# Knowledge-and-Memory-Management: v0.0.2 — Knowledge Collection & Memory Management

> Source: <https://dev.to/mage0535/knowledge-and-memory-management-v002-knowledge-collection-memory-management-5dah>
> Published: 2026-07-07 12:02:14+00:00

Version 0.0.2 of Knowledge-and-Memory-Management delivers exactly what experienced developers expect: a clean release with portable path support and streamlined knowledge ingestion. All personal paths have been replaced with the `$AGENT_HOME`

environment variable, eliminating environment-specific configuration. The focus is on robust knowledge collection from web, video, and articles, paired with a persistent memory layer for retrieval.

**Architecture and Portability**

The system is divided into two packages: `collector`

and `memory`

. Every configurable path now defaults to `$AGENT_HOME`

. This means no more hardcoded `/home/user/data/`

or `/var/lib/agent/`

entries. When `AGENT_HOME`

is not set, the system falls back to a platform-specific default, but the expectation is that you define it explicitly in containerized or orchestrated deployments. The knowledge entry model is standardized across all sources, containing `source_url`

, `media_type`

, `raw_content`

, `summary`

, `embedding`

, and `timestamp`

.

**Knowledge Collection**

All collectors adhere to a consistent interface: `collect(source: str, **kwargs) -> KnowledgeEntry`

. This makes adding new sources straightforward.

**Memory Management**

Memory is stored in a vector database (FAISS or Chroma) under `$AGENT_HOME/memory`

. The clean release retroactively updates all previous path references. The `Memory`

class supports `save`

, `delete`

, `update`

, and `search`

operations. Search uses cosine similarity on stored embeddings for semantic retrieval. Batching and optional GPU acceleration are configurable for embedding generation.

**Code Example**

Below is a short, self‑contained example showing how to leverage the portable path for collection and memory storage:

``` python
import os
from knowledge_and_memory import Collector, Memory

# Initialize with $AGENT_HOME
agent_home = os.getenv("AGENT_HOME", "/opt/agent")
collector = Collector(base_dir=agent_home)
memory = Memory()

# Collect from sources
web_entry = collector.collect_web("https://example.com/tech-article")
video_entry = collector.collect_video("https://youtube.com/watch?v=abc")
article_entry = collector.collect_article("file:///docs/research.pdf")

# Store in memory
memory.save(web_entry)
memory.save(video_entry)
memory.save(article_entry)

# Semantic query
results = memory.search("state-of-the-art in NLP", top_k=3)
for r in results:
    print(r.summary)
```

The `base_dir`

parameter ties everything to `$AGENT_HOME`

, making deployment portable. The collectors normalize content, so the memory layer receives consistent objects.

**Migration and Performance**

Existing users must update configuration files to reference `$AGENT_HOME`

and remove absolute paths. A migration script is included in the release to automate this. Performance-wise, collection pipelines are asynchronous, and memory operations are batched to reduce overhead. The vector database can operate in‑memory for low latency or persist to disk for durability.

v0.0.2 is a practical release for agent developers who need reliable memory without path headaches. Future versions will target additional source types and advanced consolidation strategies, but this clean foundation already delivers on the core promises of knowledge collection and memory management.