Using project narratives to manage memory in large OpenClaw projects

A developer on r/openclaw describes a method for managing memory challenges when working on large, multi-layered projects with OpenClaw. The core technique involves creating 'project narratives' to maintain system awareness.
The process
After every major development milestone, the developer spawns a separate OpenClaw worker to examine the entire codebase from a fresh perspective. This worker's task is to write a narrative about what it thinks the project is doing, based solely on the contents of the repository. The developer calls this resulting file the 'project narrative.'
The developer personally scans this narrative, then asks the separate worker to analyze it for issues. The worker reports on any broken pipelines, redundancies, or other problems it identifies. This report is then fed back to the core worker for evaluation and consideration.
How narratives function
According to the source, these narratives serve multiple purposes:
- They become a reference document that the main worker reviews before starting new major revisions or additions
- They help the system avoid forgetting critical maintenance tasks while focusing on new features
- They can be tweaked if the developer finds that important features or focus areas aren't being emphasized properly
- They function as historical guideposts for rolling back development processes
- They could potentially serve as a master prompt for rebuilding a project from scratch after catastrophic failure
Implementation tip
The developer emphasizes one key implementation detail: when creating a new narrative at each iteration, you should request a complete, clean recreation of the system narrative—not just a revision of the previous file. This ensures the narrative reflects the current state of the codebase without inheriting outdated assumptions.
📖 Read the full source: r/openclaw
👀 See Also

Stop Burning Claude Code Tokens on Chat Questions
A developer on r/ClaudeAI saved their weekly token cap by routing simple chat questions to cheap models like Haiku, reserving Claude Code for agent tasks like multi-file edits.

Field Report: Qwen 3.6 27B on an M2 MacBook Pro (32GB) – Painfully Slow but Smart Output
Running Qwen 3.6 27B IQ4_XS on an M2 MacBook Pro with 32GB RAM yields 7.9 t/s initially, degrading to 3.1 t/s at 52k context. Code quality impresses, but memory bandwidth is the bottleneck.

OpenClaw Implements API Cost Fix and Local Model Tool Improvements
OpenClaw has rolled out key updates addressing API usage costs and improving local model tool integrations, enhancing developer experience and operational efficiency.

OpenClaw Discord proxy fix for REST API timeout issues
A user reports fixing OpenClaw Discord connection issues where WebSocket connects but REST API calls fail with "fetch failed UND_ERR_CONNECT_TIMEOUT" errors. The solution involves creating a proxy-preload.cjs file and setting global undici proxy settings.