llm-wiki-compiler v10 now supports @Google's Open Knowledge Format. OKF is a standard for sharing compiled knowledge between tools. Before this, your wiki was portable as plain markdown but there was no shared format for two different tools to actually exchange what they compiled. Now, LLMWiki can export your wiki as an OKF bundle and import bundles from other OKF tools. Imports are review gated by default so nothing lands in your wiki until you approve it. Your compiled knowledge is no longer locked to one tool. github.com/atomicstrata/llm-…

Agent memory stops being accurate over time if you're using the wrong semantic memory pattern. Most vibe coders don't realize other patterns exist as distinct choices. They either default to RAG or copy a tutorial setup without understanding the tradeoffs. Knowing these 3 patterns means you can pick the right one for your use case instead of hitting a wall six months in ⬇️

The 3 main architectural patterns for implementing semantic memory in LLM agents are: 1) Vector store retrieval (RAG-based) The most common pattern. When your agent needs context, it runs a similarity search against the current query and pulls the closest matches. 2) Knowledge graphs Instead of a similarity search, memory is stored as entities and relationships. Your agent retrieves facts by traversing connections between nodes rather than matching embeddings. 3) Hierarchical memory with consolidation Hermes uses this pattern. It divides memory into layers where facts are extracted from raw sessions and stored separately, so retrieval pulls what your agent actually learned rather than searching through full transcripts.

llm-wiki-compiler v0.8.0 shipped We're now 1.4K Stars on GitHub! ⭐ The new release adds guided project next steps, one-command quickstart, and the first eval harness for measuring wiki quality over time. Read the full changelog below ↓

Highlights of llm-wiki-compiler v0.8.0

The most pressing challenge in AI memory is multi-agent systems. Single-agent memory is largely solved, but multi-agent memory is not. Most teams aren't aware of this until integrating it, and breaks in production after a month. Here's what actually happens when multiple agents share memory 🧵

Shared memory means every agent in the system reads from the same pool and writes back to it after every session. The idea is that what one agent learns, all agents benefit from. The break happens when writes overlap. Two agents handle the same user in parallel, store different versions of the same fact, and the memory layer keeps both with no flags raised or conflicts resolved. The system then starts retrieving contradictions and acting on whichever one scores higher at retrieval time.

Your compiled wiki now has a local web browser 🖥️ llm-wiki-compiler v0.7.0 shipped read the full changelog below ↓

v0.7.0 closes one of the items from the last roadmap post. llmwiki view gives your compiled wiki a local web UI. sidebar, search, citation chips that jump to the exact source line, and a health dashboard that doesn't re-run lint on every load.