Knowledge-and-Memory-Management v0.0.2: Portable Knowledge Collection & Memory Management

Version 0.0.2 of Knowledge-and-Memory-Management delivers a clean release focused on portable knowledge collection and memory management for autonomous agents. The primary improvement is the replacement of hard-coded personal paths with the $AGENT_HOME environment variable, ensuring seamless deployment across different machines without manual configuration. This update targets developers building agentic systems that require persistent, context-aware memory from diverse digital sources.

The $AGENT_HOME variable serves as the root directory for all knowledge artifacts—collection caches, memory vectors, and configuration files. By standardizing the base path, the system eliminates path discrepancies between environments, which is critical for containerized or distributed deployments. All internal references now use this portable base, making the setup reproducible from local workstations to production clusters.

Knowledge Collection Pipeline

The collection subsystem handles three primary inputs: web scraping, video transcripts, and article aggregation. Each source goes through a modular pipeline that extracts, normalizes, and indexes content into a unified memory format.

• Web scraping: Uses a headless browser to render JavaScript-heavy pages, extracting text and metadata. The collector respects robots.txt and implements caching to avoid redundant requests.
• Video processing: Retrieves transcripts via platform-specific APIs (e.g., YouTube captions). For external video files, it falls back to OCR-based subtitle extraction.
• Article ingestion: Parses RSS feeds, Markdown files, and PDFs, preserving structural metadata like authorship and publication dates.

All collected content is normalized into a schema that includes raw text, source URL, timestamps, and embeddings—ready for downstream memory operations.

Memory Management Architecture

The memory management module adopts a two-tier system: episodic memory for short-term interactions (with configurable TTL) and semantic memory for long-term knowledge stored in a vector database. The collection pipeline automatically deduplicates content using hash-based comparison and applies extractive summarization to compress lengthy articles. Memory retrieval is optimized via vector embeddings, enabling efficient similarity search for querying past knowledge.

Code Example

The following snippet demonstrates a basic collection and storage workflow using $AGENT_HOME:

from knowledge_memory import Agent

agent = Agent(home="$AGENT_HOME")
agent.collect.web("https://example.com/tutorial")
agent.memory.store()

Here, $AGENT_HOME defines the storage location, making the process environment-agnostic. The collector fetches the web page, extracts content, and persists it in memory under the configured base path.

Configuration and Extensibility

System settings are managed via a YAML file located at $AGENT_HOME/config.yaml. This file specifies source parameters, memory storage options (e.g., local disk vs. cloud sync), and collection schedules. The use of environment variables ensures that sensitive paths are never hardcoded. For experienced developers, the pipeline is extensible: custom source implementations can be added by subclassing the Collector base class.

Performance and Scaling

This release includes optimizations in the indexing layer, reducing memory overhead during collection. The deduplication algorithm scales with large knowledge bases by using incremental hash comparisons. Memory storage supports both local databases and remote vector stores, offering flexibility for production deployments.

Conclusion

Knowledge-and-Memory-Management v0.0.2 provides a robust foundation for agents requiring persistent, portable memory. The $AGENT_HOME approach simplifies deployment, while the multi-source collection pipeline ensures comprehensive knowledge intake from web, video, and articles. For developers building autonomous systems that need to learn and recall information, this release offers a modular, environment-agnostic solution. Future iterations will focus on advanced memory consolidation and cross-agent knowledge sharing.