openmemory
Persistent long-term agent memory for storing and querying past work, patterns, and learnings.
Installation
Follow the repository instructions for this skill and your agent. Review the files and dependencies before adding it to your workflow.
View source ↗GuidesSkill instructions
OpenMemory (Persistent Agent Memory)
You have access to OpenMemory for persistent long-term memory across sessions.
Memory Tools
| Tool | Purpose |
|---|---|
openmemory_query |
Semantic search across memories by similarity |
openmemory_store |
Store new memories with sector classification |
openmemory_list |
List recent memories for a user/agent |
openmemory_get |
Retrieve specific memory by ID |
openmemory_reinforce |
Boost salience of important memories |
Memory Sectors
Memories are classified into sectors:
| Sector | Use Case | Example |
|---|---|---|
| episodic | Events, task history | "Implemented auth flow for project X" |
| semantic | Facts, learned patterns | "Always add Context7 lookup before Rust implementation" |
| procedural | How-to knowledge | "Steps to deploy with ArgoCD" |
Usage Patterns
Before starting a task:
openmemory_query({ query: "similar implementations", sector: "episodic" })
After completing a task:
openmemory_store({
content: "Implemented OAuth2 with PKCE for React app using Effect",
sector: "episodic",
tags: ["auth", "react", "effect"]
})
For important learnings:
openmemory_reinforce({ memory_id: "mem_xyz", boost: 1.5 })
Best Practices
- Query before implementing - Check for similar past work
- Store after completing - Save successful patterns and solutions
- Reinforce important memories - Boost salience of critical learnings
- Tag memories well - Include relevant technologies and patterns
Examples
Read the usage examples in the source repository.
View source ↗Source history
- Indexed on
- 2026-01-20
- Index updated
- 2026-03-04
- Branch
- main
Index and sync dates describe this directory. View the source repository for code history.
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