OpenClaw CoreBrain Plugin: Persistent Memory for AI Coding Agents
OpenClaw Memory Plugin Solves Persistent Context Problems
The OpenClaw CoreBrain plugin addresses fundamental memory issues in OpenClaw where the AI agent writes to memory but then acts like it never happened, requiring users to re-explain everything in new sessions.
Core Problems Identified
- Memory is optional - the model decides whether to invoke memory tools and frequently skips this step
- The memory.md file lives in the context window and gets wiped during compaction
- No project separation exists, and searches often return noise with no way to trace where answers originated
CoreBrain Solution
The plugin stores memory outside the context window in a knowledge graph and auto-injects it before every query. This eliminates tool calls and the "choosing to remember" problem.
Installation and Compatibility
Install with: openclaw plugins install /openclaw-corebrain
The plugin is open-source and works with other AI agents including Claude Code, Cursor, and Codex. It can be self-hosted from the repository at RedplanetHQ/core.
📖 Read the full source: r/openclaw
👀 See Also
Rukuzu: Porting a 200,000 Line C++ Graph Database to Rust with Systematic Testing
The Rukuzu project describes a workflow for porting the 200,000-line C++ kuzu embedded graph database to Rust, using a Claude Code custom command to maintain both versions simultaneously and verify correctness through 2,700+ tests.
PeaDB: Redis-Compatible Database Coded with AI Assistants in C++20
A developer created PeaDB, a Redis 7.2.5 drop-in replacement written in C++20 using Codex, Copilot, and Claude, implementing ~147 commands with persistence, replication, and cluster support. Benchmarks show performance close to Redis.
Introducing Swarmcore: A Scalable Multi-Agent Framework in Python
Swarmcore is an open-source library for running scalable multi-agent workflows in Python, featuring sequential or parallel execution and expandable context management.
Fullerenes: Open-source persistent memory layer for coding agents cuts tokens by 64% on SWE-bench
Fullerenes uses a local SQLite knowledge graph built via Tree-sitter to give coding agents like Claude Code persistent memory, reducing token usage by 64% on SWE-bench and up to 96.6% on internal benchmarks.