Agentic Context Management
Your AI Agents Forget.Synap Makes Them Remember.
Ultra-fast retrieval. Supports 10 popular agentic frameworks & SDKs.
Free tier. No credit card. Open source.
No credit card required. Google or GitHub sign-in.
Native integrations with
LangChain
CrewAI
AutoGen (AG2)
LlamaIndex
Google ADK
Haystack
OpenAI Agents SDK
Pydantic AI
Semantic KernelVercel AI SDK coming soon
Context Management Is Harder Than It Looks
Giving AI agents short-term memory, long-term memory, and self-learning is not optional anymore. But building it right is painfully hard. Teams spend months designing memory models, tuning retrieval, fighting context rot, and debugging hallucinations, just to keep agents reliable past 20 conversation turns.
Context engineering is fast-evolving
AI agents are evolving fast. Context engineering is evolving even faster. New research, new architectures, new failure modes. What worked last quarter often breaks today. Keeping up means constant rewrites, rethinking assumptions, and chasing best practices that are not settled yet.
Context engineering is a costly distraction
Context management is a core competency, but for most teams it is the wrong one. Every hour spent balancing accuracy, latency, and token costs is an hour not spent shipping features your customers actually care about.
Anticipatory Retrieval
Synap pre-fetches context before your agent requests it. 15ms P50 latency in production. For voice AI agents, this is the difference between natural conversation and awkward pauses.
9 Framework Integrations
Native support for LangChain, CrewAI, AutoGen, LlamaIndex, Google ADK, Haystack, OpenAI Agents SDK, Pydantic AI, and Semantic Kernel. Vercel AI SDK coming soon.
Entity Resolution
When a user says "my manager" in turn 3 and "Sarah" in turn 12, Synap resolves them automatically. Cross-session, cross-conversation, without the agent doing any work.
Temporal Awareness
Context from 30 minutes ago and context from 30 days ago should not carry equal weight. Synap applies temporal decay and relevance scoring so your agent surfaces the right information at the right time.
Conscious Forgetting
When a user says "ignore what I said about the budget," Synap processes that as a retraction, not just more context to store. Contradiction handling is built into the pipeline.
Custom Memory Architectures
No universal memory model. Synap builds customized memory architectures per use case. Customer support agents and voice AI agents need different context strategies. Synap handles both.
How Synap compares
Evaluating AI memory systems? Synap scored 90.2% on LongMemEval at 15ms P50 retrieval. See how it stacks up against the other major agent memory projects.
See the full comparison across all alternatives →How It Works
Context Management, not just Memory
Synap manages agent context end-to-end. It identifies what information is relevant for an AI agent, structures it, and makes it available at the right time. This is what drives 90.2% accuracy on LongMemEval, roughly 7 points ahead of the next best system.
Anticipates Agent Needs
Synap performs proactive context retrieval. It anticipates what an agent will need and pre-fetches the right short-term and long-term context before the agent asks. P50 latency of 15ms. P99 under 300ms.
Intelligent Memorization
Synap decides what should be stored and how. It does not rely on the agent to self-identify what to remember. Memory is structured for the specific use case, with custom architectures for customer support agents, voice AI agents, and other production workloads.
Proactive Learning and Forgetting
Synap removes low-value information, consolidates related context, and reinforces important signals. It handles entity resolution, temporal decay, and contradiction detection in the background so agents operate with accurate, up-to-date context.
Start Building With Maximem Synap
No credit card required. Google or GitHub sign-in.
Frequently Asked Questions
Synap is Maximem's agentic context management layer for AI agents. It gives your agents persistent, cross-session memory with automatic entity resolution, temporal awareness, and anticipatory retrieval. Synap integrates natively with 9 frameworks (LangChain, CrewAI, AutoGen, LlamaIndex, Google ADK, Haystack, OpenAI Agents SDK, Pydantic AI, and Semantic Kernel) and scores 90.2% on the LongMemEval benchmark. Free tier available with no credit card required.
Synap offers native SDK integrations for 9 agentic frameworks: LangChain (including LangGraph), CrewAI, AutoGen (AG2), LlamaIndex, Google ADK, Haystack, OpenAI Agents SDK, Pydantic AI, and Semantic Kernel. Install the SDK, configure your API key, and start managing context with a few lines of code. Most developers are up and running in under 5 minutes. Visit docs.maximem.ai for the Quickstart guide, SDK reference, and framework-specific integration examples.
Synap provides Python and TypeScript/JavaScript SDKs, plus a REST API that works with any language. The SDK includes native wrappers for 9 agentic frameworks. Visit docs.maximem.ai for the latest SDK availability, language-specific guides, and API reference.
Synap manages memory through customized memory architectures built for each use case. It handles ingestion (deciding what to store), retrieval (surfacing the right context at the right time, including anticipatory pre-fetching at 15ms P50 latency), entity resolution (linking references like "my manager" and "Sarah" across sessions), temporal awareness (weighting recent context higher than stale context), and conscious forgetting (processing retractions and contradictions). All of this happens automatically without the agent needing to manage its own memory.
Yes. Synap is built with enterprise-grade security including encryption at rest, strict data isolation, and compliance-ready architecture. Enterprise plans include VPC/private deployment options, SSO/SAML, configurable RBAC, custom SLAs, and dedicated customer success management. Synap also supports BYOK (Bring Your Own Key) so you can use your own AI model provider credentials. Contact [email protected] for enterprise pricing and security documentation.
Synap takes a fundamentally different architectural approach. Where Mem0 applies a universal memory model (extracted facts plus embeddings), Synap builds customized memory architectures per use case. Where Zep is built around a temporal knowledge graph (Graphiti), Synap focuses on anticipatory retrieval and latency optimization. On the LongMemEval benchmark, Synap scores 90.2% accuracy compared to approximately 83% for the next best system. Synap also supports 9 agentic frameworks natively (LangChain, CrewAI, AutoGen, LlamaIndex, Google ADK, Haystack, OpenAI Agents SDK, Pydantic AI, Semantic Kernel) and delivers P50 retrieval latency of 15ms. Both Mem0 and Zep are solid tools. We encourage developers to evaluate all three against their specific use case. Read the full Synap vs Mem0 comparison at maximem.ai/compare/maximem-synap-vs-mem0 and Synap vs Zep at maximem.ai/compare/maximem-synap-vs-zep.
Supermemory is multimodal-first with connectors for documents, images, videos, and URLs. Synap is conversation-and-agent-first. If you need to process diverse content types into a searchable memory layer, Supermemory covers that well. If you need high-accuracy, low-latency context management for multi-turn AI agents (customer support, voice AI, workflow agents), that is Synap's focus. Synap's architecture is built around anticipatory retrieval (15ms P50), automatic entity resolution, temporal awareness, and conscious forgetting, which are capabilities specifically designed for agentic workloads rather than general-purpose document memory. Read the full Synap vs Supermemory comparison at maximem.ai/compare/maximem-synap-vs-supermemory.
Yes. Synap's core is open source and available on GitHub. You can self-host the full stack or use the managed platform at maximem.ai. The managed platform adds hosted infrastructure, a dashboard, analytics, and a free tier with no credit card required. The open-source release and the managed platform use the same core engine.
