cortex-memoryLong-term memory for OpenClaw agents — auto-recall before turns, auto-capture after, tools for search/save/forget.
Install via ClawdBot CLI:
clawdbot install matthewubundi/cortex-memoryGrade Fair — based on market validation, documentation quality, package completeness, maintenance status, and authenticity signals.
Calls external URL not in known-safe list
https://github.com/Ubundi/CortexUses known external API (expected, informational)
amazonaws.comAudited Apr 16, 2026 · audit v1.0
Generated Feb 23, 2026
A sales team uses Cortex to track interactions with clients across multiple meetings, storing key facts, preferences, and decisions in a knowledge graph. This enables agents to recall past conversations and relationships, improving personalized follow-ups and reducing reliance on manual notes.
A software development team integrates Cortex to store technical decisions, entity relationships (e.g., APIs, modules), and temporal changes from project discussions. This helps new team members quickly understand project history and dependencies, enhancing onboarding and collaboration.
Medical professionals use Cortex to maintain structured long-term memory of patient histories, including symptoms, treatments, and emotional insights over time. This supports accurate recall across sessions, aiding in diagnosis and continuity of care while adhering to privacy standards.
Researchers employ Cortex to store and retrieve facts, entities, and relationships from literature reviews and team discussions. It facilitates cross-session recall of key findings and temporal reasoning on evolving theories, streamlining collaborative projects and paper writing.
Law firms utilize Cortex to track case details, entity relationships (e.g., parties, evidence), and temporal changes in legal arguments. This enables quick retrieval of past case facts and superseded information, improving case preparation and strategy development.
Offer tiered subscription plans for API access, with pricing based on usage limits (e.g., number of queries or storage). This model provides recurring revenue and scales with customer growth, targeting businesses needing structured memory solutions.
Sell enterprise licenses with custom integrations, premium support, and enhanced security features. This model caters to large organizations requiring dedicated deployment and compliance, generating high-value contracts.
Provide a free tier with basic memory features and limited usage, encouraging adoption. Upsell to paid plans for advanced capabilities like full retrieval modes and higher limits, driving conversion from individual users to teams.
💬 Integration Tip
Set up environment variables correctly and verify the API connection first; use fast mode for quick checks and full mode for complex queries involving graph traversal.
Scored May 21, 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...
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...
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.