The v0.0.2 release of Knowledge-and-Memory-Management is exactly what a clean release should look like: no leftover personal paths, no hardcoded dangling in the config, and a clear split between knowledge collection and memory management.
If you've been following the 0.0.x line, this is the release where the tool finally becomes portable across machines and agents.
Here's what changed and why it matters. $AGENT_HOME: The Portability Fix The most visible change in v0.0.2 is the replacement of all absolute personal paths with the environment variable.
Previously, the agent’s knowledge store was tied to a specific filesystem layout — a dealbreaker if you're running agents in containers, across multiple users, or on ephemeral CI runners.
Now, every collection, memory index, and metadata file resolves against .
If the variable is unset, the agent falls back to a sensible default (typically ), but the contract is explicit: set once, and the entire knowledge pyramid moves with it.
Here's the core path-resolution logic that now underpins everything: This single change ripples through the whole codebase.
No more path surgery when you switch laptops.
No more hacks to move a knowledge base between team members.
Set and go.
Knowledge Collection: Web, Video, Articles The collection pipeline in v0.0.2 is built around three source types: web pages, video transcripts, and long-form articles.
Each source type has its own ingestion path, but they all converge on a common memory format.
Web: The collector fetches a URL, extracts the main content (stripping nav, footers, and boilerplate), and stores the cleaned text along with the source URL and fetch timestamp.
The emphasis is on preserving provenance — every chunk knows where it came from.
Video: Video collection relies on subtitle/transcript extraction rather than audio transcription.
This keeps the pipeline fast and deterministic.
If a video has no captions, the collector records the metadata but skips content extraction.
No fabricating transcripts.
Articles: Longer-form content (such as PDFs or full blog posts) goes through a chunking step.
The agent splits the article into manageable segments with overlapping boundaries, which later makes retrieval and memory consolidation significantly easier.
All collected items land in with a sidecar JSON metadata file.
The directory layout is stable and documented, which means you can inspect what the agent knows just by looking at the filesystem.
Memory Management: Beyond Raw Storage Storage is not memory. v0.0.2 makes that distinction explicit.
The memory management layer is responsible for deduplication, time-based decay, and consolidation.
Deduplication: If you collect the same article twice, the agent detects the URL hash and updates the existing entry instead of creating a duplicate.
Content hashes are computed on the normalized text, not the raw bytes, so minor formatting changes don't cause duplicate bloat.
Decay: Memory entries carry a timestamp.
When the agent retrieves a piece of knowledge, it refreshes that timestamp.
A pruning pass removes or archives entries that haven't been accessed in a configurable window.
This isn't AI magic — it's a simple LRU policy applied to your knowledge base.
Consolidation: The agent groups related chunks by source and by topic via a lightweight keyword overlap score.
This is not a vector store.
It's a deterministic heuristic that lets the agent say "this new article overlaps with three existing chunks" and merge them into a single memory entry.
The key design choice is that memory management is inspectable.
Everything happens on the filesystem, in JSON, with explicit timestamps.
You can delete a memory entry with , and nothing breaks.
What a Clean Release Means Here "Clean release" in the v0.0.2 notes isn't just marketing.
It means: No personal artifacts — no , no , no Windows drive letters in the codebase.
Deterministic layout — given the same , you get the same collection structure across machines.
Backward-