OpenServ

The integration is a base URL and an API key. Your prompts, tool definitions, and application logic stay untouched, whether you use the OpenAI SDK, the Anthropic SDK, the Vercel AI SDK, or LangChain. It's an OpenAI compatible endpoint first, with an Anthropic messages route and a Responses route alongside it, so /v1/chat/completions, /v1/responses, and /v1/messages all map cleanly. For a team already in production, that means the migration cost is measured in hours rather than sprints.

Each task decomposes into steps with explicit dependencies, and every step carries a defined input schema, output schema, and validation contract. A model cannot wander off the path because the structure will not let it. In practice this is what kills most parse failures, and it also stops reasoning tokens from multiplying without limit, which is where a large share of agent cost usually hides.

Shadow agents are separate reviewers that check a decision before it ships, so a hard task gets a validate-and-iterate loop instead of a single pass. Verification hints go further upstream and tell the agent what a correct answer should look like before it starts writing one. Both are opt-in through tool definitions, so you can apply the extra scrutiny to the handful of steps that need it rather than to every call.

The catalog spans OpenAI, Anthropic, and Google, with context windows up to 1M tokens and published per-million-token prices that already include the reasoning layer. You can route a routine extraction to a small model and a hard planning step to a larger one without holding three sets of credentials. Switching models is a string change in the request body, and sending different steps to different models is the simplest form of agent orchestration.

Every decision, cost, and step shows up in one dashboard, and prompts plus customer data are not stored or used for training. PromptGuard screens incoming requests for injection attempts and outgoing text for system prompt leakage, with a content filter on by default. For teams building auditable AI agents in finance or the public sector, this is the part that gets a security review signed off.

The Playground runs any supported model with and without SERV Reasoning side by side, no code required. OpenServ also ships Benchmark Tooling so you can measure cost savings and reliability gains on your own workloads before integrating anything. Vendor benchmark claims deserve skepticism, and this setup at least lets you test them against your own prompts.