Choosing Long-Term Memory for an AI agent starts with a practical question: what should the agent remember, and how will you know that it remembered correctly?
Hermes offers several approaches: curated Markdown files, a local fact database, user-modeling services, knowledge graphs, and directory-based context stores. They solve different problems and charge for different units of work.
This guide compares 10 options, including Hermes's built-in memory, with their strengths, trade-offs, integration paths, and cloud pricing. The pricing snapshot is dated October 22, 2026. All amounts are in US dollars. Annual commitments and annualized monthly prices are labeled explicitly.
This is a documentation-based comparison, not a hands-on benchmark using identical tasks. Vendor accuracy scores use different models, datasets, and cost assumptions, so they are not treated as a common leaderboard.
Start with the memory you actually need
Persistent memory can mean several things:
- Personal facts and preferences: time zone, communication style, and long-term goals.
- Project decisions and lessons: architectural choices, debugging findings, and working conventions.
- History and change: when a preference changed or why a decision was revised.
- External knowledge: documents, websites, email, and shared team material.
A small preference store and a traceable engineering knowledge base need different systems. Write down the questions you want the agent to answer before choosing the backend.
The 10 options at a glance
| Option | Main use | Deployment | Cloud entry point |
|---|---|---|---|
| Hermes built-in memory | Curated facts, preferences, conversation lookup | Local | No separate memory-service fee |
| Honcho | User modeling and reasoning-based recall | Cloud or open-source self-hosting | $2 per million ingestion tokens; reasoning extra |
| OpenViking | Directory-based knowledge and tiered loading | Self-hosting documented | No managed-service quote confirmed in this review |
| Mem0 | Automatic fact extraction and maintenance | Cloud, self-hosted server, or OSS | Free; Starter $19/month |
| Hindsight | Relationships, time, and synthesis across memories | Cloud or open-source self-hosting | Prepaid usage |
| Holographic | Lightweight local fact storage | Local | No additional model calls by default |
| RetainDB | Managed memory, hybrid search, and files | Cloud | Free; Builder $9/month |
| ByteRover | Project knowledge trees and team synchronization | Local-first, optional cloud sync | Free; Pro $15/month billed annually |
| Supermemory | User memory, document retrieval, and connectors | Cloud or licensed local server | Free; Pro $19/month |
| Memori | Tool-aware memory and production governance | OSS or managed deployments | Free allowance; production Team from $60,000/year |
Hermes allows one active external memory provider at a time, while built-in memory remains active. Additional MCP tools can coexist, but that does not automatically give them another provider's background synchronization and recall lifecycle. Hermes integration documentation
1. Hermes built-in memory
Hermes stores curated facts and preferences in MEMORY.md and USER.md. Its session_search tool can retrieve earlier conversations. Official documentation
Pros
- Human-readable files are easy to inspect, edit, back up, and migrate.
- No additional memory account, API key, or service dependency is required.
- It provides a straightforward baseline for stable project conventions and personal facts.
- It remains available alongside an external provider.
Cons
- Resident memory is bounded and needs curation as information accumulates.
- Files alone do not provide sophisticated relationship modeling or fact reconciliation.
- Sharing memory with other coding tools requires an additional integration.
- Historical recall still depends on the agent choosing useful queries.
Pricing: There is no separate SaaS fee. Injected memories still occupy the main model's context and incur its normal token costs.
Best fit: Start here when a small set of stable facts and searchable conversation history may already solve the problem.
2. Honcho
Honcho focuses on understanding users over time. It maintains representations of users and agents and offers reasoning at several depths. It has a dedicated Hermes integration and an open-source core.
Pros
- A good architectural match for persistent preferences, goals, and user modeling.
- Ordinary context retrieval is separate from deeper reasoning.
- Multiple tools can connect to a shared memory layer.
- Self-hosting gives control over the infrastructure and stored memory.
Cons
- Inferred user attributes require review; they are not equivalent to explicitly confirmed facts.
- Frequent deep reasoning can dominate the bill.
- Refresh frequency and reasoning depth need deliberate configuration.
- Self-hosting transfers model provisioning and operations to you.
Cloud pricing
| Operation | Published rate |
|---|---|
| Ingestion and processing | $2 per million input tokens |
| Context retrieval | Listed as unlimited |
| Background Dreaming | Included |
| Minimal reasoning | $0.001/query |
| Low reasoning | $0.01/query |
| Medium reasoning | $0.05/query |
| High reasoning | $0.10/query |
| Max reasoning | $0.50/query |
The current personal-memory page states that new accounts receive $5 in credits, with another $5 each billing period after adding a payment method. Recurring credits do not accumulate. This uses the live page rather than older cached signup offers. Rates, credit terms
Unlimited context retrieval does not eliminate the main agent's cost of reading that context.
Best fit: A personal assistant that should maintain a coherent understanding of the same user across sessions and tools.
3. OpenViking
OpenViking organizes context as a virtual filesystem under viking://. Agents can browse directories and load L0 abstracts, L1 overviews, and L2 source content progressively.
Pros
- The knowledge structure is visible and navigable.
- Project or directory scope can narrow retrieval.
- Summary-first loading helps control how much material reaches the agent.
- Archived sessions and extracted Markdown memories can be inspected.
Cons
- Running a service and configuring its models adds operational work.
- Generated summaries and retrieval planning can introduce errors.
- Directory design may be unnecessary overhead for a small preference store.
- Product capabilities and the subset exposed by the Hermes integration should be evaluated separately.
Cloud pricing: This review did not confirm a public first-party managed-service quote. Self-hosting costs depend on compute, storage, embeddings, and language or multimodal models. This is not a claim that other hosting arrangements or commercial agreements do not exist.
Best fit: Larger, clearly structured knowledge collections where inspecting and maintaining the organization matters. Website, Hermes integration
4. Mem0
Mem0 offers automatic fact extraction and memory CRUD operations, with a managed platform and an open-source implementation.
Pros
- A direct API for adding user-level memory to an existing agent.
- Cloud, self-hosted, and in-process OSS deployment choices.
- Explicit free and entry-level quotas make an initial trial easier to budget.
- Pro adds graph memory and Dream consolidation.
Cons
- Extraction can omit context or update a fact incorrectly.
- Lower tiers provide substantially fewer retrieval requests than add requests.
- Per-turn retrieval can exhaust the retrieval quota first.
- Pro represents a substantial price increase; its capabilities should not be assumed to exist in Starter.
- OSS deployments require separate model and storage choices.
Cloud pricing
| Plan | Monthly price | Add requests/month | Retrieval requests/month | Projects |
|---|---|---|---|---|
| Hobby | $0 | 10,000 | 1,000 | 1 |
| Starter | $19 | 50,000 | 5,000 | 1 |
| Pro | $249 | 500,000 | 50,000 | Unlimited |
| Enterprise | Custom | Listed as unlimited | Listed as unlimited | Unlimited |
Pro includes graph memory, Dream, analytics, and private Slack support. Enterprise adds features such as SLA support, on-premises deployment, audit logs, and SSO. Current pricing
Best fit: Managed fact extraction with a simple API, provided that the retrieval quota matches the intended agent loop.
5. Hindsight
Hindsight separates retain, recall, and reflect: storing information, retrieving it, and synthesizing answers across memories. It also models entities, relationships, temporal changes, and mental models. Open-source project
Pros
- A strong architectural match for questions about how a project or decision evolved.
- Explicit operations distinguish retrieval from synthesis.
- Cloud includes memory-side LLM, embedding, and reranking models.
- An upstream-maintained Hermes integration is available.
Cons
- Capturing full conversations and tool calls can increase ingestion volume.
- Reflection, model refreshes, and long-term storage have separate meters.
- Automatically refreshed knowledge pages can generate charges without a manual refresh.
- Self-hosting requires operating databases, models, and upgrades.
- Synthesized conclusions still need supporting evidence.
Cloud pricing
| Operation | Rate |
|---|---|
| Retain | $10 per million input tokens |
| Import or restore from an export archive | $5 per million input tokens; no new fact extraction |
| Recall | $0.75 per million output tokens |
| Reflect | $0.05/call |
| Retrieve Model or read a knowledge page | $0.25 per million output tokens |
| Refresh Model or create/update a knowledge page | $0.05/call |
| Iris enhanced file extraction | $7.50 per million output tokens, plus normal retain |
| Memory storage after 30 days | $0.25 per million tokens/month, accrued daily |
Cloud uses prepaid credits at $1 per credit. The homepage advertises $5 in initial credits for GitHub signup; this is not a recurring monthly allowance. Standard text extraction avoids the additional Iris charge, but retaining extracted content still costs money. Billing details, what Cloud includes
Best fit: Projects that need temporal context, entity relationships, and synthesis across multiple memories.
6. Holographic
Holographic is a local SQLite fact store combining FTS5, entity resolution, trust scoring, and holographic reduced representation retrieval. The current plugin is maintained by a community maintainer.
Pros
- No API key, network access, or additional model calls by default.
- Fact versions, provenance, and correction operations support inspection.
- A local database is straightforward to back up.
- Optional online embeddings can be enabled when needed.
Cons
- Keyword and HRR retrieval should not be equated with general language-model semantic understanding.
- Large paraphrases, implicit relationships, and complex synthesis need practical evaluation.
- Fact organization and keywords affect recall quality.
- Online embeddings change both the data flow and the cost.
- Community-maintained compatibility is worth monitoring.
Pricing: The default local path has no separate memory-model service bill. Optional embeddings are billed by the chosen provider, and main-agent tokens remain separate. No managed-service quote was confirmed in this review. Current capabilities, Hermes catalog
Best fit: A modest local store of preferences, facts, and explicit decisions with minimal additional infrastructure.
7. RetainDB
RetainDB combines managed memory, hybrid retrieval, file operations, and project features.
Pros
- Writes and context reads have separate, visible quotas.
- The entry paid tier is inexpensive.
- Memory, file knowledge, and collaboration can live in one service.
- Higher plans add delta compression and larger team limits.
Cons
- The standard route depends on a hosted service.
- Delta Compression is not included in every tier.
- The Hermes documentation identifies its standalone provider repository as needing a maintainer. That is an integration-maintenance issue, not a claim that the RetainDB service is unmaintained.
- Older documentation and current pricing can differ.
Cloud pricing
| Plan | Monthly price | Writes/month | Context reads/month | Scope |
|---|---|---|---|---|
| Starter Free | $0 | 20,000 | 5,000 | 1 project, 1 API key |
| Builder | $9 | 75,000 | 15,000 | 3 projects, 5 keys, 3 seats, Knowledge Graph |
| Pro | $29 | 250,000 | 75,000 | 10 projects, 20 keys, 10 seats, Delta Compression |
| Scale | $99 | 1,000,000 | 300,000 | Unlimited projects and keys, 50 seats |
| Enterprise | Custom | Listed as unlimited | Listed as unlimited | Contract-specific |
The pricing page also advertises 20% off annual billing. Older Hermes material lists $20/month; this table uses the current vendor pricing page. Overage behavior should be checked at checkout. Pricing, Hermes maintenance note
Best fit: Budget-conscious managed workloads with substantial read/write quotas, after checking Hermes integration compatibility.
8. ByteRover
ByteRover uses its brv CLI to manage a hierarchical knowledge tree. It is oriented toward project decisions, debugging lessons, and reusable engineering practices, with local storage and optional cloud synchronization.
Pros
- Knowledge is readable and maintainable.
- Selected material can be shared across devices and teams.
- Recall sources can be inspected.
- The Hermes integration extracts insights before context compression discards them.
Cons
- Duplicate and outdated knowledge still needs curation.
- LLM-driven search has its own latency and model cost.
- CLI installation, login, and synchronization state add operational dependencies.
- Hermes lists the standalone provider repository as needing a maintainer; the product and integration have distinct maintenance considerations.
Cloud pricing
| Plan | Price | Main scope |
|---|---|---|
| Local | Listed as free in Hermes documentation | Local tree; model costs depend on configuration |
| Free cloud sync | $0 | 100 context files, device sync, team sharing |
| Pro | $15/month billed annually | Unlimited context files, priority support |
| Enterprise | Custom | SSO/SAML, RBAC, on-premises options |
The accessible current page explicitly labels $15 as billed yearly. This review did not reliably confirm Pro's monthly-billing price or the seat-billing basis. Older retrieval-based price tables should not be substituted for the current file-based plans. Current pricing, Hermes documentation
Best fit: Coding agents that should share inspectable project knowledge with selective team synchronization.
9. Supermemory
Supermemory combines personal memory with SuperRAG document retrieval and connectors for external sources. Its Hermes plugin is maintained by the vendor.
Pros
- One integration ecosystem can cover personal memory and external knowledge.
- Supported updates under the same customId bill only newly added content on the token-ingestion meter.
- Connectors can reduce the work of ingesting documents and cloud sources.
- A local server is available for single-machine workflows.
Cons
- Credits, Memory tokens, SuperRAG tokens, queries, and operations are separate meters.
- Document identity changes or full replacements can incur fresh ingestion charges.
- Advanced PDF extraction and some connectors require higher plans.
- The current local server binary comes from a non-public codebase and is free only within its lite license limit. Open-source SDKs do not make the complete server open source.
- Local extraction models and available integrations differ from the hosted service.
Cloud plans
| Plan | Monthly subscription | Monthly included credits | Main differences |
|---|---|---|---|
| Free | $0 | $5 | APIs, plugins, 1 seat |
| Pro | $19 | $20 | Auto top-up, 3 seats |
| Max | $100 | $130 | More credits, Gmail connector |
| Scale | $399 | $600 | Advanced PDF, S3, crawler, unlimited seats |
| Enterprise | Custom | Contract-specific | Dedicated support and deployment options |
Usage rates
| Meter | Rate |
|---|---|
| Memory plain text | $5 per million SM tokens |
| Memory rich content | $10 per million SM tokens |
| SuperRAG text | $1 per million SM tokens |
| SuperRAG images, PDFs, audio, and video | $2 per million SM tokens |
| Search and traversal | $5 per million queries |
| Operations | $100 per million operations |
Subscription credits reset monthly; purchased top-up credits do not expire. An auto-top-up purchase limit is not a hard cap on the total invoice. Workflows can use multiple meters, so budget against the endpoints actually called. Plans and rates
The local server needs your extraction model. Its default embedding model is English-oriented, so multilingual workloads should evaluate alternatives. The reviewed licensing pages did not specify a numerical lite limit; it should not be described as unlimited free usage. Local deployment, Local versus Enterprise
Best fit: Workloads combining memory with documents, websites, or cloud connectors, with room for more detailed usage accounting.
10. Memori
Memori emphasizes execution traces, structured recall, and production governance. Hermes uses an externally supplied hermes-memori CLI/provider integration.
Pros
- Tool-aware memory fits production agent workflows.
- Production plans include access control, audit logging, observability, and memory pooling.
- Deployment choices cover managed multi-tenant, managed single-tenant, customer VPC, and on-premises arrangements.
- Free Cloud and OSS routes allow smaller initial experiments.
Cons
- Paid production plans are priced primarily for enterprises.
- OSS, free Cloud, and production contracts are different offers.
- Counts of memories created or recalled are not directly comparable to another provider's API-request quotas.
- The current pricing text does not specify the free allowance's renewal period.
Cloud pricing
| Plan | Published price | Scope |
|---|---|---|
| OSS | Software free | Bring your database, models, and infrastructure |
| Cloud Free | $0 | 5,000 memories created; 15,000 memories recalled |
| Team | From $60,000/year | One production agent, managed multi-tenant |
| Business | From $150,000/year | Multiple production agents, managed single-tenant |
| Enterprise | Custom | Customer VPC or on-premises, dedicated infrastructure |
Team and Business annualize to $5,000 and $12,500 per month respectively. Those are annual-contract equivalents, not published monthly purchase prices. Production plans list unlimited memories, but agent and deployment scope still differ. Pricing, Hermes integration
Best fit: Organizations buying production governance and vendor support rather than a low-cost personal memory subscription.
Budget for the whole memory loop
The memory provider's price is only part of the bill.
Main-agent tokens: A free or inexpensive recall operation can still return a large context that the main model must process.
Background work: Extraction, reconciliation, reasoning, refreshes, and consolidation may run behind a simple interaction.
Operations and migration: Self-hosting requires backups, upgrades, model configuration, and troubleshooting. Changing a service URL does not necessarily migrate stored memories.
A useful budget is:
Total cost = memory subscription or metered usage
+ extraction, retrieval, and embedding costs not included
+ main-agent tokens used to read recalled context
+ self-hosted infrastructure and maintenance
Requests, tokens, synchronized files, and generated memories measure different things. One conversation is not necessarily one billable operation.
A practical shortlist
These are architectural matches, not measured performance rankings.
| Primary requirement | Start by comparing |
|---|---|
| Minimal maintenance | Hermes built-in memory |
| Lightweight local facts | Holographic |
| Inspectable project knowledge shared across tools | ByteRover |
| Simple managed fact API | Mem0 |
| Long-term user understanding | Honcho |
| Temporal context and synthesis across memories | Hindsight |
| Navigable, directory-based knowledge | OpenViking |
| Memory plus documents and connectors | Supermemory |
| Low entry price with substantial hosted quotas | RetainDB, with integration validation |
| Production governance and vendor delivery | Memori and relevant enterprise offerings |
Keep built-in memory as a common baseline. Ask each candidate the same questions: did an old preference update correctly, can a decision be traced to its source, does information leak between projects, and does a deleted memory reappear?
Record ingestion, retrieval, background operations, and main-model tokens for a complete session. That connects recall behavior to cost and gives a more useful decision than a cross-vendor accuracy chart.
Snapshot limitations: Pricing and features were checked on October 22, 2026. ByteRover's monthly-billing price, Memori's free-allowance renewal period, Supermemory's numerical lite limit, and an OpenViking managed-service quote were not confirmed. Check the linked official documentation and checkout pages before purchasing or deploying.
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