Introducing Alchymos

AI agents are becoming more capable, but the systems behind them are also becoming more complex. A single user request can trigger multiple model calls, tool invocations, and trips across external services. Every step adds latency, cost, and another place where something can go wrong.

We built Alchymos to give teams a better infrastructure layer for those workloads. Alchymos sits alongside the clients and tools your agents already use, helping repeated work return faster while giving you a clear view of what happens across each execution.

Caching designed for agents

Traditional caches work well when requests are predictable and responses are static. Agent workloads are different. They combine natural-language prompts, model responses, tool calls, user context, and data that can change at different speeds. Caching them effectively requires more than putting a key-value store in front of an API.

Alchymos provides a multi-layer, fine-grained cache built specifically for LLM and agentic workloads. It can serve reusable results from the edge, regional caches, or local memory, reducing the distance and work required to answer a request. Exact and semantic matching help identify repeatable work even when prompts are not identical.

Teams remain in control through cache rules. You can define time-to-live settings, priorities, tags, and scopes for an organization or user. You can also invalidate entries when upstream data changes, purge them on demand, and inspect why a request produced a hit, miss, or bypass.

Faster responses and less repeated spend

Agent loops often repeat the same expensive operations: answering a familiar question, retrieving the same resource, or calling a tool with inputs seen moments earlier. Without a cache, each repetition consumes more tokens, provider capacity, and time.

By serving eligible results from cache, Alchymos reduces calls to upstream models and tools. That means lower latency for users, less load on the systems agents depend on, and fewer tokens billed for work that has already been completed. A distributed edge layer brings cached responses closer to users around the world, while layered caching gives teams the flexibility to balance speed, freshness, and resilience.

See what your agents are doing

Performance improvements are only useful when you can understand them. Alchymos includes observability for AI traffic so teams can monitor request volume, token usage, costs, errors, cache efficiency, and latency in real time.

Detailed execution traces connect the individual steps in an agent workflow. You can follow model requests and MCP tool calls, inspect their timing and cache outcomes, and identify the exact point where a workflow slowed down or failed. Views across users and teams make it easier to understand both individual requests and broader usage patterns.

This visibility turns caching from a hidden optimization into something measurable. Teams can see which actions benefit most, find avoidable misses, refine cache rules, and understand how those changes affect speed and spend.

Built to fit the stack you already have

Adopting infrastructure should not require rebuilding an agent. Alchymos is SDK-first, with lightweight TypeScript integrations designed to wrap existing clients. Teams can add caching and telemetry to OpenAI, MCP clients and servers, and Vercel AI workflows without changing the core behavior of their application.

The same foundation extends to the broader MCP lifecycle. Alchymos Insights provides visibility into MCP usage, latency, errors, and costs, while Alchymos Guard adds policy controls such as access rules and rate limits to help protect MCP clients and servers.

What we believe

Agents should not have to trade responsiveness for capability. Developers should not have to choose between moving quickly and understanding what their systems are doing. And repeated work should not carry the same cost every time it runs.

Alchymos is our answer: an agent-native layer that makes AI systems faster, more efficient, observable, and easier to operate. We are starting with caching because it creates immediate leverage across latency, cost, and reliability, while we build the surrounding tools teams need to run agents confidently in production.

If you are building agents with OpenAI, MCP, or Vercel AI, get started with Alchymos or book a demo. We would love to learn what you are building.