LLM observability is the practice of understanding what your language models and agents do in production — inputs, outputs, latency, errors, and decision quality. Histeeria provides LLM observability with a focus on judgment: not just tracing calls, but evaluating whether each agent decision was sound.

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The LLM observability stack

LayerQuestionHisteeria
InfrastructureIs the API up? Latency?Your infra / provider
TracingWhat prompts and completions ran?SDK ingest + Tracing
MonitoringWhat’s happening right now?Monitoring
EvaluationWere decisions good?Evaluation
AlertingWhen should someone act?Inbox
Histeeria owns the monitoring → evaluation → alert path for agent decisions.

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Zero-latency observability

Traditional observability can add overhead. Histeeria’s SDK is designed for production agents:
  • Async fire-and-forget — observe() never blocks your agent
  • Silent failure — API outages don’t break your app
  • No extra dependencies — Python stdlib; TypeScript native fetch

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What to observe

Send every meaningful agent turn: 
import { Histeeria } from "histeeria";

const h = new Histeeria();

const result = await chain.invoke({ input: question });

h.observe({
  input: question,
  output: result.output,
  agentId: "rag-agent",
  sessionId: threadId,
  domain: "research",
  inputTokens: result.usage?.inputTokens,
  outputTokens: result.usage?.outputTokens,
  metadata: { retriever: "pinecone", model: "gpt-4o" },
});

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Multi-step LLM chains

For RAG, ReAct, or multi-agent flows, use Tracing to capture each step under one session.

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LLM observability tools compared

Histeeria complements tracing platforms:
  • Tracing tools excel at debugging prompt chains
  • Histeeria excels at production judgment evaluation — scoring decision quality continuously
Many teams use both: trace for development, Histeeria for production reliability.

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Integrations

Works with OpenAI, Anthropic, LangChain, LlamaIndex, CrewAI, AutoGen, and custom agents. See Integrations.

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Get started

Quickstart → Python SDK or TypeScript SDK