# EverMemOS vs Honcho

EverMemOS records 24,237 monthly visits with +12% month-over-month growth and Free Trial, Free pricing. Honcho records 137,752 monthly visits with +2.2% growth and Paid, Contact for Pricing pricing.

| Metric | [EverMemOS](https://openaitools.app/tool/evermemos) | [Honcho](https://openaitools.app/tool/honcho) |
| :--- | :--- | :--- |
| Website URL | https://evermind.ai | https://honcho.dev |
| Categories | AI Developer Tools, AI Agent, Open Source AI Models, AI Models | AI Agent, AI API, AI Knowledge Management, AI Developer Tools |
| Pricing Model | Free Trial, Free | Paid, Contact for Pricing |
| Monthly Visits | 24,237 | 137,752 |
| MoM Growth Rate | +12% | +2.2% |
| Primary Market | United States | United States |
| Direct Visits | 14,588 | 59,031 |
| Review Score | 4.4 | 4.5 |
| Saved Count | 1 | 0 |
| Supported Platforms | Website | Website |

## EverMemOS

Modern LLMs forget. Context windows cap out, RAG returns similar text without real understanding, and most memory systems are built for one user and one model. EverMemOS, the flagship product of EverMind's product family alongside the EverMe personal memory hub, is designed to close that gap by acting as a memory operating system for agents: persistent, proactive and self-evolving rather than stateless.

Two capabilities stand out. First, mRAG — a multimodal retrieval and ingestion layer that pulls in PDFs, images, Word documents, spreadsheets, presentations, emails, HTML pages and URLs through one API and supports cross-modal search. Second, self-evolving memory: agent trajectories are captured as Cases, repeated patterns are distilled into reusable Skills, and the agent applies those later instead of starting from scratch each session. A Memory Bank interface gives users visibility into user, group and agent memory and lets them inspect, manage and edit generated Skills.

EverMind frames memory as the difference between a tool and an agent, and backs the claim with reproducible open benchmarks — LoCoMo, LongMemEval and AMA are all listed on the site. The product family includes EverOS for developers building agent memory and EverMe as a personal memory hub. New users receive 2,000 credits on signup, and a Mac app is available.

**Pricing & Plans:** EverMind offers a free signup with 2,000 starter credits. No fixed public price list is shown for the cloud platform, and the company positions its memory infrastructure with free and open-source components alongside paid plans.

- **mRAG multimodal memory:** Multimodal retrieval and ingestion via a single API, covering PDFs, images, Word files, spreadsheets, decks, emails, HTML and URLs.
- **Self-evolving agent memory:** Agent trajectories are recorded as Cases, distilled into reusable Skills, and reused so performance improves over time.
- **Memory Bank transparency:** Inspect, manage and edit user, group and agent memories in a dedicated Memory Bank interface.
- **Open benchmark results:** EverMind publishes reproducible results on LoCoMo, LongMemEval and AMA alongside its memory research.
- **Team and temporal memory:** Supports multi-agent coordination and group context sharing, plus temporal tracking so outdated facts are not treated as current.

**Strengths:** Tackles a real gap: persistent memory and identity for AI agents; mRAG takes many document and media types through one API; Self-evolving Skills let agents improve instead of starting from zero; Transparent Memory Bank for inspecting and editing stored memory

**Limitations:** Memory infrastructure requires developer integration effort; The memory-OS space is crowded and fast-moving, so APIs may evolve; Self-hosting the open-source core puts operational load on your team; Benchmark leadership claims still need validation on your own workloads

[Profile (Markdown)](https://openaitools.app/tool/evermemos.md)

## Honcho

Honcho positions itself as "memory that reasons" for AI agents. Developers write conversation messages into a Session, and Honcho stores and indexes them while its reasoning engine (called Neuromancer) automatically derives conclusions, patterns, and hypotheses about each entity it is tracking. A single context() call then returns a curated blend of reasoning plus relevant conversation history, keeping agents continuous without stuffing huge token blobs into every prompt.

The system is intentionally model-agnostic: it works with OpenAI, Anthropic, and custom models, and ships integrations for coding agents and chat clients such as Claude Code, Codex, Hermes Agent, OpenClaw, and others. Beyond the classic user/assistant pair, Honcho models arbitrary "Peers" — users, subagents, NPCs, or whole groups — with scoped perspectives, making it suitable for multi-agent systems, games, companions, tutoring, and customer support.

Honcho is distributed as open source, and it also offers a hosted platform with a usage-based pricing model. Ingestion is billed per million tokens stored, context() reads are unlimited and free, and on-demand reasoning queries are priced in tiers from instant cheap lookups up to research-grade deep synthesis. Standard background "Dreaming" inference is included with every workspace.

**Pricing & Plans:** Usage-based pricing on the hosted platform: ingestion billed at roughly $2 per 1M tokens stored, unlimited free reads from context(), and on-demand reasoning queries tiered from about $0.001 (instant minimal) up to $0.50 (research-grade Max). The software itself is open source.

- **Reasoning memory engine:** Neuromancer continuously derives conclusions, patterns, and hypotheses from stored messages rather than only replaying raw text.
- **One-call context():** Returns curated reasoning plus relevant conversation history in around 200ms, ready to drop into any agent loop.
- **Peer and relationship modeling:** Track users, agents, NPCs, groups, and their relationships with global or surgically scoped perspectives.
- **Model-agnostic integrations:** Works with OpenAI, Anthropic, and custom models, plus coding agents and clients like Claude Code, Codex, and OpenClaw.
- **Background Dreaming:** Asynchronous reasoning runs automatically to keep the model of each peer fresh without slowing down runtime.

**Strengths:** Open source, so you can self-host and inspect the memory layer; Reasons about users over time instead of only storing raw messages; Model-agnostic with a broad set of agent and client integrations; Unlimited free reads plus configurable token budgets reduce prompt bloat

**Limitations:** Reasoning quality and cost depend on choosing the right query tier; Pricing is developer-oriented and requires estimating token volumes; Deep reasoning tiers are asynchronous and may not fit strictly real-time flows; Self-hosting means you own infrastructure, scaling, and storage costs

[Profile (Markdown)](https://openaitools.app/tool/honcho.md)

## Frequently Asked Questions

### How do EverMemOS and Honcho compare in monthly traffic?

EverMemOS has 24,237 monthly visits, while Honcho has 137,752. Traffic estimates describe audience size, not product quality.

### How do their growth rates compare?

EverMemOS has a month-over-month growth rate of +12%; Honcho has +2.2%.

### How does pricing compare?

EverMemOS: Free Trial, Free. EverMind offers a free signup with 2,000 starter credits. No fixed public price list is shown for the cloud platform, and the company positions its memory infrastructure with free and open-source components alongside paid plans. Honcho: Paid, Contact for Pricing. Usage-based pricing on the hosted platform: ingestion billed at roughly $2 per 1M tokens stored, unlimited free reads from context(), and on-demand reasoning queries tiered from about $0.001 (instant minimal) up to $0.50 (research-grade Max). The software itself is open source.

## Alternatives

| # | Tool Name | Monthly Visits | MoM Growth Rate | Pricing Model | Summary | Profile (Markdown) |
| :--- | :--- | :--- | :--- | :--- | :--- | :--- |
| 1 | [Gemma Open Models by Google](https://openaitools.app/tool/gemma-open-models-by-google) | 4,900,914 | -13.5% | Freemium, Paid | Google's family of lightweight, open-weight AI models | [.md](https://openaitools.app/tool/gemma-open-models-by-google.md) |
| 2 | [Hermes-Agent](https://openaitools.app/tool/hermes-agent-1) | 3,638,741 | -5.4% | Freemium | A free, open-source personal AI agent that learns your projects and works across every messaging app. | [.md](https://openaitools.app/tool/hermes-agent-1.md) |
| 3 | [ModelScope](https://openaitools.app/tool/modelscope-community) | 3,085,423 | +5.1% | Free | Open-source model and dataset hub with one-click online demos | [.md](https://openaitools.app/tool/modelscope-community.md) |
| 4 | [Composio](https://openaitools.app/tool/composio) | 2,782,603 | +22.8% | Freemium, Contact for Pricing | Give your AI agents secure, authenticated access to 1,700+ apps | [.md](https://openaitools.app/tool/composio.md) |
| 5 | [Unsloth AI](https://openaitools.app/tool/unsloth-ai) | 887,457 | -17.6% | Free | Open-source, memory-efficient fine-tuning and local model running | [.md](https://openaitools.app/tool/unsloth-ai.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 | [Pinecone](https://openaitools.app/tool/pinecone) | 286,732 | -31% | Freemium, Paid, Contact for Pricing | The managed vector database and knowledge engine for production AI | [.md](https://openaitools.app/tool/pinecone.md) |
| 8 | [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) |
| 9 | [supermemory](https://openaitools.app/tool/supermemory-1) | 156,579 | -7.7% | Contact for Pricing | A memory and continual-learning engine that gives any AI agent persistent context | [.md](https://openaitools.app/tool/supermemory-1.md) |
| 10 | [Agno](https://openaitools.app/tool/agno) | 140,302 | -3.3% | Contact for Pricing, Free | Self-driving agent platform you run in your own cloud | [.md](https://openaitools.app/tool/agno.md) |

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Source: https://openaitools.app/compare/evermemos-vs-honcho

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