# Honcho vs supermemory

Honcho records 137,752 monthly visits with +2.2% month-over-month growth and Paid, Contact for Pricing pricing. supermemory records 156,579 monthly visits with -7.7% growth and Contact for Pricing pricing.

| Metric | [Honcho](https://openaitools.app/tool/honcho) | [supermemory](https://openaitools.app/tool/supermemory-1) |
| :--- | :--- | :--- |
| Website URL | https://honcho.dev | https://supermemory.ai |
| Categories | AI Agent, AI API, AI Knowledge Management, AI Developer Tools | AI API, Large Language Models (LLMs), AI Developer Tools, AI Agent |
| Pricing Model | Paid, Contact for Pricing | Contact for Pricing |
| Monthly Visits | 137,752 | 156,579 |
| MoM Growth Rate | +2.2% | -7.7% |
| Primary Market | United States | United States |
| Direct Visits | 59,031 | 71,163 |
| Review Score | 4.5 | 4.5 |
| Saved Count | 0 | 0 |
| Supported Platforms | Website | Website |

## 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)

## 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 Honcho and supermemory compare in monthly traffic?

Honcho has 137,752 monthly visits, while supermemory has 156,579. Traffic estimates describe audience size, not product quality.

### How do their growth rates compare?

Honcho has a month-over-month growth rate of +2.2%; supermemory has -7.7%.

### How does pricing compare?

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. 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 | [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) |
| 2 | [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) |
| 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 | [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) |
| 5 | [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) |
| 6 | [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) |
| 7 | [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) |
| 8 | [AIsa](https://openaitools.app/tool/aisa) | 89,162 | -10.3% | Freemium | One key, one schema, 6,000+ APIs for your AI agents | [.md](https://openaitools.app/tool/aisa.md) |
| 9 | [Qdrant](https://openaitools.app/tool/qdrant-io) | 72,992 | -38.4% | Freemium, Contact for Pricing | Provides high-performance open-source vector similarity search, requiring custom schema design for agent memory management. | [.md](https://openaitools.app/tool/qdrant-io.md) |
| 10 | [HydraDB OSS](https://openaitools.app/tool/hydradb-oss) | 29,635 | +5.8% | Freemium, Contact for Pricing | Delivers robust open-source graph storage on object stores, but is a database rather than a turnkey agent memory API. | [.md](https://openaitools.app/tool/hydradb-oss.md) |

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Source: https://openaitools.app/compare/honcho-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)