Koreshield vs Maximem Synap: Features, Pricing & Which Is Better (2026)
A side-by-side comparison of Koreshield and Maximem Synap — features, pricing, and ideal use cases — to help you decide which AI tool fits your workflow.
Koreshield
Koreshield Labs Ltd.
Runtime trust layer that scans customer input, retrieved context and proposed tool calls in AI support workflows before they become trusted instructions.
Key features
- Customer Input Scanning: Inspects messages, attachments and externally controlled text before model execution so injected instructions never reach the model as trusted input.
- Retrieved Context Screening: Keeps poisoned tickets, CRM notes and RAG documents from inheriting system-level authority inside the prompt.
- Proposed Action Evaluation: Checks tool calls against trust, approval and authorization limits before they are executed.
- Detect and Enforce Modes: Run in detect mode to record threats without interrupting traffic, then enable enforcement only where fallback behavior is ready.
- Decision Evidence Trail: Records severity, decision and reason for every request, retained for 7 or 30 days depending on plan, for review and audits.
- Scan API with Key Management: Server-side API keys and endpoints such as /v1/rag/scan make integration with existing support stacks straightforward.
- Flexible Deployment: Use the hosted console or run the FastAPI + PostgreSQL service in your own infrastructure, with VPC, self-hosted and air-gapped options for Enterprise.
Best for
- Prompt Injection Defense: Blocking malicious instructions hidden in customer messages before an AI support agent acts on them.
- RAG Poisoning Protection: Preventing tampered knowledge-base articles or ticket histories from overriding system prompts.
- Agent Action Guardrails: Holding refunds, account changes or other tool calls for policy review when trust limits are exceeded.
- Safe Production Rollout: Measuring real threat and false-positive rates in detect mode before enforcing on live traffic.
- Security Audits: Exporting decision evidence to show auditors and security teams why each request was allowed or held.
Maximem Synap
Maximem
Maximem Synap is an agentic context management platform that gives AI agents persistent, scoped memory with sub-15ms retrieval.
Key features
- Two-Call Memory API: Record messages asynchronously and fetch ranked, prompt-ready context before each reply — writes never block the agent.
- Three-Layer Memory Model: Separate organizational, long-term per-user and short-term session memory with customer and client scoping for multi-tenant isolation.
- Entity Resolution & Structured Extraction: Converts raw dialogue into structured facts and merges aliases of the same person or company automatically.
- Anticipatory Retrieval: Pre-fetches context during the conversation for under 15ms P75 retrieval, fast enough for voice agents.
- Agentic Compaction & Consolidation: Background cycles compact and consciously forget stale information to keep context lean and cut token costs.
- Hybrid Vector, Graph & File Storage: Retrieval nets across semantic vectors, entity relationship graphs and raw documents, with FAST/ACCURATE/HYBRID modes.
- Broad Framework Support: Python and TypeScript SDKs, REST and WebSocket APIs, a hosted MCP endpoint and native adapters for 23 frameworks including LangChain, CrewAI, OpenAI Agents and Claude Agent SDK.
- Enterprise Controls: BYOK model providers, audit trails, webhooks, custom memory schemas, and VPC deployment, SSO/SAML and RBAC on enterprise plans.
Best for
- Personalized Support Agents: Customer service bots remember prior issues and preferences across sessions instead of re-asking users.
- Voice Agents: Real-time voice assistants pull user context in milliseconds without conversational pauses.
- Multi-Tenant SaaS Assistants: B2B products keep each customer's memory isolated while sharing organization-wide policies and product facts.
- Token Cost Reduction: Teams replace full-history prompt stuffing with compact ranked context to lower LLM spend.
- Long-Running Copilots: Coding or productivity assistants recall what a user worked on days or weeks earlier.
- Replacing Homegrown RAG Memory: Engineering teams avoid building and maintaining their own extraction, vector DB and ranking pipeline.