Memonto

At a glance

• Editor score
  5.4 / 10
• Pricing
  Open source
• Best for
  Ontology-driven self-hosted memory projects
• Paid from
  None
• Semantic memory
  Yes
• Facts checked
  13 Oct 2026

• Where it wins

  • Extracts ontology-matching facts from unstructured text
  • Supports contextual summaries and URI or SPARQL graph retrieval
  • Offers ephemeral, Jena-backed and Chroma-backed configurations

• Where it doesn't

  • Requires developers to define and work with an RDF ontology
  • The documented SDK language is Python
  • Self-hosted deployment puts configuration and operation with the project team

Our verdict on Memonto

Memonto is an open-source Python library for developers adding long-term memory to AI agents. Its defining choice is an RDF knowledge graph shaped by a user-defined ontology: the library extracts matching facts from unstructured text, then supports contextual recall and summaries or raw graph retrieval. That makes it a fit for solo, small and mid-market projects that need structured, self-hosted memory rather than a managed memory service. Projects that do not want to define an ontology or operate their own deployment may prefer a simpler or hosted approach.

The retrieval and storage options address different needs. Contextual retrieval can use Chroma, while raw knowledge-graph data can be queried by URI or SPARQL. For storage, Memonto supports ephemeral in-memory operation and persistent Apache Jena triple-store mode. Automatic ontology expansion can extend the ontology as information is processed. Together, these features make Memonto more than a vector-recall layer, but also mean teams should decide how they want structured facts and contextual results to work together. The library exposes synchronous and asynchronous APIs; the documented SDK language is Python.

The published product details name OpenAI and Anthropic as LLM providers, alongside Chroma and Apache Jena for storage. Its open-source model and self-hosted deployment suit developers who want control over the memory stack and are comfortable integrating its components. Choose Memonto when ontology-backed facts, graph access and configurable persistence are central requirements. It is a weaker fit for teams seeking a managed service, a non-Python SDK, or memory that requires no ontology design or operational ownership.

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Memonto pricing

Memonto fact sheet

Memonto integrations

Memonto lists 4 integrations on its own site.

• OpenAI
• Anthropic
• Chroma
• Apache Jena

Alternatives to Memonto

• CACloudflare Agent MemoryA cloud API for scoped, searchable memory across AI agent profiles and conversations.9.2
• MMem0A flexible memory layer with semantic, graph, and self-hosted deployment options.8.8
• MMMCP Memory ServiceA broad open-source memory layer for teams that can manage deployment and configuration.7.9

See all Memonto alternatives →

Featured on iTechGuides

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Reviewed by iTechGuides Editors · Editorial team · Updated Oct 2026

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Last updated 20 Oct 2026 · How we research and update