgets everything between sessions. Rekindle fixes that. For Claude Code users who lose time re-explaining project context every session.

MemOS (Memory Operating System) is a memory management operating system designed for AI applications. Its goal is: to enable your AI system to have long-term memory like a human, not only remembering what users have said but also actively invoking, updating, and scheduling these memories.

A Byzantine fault-tolerant MCP Memory server that uses SQLite write-ahead logging and FAISS hybrid vector search for deterministic, secure memory operations.

A local MCP server that maintains a persona profile (contact library) for AI assistants, allowing them to create, search, retrieve, supplement, and correct information about people mentioned in conversations via tools like search_profiles, get_profile, create_profile, update_profile, add_facts, update_fact, and delete_fact.

A personal knowledge hub MCP server that enables LLMs to query structured information about Akib Dabgar's skills, experience, projects, and goals using Dynamic Context Retrieval for efficient context usage.

Gives AI coding agents persistent, evolving knowledge about a codebase, enabling them to store and retrieve observations about architecture, conventions, gotchas, and recent work context.

Exposes tools to ingest FigJam boards, retrieve context summaries, and answer free-form questions about the board content.

MCP server for intelligent, use-adaptive Obsidian vault search. It uses Hebbian learning and graph centrality to rank files by usage and relevance.

Connects AI assistants like Cursor and Claude to your local Daylens timeline so they can answer questions about your past work sessions, no cloud or API keys needed.

With Memori's MCP server, your agent can retrieve relevant memories before answering and store durable facts after responding, keeping context across sessions without any SDK integration. With MCP, it can: Store stable user facts and preferences after answering using the advanced_augmentation tool Recall relevant memories before answering using the recall tool Maintain context across sessions us

Provides local persistent working memory for AI agents, allowing them to load rules, preferences, and project state before tasks and save validated experiences after successful completion. Works with stdio MCP clients via a local JSON file.

Intelligent context manager for AI coding assistants that uses a three-level memory system (core, active, archive) to remember project context across conversations.

Interacts with LogSeq via its API.

Read-only MCP server that lets Claude or any MCP client search and retrieve metadata, notes, full text, citations, and BibTeX from your local Zotero library via its built-in API.

Enables storing and querying structured information about software code entities (classes, functions, files) and their relationships (calls, imports) along with qualitative observations like design decisions and change rationale.

Enables AI agents to record, recall, correct, and forget evidence-backed factual claims with temporal history, while explaining whether remembered information is current, historical, or contested.