Let an agent persist and retrieve conversation, user, task, or operating context across steps and sessions. This comparison covers products mapped to the task for AI agents as the direct users.
Companies collected
7
Products compared
11
Audience
AI Agents
Market observation
Store & Retrieve Memory products are built for AI agents
Dedicated memory services, temporal graphs and agent-native shared state fit this task. RAG retrieval alone does not establish persistent memory; adoption evidence here is largely indirect or company-reported.
SOTA2 collected 7 companies and 11 products with direct evidence that AI agents use the product to perform this task.
Compatibility alone is not sufficient: the audience must be a first-class user of the product.
Ranked comparison
Ranking
We compared 11 products from 7 companies and show the first 6 positions below.
Open-source Context Graph framework that powers Zep.
Why #1
Temporal graph extraction, bi-temporal facts and vector/BM25/graph retrieval provide concrete memory mechanisms, with a built-in MCP server for direct agent access.
DescriptionPrimary Use CasesPricing
Task fitStrong
Adoption evidenceModerate
Product evidenceStrong
PricingStrong
Market fitStrong
Best for
Agents accessing temporal, relationship-rich memory through MCP.
Pricing
Open-source under permissive license
What to verify
The reported 20k GitHub stars do not establish production deployments; hosting and operating costs are unspecified.
Graph-vector database for AI memory combining knowledge graphs, vectors, temporal, and full-text search in one Rust engine for agent memory, RAG, and company brains.
Why #3
Explicitly provides persistent, queryable agent memory combining graph relationships, vectors, temporal context and full-text search in one engine.
DescriptionPrimary Use CasesPricingSocial Following
Task fitStrong
Adoption evidenceLimited
Product evidenceStrong
PricingStrong
Market fitStrong
Best for
Agents needing relationship-aware, semantic and temporal memory queries.
Pricing
$0.061 / vCPU / hr
What to verify
Production deployments are not documented, and the quoted vCPU rate does not establish total operating cost.
Distributed filesystem for real-time, multi-agent collaboration.
Why #4
An agent-focused distributed filesystem shares files and session context across sandboxes, supported by Blaxel's explicit persistent-shared-memory positioning.
DescriptionPrimary Use CasesPricingFree Plan Or Trial
Task fitStrong
Adoption evidenceLimited
Product evidenceModerate
PricingModerate
Market fitStrong
Best for
Agents sharing files and session context across Blaxel sandboxes.
Pricing
Free during beta.
What to verify
Post-beta pricing and retention or durability guarantees are not supplied.
A middleware runtime SDK that integrates with agent frameworks to provide reasoning reuse, semantic file memory, loop detection, tool supervision, context compression, and reaso...
Why #6
Its agent-framework runtime SDK supplies semantic file memory and reasoning reuse, while the company explicitly describes remembering past solutions with benefits across runs.
DescriptionPrimary Use CasesY Combinator
Task fitStrong
Adoption evidenceLimited
Product evidenceStrong
PricingLimited
Market fitStrong
Best for
Agents reusing prior solutions and file context across runs.
Pricing
Pricing not published
What to verify
Standalone memory read/write interfaces, retention controls and pricing are not specified.
