Asynchronous ingestion
Record conversation turns or submit documents through the SDK or API. The write call returns immediately while ingestion and subsequent processing run asynchronously without blocking the agent.
Synap
Synap is an agentic context-management platform that gives AI agents persistent, scoped memory across conversations, users, and organizations. It helps developers capture, organize, compact, and retrieve relevant context through SDKs, APIs, MCP, and framework adapters.
Agent memory and context management
Synap is an agentic context-management platform for developers building AI agents. It records conversations and documents, turns them into structured and scoped memory, and retrieves relevant context for later agent responses across sessions and users.
The platform is designed to manage more than a single conversation window: short-term session context, long-term personal memory, and organizational knowledge can be kept in separate scopes. Synap combines asynchronous ingestion, structured extraction, vector and graph storage, anticipatory retrieval, consolidation, and compaction so developers do not have to assemble those memory operations themselves.
Core capabilities
Structured memory extraction
Synap categorizes content, extracts structured memory, chunks information, resolves entities, and organizes the results instead of treating the conversation as unprocessed text.
Multi-store memory
Processed memories are written to vector storage for semantic similarity, graph storage for entity relationships, and file storage for source documents. Scope identifiers govern where information is stored.
Agentic and anticipatory retrieval
The system searches applicable scopes in parallel and can use fast, accurate, or hybrid retrieval. Context can be pre-fetched during a conversation, with under 15 ms P75 in-conversation retrieval reported on the product site.
Compaction and consolidation
Background consolidation and agentic compaction reduce accumulated context while retaining relevant signal. Synap reports validated compaction and includes consolidation and compaction across plans.
Configurable memory architecture
A context architecture is generated per agent to govern extraction, scoping, retention, and related behavior. The product also supports custom memory schemas and organizational, long-term, and short-term memory layers.
Practical use cases
Persistent personal assistants
Record user conversations over days or months and retrieve established preferences, facts, and prior decisions when the agent responds in a new session. Entity resolution is intended to connect names or aliases that refer to the same person.
Shared organizational knowledge
Store product facts, policies, and other shared organizational knowledge separately from personal memories so multiple agents can use the appropriate company context without exposing unrelated customer data.
Low-latency conversational agents
Pre-fetch relevant context while a live conversation is still in progress so an interactive or voice-oriented agent can receive memory without waiting for a separate retrieval step at response time.
Long-lived project and support workflows
Capture long-running project discussions and use consolidation and compaction to keep prompts focused as the history grows, while retaining useful information for later turns.
Custom agent architectures
Give each agent a generated or customized memory architecture that controls extraction, retention, and scoping when a team needs behavior tailored to its application rather than a generic memory store.
Pros and Cons
Pros
- Supports persistent memory across conversations, channels, and months rather than only recent turns.
- Uses structured extraction, entity resolution, scoping, provenance, and multiple storage types to organize context.
- Includes fast, accurate, and hybrid retrieval modes, anticipatory retrieval, compaction, and consolidation.
- Offers Python and TypeScript SDKs, a REST API, hosted MCP endpoint, WebSocket APIs, webhooks, and native adapters for 23 agent frameworks.
- Provides a free tier and states that every plan includes the full core capability set, with plan differences focused on volume and service level.
Cons
- The available evidence does not provide a definitive framework-by-framework description of what each of the 23 native adapters supports.
- Usage is credit-based, and ingestion and compaction costs vary with document or history length; the Trial tier has a hard monthly cap.
- The product’s richer deployment and model-provider options vary by plan, with BYOK and on-premise deployment reserved for eligible tiers.
FAQ
How do you integrate Synap into an AI agent?
Synap provides Python and TypeScript SDKs, a REST API for other languages, a hosted MCP endpoint, and native adapters for 23 agent frameworks. Messages can be recorded asynchronously, with an ingestion ID returned immediately.
What happens to information sent to Synap?
You record conversation turns or submit documents through the SDK or API. Synap extracts structured memories, resolves entities, stores information across vector, graph, and file stores, and retrieves relevant context for later turns.
Which retrieval and memory capabilities are included?
Synap supports fast, accurate, and hybrid retrieval modes. The pricing page states that all plans include these modes, along with anticipatory retrieval, consolidation, agentic compaction, custom memory schemas, and memory-architecture customization.
Can Synap separate personal, organizational, and session context?
The product supports organizational memory shared across users and tenants, long-term memory per person across sessions, and short-term memory for the current session. Scope identifiers determine where ingested information belongs.
Is there a free tier, and can Synap be self-hosted?
A free Trial tier is available without a credit card. Paid plans add higher credit volumes, more agent or instance capacity, support options, longer audit trails, and—on eligible plans—bring-your-own-key support. Enterprise plans include an on-premise deployment option.
Quick Facts
- Category
- AI developer tool
- Primary users
- Teams building AI agents
- Access methods
- Python and TypeScript SDKs, REST API, WebSocket APIs, hosted MCP endpoint
- Framework coverage
- Native adapters for 23 agent frameworks
- Reported retrieval performance
- Under 15 ms at P75 for in-conversation retrieval
- Availability
- Free Trial tier; paid plans and Enterprise options available
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