Credentials. The setup needs LLM and embedding API keys, which must be exported in your shell and never committed to the repo.
Who it is forAn agent developer whose harness forgets user details between sessions and needs a documented policy for when to recall, capture, and forget.
The problemAgents lose prior context and have no consistent rule for when to retrieve memory, store turns, or honor deletion requests.
The outcomeYour harness loads a written memory operating policy and can call AMA memory operations through its own tool interface.
What you need first
- Python 3.10 or newer
- Node.js 22.22.3 or newer (24.15 or newer also supported)
- pnpm 11 or newer
- An OpenAI-compatible chat-completions endpoint and embedding endpoint, with an API key
- Git, to clone the repository
Steps
git clone https://github.com/Sherlockwz/AMA.gitcd AMAcorepack enable pnpm./scripts/setup.sh- Export
AMA_LLM_API_KEY,AMA_LLM_BASE_URL, andAMA_EMBEDDING_URLin your shell, pointing to your OpenAI-compatible endpoints. - Open
SKILL.mdand map its retrieve, capture, synthesize, inspect, and delete rules onto your harness tool interface, using the six operations intools/.
Prompt for your agent
In the AMA repo at <path-to-AMA>, read SKILL.md and list each rule about when to retrieve prior context, capture turns, synthesize episodes, inspect state, and honor deletion requests. Expected output: a bulleted list of those rules with the heading each came from. Verify that every rule you list appears in SKILL.md text and none is invented.
Caveat. The README describes the six operations and the policy only in general terms, so you must read the tools/ folder to see their exact interfaces before binding them.
Use this in your AI agent
Ask your agent
Using the Deduped MCP, get the setup steps for Sherlockwz/AMA and "Add AMA long-term memory policy to an agent harness"
Add Deduped to Claude Code once
claude mcp add --transport http deduped https://dedupedai.com/mcp
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