Question 1

How does Palimo compare to Mem0?

Accepted Answer

Mem0 calls frontier models like GPT-5 and Gemini 3.1 for memory extraction on every turn, meaning your data routes through OpenAI or Google and you pay frontier API pricing at scale. Palimo uses a fine-tuned local archivist LLM that runs on your infrastructure — zero per-call inference cost and your data never leaves your servers.

Question 2

Does Palimo require a GPU?

Accepted Answer

No. The archivist model is quantized to run efficiently on CPU. A GPU will speed up inference but is not required. The model runs well on Apple Silicon (M-series Macs) and standard Linux servers.

Question 3

What are the 9 memory operations?

Accepted Answer

Palimo's archivist supports 9 structured operations: INSERT (new fact), SUPERSEDE (replace outdated fact), MERGE (combine duplicates), CLARIFY (add detail), EXPIRE (time-bound removal), REAFFIRM (strengthen existing), DELETE (remove), UPDATE (modify), and IGNORE (skip irrelevant). This goes far beyond basic CRUD — it handles contradictions, temporal facts, and knowledge evolution.

Question 4

How do I integrate Palimo with my AI agent?

Accepted Answer

Three lines of code: (1) Before your LLM call, use mem.enrich() to retrieve relevant memory context. (2) Include that context in your system prompt. (3) After the LLM responds, use mem.commit() to let the archivist process the conversation. Works with any LLM — OpenAI, Anthropic, local models, LangChain, or custom agents.

Question 5

Is Palimo open source?

Accepted Answer

Palimo is a self-hosted product — it runs on your infrastructure and your data never leaves your servers. The Python SDK client is available via pip. The core archivist model and memory pipeline are proprietary, fine-tuned to outperform frontier-prompted memory extraction.