pgmemoryPersistent semantic memory for OpenClaw agents — PostgreSQL + pgvector
Install via ClawdBot CLI:
clawdbot install jbushman/pgmemoryGrade Fair — based on market validation, documentation quality, package completeness, maintenance status, and authenticity signals.
Sends data to undocumented external endpoint (potential exfiltration)
Post → https://github.com/pgvector/pgvectorCalls external URL not in known-safe list
https://api.voyageai.com/v1/embeddingsUses known external API (expected, informational)
api.openai.comAI Analysis
The external API calls (Voyage AI, OpenAI) are consistent with the skill's stated purpose of generating embeddings for semantic memory. The 'UNKNOWN_DATA_SINK' signal for pgvector is a false positive, as it refers to a PostgreSQL extension, not an active data exfiltration endpoint. No evidence of credential harvesting, hidden instructions, or obfuscation was found in the provided definition.
Generated Mar 21, 2026
Agencies managing multiple client projects can use pgmemory to persist architectural decisions, client-specific constraints, and infrastructure setups across development sessions. This ensures new team members or AI agents quickly access critical project histories, reducing onboarding time and maintaining consistency.
DevOps teams can store infrastructure changes, deployment configurations, and system constraints in pgmemory. This allows AI agents to recall past incidents and solutions, automating troubleshooting and ensuring compliance with operational standards across sessions.
Support teams using OpenClaw agents can leverage pgmemory to remember customer preferences, common issues, and resolution strategies. This enables persistent context across interactions, improving response accuracy and personalization without manual data entry.
Researchers can document hypotheses, data processing decisions, and analytical constraints in pgmemory. AI agents can then retrieve past insights and methodologies, facilitating reproducible studies and accelerating iterative analysis across multiple sessions.
Offer pgmemory as a managed service with tiered pricing based on memory storage, query volume, and support levels. Revenue comes from monthly subscriptions, targeting businesses needing persistent AI memory without managing PostgreSQL infrastructure.
Provide custom setup, configuration, and training services for organizations integrating pgmemory into their OpenClaw workflows. Revenue is generated through project-based fees and ongoing maintenance contracts, focusing on complex enterprise deployments.
Monetize pgmemory by offering premium support, advanced features, and guaranteed SLAs for the open-source version. Revenue streams include annual support subscriptions and paid plugins for enhanced functionality like advanced analytics.
💬 Integration Tip
Ensure PostgreSQL with pgvector is properly configured before setup, and regularly run the decay cycle to manage memory expiration and maintain performance.
Scored Apr 19, 2026
Audited Apr 16, 2026 · audit v1.0
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