Maximem Synap gives developers a memory layer for AI agents. It turns past conversations into useful context that an agent can retrieve in a later session, so the agent can continue a task or remember a preference without receiving the entire chat history again.

What is Synap, in plain language?

Think of Synap as a notebook that an AI agent can consult between conversations. Your application sends conversation content to Synap; before a later reply, it asks for relevant context about the user. Synap returns facts and preferences for the application to include in the next model request. The developer still controls the agent and decides how to use the returned material. Synap is a developer service, rather than a ready-made chatbot for an end user.

That distinction matters for support, sales and voice agents that meet the same person more than once. Remembering a previously stated preference or an ongoing issue can prevent repeated questions. The memory layer also separates information at client, customer and user levels, so shared organizational knowledge can be available where intended while personal context stays in the right scope.

How does the memory reach an agent?

The documented path has two sides: ingest after an interaction and retrieve before a later model call. An ingestion request can contain a conversation turn; processing then extracts useful facts, preferences and events asynchronously. A later context request supplies the appropriate user and conversation identifiers and a search query. Your application reviews the returned context and inserts relevant parts into its agent prompt.

Because ingestion is asynchronous, do not design a workflow that assumes a new fact is available immediately after writing it. Test recall with a later request and check that a correction or changed preference replaces stale context as expected. For a multi-tenant application, verify customer and user scoping with separate test accounts before using real customer data.

A practical first integration

Start in the Synap dashboard by creating a Client and an isolated Instance for your agent. The official quickstart says the instance needs a use-case Markdown file when it is created; plan this description carefully because that file cannot be replaced afterward. Generate an API key, keep it outside source control, then install the Python or JavaScript/TypeScript SDK and initialize it with the key and instance ID.

Next, send a sample conversation turn with a user ID. In a separate later request, fetch context for that user using a question such as which preference the person mentioned. Confirm the returned facts before placing them in the agent prompt. The product also offers a REST API, a hosted MCP endpoint and framework adapters; consult the relevant integration guide if your application already uses an agent framework. The open-source repository covers the SDK, so do not assume the managed service itself is an out-of-the-box open-source deployment.

Plans, limits and data decisions

On the public Synap pricing page, checked 25 September 2026, the promotional Trial is displayed as free with 12,500 credits per month, one agent or instance, and a hard cap when credits run out. The Starter launch offer is displayed in US dollars at $19 per month with 62,500 monthly credits and paid overage. These are public USD displays, not verified local checkout prices; check the page again before buying. The page also lists higher-volume plans and custom Enterprise pricing. On-premise deployment is an Enterprise option that requires a discussion with Maximem.

The security and privacy page states that the managed service stores data in the United States, encrypts it in transit and at rest, and isolates tenants. Memories remain until deleted, with retention policies configurable by data type. Teams handling sensitive information should review the full policy and their own contractual requirements before sending production conversations. Avoid using a public memory service as a shortcut around access controls or consent.

Common questions

Does Synap remember every message verbatim?

Its described workflow extracts and retrieves useful facts, preferences and events from ingested conversations. Treat the returned context as a selected memory view, then check whether it contains the detail your agent needs.

Can I use it without a particular agent framework?

Yes. The product documents Python and JavaScript/TypeScript SDKs, a REST API and a hosted MCP endpoint. Framework adapters are available, but the basic ingest-and-fetch workflow does not require choosing one of them.

What happens when the free allowance is used?

The current Trial table describes a hard cap with no overage. Paid plans show credit allowances and overage rules. Check current plan terms because promotional prices and limits can change.

Where is the data stored?

The official security page says managed data is stored in the United States. It also describes encryption, tenant isolation and configurable retention. Review the full data-handling details for a production or regulated use case.

Official resources and developer community

Synap quickstart — instance setup, credentials and the first ingest-and-fetch test.
Synap SDK on GitHub — source code and developer examples.
Synap pricing — current credit allowances and plan conditions.

Maximem Synap: AI Agent Memory and Context