Agents Memory review
Governed graph and profile memory with APIs, lifecycle controls and self-hosting.
Reviewed by iTechGuides Editors · Editorial team · Updated Oct 2026
Agents Memory gives developers and businesses infrastructure for AI-agent memory across conversation, visitor and merchant scopes. It stores interactions, extracted facts, user profiles and conversation context, then exposes that information through a management dashboard, HTTPS API, Python SDK and Node SDK. Its memory model covers semantic, episodic, profile and graph use cases, making it suitable for teams that need persistent context across conversations rather than a simple session cache.
Pricing starts with a Free plan that includes Core Temporal Graph access, standard multi-modal extraction, all three memory scopes, full API and SDK access, community support and a 90-day memory archive. Pro is listed at $35 and adds Advanced Temporal Graph, multi-modal extractors, sub-200ms retrieval, a Fact Invalidation Engine, memory lifecycle configuration and webhooks. Scale is listed at $499 and adds automated business connectors, custom ontology modeling, an analytics dashboard, a dedicated account manager, 24/7 priority support and SSO/SAML. Paid plans may be billed monthly or annually under the terms.
The platform’s standout capability is temporal graph memory with fact versioning and contradiction detection, paired with persistent profiles consolidated across conversations. Lifecycle configuration and temporal decay provide controls for how long information remains useful, while data export and deletion controls support operational governance. Agents Memory runs through the web and API, with cloud and self-hosted deployment; custom VPC or on-premise deployment is offered on Scale. Choose it when governed, multi-scope memory and deployment control matter. Teams needing only lightweight conversation storage, or advanced connectors and identity controls without Scale, should consider a narrower tool.
Agents Memory pros and cons
- Where it wins
- Temporal graphs track versions and detect contradictory facts
- Hybrid retrieval combines semantic, recency and frequency signals
- Cloud and self-hosted deployment with API, SDKs and webhooks
- Where it doesn't
- Automated connectors and custom ontology modeling require Scale
- SSO/SAML and 24/7 priority support are limited to Scale
- Self-hosted deployment is available only with the Scale plan
Agents Memory fact sheet, pricing and score →
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