linkgo

ai-memory vs pumaDB: Features, Pricing & Which Is Better (2026)

A side-by-side comparison of ai-memory and pumaDB — features, pricing, and ideal use cases — to help you decide which AI tool fits your workflow.

a

ai-memory

AkitaOnRails

Free

Open-source, self-hosted long-term memory server that lets Claude Code, Codex, Cursor and other agent CLIs share context and hand off sessions.

Key features

  • Cross-Agent Handoffs: Typed, claim-once handoffs let you continue a session in a different agent CLI.
  • Automatic Capture: Lifecycle hooks record prompts, tool calls and session boundaries, sanitized before storage.
  • Markdown Wiki Storage: Git-backed wiki of .md files is the source of truth, with a rebuildable derived database index.
  • Multi-Mode Search: Full-text, entity and graph search plus optional vector search.
  • Team Sharing: Per-project knowledge sharing with multi-user auth, per-person attribution and an audit log.
  • Self-Hosted Single Binary: Rust binary or Docker server running on your laptop, homelab or LAN host.
  • Zero-LLM Default: Capture and search work without an LLM API key.
  • Broad Agent Support: Works with Claude Code, Codex, Cursor, Gemini CLI, OpenCode, Kiro CLI, Grok Build CLI and more.

Best for

  • A developer starts a feature in Claude Code and continues it in Codex without re-explaining the project.
  • A team shares project knowledge across members' coding agents with an audit trail.
  • An engineer keeps memory on a homelab server so every laptop and agent sees the same context.
  • A privacy-focused user runs fully local memory capture with no LLM calls or cloud service.
  • A maintainer browses and edits agent memories as plain markdown in a git repository.
  • A developer onboards a new agent CLI to an existing project through MCP install commands.
View ai-memory details
p

pumaDB

pumaDB

Paid

Durable JSON memory API for agents that stores and serves agent memory via hosted MCP or REST without requiring database setup.

Key features

  • Hosted MCP Endpoint: Provides a managed MCP interface so agents can connect to a memory control plane without self-hosting infrastructure or managing databases.
  • REST API Compatibility: Offers a standard REST API for inserting, querying, and retrieving JSON memory rows from existing services and agent frameworks.
  • Durable JSON Row Storage: Persists structured JSON rows as durable memory entries, enabling stateful behavior across agent sessions and long-lived context retention.
  • Memory Review and Inspection: Includes capabilities to review stored memories so developers and auditors can inspect agent state and historical interactions.
  • No-Database Setup: Eliminates the need to provision, configure, or maintain a dedicated database project — simplifying prototyping and production deployment.
  • Lightweight Integration: Designed for quick integration with agent systems and assistants, reducing engineering overhead to add persistent memory.
  • Hosted MCP and REST endpoints for integrations
  • Store arbitrary JSON rows as durable memory
  • Durable agent memory without a separate database project
  • Review and retrieve persisted agent memory
  • Simple API surface to connect agents to persistent storage

Best for

  • Persistent Conversational Context: Store user preferences and past conversation turns as JSON so chatbots can recall history across sessions.
  • Stateful Autonomous Agents: Provide long-term memory for agents that require recall of decisions, tasks, and learned information over time.
  • Rapid Prototyping without DB Work: Enable developers to build and test memory-enabled agents without provisioning or maintaining database infrastructure.
  • Audit and Debugging of Agent Behavior: Review stored memory entries to trace agent reasoning, reproduce issues, and validate decision contexts.
  • Cross-Service Memory Sharing: Use REST or MCP interfaces to share agent memory between microservices, chat platforms, and orchestration layers.
  • User Profile Management for Assistants: Persist structured user data (preferences, settings, history) as JSON to personalize assistant responses.
  • Maintain long-term memory for conversational agents
  • Persist agent state and interaction history as JSON
  • Enable stateless agents to access shared durable memory across sessions
  • Prototype agents quickly without managing database infrastructure
View pumaDB details