# Mesa vs supermemory

Mesa records 6,563 monthly visits with +48.4% month-over-month growth and Freemium pricing. supermemory records 156,579 monthly visits with -7.7% growth and Contact for Pricing pricing.

| Metric | [Mesa](https://openaitools.app/tool/mesa-dev) | [supermemory](https://openaitools.app/tool/supermemory-1) |
| :--- | :--- | :--- |
| Website URL | https://mesa.dev | https://supermemory.ai |
| Categories | AI Agent, AI API, AI Developer Tools, AI Workflow | AI API, Large Language Models (LLMs), AI Developer Tools, AI Agent |
| Pricing Model | Freemium | Contact for Pricing |
| Monthly Visits | 6,563 | 156,579 |
| MoM Growth Rate | +48.4% | -7.7% |
| Primary Market | United States | United States |
| Direct Visits | 2,568 | 71,163 |
| Review Score | 4.3 | 4.5 |
| Saved Count | 0 | 0 |
| Supported Platforms | Website | Website |

## Mesa

Mesa solves a problem that shows up as soon as agents do real work: their compute is ephemeral, but the files they read and write should not be. Mesa provides a versioned virtual filesystem, MesaFS, that agents can mount like a normal directory, read and write with standard Unix tooling, and fork instantly. Repositories are version-controlled with branching, history and diffs, so every change an agent makes is traceable and reversible.

The design targets parallel agent workloads. Instead of locking files or letting agents clobber each other, you fork a workspace in milliseconds, run dozens of agents on isolated branches, and merge whichever result wins. Because MesaFS materializes files on demand, an agent can start working without cloning an entire repository, which keeps time-to-first-token low. Sub-50ms p95 random reads on large files and unlimited concurrent writers are the headline performance claims.

Beyond speed, Mesa adds the governance enterprises ask for: pause an agent for human review and resume from the exact same workspace state, snapshot the filesystem at any step and roll back in a single API call, version skills, memories and learned context as files the whole fleet can read and write, and enforce fine-grained access with short-lived tokens. Mesa can be deployed in your own AWS, GCP or Azure tenancy with the vendor operating only the control plane.

**Pricing & Plans:** Usage-based with three tiers. Free: $0/month including 50 GB storage, 1,000 repositories, 200 GB/month egress and unlimited virtual filesystem reads. Scale: pay-as-you-go at about $0.20/GB/month for storage and $0.11/GB egress for Git and REST API traffic, with the free allowance included. Enterprise: custom pricing with volume and committed-use discounts, self-hosted bring-your-own-cloud deployments, SOC 2 Type II and dedicated support. Reads through MesaFS are not metered.

- **Versioned virtual filesystem (MesaFS):** A 100% POSIX-compatible mount so agents and sandboxes use familiar Unix tools, with persistence, sync and version history handled transparently underneath.
- **Instant branches and parallel agent swarms:** Fork a workspace in milliseconds to run dozens of agents on isolated branches, then pick the best result without file locking.
- **Checkpoints and rollbacks:** Snapshot the entire filesystem at every step and restore any past state with a single API call when an agent goes off track.
- **Human-in-the-loop governance:** Pause any agent for review, build approval queues and policy gates, then resume from the exact same workspace state.
- **Enterprise-ready deployment:** Bring-your-own-cloud on AWS, GCP or Azure, SOC 2 Type II, AES-256 at rest, TLS 1.3 in transit, full access logging and fine-grained per-branch ACLs.

**Strengths:** Combines Git-style versioning with a fast, mountable filesystem; Instant forks make parallel agent swarms practical without locking; Strong enterprise story: BYOC, SOC 2 Type II, short-lived scoped tokens; Generous free tier with unlimited MesaFS reads

**Limitations:** Aimed at developers, so it requires engineering effort to adopt; Egress from Git and the REST API is metered even on paid tiers; Some capabilities are still framed as early access, so availability may vary

[Profile (Markdown)](https://openaitools.app/tool/mesa-dev.md)

## supermemory

Most LLM applications treat memory as an afterthought: chat history is stuffed into the prompt until the window fills up, and everything is lost on the next session or the next model switch. supermemory attacks that problem as infrastructure. Its learner-1 model reads incoming material — conversations, files, URLs, raw data — and performs update, merge, infer and forget operations against a vector-graph database, so learnings stay dense, interconnected and time-aware rather than piling up as an ever-growing transcript.

At request time the relevant memories are injected as tokens directly into the agent's context, which keeps the approach model-agnostic and harness-agnostic. Because learning and memory live outside the model providers, the same accumulated understanding travels with an application whether it runs on Claude, GPT-family models, Gemini or open-weight models, and whether it is driven by an SDK, a plugin or MCP.

The company positions this as the hard part of agent infrastructure and backs it with published numbers: sub-second recall on production traffic, roughly a trillion tokens processed per month, benchmarks on LongMemEval, LoCoMo and ConvoMem, and cost comparisons against markdown-file memory at large corpora. Enterprise features include SOC 2 Type II certification, a HIPAA BAA on higher tiers, GDPR compliance and deployment options ranging from cloud to VPC, on-premises and air-gapped environments.

**Pricing & Plans:** Pricing is split between self-serve tiers and enterprise contracts, which is why the site lists a contact-for-pricing model. Free, Pro and Max tiers cover standard PDF extraction and personal use, while Scale and Enterprise add advanced document extraction with OCR and figure/diagram descriptions, higher limits, security guarantees such as SOC 2 Type II and HIPAA BAA, and deployment choices including your VPC, your data center or air-gapped setups. Exact figures should be confirmed on the pricing page for current rates.

- **learner-1 memory model:** A dedicated model extracts, merges, infers and forgets across stored context so the memory graph keeps improving instead of simply accumulating.
- **Vector-graph memory store:** Learnings link to the entities, sources and moments around them, storing not just content but a time-aware map of how facts relate.
- **Model, harness and MCP agnostic:** Memory lives outside model providers and is used through APIs, SDKs, plugins and MCP, so context carries across models and tools.
- **Real-time context injection:** Relevant memories are injected as tokens into the prompt at request time, giving agents relevant context without unbounded transcripts.
- **Enterprise deployment and compliance:** SOC 2 Type II, HIPAA BAA on higher tiers, GDPR compliance, plus deployment in the cloud, your VPC, your data center or air-gapped.

**Strengths:** Memory is decoupled from model providers, so accumulated context survives model swaps; Rich engineering evidence, including published benchmarks, latency figures and cost comparisons; Multiple integration surfaces — REST API, SDKs, plugins and MCP; Strong enterprise posture with SOC 2 Type II, HIPAA BAA, GDPR and air-gapped deployment options

**Limitations:** Self-serve pricing on the public site is thin, and exact tier costs may require a sales conversation; It is infrastructure rather than an end-user app, so there is real integration work before you see value; Benchmark results and adoption figures are vendor-reported and worth validating against your own workload

[Profile (Markdown)](https://openaitools.app/tool/supermemory-1.md)

## Frequently Asked Questions

### How do Mesa and supermemory compare in monthly traffic?

Mesa has 6,563 monthly visits, while supermemory has 156,579. Traffic estimates describe audience size, not product quality.

### How do their growth rates compare?

Mesa has a month-over-month growth rate of +48.4%; supermemory has -7.7%.

### How does pricing compare?

Mesa: Freemium. Usage-based with three tiers. Free: $0/month including 50 GB storage, 1,000 repositories, 200 GB/month egress and unlimited virtual filesystem reads. Scale: pay-as-you-go at about $0.20/GB/month for storage and $0.11/GB egress for Git and REST API traffic, with the free allowance included. Enterprise: custom pricing with volume and committed-use discounts, self-hosted bring-your-own-cloud deployments, SOC 2 Type II and dedicated support. Reads through MesaFS are not metered. supermemory: Contact for Pricing. Pricing is split between self-serve tiers and enterprise contracts, which is why the site lists a contact-for-pricing model. Free, Pro and Max tiers cover standard PDF extraction and personal use, while Scale and Enterprise add advanced document extraction with OCR and figure/diagram descriptions, higher limits, security guarantees such as SOC 2 Type II and HIPAA BAA, and deployment choices including your VPC, your data center or air-gapped setups. Exact figures should be confirmed on the pricing page for current rates.

## Alternatives

| # | Tool Name | Monthly Visits | MoM Growth Rate | Pricing Model | Summary | Profile (Markdown) |
| :--- | :--- | :--- | :--- | :--- | :--- | :--- |
| 1 | [Composio](https://openaitools.app/tool/composio) | 2,782,603 | +22.8% | Freemium, Contact for Pricing | Focuses on tool integrations and authentication for agents, complementing memory engines rather than storing cross-session context. | [.md](https://openaitools.app/tool/composio.md) |
| 2 | [BLACKBOX.AI](https://openaitools.app/tool/blackbox-ai-1) | 622,975 | -27.2% | Free Trial, Paid | High-trust frontier inference, encrypted end to end | [.md](https://openaitools.app/tool/blackbox-ai-1.md) |
| 3 | [LiteLLM](https://openaitools.app/tool/litellm) | 576,967 | +7.9% | Freemium, Contact for Pricing | One open-source AI gateway for 100+ LLM providers | [.md](https://openaitools.app/tool/litellm.md) |
| 4 | [Treg](https://openaitools.app/tool/treg) | 452,935 | +223.4% | Freemium | One key for 4,100+ agent tools, pay per call | [.md](https://openaitools.app/tool/treg.md) |
| 5 | [Mastra](https://openaitools.app/tool/mastra) | 318,954 | -5% | Freemium, Contact for Pricing | The TypeScript framework for building, deploying, and observing AI agents | [.md](https://openaitools.app/tool/mastra.md) |
| 6 | [Merge](https://openaitools.app/tool/merge-dev) | 313,201 | +1.7% | Freemium, Contact for Pricing | The universal context engine connecting agents to data, tools, and models | [.md](https://openaitools.app/tool/merge-dev.md) |
| 7 | [Morph](https://openaitools.app/tool/morph-apply-ai-edits-to-files-fast) | 306,961 | +9.5% | Free Trial, Contact for Pricing | Fast Apply and agent-tuned inference for coding agents | [.md](https://openaitools.app/tool/morph-apply-ai-edits-to-files-fast.md) |
| 8 | [Pinecone](https://openaitools.app/tool/pinecone) | 286,732 | -31% | Freemium, Paid, Contact for Pricing | Offers enterprise-grade scalable vector search and RAG storage, but lacks supermemory's automated agent memory graph abstractions. | [.md](https://openaitools.app/tool/pinecone.md) |
| 9 | [Portkey](https://openaitools.app/tool/portkey-ai) | 195,650 | -9.4% | Freemium, Paid, Contact for Pricing | AI gateway, observability and guardrails for production LLM apps | [.md](https://openaitools.app/tool/portkey-ai.md) |
| 10 | [Honcho](https://openaitools.app/tool/honcho) | 137,752 | +2.2% | Paid, Contact for Pricing | Provides open-source persistent user modelling for stateful agents, but focuses more on conversational psychology than universal vector graphs. | [.md](https://openaitools.app/tool/honcho.md) |

---
Source: https://openaitools.app/compare/mesa-dev-vs-supermemory-1

More Markdown: [Categories](https://openaitools.app/categories.md) · [Traffic rankings](https://openaitools.app/rankings.md) · [llms.txt](https://openaitools.app/llms.txt)