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Kimi Code forgets project decisions between sessions. The Mem0 plugin captures completed work, recalls relevant context before a response, and gives Kimi explicit memory tools and skills.
Current plugin version: 0.3.1.

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Prerequisites

  1. A Mem0 Platform account and API key:
  2. Kimi Code with plugin support.
  3. Python 3.10+ and Git on your machine.

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Quick start

Export the key in the shell where you start Kimi Code:
Inside Kimi Code, install the native plugin bundle and reload the session:
Run /plugins info mem0 to confirm that the plugin, MCP server, skills, hooks, and sidekick agent loaded.

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What you get

  • Automatic capture: Kimi records completed exchanges locally and flushes durable project knowledge to Mem0 in the background.
  • Automatic recall: Relevant memories are added before Kimi answers the first prompt in a session, if that prompt has at least 20 characters.
  • Explicit search: Kimi can call search_memories when it needs a more specific answer.
  • Six memory skills: Search, status, remember, forget, pause, and resume use the same memory behavior as the other Mem0 coding-agent plugins.
  • Project scoping: Memories stay attached to the repository, with separate personal and shared project lanes.
  • Kimi sidekick: A focused subagent can investigate or implement a bounded task in a separate context. Filesystem isolation depends on the environment Kimi provides.
Credentials are redacted before memory capture. If Mem0 is unavailable, hooks fail open so Kimi can continue its normal work.

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How it works

Kimi’s native lifecycle events are translated into the shared Mem0 memory lifecycle:

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Verify the plugin

In one session, say:
Start a new Kimi session in the same repository and ask:
Kimi should return ORCHID-9274 from memory.

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Managing the plugin

Run /reload or start a new session after enabling, disabling, or reinstalling the plugin.

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Search and capture

The local search_memories tool accepts query, top_k, category, scope (repo, dir, or mine), and optional run_id with every scope. Use a known session ID to recall memories saved in that session; omit it to search across sessions. See search scopes for the shared Python search contract, including legacy repository memory compatibility. Captured prompts and responses retain their full redacted text without a per-message character cutoff. Large extraction inputs are split across requests without dropping message text; recall output and tool-result previews have separate limits.

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Troubleshooting

Cursor

Add the same persistent project memory to Cursor

Claude Code

Use Mem0 with Claude Code and its isolated sidekick
Using Mem0? Star us on GitHub to help more developers discover memory for AI apps.