AI Decision System
Replayable, offline-verifiable records for AI-assisted go/no-go decisions: experiment promotions, typed model answers and human-approved actions.
LLM 애플리케이션 성능을 추적하고 엔지니어링 팀의 프롬프트를 관리합니다
Track LLM application performance and manage prompts for engineering teams
AI가 요약 · README에서
개발 도구Python · TypeScriptGitHub @respanai직접 설치운영자 미확인
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로그인한 뒤 확인 파일을 만들어 이 저장소의 기본 브랜치에 30분 안에 커밋해 주세요. 다른 브랜치나 포크의 파일은 인정하지 않습니다. 파일을 올린 뒤 아래에서 확인하면 관리자 승인 없이 관리할 수 있습니다. 자격을 유지하려면 파일을 남겨 두세요.
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README 전체를 불러오는 중입니다. 아래는 저장된 발췌입니다.
Observability, prompt management, and evals for LLM engineering teams.
Respan's library for sending telemetries of LLM applications in OpenLLMetry format.
Replace openai with the provider SDK your application uses. The facade supplies the eligible first-party instrumentation adapters; TypeScript users should not install with --omit=optional because those adapters are optional dependencies of @respan/respan.README 전문 보기 ↗Respan's library for sending telemetries of LLM applications in OpenLLMetry format.
pip install respan-ai openai
npm install @respan/respan openai
Replace openai with the provider SDK your application uses. The facade supplies the eligible first-party instrumentation adapters; TypeScript users should not install with --omit=optional because those adapters are optional dependencies of @respan/respan.
| Variable | Required | Description |
|---|---|---|
RESPAN_API_KEY | Yes | Your Respan API key. Authenticates both proxy and tracing. Get it from the platform. |
RESPAN_BASE_URL | No | Defaults to https://api.respan.ai/api. |
The quickstart below routes OpenAI through the Respan gateway, so RESPAN_API_KEY authenticates both inference and telemetry export. When calling a provider directly, configure that provider's credentials and endpoint normally; Respan still uses RESPAN_API_KEY for telemetry.
import os
from openai import OpenAI
from respan import Respan
# Omit `instrumentations` to auto-discover the matching bundled adapter.
respan = Respan()
# Respan API key authenticates both proxy and tracing
respan_api_key = os.environ["RESPAN_API_KEY"]
respan_base_url = os.getenv("RESPAN_BASE_URL", "https://api.respan.ai/api")
client = OpenAI(api_key=respan_api_key, base_url=respan_base_url)
response = client.chat.completions.create(
model="gpt-4.1-nano",
messages=[{"role": "user", "content": "Say hello in three languages."}],
)
print(response.choices[0].message.content)
respan.shutdown()
import OpenAI from "openai";
import { Respan } from "@respan/respan";
const respanBaseURL =
process.env.RESPAN_BASE_URL ?? "https://api.respan.ai/api";
// Omit `instrumentations` to auto-discover the matching bundled adapter.
const respan = new Respan({
apiKey: process.env.RESPAN_API_KEY,
baseURL: respanBaseURL,
});
await respan.initialize();
const client = new OpenAI({
apiKey: process.env.RESPAN_API_KEY,
baseURL: respanBaseURL,
});
const response = await client.chat.completions.create({
model: "gpt-4.1-nano",
messages: [{ role: "user", content: "Say hello in three languages." }],
});
console.log(response.choices[0].message.content);
await respan.shutdown();
See your traces in the Respan platform.
Install the facade and the provider SDK, omit the instrumentations option, and initialize Respan before the first provider call. Missing provider SDKs are skipped without failing startup and are visible through the instrumentation status API.
| Provider | Python SDK | TypeScript SDK |
|---|---|---|
| OpenAI | openai | openai |
| Azure OpenAI | openai | openai |
| Anthropic | anthropic | @anthropic-ai/sdk |
| Vertex AI | google-cloud-aiplatform | @google-cloud/vertexai |
| Google GenAI | google-genai | — |
| AWS Bedrock | boto3 | @aws-sdk/client-bedrock-runtime |
| Cohere | — | cohere-ai |
| Together AI | together | together-ai |
| OpenRouter | — | @openrouter/sdk |
| Writer | — | writer-sdk |
| Ollama | ollama | — |
respan-ai bundles the seven eligible Python adapters. A normal @respan/respan install includes the nine TypeScript adapters as optional dependencies.
Inspect the result after construction in Python with respan.get_auto_instrumentation_status(), or after await respan.initialize() in TypeScript with respan.getInstrumentationStatus().
LLM wrappers, agent frameworks, application frameworks, protocol/tooling integrations, observability bridges, and vector databases remain explicit opt-ins to prevent duplicate spans. Supplying instrumentations selects explicit mode and disables automatic discovery by default.
Python can intentionally combine explicit plugins with direct-SDK automatic discovery by setting is_auto_instrument=True. Use Respan(is_auto_instrument=False) to disable automatic discovery entirely.
In TypeScript, use disabledInstrumentations: ["openrouter"] to disable one automatic adapter, or instrumentations: [] to disable all automatic discovery. TypeScript explicit mode is currently exclusive and cannot be combined with automatic discovery.
Structure traces with @workflow / @task (Python) or withWorkflow / withTask (TypeScript). See the decorators example for details.
Attach customer_identifier, thread_identifier, and metadata to all spans in scope. See the attributes example.
The SDKs batch spans in memory and retry, but never write to disk, so spans still queued during a long outage or a process crash are lost. For long-running agents that must not lose spans, run the packaged Respan Collector next to the application — the upstream OpenTelemetry Collector with a persistent, bounded queue — and point RESPAN_BASE_URL at it (respan collector start or docker compose -f collector/docker-compose.yaml up -d).
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