Pieces
AI memory layer that captures context from your work so you and your AI tools can recover decisions and resume where you left off.
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About Pieces
Pieces is listed in our memory & RAG category and is aimed at people who want a ready-to-use product. AI memory layer that captures context from your work so you and your AI tools can recover decisions and resume where you left off. Pricing, as the maker states it: Paid. Maturity: Active. We last checked the official link on 2026-10-18.
Pieces at a glance
- Category: Memory & RAG
- Built for: People and teams who want a ready-to-use AI product
- Pricing: Paid (as the maker states it)
- Official site: pieces.app
- Listed on AgentScout: Oct 18, 2026
Pieces FAQ
What is Pieces?
AI memory layer that captures context from your work so you and your AI tools can recover decisions and resume where you left off.
How much does Pieces cost?
Pieces's pricing, as the maker states it: Paid. Check the official site for current plans.
What are alternatives to Pieces?
Similar tools include Zep, Claude-Mem, AgentMemory, projectmem, OpenViking and Beacon.
Facts from our link-checked listing and the official site. See something wrong? Suggest an edit.
Pieces ratings and reviews
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Pieces alternatives
Other memory & RAG tools in our directory. See all memory & RAG tools →
Zep
Context layer that unifies business data, documents and conversations into memory so agents complete tasks correctly.
Claude-Mem
Gives coding agents memory across sessions by capturing what they do, compressing it and feeding the relevant context back in later.
AgentMemory
Persistent memory layer for AI coding agents.
projectmem
A local-first memory layer for coding agents that records issues, attempts, fixes and decisions and warns your agent before it repeats a past mistake.
OpenViking
Context database for AI agents that keeps memory, knowledge and skills in one inspectable virtual filesystem agents can navigate.
Beacon
An open-source memory layer that captures traces from coding agents across harnesses and turns them into self-improving memory and visibility into what your agents did.
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