Agent Long-Term Memory

Other

Three-tier long-term memory for AI agents — short-term + entity + episodic. 三层长期记忆架构：短期记忆 + 实体画像 + 情景记忆。跨项目共享用户画像，让 AI 真正记住你。

Install

openclaw skills install agent-long-term-memory

Agent Long-Term Memory · AI 智能体长期记忆

Install from GitHub · 从 GitHub 安装

git clone https://github.com/exp007/agent-long-term-memory.git ~/.codex/skills/agent-memory

Three-tier persistent memory shared at ~/.codex/agent_memory/ across all projects.

三层持久记忆，数据存在 ~/.codex/agent_memory/，所有项目共享。

Quick Start · 快速开始

from agent_memory import get_memory

mem = get_memory()
mem.remember("name", "Alice")
mem.recall("name")                    # -> "Alice"
mem.recall("favorite color")          # -> None (not yet stored)

API Reference · API 参考

Tier 2: Entity Memory · 实体记忆（结构化事实，SQLite）

Method	Signature	Description
`remember`	`(key, value, evidence="", confidence=1.0)`	Store a structured fact · 存储结构化事实
`remember`	`(content, tags=None, ...)`	Store a fact in v1 compat mode
`recall`	`(key_or_query, limit=10, tags=None)`	Lookup by key or search by content · 按 key 精确查或按内容搜
`recall_card`	`(key)`	Get the full EntityCard · 获取完整卡片
`get_profile`	`()`	Return all entity cards · 获取全部画像
`search_entities`	`(keyword)`	Fuzzy search across keys and values · 模糊搜索
`forget_entity`	`(key)`	Delete an entity card · 删除
`clean_stale`	`(threshold=0.3)`	Remove low-confidence cards · 清理低置信度
`entity_count`	property	Number of entity cards · 卡片数量

Tier 3: Episodic Memory · 情景记忆（对话片段，ChromaDB）

Method	Signature	Description
`archive`	`(content, summary="")`	Store a conversation chunk · 存储对话片段
`recollect`	`(query, n_results=5)`	Semantic search · 语义检索
`episodic_count`	property	Number of stored episodes · 片段数量

Tier 1: Short-Term Memory · 短期记忆（滑动窗口）

Method	Signature	Description
`add_turn`	`(user_text, assistant_text)`	Record a turn · 记录一轮对话
`get_recent`	`(n=None)`	Get recent messages · 获取最近消息
`clear_short_term`	`()`	Clear buffer · 清空缓冲区

RAG Context Building · RAG 上下文构建

Method	Signature	Description
`build_context`	`(user_query="", episodic_top_k=3)`	Full MemoryContext · 完整上下文
`build_system_extension`	`(user_query="", episodic_top_k=3)`	Prompt injection string · 系统提示扩展

Auto Extraction · 自动抽取

Method	Signature	Description
`auto_remember`	`(conversation_text)`	Extract entities from text · 从对话中抽取实体。有 OpenAI key 用 LLM，无则用正则兜底。

v1 Compat API · v1 兼容接口

add_fact, get_fact, get_facts, list_facts, forget, supersede, forget_stale, learn, get_lessons, apply_lesson, track_entity, get_entity, update_entity, list_entities, link_fact_to_entity, stats, export_json, close

Standard Integration Pattern · 标准集成流程

session start:       mem = get_memory(); inject mem.get_profile() into system prompt
every user message:  mem.add_turn(user_msg, assistant_msg)
significant facts:   mem.remember(key, value, evidence)
                     mem.auto_remember(conversation_text)  # auto-extract · 自动抽取
conversation end:    mem.archive(full_conversation, summary)
periodic cleanup:    mem.clean_stale(0.3); mem.forget_stale(30)
shutdown:            mem.close()

Dependencies · 依赖

pip install chromadb>=0.4.0 openai>=1.0.0

OpenAI key is optional — if unset, entity extraction falls back to regex patterns. OpenAI key 可选——不配也能用正则兜底。