beta-agent-memory
Long-term memory systems for AI agents. Implements vector memory, entity tracking, conversation summarization, and persistent context across sessions.
Best use case
beta-agent-memory is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Long-term memory systems for AI agents. Implements vector memory, entity tracking, conversation summarization, and persistent context across sessions.
Teams using beta-agent-memory should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/beta-agent-memory/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How beta-agent-memory Compares
| Feature / Agent | beta-agent-memory | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
Long-term memory systems for AI agents. Implements vector memory, entity tracking, conversation summarization, and persistent context across sessions.
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
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SKILL.md Source
# Agent Memory System
Give your AI agent persistent, long-term memory across conversations and sessions.
## Memory Types Implemented
### Episodic Memory
Stores episodes/events from conversations:
- Key facts extracted per conversation
- Decisions made and context
- User preferences and patterns
- "Remembering" past interactions
### Semantic Memory
Structured knowledge storage:
- Entity definitions and relationships
- Facts about the world
- Domain knowledge base
- Learned procedures
### Procedural Memory
Agent's own capabilities:
- Known skills and tools
- How to use different APIs
- Response patterns that worked
## Architecture
```
User Input
↓
Short-term (current session context)
↓
Memory Retrieval → Top-k relevant memories (vector search)
↓
Context Injection → Combined prompt
↓
LLM Response
↓
Memory Storage → Extract new facts, update entities
```
## Features
- **Vector-based storage** (ChromaDB or Pinecone)
- **Entity extraction** (spaCy NER)
- **Conversation summarization** (every N turns)
- **Relevance scoring** for retrieval
- **Forgetting/summarization** of old memories
## Use Cases
- Personal AI assistant that remembers you
- Customer support agent with context
- Research agent with persistent knowledge
- Trading agent with market memory
- Personal CRM (remembering people and their context)
## Technical Stack
- ChromaDB / Pinecone (vector store)
- spaCy (entity extraction)
- LangChain (memory abstractions)
- PostgreSQL (structured memory)
## Pricing
| Type | Context Window | Price |
|------|-----------------|-------|
| Basic | 100K tokens | $100 |
| Pro | 1M tokens | $300 |
| Enterprise | Unlimited | $800 |
---
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