Oracle Agent Memory (Python SDK)

Neo4j Agent Memory is a graph-native memory system for AI agents developed as a Neo4j Labs project. It stores conversations and messages as short-term memory, builds long-term knowledge graphs using the POLE+O model, and records entities, relationships, reasoning traces, tool usage, decisions, preferences, and facts for later retrieval. The system combines vector and text search, entity resolution and deduplication, similar-task retrieval, multi-stage entity and relationship extraction, background enrichment, and geospatial queries. Python and TypeScript clients support hosted NAMS usage or direct connections to Neo4j and Neo4j AuraDB through Bolt. An MCP server exposes capabilities for searching memory, retrieving context, storing messages, managing entities and preferences, inspecting graph data, and recording reasoning. Neo4j Labs describes it as actively maintained but not officially supported.

agent-memory is a local-first, agent-agnostic long-term memory runtime for AI agents. It stores memories as plain Markdown files, with a rebuildable SQLite index used as a cache rather than the source of truth. Conversation-boundary hooks trigger writes, while an independent sleep-time Manage pass consolidates, ages, and forgets memories by value; unattended deletion is limited to proposals that require confirmation, and superseded or archived material remains available. The runtime provides three recall paths: deterministic MEMORY.md injection at session start, BM25 search with progressive disclosure, and direct access through the store's directory tree using tools such as ls and grep. Memory records use frontmatter for names, abstracts, status, timestamps, links, weights, and provenance, with free-form Markdown bodies. Its CLI, MCP server, and host-agent hooks share the same validation, hash-diff, and reindexing path, and it can be used by agents that run shell commands, including Claude Code and Codex CLI. The repository documents a Python 3.12-or-later setup using uv, commands for initialization, recording, recall, rebuilding, sleep-time consolidation, and proposal approval, plus host setup for Claude Code and Codex. It does not include an LLM client; reasoning is delegated to the host agent's CLI, so the library requires no separate model keys or billing surface.

OptMem is a permanent-memory tool for AI agents. It installs as a dependency-free Python 3 script and integrates through a prompt block added to an agent's AGENTS.md or CLAUDE.md file. The agent uses `memo wake` at session startup, `memo note` to append one-line memories, `memo recall` for exact regular-expression searches, and `memo zoom` to navigate summaries. Raw memories are stored in an append-only `LOG.txt`; a binary tree of one-line summaries provides a compact reading view, with summaries rebuilt when needed. `memo nap` processes due merges, while `memo forget` discards a summary so the next nap can rebuild it. Memory can be kept in a configurable directory such as a synced folder or Git repository, and the system is designed to persist across sessions, context compaction, models, and vendors.

TencentDB Agent Memory is a self-hostable, team-level memory hub for AI agents. It extracts conversations and tasks into reusable Chat Memory and Skills, and converts documents and code into an LLM Wiki and CodeGraph. These assets can be reviewed, versioned, governed, shared, routed, and reused across agents, frameworks, and team members; existing documents, codebases, and agent sessions can also be imported to reduce cold-start work. The system provides a shared memory server and a proxy that preserves the agent protocol, so supported clients can use the same memory by pointing their base URL at the proxy without plugins, hooks, or an MCP server. The repository deploys memory-core, memory-hub, and the proxy together, with a local management panel and configuration for separate memory and proxy LLM parameters. Documented integrations include DeepSeek Harness, Claude Code, Codex, CodeBuddy, WorkBuddy, Hermes, and OpenClaw.

OKF Agent Memory is a Git-native persistent memory layer for AI coding agents, developed as a pure Go CLI and library. It stores project knowledge as plain Markdown files with YAML front matter in a repository, following the vendor-neutral Open Knowledge Format (OKF) v0.2 rather than relying on an external vector database. The tooling parses and validates OKF bundles, builds an in-memory BM25 index for local lexical search, and provides progressive disclosure through hierarchical index files and link graphs. Its search-before-write workflow queries existing concepts before creating new ones; concepts can include provenance, trust tiers, lifecycle metadata, and code references for linking knowledge to source files. The standalone executable also supports creating and updating concepts, bootstrapping the memory structure into a project, and running an embedded Model Context Protocol (MCP) server over stdio for agent platforms. The repository reports sub-300-microsecond concept search and approximately 4-millisecond graph validation for its benchmark cases, with no external databases or API costs for retrieval. It is distributed under the MIT License.