Neo4j Agent Memory

NAMS is a hosted, graph-native memory service from Neo4j Labs for LLM agents. Backed by Neo4j Aura, it captures messages, entities, tool calls, and reasoning traces into workspace-isolated graphs, with short-term conversation memory, long-term knowledge, synthesized observations, entity merging, hybrid vector and text search, and REST and MCP interfaces. Agents connect with a workspace API key, after which NAMS extracts and embeds memory automatically and supports graph, semantic, and three-tier context retrieval. Its skill-distillation feature runs a seven-stage Scope → Extract → Consolidate → Synthesize → Gate → Package → Publish pipeline that turns a scoped portion of the memory graph into a signed, provenance-grounded SKILL.md package. The process includes grounding, coverage, specification linting, PII redaction, human sign-off, typed procedure graphs, cryptographic attestation, composition of shared sub-procedures, and drift detection for stale or contradicted claims. A dashboard provides workspace and skill-distillation interfaces; the service also offers plugins for Claude Code, Gemini CLI, Codex, and OpenCode, and supports bring-your-own LLM keys or an external Neo4j deployment.

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ages, and forgets memories by value; unattended deletion is limited to proposals that require confirmation, and superseded or archived material remains available.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.

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.

Agent Memory Repo is an open standard and agent skill for carrying context between coding-agent sessions in a separate Git repository. Agents load a short MEMORY.md entry point, follow links to Markdown notes, projects, queries, and scripts, then update and commit memories as they learn. The repository can support personal memory, shared team knowledge, and agent swarms; Git provides version history and flags conflicting edits when multiple agents update the same files. The skill can be installed for Devin CLI or through the skills installer for Claude Code, Cursor, and other agents, and requires Node.js and Git for the latter. A separate persistent local or remote repository is needed for memory across machines or cloud sessions. The specification was developed by Cognition, is open to ecosystem contributions, and is licensed under MIT.

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.