Structured memory ingestion
Conversation messages are ingested asynchronously and processed into structured memory rather than being stored only as raw dialogue. The pipeline can write to vector, graph, and file stores.
Maximem Synap
Freemium
Maximem Synap is an agentic memory and context-management platform for AI agents. It records conversations, builds structured and scoped memory, and retrieves concise context through SDKs, APIs, MCP, and native adapters for 23 agent frameworks.
What is Maximem Synap?
Maximem Synap is an agentic memory and context-management platform for AI agents. It records conversations as they happen, converts dialogue into structured memory, and returns ranked context before an agent responds. The system separates short-term session context from long-term user memory and shared organizational knowledge, with scoping intended to keep information isolated between users and tenants.
Synap is designed to reduce repeated questions, stale context, oversized prompts, and slow retrieval in applications that need continuity across sessions or agents. Developers can use Python or TypeScript SDKs, a REST API, a hosted MCP endpoint, or native adapters for 23 agent frameworks. The platform reports 92% LongMemEval accuracy, 93.2% LoCoMo accuracy, and under 15 ms P75 in-conversation retrieval.
What can Maximem Synap do?
Scoped memory architecture
Synap supports short-term session context, long-term per-person memory, and organizational knowledge. Custom architectures govern extraction, scoping, retention, and related behavior for an agent.
Agentic retrieval
Retrieval combines semantic similarity, graph relationships, and document content. FAST, ACCURATE, and HYBRID retrieval modes are available, with anticipatory retrieval designed to pre-fetch likely-needed context.
Compaction and consolidation
Agentic compaction reduces conversation history to relevant signal, while background consolidation updates memory over time. Compaction is described as validated and includes an indication of how it performed.
Entity resolution and provenance
The system can resolve references such as a person’s full name and abbreviation to one entity. Memories retain source information and what a memory replaced, supporting traceability and updates.
Developer access and integrations
Synap provides Python and TypeScript SDKs, REST and WebSocket APIs, webhooks, event streams, a hosted MCP endpoint, A2A protocol support, and native adapters for 23 agent frameworks.
Use Cases
“Healthcare and mental health continuity”
Maintain structured patient profiles, treatment timelines, preferences, and outcomes across sessions and agents. Source information and replaced memories can help make the history traceable.
“Cross-channel customer support”
Surface prior conversations, resolutions, open threads, and customer sentiment before an agent responds across chat, email, phone, or SMS, reducing the need for customers to repeat their issue.
“Sales and revenue intelligence”
Connect calls, emails, messages, objections, and buying signals to a deal. Sales representatives can inherit deal context during handoffs and review patterns across similar opportunities.
“Voice AI”
Pre-fetch relevant context during a live conversation so retrieval does not require a turn-boundary pause. Synap also describes updating memory when a user corrects a fact mid-turn.
“Multi-agent workflows”
Give multiple agents access to shared organizational or user context while retaining scope boundaries, so information learned by one agent can be available to other authorized agents.
Frequently Asked Questions
How is Synap added to an agent workflow?
A typical workflow records conversation messages as they occur, then calls Synap before the agent replies to fetch relevant context. Developers can use the Python or TypeScript SDKs, REST or WebSocket APIs, a hosted MCP endpoint, or framework adapters.
What does Synap return during retrieval?
The SDK returns a ranked set of relevant facts, including a formatted context field that can be inserted into an agent prompt. Retrieval can use fast, accurate, or hybrid modes.
Can Synap support more than one agent?
Yes. The product describes shared organizational and user memory for multi-agent workflows, with scope boundaries intended to control which agents can access particular information. Plan limits vary by the number of agents or instances.
How are credits calculated?
The pricing page states that fast retrieval costs one credit per query, hybrid retrieval costs two, and accurate retrieval costs three. Ingestion costs between 2 and 25 credits per document, while compaction costs between 2 and 8 credits per call, depending on length. Cached reads cost a fraction of one credit.
Can Synap be self-hosted?
The pricing page states that Enterprise plans include an on-premise deployment option. Teams needing self-hosting, air-gapped deployment, or detailed architecture requirements should confirm availability and requirements with Maximem.
Quick Facts
- Category
- Developer Tool
- Primary users
- AI application developers and teams building agentic workflows
- Core function
- Persistent memory and context management for AI agents
- Access methods
- Python and TypeScript SDKs, REST, WebSocket, hosted MCP, and framework adapters
- Framework coverage
- Native adapters for 23 agent frameworks
- Reported performance
- 92% LongMemEval, 93.2% LoCoMo, and under 15 ms P75 in-conversation retrieval
Maximem Synap Traffic Analysis
Traffic data is for reference only.
- Domain Rating
- 38
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