Analog AI

What is Analog AI?

Analog AI is an AI platform that develops infrastructure for agents’ cognition and memory; its core product is called Analog OS. It combines large language models with long-term dynamic memory, symbolic reasoning, and skill learning, with the goal of enabling agents to retain knowledge across sessions and carry out more stable workflows.

This product is neither IBM’s hardware for simulating memory computing nor the sports coaching application with the same name. The details listed should be based on the agent operating system, APIs, Python SDK, and digital human capabilities as described on Analog AI’s official website.

How does an Analog OS work?

Traditional large models do not retain each conversation permanently; agents that operate for extended periods tend to lose facts, preferences, and operational experience. Analog OS adds layers for persistent memory and reasoning outside the model, organizing information into facts, experiences, and processes.

• Fact: It records relatively stable knowledge, relationships, and user preferences.
• History: Records the questions raised by the user, the actions taken by the agent, and the results.
• Process: Records tool calls and successful methods for multi-step tasks.
• Time and place: Distinguishing information that is valid only during specific periods or in certain settings.
• Permissions: Identify the actions that different users and roles can perform.

The system will also gradually reduce the relevance of outdated information, and assign those processes that are repeated successfully to smaller models for processing. The actual performance still depends on data quality, conflict resolution methods, model selection, and the design of access permissions.

Core module

1. DeepThink inference module

DeepThink is designed for dealing with complex logic and problem-solving; it does not provide an immediate single answer, but rather evaluates various hypotheses, checks for contradictions, and adjusts its reasoning. The official website states that it is capable of performing first-principles reasoning, multi-step reasoning, and hypothesis-based reasoning.

• Derive new conclusions based on existing facts.
• A contradiction between the stored memory and the current input is detected.
• Ask follow-up questions about unclear information.
• Predict the possible outcomes under different conditions.
• Maintain explainable decision-making criteria.

\"Explainability\" does not mean that the reasoning is necessarily accurate or safe. In high-risk scenarios, it is still necessary to retain the original evidence, verify the intermediate conclusions, and implement human approval.

2. DeepAct execution module

DeepAct is responsible for converting reasoning into actions: it can invoke tools, run code, access APIs, and adjust subsequent steps based on the results obtained. It also learns from successful processes and routes repetitive tasks to models that are less costly to use.

Execution capabilities can amplify the impact of errors; therefore, it is necessary to restrict the permissions of tools, the amount of transactions that can be carried out, file access, and the ability to send external messages. Any irreversible action must require explicit confirmation.

3. Persistent dynamic memory

The Analog OS continuously organizes information across sessions, updating or discarding old facts based on new evidence. It not only performs searches for similar texts but also tries to retain knowledge related to time, causality, permissions, and procedures.

Erroneous memories can also continue to influence subsequent decisions. The application should allow users to view, correct, delete, and export memories, while keeping a record of any changes made.

4. Program and skill learning

Agents can learn the procedures by using multi-step tools, analyze failures, and reuse the successful steps in subsequent tasks. This approach is suitable for customer service, internal operations, research, and the automation of repetitive tasks.

Before reusing a process, it is necessary to check whether the external systems have changed. Once the interfaces, policies, prices, and permissions change, the old processes may cease to be secure.

5. Multiple users and permission identification

The official website states that the system is able to distinguish between different users, permissions, and organizational roles, and to adjust the responses or actions accordingly. It can also record that certain facts are valid only at specific times and places.

These capabilities need to be integrated with systems for managing identities, authorizations, and auditing. Relying solely on models’ ability to identify natural language roles is not sufficient to protect sensitive data.

6. Research on emotions and digital humans

Analog AI also explores emotional intelligence and virtual human interfaces; its public page lists emotions such as surprise, confusion, excitement, happiness, disappointment, care, and confidence. Virtual humans can enable text or voice-based interaction, and they come in either realistic or low-fidelity formats.

Simulating emotions is a form of interactive design; it does not imply that the system possesses consciousness or real feelings. When used with customers, it is important to clearly indicate that it is an AI, so as to prevent users from mistakenly thinking they are communicating with a human being.

Access method

Compatible chat interface

The official website states that it provides APIs compatible with chat completion mechanisms, allowing integration with existing proxy environments such as OpenClaw and Hermes. Teams can incorporate Analog OS’s memory and reasoning capabilities without having to rewrite the application entirely.

Python SDK

The Python SDK is designed for custom agent projects such as LangChain and CrewAI, and it is used to integrate long-term memory into existing workflows. Currently, access to it is limited; developers need to register or request permissions.

Cloud Proxy Creator

Users can also create chat agents with persistent memory within the official app, and upload files as input for knowledge storage. This service is currently available on a selective basis or in an early adoption phase.

What scenarios are it suitable for?

• Customer service agents that need to remember user preferences across sessions.
• Enterprises hope to reuse successful operational processes in their automation systems.
• Long-term research, project management, and internal knowledge assistant.
• Multi-user agents that require reasoning regarding time, causality, and permissions.
• Development teams that use LangChain or CrewAI to build agents.
• Enterprise services that require text, voice, or digital human interfaces.

How to connect to Analog AI?

1. Clarify which facts, experiences, and procedures the agent is required to keep record of.
2. Apply for cloud services or API access and create a testing environment.
3. Choose a compatible chat interface or Python SDK to integrate with existing applications.
4. Set minimum permissions for each type of user and tool.
5. Prepare a test set that includes contradictions, outdated facts, and multi-user isolation.
6. Check whether memory writes, updates, deletions, and restores are traceable.
7. Set manual approval and maximum risk limits for executing actions.
8. Deploy it only after comparing accuracy, latency, model cost, and business outcomes.

Pricing and Versions

As of August 26, 2026, the official website lists chat agents, agent memory, and interactive digital avatars as separate services. The first two options are available with a free trial followed by pay-per-use pricing, though no unified price is disclosed.

The starting price for digital avatars corresponds to the reference configuration specified on the official website, which involves at least 25 calls per day, each lasting 5 minutes; however, the specific parameters regarding concurrency, voice quality, models, and customization options need to be confirmed by the sales team. The price was verified on August 26, 2026, and the final amount will depend on the account usage, the settlement details, and the contract terms.

How to evaluate pay-as-you-go costs?

• Confirm how memory writing, retrieval, reasoning, and tool execution are charged separately.
• Count the number of model calls and memory operations for each business task.
• Compare the cost reduction and quality changes brought about by smaller model routing.
• Ask whether file storage, voice, digital avatars, and third-party models are charged separately.
• Set separate budget limits for development, testing, and production environments.
• Explanation of billing for failed calls, retries, and timeouts.

How to interpret the baseline score?

The official website displays results from BEAM, Microsoft State-Bench, and HotPotQA, stating that these models are superior in certain memory-related tasks. It also shows the cost of processing tasks for different model configurations.

These results are based on data provided by the manufacturers, and the catalog should not present them as separate certifications. Purchasers need to verify the version of the dataset, the evaluation scripts, the baseline models, the number of times the tests were run, and whether the results can be reproduced.

Security and Governance

• Memory is strictly isolated by organization, user, and project.
• Record the source, time, and confidence level of each fact.
• Users are allowed to view, correct, and delete incorrect memories.
• Set retention periods and rules prohibiting writes for sensitive information.
• Limit the tools, parameters, and external systems that agents can call.
• Manual approval and thorough auditing are applied to high-risk actions.

Long-term memory stores personal preferences, business facts, and operational history; it may be more sensitive than a single conversation. Before going live, it is necessary to verify the data location, encryption methods, deletion procedures, backup strategies, and any third parties involved in processing the data.

Is Analog AI open source?

For the Analog OS cloud services, APIs, and digital human platform, no verifiable, complete official open-source repositories have been found; therefore, they should be classified as commercial or access-restricted products. The official website mentioned an open-source memory module, but the available public information is not sufficient to confirm the existence of an official repository for it, its version numbers, or the status of its ongoing maintenance.

A-MEM, IBM AIHWKit, or other memory-related projects found on GitHub are not the same product as Analog AI. Without an official confirmation of ownership, it is not appropriate to mix together different open-source licenses and installation methods.

Product advantages

• Integrate the memorization of facts, experiences, and processes into the same agent layer.
• Pay attention to contradictions, time, permissions, and multi-step reasoning.
• Existing proxy frameworks can be integrated through APIs and Python SDKs.
• Attempt to learn from successful processes and route to smaller models.
• It also studies text, voice, and digital human interactions.

Product limitations

• APIs and SDKs are not currently fully accessible to the public.
• The chat and memory services do not have publicly specified, clear pay-per-use rates.
• The baseline scores on the official website still need to be independently reproduced.
• Long-term memory can lead to privacy issues, the accumulation of errors, and permission risks.
• There is no verifiable official open-source version of the complete product.

Frequently Asked Questions

Is Analog AI the same as IBM’s simulated AI hardware?

No. The term Analog AI here refers to a platform for long-term memory and symbolic reasoning designed for software agents, and it is not IBM’s tool for simulating memory computing.

What is Analog OS mainly used to store?

It divides memories into facts, experiences, and processes, and attempts to deal with issues such as time, location, permissions, contradictions, and outdated information.

Can it be integrated with LangChain or CrewAI?

The official website states that the Python SDK can be used with custom solutions such as LangChain and CrewAI, but currently registration or an application is required to gain access; the specific compatibility details should be referred to in the documentation.

Is there a free version of Analog AI?

Chat agents and agent memories offer a free trial along with pay-as-you-go pricing; the public page does not specify a fixed free quota or a uniform price.

How much does a digital human cost?

The official website indicates that the starting price is $300 per month, with possible upfront fees; the final cost depends on the number of calls, the image used, the voice quality, the model selected, and any customization requirements.

Is Analog AI open-source?

The core platform is not an open-source product that has been officially recognized. No complete official repository that can be verified exists at present, and third-party projects with the same name cannot be regarded as its source code.