agent-memory
Implement agent memory - short-term, long-term, semantic storage, and retrieval
- Installs
- 66
- Last 24h
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- Security
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Install
Works with every agent the skills CLI supports.
npx skills add https://github.com/pluginagentmarketplace/custom-plugin-ai-agents --skill agent-memory- 01
Install
Run the command in your project. The CLI asks which agents to install for.
- 02
Describe the task
Ask your agent for something this skill covers. It loads agent-memory automatically when relevant.
- 03
Review the result
The agent follows the SKILL.md steps, scripts and references bundled with the skill.
SKILL.md
The instructions your agent reads when the skill is triggered.
Agent Memory
Give agents the ability to remember and learn across conversations.
When to Use This Skill
Invoke this skill when:
- Adding conversation history
- Implementing long-term memory
- Building personalized agents
- Managing context windows
Parameter Schema
| Parameter | Type | Required | Description | Default |
|---|---|---|---|---|
task | string | Yes | Memory goal | - |
memory_type | enum | No | buffer, summary, vector, hybrid | hybrid |
persistence | enum | No | session, user, global | session |
Quick Start
from langchain.memory import ConversationBufferWindowMemory
# Simple buffer (last k messages)
memory = ConversationBufferWindowMemory(k=10)
# With summarization
from langchain.memory import ConversationSummaryBufferMemory
memory = ConversationSummaryBufferMemory(llm=llm, max_token_limit=2000)
# Vector store memory
from langchain.memory import VectorStoreRetrieverMemory
memory = VectorStoreRetrieverMemory(retriever=vectorstore.as_retriever())
Memory Types
| Type | Use Case | Pros | Cons |
|---|---|---|---|
| Buffer | Short chats | Simple | No compression |
| Summary | Long chats | Compact | Loses detail |
| Vector | Semantic recall | Relevant | Slower |
| Hybrid | Production | Best of all | Complex |
Multi-Layer Architecture
class ProductionMemory:
def __init__(self):
self.short_term = BufferMemory(k=10) # Recent
self.summary = SummaryMemory() # Compressed
self.long_term = VectorMemory() # Semantic
Troubleshooting
| Issue | Solution |
|---|---|
| Context overflow | Add summarization |
| Slow retrieval | Cache, reduce k |
| Irrelevant recall | Improve embeddings |
| Memory not persisting | Check storage backend |
Best Practices
- Use multi-layer memory for production
- Set token limits to prevent overflow
- Add metadata (timestamps, importance)
- Implement TTL for old memories
Related Skills
rag-systems- Vector retrievalllm-integration- Context managementai-agent-basics- Agent architecture
References
FAQ
What does the agent-memory skill do?
Implement agent memory - short-term, long-term, semantic storage, and retrieval
How do I install agent-memory?
Run npx skills add https://github.com/pluginagentmarketplace/custom-plugin-ai-agents --skill agent-memory in your project. The skills CLI detects the agents you use and copies the skill into the right folder. For Claude Code you can also place the skill folder at ~/.claude/skills/agent-memory/ (personal) or .claude/skills/agent-memory/ (project).
Which AI agents can use agent-memory?
Any agent that supports the open Agent Skills format (a folder with a SKILL.md file), including Claude Code, Codex, Cursor, GitHub Copilot, Gemini CLI, Windsurf, Cline, OpenCode and more.
Is agent-memory safe to install?
No partner security audit is available yet. Skills can run commands and scripts, so read the SKILL.md and any bundled files before installing.
Who maintains agent-memory?
It is published by pluginagentmarketplace in pluginagentmarketplace/custom-plugin-ai-agents. It has 66 recorded installs.
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