AI Decision System
Replayable, offline-verifiable records for AI-assisted go/no-go decisions: experiment promotions, typed model answers and human-approved actions.
翻譯準備中
Tracify is an AI observability platform that helps developers monitor, debug, and optimize AI agents in production. Trace every decision, tool call, failure, retry, latency spike, and cost in one clear view.
翻譯準備中
取自儲存庫 README
正在載入完整 README。 以下為已儲存的摘錄。
Tracify is an observability and failure-prevention platform for AI agents. It captures agent runs, LLM generations, tool calls, costs, evaluations, and failures so teams can understand what happened, fix it, verify the fix, and prevent the same failure from returning.
Run the example once, then open the Tracify dashboard. The canonical guide is docs/quickstart.md.檢視完整 README ↗Uncoil is an observability and failure-prevention platform for AI agents. It captures agent runs, LLM generations, tool calls, costs, evaluations, and failures so teams can understand what happened, fix it, verify the fix, and prevent the same failure from returning.
capture → diagnose → assign → fix → verify → prevent recurrence
npm install uncoil-sdk
export UNCOIL_API_KEY=uncoil_sk_live_...
import { traceAgent, llmCall, toolCall } from "uncoil-sdk";
const run = traceAgent(async (question: string) => {
await llmCall({ input: question, output: "I will look that up.", modelId: "gpt-4o-mini", costUsd: 0.001 });
await toolCall({ toolName: "search", input: { query: question }, output: "Search completed", latencyMs: 120 });
return "Done";
}, { environment: "development", release: "quickstart" });
await run("What is agent observability?");
pip install uncoil-sdk
export UNCOIL_API_KEY=uncoil_sk_live_...
from uncoil import trace_agent, llm_call, tool_call
@trace_agent(environment="development", release="quickstart")
async def run_agent(question: str):
llm_call(input_data=question, output_data="I will look that up.", model_id="gpt-4o-mini", cost_usd=0.001)
tool_call(tool_name="search", input_data={"query": question}, output_data="Search completed", latency_ms=120)
return "Done"
Run the example once, then open the Uncoil dashboard. The canonical guide is docs/quickstart.md.
| Surface | Location | Status |
|---|---|---|
| Web app and API | src/, convex/ | Active |
| TypeScript SDK | packages/ts-sdk/ | Active; package uncoil-sdk |
| Python SDK | packages/python-sdk/ | Active; package uncoil-sdk |
| Playwright package | packages/playwright/ | Active |
| Documentation | content/docs/, docs/ | Active |
| Tinybird analytics | tinybird/ | Active |
| Regional configuration | config/, docs/regional-cloud-runbook.md | Active |
| CLI / MCP / curated examples | Planned after contracts stabilize |
SDK / OTLP → Next.js ingest → asynchronous processing
→ Tinybird raw spans and analytics
→ Convex metadata, run summaries, workflow state, evaluations
→ dashboard and future CLI / CI / MCP consumers
Tinybird is the analytical store for high-volume telemetry. Convex is the application store for project-owned metadata and workflow state. SDK telemetry fails open so an observability outage does not interrupt a customer agent.
The hosted application is the supported path. Regional availability and data-flow claims are documented on the public status, security, and data regions surfaces. The EU deployment currently discloses that asynchronous event processing transits the United States; it must not be described as end-to-end EU-resident until that boundary is removed or the product contract changes.
npm ci --legacy-peer-deps
npm run typecheck
npm run lint
npm run test:content
npm run test:activation
npm run test:sdk:ts
npm run test:sdk:python
npm run build
See docs/releasing.md before publishing or deploying.
API keys are hashed server-side and shown only once. Redaction is applied before asynchronous telemetry processing. Do not send secrets or unnecessary personal data in trace metadata.
The application and SDKs are MIT licensed unless a subdirectory states otherwise.