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Get Started Free →majiayu000/building-with-agent-memory
Build persistent memory systems for AI agents using Mem0, claude-mem, or custom implementations.
Passed 18 of 21 test cases with the skill, and 12 of 21 without it.
and 4 more in the full report
6 fixed13 passed either way3 still failing0 broke
Tokens, turns and time describe resource use, not a dollar price.
One of 6 test cases the skill fixed. Same model, same prompt, with and without the skill.
When running Mem0 with default out-of-the-box local configuration without external cloud parameters, where are the vector store embeddings and interaction history database stored by default?
| Checked | Without skill | With skill |
|---|---|---|
| The response identifies Qdrant on disk at /tmp/qdrant as the default vector store location. | Missed | Passed |
| The response identifies SQLite at ~/.mem0/history.db as the default history database location. | Passed | Passed |
The instructions your model reads when the skill loads.
Production-grade memory layers for AI agents that persist context across sessions.
| Need | Tool | Why |
|---|---|---|
| Simple memory for any agent | Mem0 | Open-source, Python SDK, minimal setup |
| Claude Code agent memory | claude-mem | Automatic hooks, 3-layer retrieval |
| Enterprise/self-editing memory | Letta (MemGPT) | Agent-driven memory management |
| Custom memory | RAG + state management | Full control |
pip install mem0ai
export OPENAI_API_KEY="your-key"
from mem0 import Memory
m = Memory()
# Add memory from conversation
messages = [
{"role": "user", "content": "Hi, I'm Alex. I love basketball and gaming."},
{"role": "assistant", "content": "Hey Alex! I'll remember your interests."}
]
m.add(messages, user_id="alex")
# Search memories
results = m.search("What do you know about me?", filters={"user_id": "alex"})
test cases passed, vs 12 / 21 without
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