DEDUPED Hot AI repos, turned into use cases Get 5 a day

What you can do with Agent Memory

agent-memory is a Python runtime that gives AI agents long-term memory stored as plain Markdown files, with local ranked retrieval and a sleep-time consolidation layer, and Claude Code and Codex share one store.

Trended earlier

tigerless-labs/agent-memory on GitHub

Third-party repos we don't own or vouch for. AI-generated use cases can be wrong: review everything and run at your own risk.

What do you want to do?

5 use cases

  • Share one memory of decisions across Claude Code and Codex

    Let Claude Code and Codex read and write the same store so past decisions carry into new sessions.

    View prompt
    Use the agent-memory repo at <store path> to recall memories about <topic> with `mem recall` and `mem context`. Return the file paths and one-line abstracts that matched. Verify that the same paths come back when the query is run from Claude Code and from Codex.

  • Recover why a past decision was made and what it said then

    Pull the reasoning behind an old choice and check what the memory said on a given date.

    View prompt
    Use the agent-memory repo at <store path> to find the memory about <decision topic>, then show its outline and the version valid on <date>. Expected output: the memory path, its outline, and the as-of answer. Verify that the as-of answer differs from the current file when the memory was superseded.

  • Correct a stale memory without losing its history

    Update or replace a wrong memory while keeping the old version for lookup.

    View prompt
    Use the agent-memory repo at <store path> to correct the memory at <memory path> so it reflects <new fact>, keeping the old version for as-of recall. Expected output: the new memory path and the superseded status of the old one. Verify that a normal recall returns only the new version.

  • Rebuild a broken memory index from the Markdown files

    Delete the rebuildable index cache and regenerate it without losing any memory.

    View prompt
    Inside the agent-memory store at <store path>, run `rm -rf .index/ && mem rebuild`, then run a recall for <known topic>. Expected output: a non-empty L0 list. Verify that the count of Markdown memory files is the same before and after the rebuild.

  • Review what the sleep-time pass changed and proposed

    Check the dream report and dream status to see what consolidation added, updated, or proposed to delete.

    View prompt
    Inside the agent-memory store at <store path>, run `mem --json dream-status` and read the latest file in dream-reports/. Return the Dream state, the list of moved memories, and any proposed deletions. Verify that no deletion is applied unless it was confirmed.

How to use Agent Memory

  1. Check you have what it needs: Python environment able to install the agent-memory package, as described in the README; Claude Code or Codex CLI installed on the machine; A store directory set as AGENT_MEMORY_STORE.
  2. Open tigerless-labs/agent-memory on GitHub and read the README and license.
  3. Run the setup step described in the README so the host agents use the shared store at AGENT_MEMORY_STORE.
  4. Run mem recall "why files instead of a database" to see the ranked L0 list of matching memories.
  5. Run mem context "why files instead of a database" to get the top hits expanded in one call.
  6. Open the same question in Codex and check that it returns the same memory paths.

These steps come from our top use case, Share one memory of decisions across Claude Code and Codex, which also has a copy-paste agent prompt.

Agent Memory alternatives · Best AI repos for Software and DevOps

About this repo

Requirements

  • Python environment able to install the agent-memory package, as described in the README
  • Claude Code or Codex CLI installed on the machine
  • A store directory set as AGENT_MEMORY_STORE
  • Shell access to run the mem commands
Maintain Agent Memory? Add the Featured on Deduped badge

Paste this into your README:

[![Featured on Deduped](https://dedupedai.com/badge/tigerless-labs-agent-memory.svg)](https://dedupedai.com/use-cases/tigerless-labs-agent-memory/?ref=badge)

More badge styles and HTML

Questions about Agent Memory

What is Agent Memory?

agent-memory is a Python runtime that gives AI agents long-term memory stored as plain Markdown files, with local ranked retrieval and a sleep-time consolidation layer, and Claude Code and Codex share one store. It is an open-source project on GitHub: https://github.com/tigerless-labs/agent-memory.

Who is Agent Memory for?

Developers who use Claude Code or Codex across many sessions and want decisions and context to carry over without an API key.

What can I do with Agent Memory?

5 use cases so far, for example: Share one memory of decisions across Claude Code and Codex; Recover why a past decision was made and what it said then; Correct a stale memory without losing its history. Each has steps and a copy-paste agent prompt.

What do I need to run Agent Memory?

Python environment able to install the agent-memory package, as described in the README; Claude Code or Codex CLI installed on the machine; A store directory set as AGENT_MEMORY_STORE; Shell access to run the mem commands.

What are alternatives to Agent Memory?

Other hot AI repos in Software and DevOps: Harness2, iFixAi, Orca, Diagram Design, AI Memory.

Is Agent Memory safe to run?

Deduped does not own, audit or vouch for it. Read the README and license, run it on your own systems first and review every command before you run it.