Agents need a new way to get reliable context
As models get better at reasoning, context is what separates a dependable agent from an unpredictable one. Agents retrieve, remember, and traverse relationships inside one reasoning loop. The infrastructure gives them three systems. Each one is built for agents. None of them knows about the other two.
Retrieval returns chunks. Memory returns facts. Graph returns relationships. Teams write the code that merges them into one context window. They write another layer for writes, deciding what gets stored, where, and when the rest of the stack can see it. An agent writes a fact, and a second agent queries it before the index catches up. Every system is healthy and the agent still gets the wrong context. Teams end up hiring specialists and spending months building a knowledge platform before they can build their agent.
We don’t think that’s a tooling problem. We think it’s an architecture problem.
Ninebark is the observable knowledge engine for agents.
One request returns the documents, the memories, and the relationships that apply, merged and ranked for the model. One write path puts what an agent learns where the next agent’s read will find it. Retrieval, memory, and graph run underneath as a single engine instead of three services behind an SDK. Governance and observability cover the whole loop. When an agent fails, teams see what it read, what it wrote, and how the context was assembled.
Run Ninebark where you run your agents. Deploy it on your cloud or use it as a fully managed service. Any model can use it.
We’ve seen this break before.
We’re a small team with over a decade of experience spanning search, infrastructure, and product. We’ve watched context throttle agents at scale, and we solved it the hard way, by assembling the fragmented stack ourselves.
Now we want you to skip that pain and spend your time building what matters most. That’s why we’re building Ninebark.