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Documentation Index

Fetch the complete documentation index at: https://docs.aevyra.ai/llms.txt

Use this file to discover all available pages before exploring further.

aevyra-origin diagnoses why your agent failed. Point it at your pipeline and a rubric; it runs the pipeline, grades it with a judge, and returns a ranked list of culprit spans — each with severity, confidence, grounded reasoning, and a fix_type that tells you exactly where the repair effort belongs. 
pip install aevyra-origin
Not all agent failures are prompt failures. Origin classifies each culprit into one of six fix types so you know what to actually change: 
retrieve  [primary, confidence=0.89, fix=retrieval]
  → The refund policy doc wasn't in the retrieved set.
  → Fix the index, not the prompt.

classify  [contributing, confidence=0.44, fix=routing]
  → Sent the query to the wrong topic corpus.
  → Fix the routing classifier.

answer    [minor, confidence=0.18, fix=prompt]
  → Defaulted to a generic apology given missing context.
  → Reflex can fix this.
Only fix_type="prompt" spans are candidates for Reflex — the others (retrieval, routing, tool_schema, infrastructure) need a different intervention. Origin tells you which is which so you don’t waste time rewriting prompts that won’t help.

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Where Origin fits

Origin is the diagnosis stage in the Aevyra stack: Witness captures the execution trace. Verdict scores it. Origin reads both, pinpoints the failure, and classifies the fix type. When the fix is in a prompt, Reflex can act on it automatically. For every other failure type — a bad retrieval index, an ambiguous tool schema, a mis-routing — Origin tells you exactly where to look so you don’t waste time rewriting prompts that won’t help.

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What it diagnoses

fix_typeWhat it meansWho fixes it
promptThe instructions or context in the prompt need changingReflex
tool_schemaThe tool’s input schema is ambiguous; the LLM called it wrongSchema redesign
retrievalThe retrieval step fetched wrong, irrelevant, or missing docsIndex / embedding fix
routingThe pipeline sent the query down the wrong branch or toolRouting logic fix
infrastructureTimeout, rate limit, auth error, quota exceededOps / infra fix
unknownOrigin could not determine the fix typeManual review

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Three attribution methods

Origin ships three methods that can run independently or together: LLM-as-critic (method="critic") reads the rubric, score, and full trace in one LLM call and returns a ranked list of culprit spans. Fast, general, works for any rubric. Score decomposition (method="decomposition") breaks the rubric into its underlying criteria, attributes each criterion to a span, and aggregates blame across failed criteria. Better at surfacing distributed failures where multiple spans each contributed. Ablation (method="ablation") replaces each span’s output with a neutral placeholder, replays the pipeline via a user-supplied runner, and re-scores. The only method that makes a causal claim — a large score drop means the span is genuinely responsible. method="all" (default) runs critic and decomposition always (two LLM calls), adds ablation when you supply a runner, and merges the results with a corroboration bonus for spans named by multiple methods.

Quick start

Diagnose your first pipeline failure in under 5 minutes

Tutorial

Full walkthrough: a plan-act-respond agent that gets the wrong answer

Methods

Critic, decomposition, ablation — when to use each

API reference

Attribution, NodeAttribution, PromptAttribution

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Works with any LLM

Claude, OpenAI, OpenRouter, Ollama, vLLM, or any OpenAI-compatible endpoint: 
pip install aevyra-origin              # Claude included by default
pip install aevyra-origin[openai]      # add OpenAI, OpenRouter, Together, Groq, Ollama

from aevyra_origin.llm import anthropic_llm, openai_llm

llm = anthropic_llm(model="claude-sonnet-4-5")
# or
llm = openai_llm(model="gpt-4o")
# or local
llm = openai_llm(model="llama3.1:8b", base_url="http://localhost:11434/v1", api_key="ollama")
Python 3.10+. Apache-2.0 licensed.