EverMemOS
Summary
Long-horizon AI agents fail not because their reasoning breaks, but because their memory does — every session starts blank, every context window expires, and your users explain themselves again from scratch.
EverMemOS, built by EverMind, is a memory infrastructure layer that gives AI agents persistent, inspectable, and portable memory across sessions, platforms, and model providers. The vendor describes multimodal ingestion, so agents can encode not just text exchanges but structured context from multiple input types. Self-hosted deployments run under an Apache 2.0 license, which means teams with data-residency requirements can own the stack entirely. The ceiling appears when memory graphs grow dense — community reports suggest retrieval latency climbs before tuning is required, and teams building high-throughput customer support pipelines report needing to manage memory pruning manually. Teams that need memory to double as a full observability or analytics layer find they are adding a second tool alongside it.
Bottom line: EverMemOS earns its place as the memory substrate for a personalized AI companion or a multi-agent system where context continuity is the whole product — but teams expecting drop-in memory for a stateless REST API service will need to instrument the integration themselves.
Pricing Plans
Subscription- Free Tier
- Full self-hosted open source version available with no usage limits beyond infrastructure
Open Source Self-Hosted
Full EverOS runtime under Apache 2.0, local-first with Docker
- Self-hosting
- Markdown export
- Multimodal ingestion
- Self-evolving skills
EverOS Cloud Enterprise
Managed cloud service with enterprise features
- Managed hosting
- Support
- Additional enterprise capabilities
View full pricing on evermind.ai →
Pricing may have changed since last verified. Check the official site for current plans.
Community Performance Report Card
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Pros
Sign in to edit- Apache 2.0 self-hosted deployment, so teams with data-residency or compliance requirements keep memory data inside their own infrastructure without negotiating a custom data-processing agreement.
- Model-agnostic memory storage, which means swapping the underlying LLM — whether for cost, capability, or vendor risk reasons — does not erase accumulated user context the way model-native memory solutions do.
- Multimodal memory ingestion described by the vendor, so agents handling inputs beyond plain text — images, structured data, hardware sensor context — do not require a separate storage abstraction for each input type.
- API-first design, which means EverMemOS plugs into an existing agent runtime rather than forcing a platform migration, avoiding the all-or-nothing switching cost that embedded memory solutions impose.
- Memory portability across platforms, so context accumulated in one agent deployment can be accessed by another agent in the same ecosystem — critical for multi-agent systems where one agent's output is another agent's starting context.
Cons
Sign in to edit- There is no built-in interface for inspecting, editing, or pruning stored memory — a product manager or support lead who needs to audit what an agent remembers about a customer must wait for an engineering build or a third-party tool, which delays any compliance or quality review workflow.
- Memory retrieval latency increases as the memory graph grows without active pruning policies, and EverMemOS does not automate pruning out of the box — teams running high-volume customer support deployments end up writing and maintaining their own memory lifecycle management layer.
- The platform provides no agent execution, no prompt routing, and no workflow canvas, which is the right architectural choice but means teams evaluating tools that bundle memory with agent orchestration — Mem0 or platform-native memory in LangGraph, for example — will find EverMemOS requires more integration work and will move to those alternatives when build capacity is the constraint.
About
- Platforms
- Python library, Docker/self-hosted, Cloud
- API Available
- Yes
- Self-Hosted
- Yes
- Last Updated
- 2026-08-16T03:46:14.375Z
Best For
Who it's for
- Developers building long-horizon agent systems
- Teams needing portable, inspectable memory across platforms
- Applications requiring multimodal memory ingestion
- Self-hosting users wanting Apache 2.0 licensed infrastructure
What it does well
- Multi-agent orchestration and team context sharing
- Personalized AI companions with behavioral consistency
- Company knowledge bases that evolve with team interactions
- Customer support agents that retain issue history and preferences
- AI wearables and hardware with conversational memory
Integrations
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Sign Up to ContributeFrequently Asked Questions
- Is EverMemOS free?
- EverMemOS has a permanent free tier alongside paid upgrades. You can keep using a baseline version indefinitely without paying.
- Is EverMemOS open source?
- No — EverMemOS is a closed-source tool. Source code is not publicly available.
- Does EverMemOS have an API?
- Yes. EverMemOS exposes a developer API. See the official documentation at https://evermind.ai for details.
- Can I self-host EverMemOS?
- Yes. EverMemOS supports self-hosting on your own infrastructure.
- When was EverMemOS released?
- EverMemOS was first released in 2025.
- What platforms does EverMemOS support?
- EverMemOS is available on: Python library, Docker/self-hosted, Cloud.
Curated lists that include this category
Memory that survives across sessions
Long-horizon AI agents fail not because their reasoning breaks, but because their memory does — every session starts blank, every context window expires, and your users explain themselves again from scratch.
What it offers
EverMemOS, built by EverMind, is a memory infrastructure layer that gives AI agents persistent, inspectable, and portable memory across sessions, platforms, and model providers. The vendor describes multimodal ingestion, so agents can encode not just text exchanges but structured context from multiple input types. Self-hosted deployments run under an Apache 2.0 license, which means teams with data-residency requirements can own the stack entirely. Pricing follows a subscription model, though the full self-hosted open source version carries no usage limits beyond infrastructure.
Supported platforms include a Python library, Docker/self-hosted, and cloud. Integrations cover Claude Code, Codex, OpenClaw, Hermes, MCPs, and custom agent loops. API access is available.
Trade-offs
Community reports note that retrieval latency climbs as memory graphs grow dense, and teams must handle tuning themselves. There is no built-in interface for inspecting, editing, or pruning stored memory, and the platform does not automate pruning out of the box.
Who it is for / who should skip it
EverMemOS suits developers building long-horizon agent systems, teams needing portable memory across platforms, applications that require multimodal ingestion, and self-hosting users who want Apache 2.0 licensed infrastructure. Skip it if your workflow needs a ready-made audit interface or automated memory lifecycle management.