AgentRAM review
A focused cloud memory API with scoped storage, shared namespaces, and SDK support.
Reviewed by iTechGuides Editors · Editorial team · Updated Oct 2026
AgentRAM is a hosted HTTP memory service for developers building AI agents and multi-agent applications. It stores and retrieves named text memories through a REST API, with separate per-agent scopes and shared namespaces for team workflows. Optional TTL expiry can remove entries automatically, while temporal assertions preserve provenance and history as values change. The service targets solo developers and small to mid-market teams that want persistent key-value memory without running a database, vector store, embedding pipeline, or agent framework.
Its main strength is straightforward platform access. The REST API is available through official Python and TypeScript SDKs, and an MCP server connects with MCP clients including Claude Desktop, Cline, Cursor, Windsurf, Claude Code, Codex, and the OpenAI Agents SDK. This combination suits teams that need one memory layer across different agent runtimes. The published plans show Starter with read, write, and delete operations, full API access, and email support; Builder includes the same core operations and API access with priority support. A free plan is also available, making the service approachable for initial projects.
AgentRAM is intentionally narrow rather than a broad memory platform. Literal text search covers keys and values, but there is no semantic memory, graph memory, or built-in AI feature set. Deployment is cloud-only, so teams requiring self-hosting or infrastructure control should consider another option. Choose AgentRAM when scoped key-value memory, temporal history, and MCP connectivity match your architecture; look elsewhere when retrieval must understand meaning, model relationships, or run inside your own environment.
AgentRAM pros and cons
- Where it wins
- Per-agent scopes and shared namespaces support multi-agent workflows
- Temporal assertions retain provenance and change history
- Official Python, TypeScript, and MCP integrations broaden access
- Where it doesn't
- Search is limited to literal text across keys and values
- Cloud deployment leaves no self-hosted option
- No semantic, vector, or graph memory features
AgentRAM fact sheet, pricing and score →
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