faya-session-memoryPersistent session memory system that prevents knowledge loss after context compaction. Converts session transcripts to searchable Markdown, builds an auto-u...
Install via ClawdBot CLI:
clawdbot install moltbotmolty-del/faya-session-memoryGrade Fair — based on market validation, documentation quality, package completeness, maintenance status, and authenticity signals.
Generated Mar 21, 2026
A customer support team uses an AI agent to handle ongoing technical support cases. The session memory system prevents loss of specific troubleshooting steps, customer preferences, and previous solutions discussed across multiple sessions. This ensures consistent support quality even when context windows are compacted during long-running conversations.
A law firm employs an AI agent to assist with case research and document preparation. The glossary tracks all mentioned cases, legal precedents, client names, and key decisions across research sessions. This prevents the agent from forgetting specific case details or legal citations after context compaction during extended research periods.
A consulting firm uses an AI agent to track project progress, stakeholder discussions, and decision logs across multiple client engagements. The three-layer memory architecture maintains detailed records of who said what, when decisions were made, and project timelines, ensuring continuity across weekly status meetings and planning sessions.
A medical research team utilizes an AI agent to document experimental protocols, researcher discussions, and data analysis decisions. The session memory preserves specific methodological details, researcher contributions, and analytical choices that might be lost during context summarization in long-term research projects.
A development team employs an AI agent during sprint planning, code reviews, and architecture discussions. The system maintains searchable records of technical decisions, bug discussions, and feature specifications across multiple development sessions, preventing knowledge loss when context is compacted during extended coding sessions.
Offer the session memory system as a cloud-based service with tiered pricing based on session volume and storage capacity. Include automated glossary generation, cron job management, and API access for integration with existing AI platforms. Provide premium support for custom entity detection and integration assistance.
Sell perpetual licenses to large organizations with custom deployment options, including on-premises installation and integration with proprietary AI systems. Include professional services for initial setup, customization of entity detection patterns, and training for internal teams. Offer annual maintenance contracts for updates and support.
Provide specialized consulting services to implement the session memory system within existing AI workflows. Services include assessment of current knowledge loss issues, customization of glossary patterns for specific industries, integration with existing cron systems, and training for optimal usage patterns.
💬 Integration Tip
Start by populating KNOWN_PEOPLE and KNOWN_PROJECTS in the glossary script before full implementation, as this dramatically improves entity detection accuracy from the beginning.
Scored Apr 19, 2026
Search and analyze your own session logs (older/parent conversations) using jq.
Typed knowledge graph for structured agent memory and composable skills. Use when creating/querying entities (Person, Project, Task, Event, Document), linkin...
Audit, clean, and optimize Clawdbot's vector memory (LanceDB). Use when memory is bloated with junk, token usage is high from irrelevant auto-recalls, or setting up memory maintenance automation.
Enable and configure Moltbot/Clawdbot memory search for persistent context. Use when setting up memory, fixing "goldfish brain," or helping users configure memorySearch in their config. Covers MEMORY.md, daily logs, and vector search setup.
Local memory management for agents. Compression detection, auto-snapshots, and semantic search. Use when agents need to detect compression risk before memory loss, save context snapshots, search historical memories, or track memory usage patterns. Never lose context again.
You MUST use this for gathering contexts before any work. This is a Knowledge management for AI agents. Use `brv` to store and retrieve project patterns, dec...