Cavemem
Local-first persistent memory for MCP coding agents that cuts token spend via caveman compression.
Cavemem is a smart token-saver for Claude Code and Caveman Code users, with real compression and a free local tier. But its value is tightly coupled to the Caveman ecosystem, so standalone utility is thin. If you're already in MCP agent development, it's a no-regret add-on; otherwise, you may find better standalone options.
Verified 6d ago · liveness 72/100 · cite: rightaichoice.com/tools/cavemem
- Developers building agentic coding assistants with MCP
- Teams using Claude Code or Caveman Code to reduce token spend
- Power users seeking to cut token spend on repeat context
- Engineers wanting local-first persistent memory with no cloud
- Non-developers or business users needing a GUI
- Teams wanting fully managed cloud solution (Cloud still waitlist)
- Users needing real-time sync across many machines (local-first)
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Skip Cavemem if you're not using an MCP-compatible coding agent or if you need a fully managed cloud memory solution — the local-first design and ecosystem coupling will frustrate you.
Going past the free local wrap's single seat requires a paid plan, starting at $29/mo for Indie.
Cavemem's free tier is a great entry point for solo developers on Claude Code or Caveman Code, saving you tokens with zero upfront cost. Compared to managed memory tools like Mem0 or Zep (which charge per request or monthly fees even for basic use), Cavemem's local-first approach is cheaper for heavy local use. But for teams needing cloud sync and verified savings, the Indie ($29/mo) and Team ($349/mo) tiers are pricier than some standalone memory APIs — weigh the ecosystem integration.
In short
Cavemem — Local-first persistent memory for MCP coding agents that cuts token spend via caveman compression. Best for Developers building agentic coding assistants with MCP, Teams using Claude Code or Caveman Code to reduce token spend, Power users seeking to cut token spend on repeat context. Free to start; paid plans from $29/mo.
What's new in Cavemem
Checked 4 days agoAcross the latest 7 updates: 1 launch and 6 news mentions.
Caveman is live on GreenPT
Caveman Skill now runs at API level on GreenPT. No install, nothing to maintain. First platform integration.
Field report: How removing tokens raised the bill
Token counter claimed 96.2 million saved. Paired trials found a higher bill. Denominator explains both.
Field report: Your agent pays before it works
One agent loaded 224,655 characters of tool schemas before first action. Most came from plugins it might never call.
Field report: The 47 tokens your agent should never summarize
Compaction can preserve job while deleting rules. Benchmark found 47-token buffer prevented failure.
Field report: The average stayed flat. 125 tasks changed outcome.
998-task replay gained seven net passes while 125 outcomes flipped. Aggregate scores hid both directions.
Field report: A benchmark printed 26.5%. We refused the result.
Nine quality-held pairs produced attractive diagnostic and three publication blockers. Number stayed out of claims.
Efficiency and the token economy
Price of token collapsed and bill went up. What follows from reading that correctly.
What people actually say about Cavemem — is it worth it?
We ran a structured research pass across product reviews, community discussions, and post-purchase forum threads to surface the patterns vendors won't publish themselves. Below: the recurring strengths, the hidden costs people mention most, and the cohort that consistently regrets adopting this tool.
22 mentions across 1 source (YouTube) · researched Aug 13, 2026.
- +Local-first SQLite storage keeps data private and offline.
- +Token-efficient recall reduces per-invocation costs significantly.
- +Simple npm install and MCP server setup.
- +No external vector database or cloud dependency.
- +Lossless content-addressed compression preserves fidelity.
- −Zero independent community reviews or user experiences found.
- −Name collides with a 1981 movie, hurting searchability.
- −Cloud sync and dashboard are waitlisted, not fully available.
- −Less suitable for non-developers or fully managed setups.
- −Requires intermediate knowledge of MCP and CLI tools.
- • Cloud sync requires waitlist approval — not instantly purchasable.
- • No transparent pricing listed for paid tiers.
Viability Score
How well maintained and how widely used is Cavemem? Built from what the vendor actually publishes (docs, changelog, tutorials, integrations, pricing), whether the site is live, and how much real users discuss it. How we calculate this
Last calculated: September 2026
How we score →Key Features
- Persistent memory for coding agents via MCP
- Local SQLite database with FTS5 and vector index
- Content-addressed compression for memory entries
- Recoverable compression via content-addressed handles
- Integration with Caveman compression engine
- MCP server tools: store, query, forget memories
- Local-first, no cloud dependency
- Token-efficient recall reduces re-sending context
- Compatible with 30+ MCP-compatible agents
- Install via npm: npm install -g cavemem
- Part of Caveman ecosystem: engine, proxy, code, memory
- Lossless memory storage and retrieval
- Open-source under MIT license
- Cloud sync and dashboard (paid tiers)
- Hosted gateway for remote access (paid tiers)
About Cavemem
Cavemem is a local-first persistent memory layer built on the Model Context Protocol (MCP), designed for coding agents like Claude Code and Caveman Code. It stores agent memories in a SQLite database with FTS5 and vector indexing, enabling efficient recall of past interactions without re-sending full context. This cuts token consumption per invocation and speeds up agent responses, making it a cost-effective addition to AI-assisted development workflows. Developers using MCP-compatible agents can offload long-term memory to Cavemem's local store, which uses content-addressed compression to minimize token usage while preserving fidelity. Installation is a single npm command (`npm install -g cavemem`), and the MCP server exposes tools for storing, querying, and forgetting memories. No external vector databases or cloud dependencies are required for basic operation. Key features include lossless compression via content-addressed handles, token-efficient retrieval, and compatibility with 30+ MCP-compatible agents. Cavemem integrates deeply with the Caveman ecosystem (engine, proxy, code) but can also be used standalone with any MCP agent. It's open-source under MIT and backed by the Caveman project's 72.8k+ GitHub stars. The free tier includes one seat for the local wrap, with paid tiers adding cloud sync and a dashboard. Versus standalone solutions like Mem0 or Zep, Cavemem offers tighter integration with coding agents and the Caveman compression pipeline, but it's less suited for non-developer use cases or fully managed cloud scenarios—Caveman Cloud is still in design-partner preview. Its local-first design prioritizes privacy and cost control, making it a strong pick for developers who want to own their agent memory stack.
Behind the Verdict
Cavemem is a niche tool, and it knows it. If you live inside Claude Code or any of the 30+ MCP-compatible agents, the pitch is hard to ignore: your agent stops re-reading the same context every session, and your token bill shrinks accordingly. The local-first design means your memories stay on your machine—no cloud, no data egress—which is a real privacy win for teams with strict data-handling rules. When should you pick this? When you're building agentic coding workflows and want persistent memory without the recurring token cost of re-sending large context windows. The free tier gives you a local wrap seat for nothing, so you can validate the savings before paying a cent. And if you're already using Caveman's compression engine, this slots in as the memory layer that completes the stack. When should you pass? If you need a fully managed, cloud-hosted solution, Cavemem isn't there yet—Caveman Cloud is still in design-partner preview, with paid plans on a waitlist. Non-developers will bounce off the CLI-first setup, and teams wanting real-time sync across many machines should look at SaaS alternatives like Mem0 or Zep, which handle distributed memory out of the box. Compared to those alternatives, Cavemem's advantage is depth over breadth. It's purpose-built for coding agents, so the integration is tighter—but if you need memory for general chatbots or non-coding use cases, standalone solutions will serve you better. The compression pipeline is the differentiator, but it only shines when you're pushing large, structured context through an MCP agent. In practice, the token savings are real but not magical. You'll see the biggest wins on repetitive tasks—code reviews, diff analysis, table-heavy prompts—where compression can cut output tokens by 65% or more. The
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Real-world workflow fit
Concrete scenarios for the personas Cavemem actually fits — and what changes day-one when you adopt it.
You're tired of re-explaining your codebase to Claude every session. Install Cavemem via npm and let it store memory of past fixes.
Outcome: Claude recalls your conventions and avoids re-asking, cutting token spend and speeding up responses.
Your team's agents keep losing context between tasks. Set up Cavemem with the Team plan to share memory and save on token costs.
Outcome: Agents share a persistent memory, reducing re-sent context and lowering token bills across the team.
You want to build a custom agent with persistent memory. Use Cavemem's MCP server to store and query memories locally.
Outcome: You get a lightweight memory layer without cloud dependencies, and you can integrate it into any MCP-compatible agent.
Use Cases
- Store and recall agent context across sessions to avoid re-sending the same data
- Reduce token consumption by compressing memory entries with content-addressed handles
- Integrate with Claude Code via MCP to give it persistent memory of past interactions
- Use as a building block for custom coding agents that need long-term recall
- Pair with Caveman Code to halve token usage while maintaining agent effectiveness
- Evaluate memory retrieval patterns locally before committing to a cloud provider
Models Under the Hood
as of 2026-08-31
Limitations
- Cavemem is an open-source (MIT) local-first memory tool for MCP coding agents.
- Usage of the local wrap is free for one seat with your own keys and no account required, but additional seats, cloud sync, and hosted gateway features require paid plans or joining a waitlist.
- The verified savings ledger currently only accepts provider-causal Anthropic cache evidence, and automatic receipt signing remains disabled.
as of 2026-08-19
Verification history
We have re-verified Cavemem 7 times since . Each pass re-reads the vendor's own pages and re-checks every listed field against that evidence; passes where nothing had changed are marked as such.
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
Showing the 6 most recent of 7 verification passes.
Free to cite with attribution — this page re-verifies continuously.
12-month cost
Project the real annual outlay, including the implied monthly cost when only an annual tier is published.
Vendor list price only. Add-on usage, seat overages, and contract minimums are surfaced under Hidden costs & gotchas.
Plans compared
For each published Cavemem tier: who it actually fits, and what it adds vs. the previous tier. Cross-reference the cost calculator above for projected annual outlay.
Free
$0
Ideal for
Solo developer using Claude Code or Caveman Code locally, with your own API keys, wanting to cut token spend without a subscription.
What this tier adds
Starting tier: includes local wrap with one seat, MIT skill/extension, and inferred savings computed locally; no account required.
Indie
$29/mo
Ideal for
Individual developer who wants cloud sync and a hosted gateway to access savings dashboard from anywhere, with a budget of $29/mo.
What this tier adds
Adds hosted gateway, synced dashboard with inferred headroom, and 2,900 credits/month for agent compute; still one seat.
Team
$349/mo
Ideal for
Small team of up to 10 developers wanting eval-gated rollout, team seats, and receipt verification for token savings.
What this tier adds
Includes 10 seats (extra $29/seat), 34,900 credits/month, eval-gated rollout with automatic rollback, receipt export + Ed25519 verification.
Enterprise
Custom
Ideal for
Large organizations needing SSO, audit logs, RBAC, and on-prem/private deployment; commited contract with zero data collection.
What this tier adds
Adds OIDC SSO, audit log, RBAC, governance, on-prem/BYOC deploy + OEM embedding, provider-invoice reconciliation (planned).
Where the pricing makes sense
The company stage and team size where Cavemem's pricing actually pencils out — and where peers do it cheaper.
Cavemem's free tier is a great entry point for solo developers on Claude Code or Caveman Code, saving you tokens with zero upfront cost. Compared to managed memory tools like Mem0 or Zep (which charge per request or monthly fees even for basic use), Cavemem's local-first approach is cheaper for heavy local use. But for teams needing cloud sync and verified savings, the Indie ($29/mo) and Team ($349/mo) tiers are pricier than some standalone memory APIs — weigh the ecosystem integration.
Setup time & first value
How long it actually takes to get something useful out of Cavemem — broken out by persona, not the marketing-page minute.
Solo developer: install via npm and configure MCP in under 10 minutes. Team: add seats and configure cloud sync in about 30 minutes. Power user: integrate MCP tools into a custom agent in about an hour.
Switching to or from Cavemem
How to bring data in from common predecessors and how to get it back out — written for the switcher, not the buyer.
- →From Mem0: Export your memories and import them into Cavemem's SQLite database for local, compression-first recall.
- ↗To Mem0: Export your Cavemem SQLite data and import into Mem0's cloud if you need managed sync.
Integrations
Resources & Guides
Tutorials & Learning
Official links
Featured Head-to-Head Comparisons
Cavemem vs Spider Cloud
Choose Spider Cloud if your AI agent needs live web data for RAG or scraping — its Rust-powered engine and 1,000+ ready-made scrapers make data ingestion cheap and fast. Choose Cavemem if you build coding agents and want to slash token costs by retaining context locally via MCP. They solve different problems: one pulls external data, the other remembers internal conversation history.
Cavemem vs Voyage Ai
Choose Voyage AI if you need high-accuracy embedding models and rerankers for enterprise RAG pipelines, especially for finance or legal documents, and have a budget for a contact-sales pricing model. Choose Cavemem if you are a developer building agentic coding assistants with MCP and want a token-efficient, local-first persistent memory layer to reduce repeated context – it's free to use locally. These tools serve fundamentally different needs: one is for retrieval quality, the other for agent memory efficiency.
Cavemem vs Temporal Ai
Choose Temporal AI if you need to build fault-tolerant, long-running orchestration for AI agents or microservices – it survives crashes and retries automatically. Choose Cavemem if you're a developer looking to reduce token costs when repeating context to coding agents like Claude Code, and you prefer a local-first, MCP-native memory solution. For a team building reliable production agent workflows, Temporal is the proven heavyweight; for individual developers optimizing agent memory, Cavemem is lean and token-efficient.
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Frequently Asked Questions
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