CMEM vs PromptShark

CMEM and PromptShark are both inference engines & infra tracked by AIDiveForge. Below is a side-by-side comparison of pricing, capabilities, platforms, and ownership — sourced from each tool's live website and verified before publishing.

CMEM

The open-source claude-mem engine hooks into Claude Code, Cursor, Windsurf, and CLI agents, writing decisions and dead ends into a local SQLite observations database as your agent works. CMEM Cloud mirrors that database behind a private MCP endpoint any agent or IDE can read, so the context one agent built in one session is available to the next one without manual handoff. Vector search over the observations store means retrieval is semantic, not keyword-based — you query by meaning, not by remembering what you typed three sprints ago. The ceiling appears at the team coordination layer: role-based read/write scoping and per-project isolation are paid-only features, so solo developers get the full engine but teams hit a paywall before they get the shared-brain behavior the product is built around.

PromptShark

PromptShark is a local MITM proxy written in Go and C++ that sits between your agent and any OpenAI-compatible API endpoint. Every request and response pair is captured, logged, and surfaced in a real-time dashboard — no changes to your agent code, just a single base_url swap. The loop detector flags infinite tool-calling cycles automatically. The time-travel replay feature lets you re-run or edit any historical API step without firing a live request, which means no extra token spend during debugging. The self-hosted, MIT-licensed architecture means your prompts and API keys never leave your own infrastructure.

Attribute	CMEM	PromptShark
Pricing	Paid	Free
Free trial	No	No
Open source	No	Yes
Has API	Yes	Yes
Self-hosted option	Yes	Yes
Platforms	Mac, Windows, Linux, mobile	Cross-platform (Go binary + Docker)
Pros	Zero-config install via npx hooks the engine into Claude Code, Cursor, Windsurf, and CLI agents without a separate account, so you get structured observation capture running before you finish reading the docs. Offline-first local SQLite database means the memory layer keeps working when the network drops, and sync catches up when connectivity returns — so a spotty connection does not cost you a session's worth of captured context. Vector search over the observations store retrieves by semantic meaning rather than exact keyword match, so querying 'why did we avoid Node for cold starts' surfaces the right decision even if you never wrote it in those words. CMEM Cloud mirrors the local database behind a single private MCP endpoint, so switching from Claude Code on your laptop to Gemini CLI on a server is a URL already in your config — not a re-export and re-import. Brainbeats route context to the right agent at the moment a stored observation becomes relevant, so agents that need briefing get it without a human manually queuing context before each run.	Zero-instrumentation integration via a single base_url swap, so you get full request visibility without touching your agent's codebase or adding an SDK dependency. Automatic infinite tool-calling loop detection, which means runaway agent cycles are flagged before they drain your token budget rather than after you read the invoice. Per-step token usage and USD cost tracking surfaced in real time, so you can attribute spend to specific agent decisions rather than receiving an opaque monthly total. Time-travel replay lets you re-run and edit any past API step without issuing a live request, so iterating on a prompt variant costs nothing in tokens during a debugging session. MIT-licensed and fully self-hostable via Docker, which means your prompts, responses, and API keys remain on your own infrastructure with no third-party data handling.
Cons	Shared team memory, per-project scoping, and role-based read/write access are paid-only CMEM Cloud features — a team that installs the open-source engine expecting a shared brain across multiple developers hits that wall immediately and either upgrades or sets up a separate MCP server to share the database themselves. The tool captures observations from agent sessions but does not run, schedule, or coordinate agents — teams that want agents to trigger other agents based on memory state still need a separate orchestration layer, and at that point claude-mem is one component inside a larger system they are building and maintaining. Teams with strict data residency requirements who cannot route codebase observations through a third-party cloud endpoint have the self-hosted path, but the vendor page does not describe a self-hosted CMEM Cloud option — only the local engine and the vendor-hosted cloud tier — meaning the private MCP link feature is unavailable without the managed service.	The proxy is scoped to OpenAI-compatible API calls; agents using Anthropic, Google, or other non-OpenAI-format endpoints get no coverage, and teams with a mixed provider setup gain only partial visibility. The docs describe no multi-user access controls or role separation, so the tool cannot be safely exposed across a development team without additional network-layer restrictions — teams beyond a single developer typically add a separate access layer or switch to a purpose-built observability platform. There is no alerting or notification system described in the repository; when a loop fires at 2 AM, the dashboard captures it, but nobody is paged — teams with production monitoring requirements move to a dedicated tracing platform such as LangSmith or Langfuse that integrates with existing alerting pipelines. With only 3 commits and 1 star at the time of curation, the project is early-stage; the community reports no track record of sustained maintenance, which is a real risk if you build a debugging workflow around it and a breaking API change in the upstream OpenAI spec goes unpatched.

Bottom line

CMEM is paid while PromptShark is free; PromptShark is open source. Choose based on which difference matters most for your workflow.

Frequently asked questions

What is the difference between CMEM and PromptShark?

CMEM is Paid, while PromptShark is Free and open source. Compare pricing, free trial, API, platforms, and pros/cons in the table above on AIDiveForge.

Is CMEM better than PromptShark?

It depends on your workflow. Use the side-by-side attributes (pricing, open source, API, self-hosted, platforms) to decide. AIDiveForge does not rank a universal winner — we publish verified facts so you can choose.