engramclawSistema de memoria persistente para agentes IA. Usa mem_save después de bugfixes, decisiones, descubrimientos, cambios de config. Usa mem_search cuando el us...
Install via ClawdBot CLI:
clawdbot install DragonJAR/engramclawGrade Fair — based on market validation, documentation quality, package completeness, maintenance status, and authenticity signals.
Accesses system directories or attempts privilege escalation
sudo mvCalls external URL not in known-safe list
https://github.com/Gentleman-Programming/engramAudited Apr 17, 2026 · audit v1.0
Generated Mar 21, 2026
A development team uses Engram to track bug fixes, architectural decisions, and code patterns across multiple sessions. When a developer starts a new task, they search for similar past work to avoid duplication and ensure consistency in technical decisions.
Researchers employ Engram to log discoveries, configuration changes, and experimental results from AI model training. At the start of each session, they retrieve previous context to build upon findings, and save summaries after significant breakthroughs.
Support agents use Engram to record solutions to common technical issues and user-specific configurations. When a user mentions a problem, the agent searches memory for past fixes, speeding up resolution and maintaining a knowledge base of effective troubleshooting steps.
Project managers integrate Engram to store decisions, milestones, and configuration changes from project meetings. At session ends, they create summaries to preserve context, ensuring continuity across team handoffs and reducing onboarding time for new members.
Tutors leverage Engram to remember student progress, learning patterns, and customized lesson plans. When starting a session, they retrieve past context to tailor instruction, and save insights after each lesson to adapt future teaching strategies.
Offer Engram as a cloud service with tiered pricing based on memory storage capacity and number of AI agents. Revenue comes from monthly or annual subscriptions, targeting enterprises and development teams needing persistent memory for collaborative AI workflows.
Sell perpetual licenses or annual enterprise contracts for on-premises deployment, including premium support and customization. This model appeals to large organizations with strict data privacy requirements, generating revenue through upfront fees and maintenance renewals.
Provide a free version with basic memory functions and limited storage, while charging for advanced features like enhanced search, analytics, and integration with other tools. Revenue is driven by upgrades from individual developers and small teams to premium tiers.
💬 Integration Tip
Ensure MCPorter and Engram binaries are correctly installed and registered via MCP to enable seamless tool calls; the agent must actively decide when to use memory functions based on context significance.
Scored Jun 19, 2026
Work with Obsidian vaults (plain Markdown notes) and automate via notesmd-cli.
Work with Obsidian vaults (plain Markdown notes) and automate via obsidian-cli.
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