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

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

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.

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.

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.

Freemium

Analytics for MCP servers — see which AI clients connect, which tools they call, whether the work completes and what to fix.

One-line install: Drop the @trackmcp/sdk into an existing TypeScript or Python MCP server with no manual event tagging
Client breakdown: See the share of traffic coming from Claude, Cursor, ChatGPT and custom agents
Tool analytics: Per-tool call volume, adoption, latency percentiles and health status ranked in one table
Workflow paths: Follow sessions from first request to result and see exactly where they stop
Outcome tracking: Completion rates, sessions that reached a tool and returning clients within seven days
Hidden-error detection: Flags calls that report 200 OK while returning isError, with retry counts and a suggested fix
Real-time dashboard: Events appear as they happen across production and staging environments
Alerts: Slack and webhook notifications when a tool starts failing or a workflow degrades

An MCP server author finds out which of their tools agents actually call and which have never been used
A team diagnoses why a checkout workflow stops at 38% instead of completing, by replaying the session path
A maintainer catches a tool failing 94% of calls behind a 200 OK response that logs never surfaced
A product team measures whether new clients keep coming back within seven days of first connecting
An engineer compares latency and error rates across production and staging before shipping a schema change
A company decides which MCP tools to invest in by ranking them on adoption rather than guesswork