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
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Benchmark Results: Claude Agent Swarm with Memory System Shows 30-43% Token Cost Savings
A developer tested a 6-agent Claude swarm on a 40-point coding task with and without a custom memory system called Stompy. Results show Sonnet 4.6 with memory achieved perfect scores at $3.98 vs $7.04 without, while Haiku 4.5 failed completely without memory but scored 39/40 with it.
MCP Server Indexes Codebases into Knowledge Graph for 10x Token Reduction
A new MCP server called codebase-memory-mcp parses codebases into a persistent knowledge graph using tree-sitter, reducing token usage by at least 10x for structural queries. Benchmarked across 35 real-world repositories, it replaces file-by-file exploration with graph queries.
OmniCoder-9B: 9B Parameter Coding Agent Fine-Tuned on 425K Agentic Trajectories
Tesslate released OmniCoder-9B, a 9-billion parameter coding agent model fine-tuned on Qwen3.5-9B's hybrid architecture. It was trained on 425,000+ curated agentic coding trajectories from Claude Opus 4.6, GPT-5.4, GPT-5.3-Codex, and Gemini 3.1 Pro.