See failures, cost, and latency in productionYou add traces, usage and cost accounting, cache policy, cancellation, bounded retries, and safe logs. Debug output becomes evidence for tests and operations.cppacademyacademy/topics/production-observabilitywebsite/content-src/academy/course.mjsacademySee failures, cost, and latency in production
You add traces, usage and cost accounting, cache policy, cancellation, bounded retries, and safe logs. Debug output becomes evidence for tests and operations.
9 focused minutesNot started
Unit example (nearest native match)
See the idea in context
auto program = axllm::ax("text:string -> label:string");
auto usage = program.getUsage();
Trace the run
tracer connects model and tool activity to the surrounding request.
Make cancellation possible
abortSignal lets callers stop work that is no longer useful.
Control repeated work
contextCache makes reuse an explicit operational policy.
Run itIn your own project
include(FetchContent)
FetchContent_Declare(axllm GIT_REPOSITORY https://github.com/ax-llm/ax GIT_TAG main SOURCE_SUBDIR packages/cpp)
FetchContent_MakeAvailable(axllm)
target_link_libraries(your_app PRIVATE axllm::axllm)
#include <axllm/axllm.hpp>
auto llm = axllm::ai("openai", axllm::object({{"apiKey", std::getenv("OPENAI_API_KEY")}}));
auto classify = axllm::ax("review:string -> sentiment:class \"positive, negative, neutral\"");
Set OPENAI_APIKEY in your environment before running provider-backed code.
In the ax repo
From a clone of the ax repo:
npm run example -- cpp src/examples/cpp/long-agents/smart_defaults_agent.cpp