runtime-narrative 1.0.1

Model execution as human-readable stories with lean/rich failure diagnostics and optional LLM analysis

runtime-narrative

Turn any Python execution into a traceable story composed of named stages. Get minimal logs when everything works — and surgical, LLM-powered diagnostics the moment something breaks.

▶ Story started: Import Customers
✔ Load CSV           0.012s
✔ Validate Data      0.004s

❌ Failure at: Insert Records

  ValueError: duplicate customer id
  Location:   app/db.py:47  insert_row
  Code:       raise ValueError("duplicate customer id")
  Chain:      ValueError ← sqlite3.IntegrityError

  ## Exact Why
  A record with the same customer_id already exists (UNIQUE constraint).

  ## Targeted Fix
  Use INSERT OR IGNORE, or check for an existing row before inserting.

---

Contents

• Installation
• Quick start
• Decorators
• Auto-instrumentation
• Failure diagnostics
• Failure analyzers
• Renderers
• Framework integrations
• Async task groups
• Persistence and CLI
• Alert routing
• Testing utilities
• Custom renderers and analyzers
• Environment variables

---

Installation

pip install runtime-narrative

Optional extras unlock additional renderers and integrations:

Extra	What it installs
console	typer — colored terminal output in ConsoleRenderer
fastapi	starlette — RuntimeNarrativeMiddleware
otel	opentelemetry-api, opentelemetry-sdk — OtelRenderer, OtelLogRenderer, OtelMetricsRenderer
prometheus	prometheus-client — PrometheusRenderer
anthropic	anthropic — AnthropicFailureAnalyzer
django	django — RuntimeNarrativeDjangoMiddleware / SyncMiddleware
celery	celery — NarrativeTask, connect_narrative
grpc	grpcio — RuntimeNarrativeInterceptor / AsyncInterceptor
all	Everything above

pip install "runtime-narrative[console,fastapi,anthropic]"
pip install "runtime-narrative[all]"

---

Quick start

Sync

from runtime_narrative import story, stage

with story("Import Customers"):
    with stage("Load CSV"):
        rows = load_csv("customers.csv")

    with stage("Validate Data"):
        validate(rows)

    with stage("Insert Records"):
        db.insert(rows)

ConsoleRenderer is the default. On failure it prints the exact frame, a source snippet, the exception chain, and a compressed stack summary — no configuration needed.

Async

import asyncio
from runtime_narrative import story, stage

async def run():
    async with story("Sync Pipeline"):
        async with stage("Fetch Orders"):
            orders = await fetch_orders()

        async with stage("Process Orders"):
            await process(orders)

asyncio.run(run())

story and stage are dual sync/async context managers — use with or async with interchangeably.

Track progress

Declare the total stage count upfront so progress_percent is accurate at every stage boundary:

with story("Import Customers", total_stages=3) as runtime:
    with stage("Load CSV"):    ...   # 33%
    with stage("Validate"):   ...   # 66%
    with stage("Insert"):     ...   # 100%

Or set it dynamically after the story starts:

with story("Batch Job") as runtime:
    items = fetch_batch()
    runtime.set_total_stages(len(items))
    for item in items:
        with stage(f"Process {item.id}"): ...

---

Decorators

Wrap functions without restructuring call sites. The library detects async def automatically:

from runtime_narrative import runtime_narrative_story, runtime_narrative_stage

@runtime_narrative_stage("Load CSV")
def load_csv() -> list[str]:
    return open("customers.csv").read().splitlines()

@runtime_narrative_stage("Insert Records")
def insert(rows: list[str]) -> None:
    db.insert_many(rows)

@runtime_narrative_story("Import Customers")
def run() -> None:
    rows = load_csv()
    insert(rows)

@runtime_narrative_story accepts the same keyword arguments as story(): renderers, failure_analyzer, background_analysis, diagnostics_config, failure_diagnostics, allow_rich_in_production, app_roots, redact_extra, total_stages, dry_run.

---

Auto-instrumentation

Instrument an entire class or module without wrapping every function individually.

@narrative_class

Every public instance method becomes a stage. Stage names are ClassName.method_name:

from runtime_narrative import narrative_class, no_stage, story

@narrative_class
class OrderService:
    def validate(self, order: dict) -> None: ...   # → "OrderService.validate"
    def charge(self, order: dict) -> str: ...      # → "OrderService.charge"
    def fulfill(self, order: dict) -> str: ...     # → "OrderService.fulfill"

    @no_stage
    def _log(self, msg: str) -> None: ...          # excluded

svc = OrderService()
with story("Process Order", total_stages=3):
    svc.validate(order)
    svc.charge(order)
    svc.fulfill(order)

Skipped by default: names starting with _, @no_stage-marked methods, @property, @staticmethod, @classmethod, and inherited methods. Opt in to class and static methods explicitly:

@narrative_class(instrument_classmethods=True, instrument_staticmethods=True)
class Factory:
    @classmethod
    def create(cls): ...       # → "Factory.create"

    @staticmethod
    def validate(x): ...       # → "Factory.validate"

    @classmethod
    @narrative_stage("Build Widget")
    def build(cls): ...        # → "Build Widget" (custom name)

@narrative_stage

Override the stage name on a single method or standalone function. Prevents double-wrapping when used inside @narrative_class:

from runtime_narrative import narrative_class, narrative_stage

@narrative_class
class ReportService:
    @narrative_stage("Generate PDF Report")
    def generate(self, data: dict) -> bytes: ...   # custom name

    def archive(self, report: bytes) -> None: ...  # "ReportService.archive" (default)

When name is omitted, the function name is title-cased: validate_order → "Validate Order".

@no_stage

Exclude any method or function from all auto-instrumentation:

from runtime_narrative import no_stage

@no_stage
def _cache_lookup(key: str): ...

instrument_module

Instrument all public callables in an existing module in-place. Classes get @narrative_class; top-level functions are wrapped directly. Imported symbols are not touched:

import runtime_narrative
import myapp.services as svc

runtime_narrative.instrument_module(svc)

auto_instrument

Register a sys.meta_path import hook that instruments every app module on import. Only modules whose source path is under app_roots (default: cwd) are affected — stdlib and installed packages are unaffected:

import runtime_narrative

finder = runtime_narrative.auto_instrument()

# All subsequent imports of app modules are instrumented automatically.
from myapp.services import OrderService

# Stop at any point:
import sys
sys.meta_path.remove(finder)

Pin to specific directories:

runtime_narrative.auto_instrument(app_roots=["/app/src", "/app/workers"])

---

Failure diagnostics

Lean vs rich mode

Mode	What is captured
lean (default)	Primary frame, source snippet (±2 lines), exception chain, compressed stack summary
rich	Everything in lean + local variable values for up to 2 frames, with automatic secret redaction

RUNTIME_NARRATIVE_FAILURE_DIAGNOSTICS=rich python app.py

Or per-story:

with story("Import", failure_diagnostics="rich"):
    ...

Production safeguards

When RUNTIME_NARRATIVE_ENV=production:

• Tracebacks are capped at 8 000 characters.
• rich mode is silently downgraded to lean to prevent local variable leakage in logs.

Override the downgrade when needed:

with story("Debug", failure_diagnostics="rich", allow_rich_in_production=True):
    ...

Secret redaction

Rich mode automatically redacts local variables whose key names contain any of: password, secret, token, api_key, apikey, authorization, cookie, session, credential.

Extend the list with project-specific names:

with story("Import", failure_diagnostics="rich", redact_extra=["internal_id", "org_key"]):
    ...

For more expressive rules, use FailureDiagnosticsConfig:

from runtime_narrative import FailureDiagnosticsConfig

config = FailureDiagnosticsConfig(
    failure_diagnostics="rich",
    redact_patterns=("^internal_.*", r"\bpii\b"),   # regex, case-insensitive re.search
    redact_callback=lambda key: key.startswith("priv_"),
)

with story("Import", diagnostics_config=config):
    ...

redact_callback exceptions are caught and treated as non-redact. Both redact_patterns and redact_callback are available on FailureDiagnosticsConfig and flow through merge().

Full FailureDiagnosticsConfig reference

from runtime_narrative import FailureDiagnosticsConfig

config = FailureDiagnosticsConfig(
    runtime_environment="production",       # "development" | "production"
    failure_diagnostics="lean",             # "lean" | "rich"
    allow_rich_in_production=False,
    app_roots=("/app/src",),                # paths used for primary frame selection
    redact_extra=("internal_id",),          # additional substring matches
    redact_patterns=("^priv_",),            # regex patterns (case-insensitive)
    redact_callback=lambda k: k.endswith("_key"),
    max_traceback_chars=12_000,             # development cap (None = unlimited)
    production_traceback_cap=8_000,
    max_locals_per_frame=12,
    max_local_value_len=200,
    max_local_depth=2,
    max_frames_with_locals=2,
    snippet_context_lines=2,
)

with story("Import", diagnostics_config=config):
    ...

FailureDiagnosticsConfig.from_env() reads the standard environment variables. FailureDiagnosticsConfig.merge(base, **overrides) layers per-story overrides on a base config.

---

Failure analyzers

Analyzers receive structured failure data and return a diagnosis string that is attached to FailureOccurred and displayed by renderers. All analyzers are optional — if the endpoint is unreachable, your exception propagates normally.

OllamaFailureAnalyzer

Calls a local Ollama instance or any /api/generate-compatible endpoint:

from runtime_narrative import OllamaFailureAnalyzer, story

analyzer = OllamaFailureAnalyzer(
    model="llama3",
    endpoint="http://127.0.0.1:11434/api/generate",  # default
    timeout_seconds=60.0,
    max_context_chars=8000,
)

with story("Import Customers", failure_analyzer=analyzer):
    ...

LLMFailureAnalyzer

Calls any OpenAI-compatible /v1/chat/completions endpoint (OpenAI, vLLM, llama.cpp, LM Studio, Ollama OpenAI mode):

from runtime_narrative import LLMFailureAnalyzer

analyzer = LLMFailureAnalyzer(
    model="gpt-4o-mini",
    endpoint="https://api.openai.com/v1/chat/completions",
    api_key="sk-...",
    max_context_chars=8000,
)

AnthropicFailureAnalyzer ([anthropic] extra)

Uses the Anthropic Claude API. Reads ANTHROPIC_API_KEY from the environment by default:

from runtime_narrative import AnthropicFailureAnalyzer

analyzer = AnthropicFailureAnalyzer(
    model="claude-haiku-4-5-20251001",   # default
    api_key="sk-ant-...",                # or set ANTHROPIC_API_KEY
    max_tokens=1024,
    timeout_seconds=30.0,
)

Both analyze_failure() (sync) and analyze_failure_async() (async) are implemented. The async path uses anthropic.AsyncAnthropic natively — no thread offloading.

Structured output

All analyzers request a JSON response (exact_why, evidence, targeted_fix, code_changes) and format it into guaranteed ## Header\ncontent sections. Falls back to raw text when the model returns non-JSON.

Context budget

Tracebacks are trimmed from the top (keeping the most recent frames) when the prompt would exceed max_context_chars. If the budget is exhausted before any traceback fits, a <traceback omitted> marker is used:

analyzer = LLMFailureAnalyzer(model="llama3", max_context_chars=4000)

DeduplicatingAnalyzer

Wraps any analyzer with an LRU cache keyed by SHA-256(error_type, filename, lineno, exception_chain). Prevents redundant LLM calls for the same recurring error. None results (timeouts, network errors) are never cached:

from runtime_narrative import DeduplicatingAnalyzer, AnthropicFailureAnalyzer

analyzer = DeduplicatingAnalyzer(
    AnthropicFailureAnalyzer(),
    max_cache_size=256,
)

Thread-safe; async-aware (delegates to analyze_failure_async when available).

Background analysis

background_analysis=True emits FailureOccurred immediately (with llm_analysis=None), then runs the LLM as a background asyncio.Task. When the task completes, LLMAnalysisReady is emitted:

async with story(
    "Process Order",
    failure_analyzer=analyzer,
    background_analysis=True,
):
    ...
# Response is not delayed by LLM latency.
# LLMAnalysisReady fires when analysis is ready.

FailureAnalyzer protocol

All built-in analyzers satisfy the @runtime_checkable FailureAnalyzer protocol. Use it to type-check custom analyzers:

from runtime_narrative import FailureAnalyzer

assert isinstance(my_analyzer, FailureAnalyzer)

---

Renderers

A renderer is any object with handle(self, event: object) -> None (sync) or async def handle(self, event: object) -> None (async). Pass a list to story(), middleware, or a decorator. Async renderers are awaited inside async with story(...) including for stage events.

Renderers never crash a story — if a renderer raises, a warning is printed to stderr and the next renderer continues.

ConsoleRenderer (default)

Colored terminal output for local development. Falls back to print and ASCII glyphs (>, [ok], [FAIL]) when typer is absent or the terminal is not UTF-8 (e.g. Windows cp1252):

from runtime_narrative.renderer.console import ConsoleRenderer

with story("My Pipeline", renderers=[ConsoleRenderer()]):
    ...

JsonRenderer

One structured JSON object per lifecycle event. Suitable for log aggregators (Datadog, CloudWatch, Loki):

from runtime_narrative import JsonRenderer

with story("My Pipeline", renderers=[JsonRenderer()]):
    ...

# Write to a file
with story("My Pipeline", renderers=[JsonRenderer(output=open("stories.jsonl", "a"))]):
    ...

On failure, FailureOccurred carries the full diagnostics payload: exact location, stack frame classifications, source snippet, local variables (rich mode), compressed stack summary, and traceback text.

RotatingJsonRenderer

Same as JsonRenderer but writes to a rotating log file using path.1 / path.2 semantics. No external dependencies:

from runtime_narrative import RotatingJsonRenderer

renderer = RotatingJsonRenderer(
    "stories.jsonl",
    max_bytes=10_485_760,  # rotate at 10 MB (default)
    backup_count=5,        # keep .1 through .5 (default)
)

HtmlReportRenderer

Writes a self-contained HTML report on StoryCompleted. Includes a story header, per-stage duration bar chart, and a failure detail section with traceback and optional LLM analysis:

from runtime_narrative import HtmlReportRenderer

with story("ETL Run", renderers=[HtmlReportRenderer("report.html", open_browser=True)]):
    ...

open_browser=True calls webbrowser.open() on the generated file after writing.

SqliteStoryRenderer

Persists all six lifecycle events to a SQLite database. No extra dependencies. See Persistence and CLI:

from runtime_narrative import SqliteStoryRenderer

with story("Nightly ETL", renderers=[SqliteStoryRenderer("stories.db")]):
    ...

OtelRenderer ([otel] extra)

Maps narrative events to OpenTelemetry spans. Each story is a root span; each stage is a child span:

from runtime_narrative import OtelRenderer

renderer = OtelRenderer(
    exclude_stages={"Health Check"},  # never create child spans for these stages
    min_duration_ms=5.0,              # suppress spans shorter than 5 ms
    max_attribute_length=8192,        # truncate long string attributes (default)
)

Attributes on failure spans: error.type, error.message, code.filepath, code.lineno, code.function, error.stack_trace, narrative.exception_chain. LLMAnalysisReady adds a llm_analysis_ready span event with narrative.llm_analysis.

exclude_stages stages that fail still mark the root span ERROR — only the child span is suppressed.

OtelLogRenderer ([otel] extra)

Emits all six lifecycle events as OpenTelemetry log records via opentelemetry._logs:

Event	Severity
StoryStarted, StoryCompleted, LLMAnalysisReady	INFO
StageStarted, StageCompleted	DEBUG
FailureOccurred	ERROR with error.type, code.filepath, error.stack_trace, etc.

Automatically correlates trace_id/span_id from the ambient OTel context so logs link to their enclosing spans:

from runtime_narrative import OtelLogRenderer

renderer = OtelLogRenderer(logger_name="my_app")

OtelMetricsRenderer ([otel] extra)

Emits four OTel instruments via the OpenTelemetry Metrics API:

Instrument	Type	Labels
narrative.stage.duration	Histogram (s)	story_name, stage_name
narrative.story.duration	Histogram (s)	story_name, success
narrative.story.failures	Counter	story_name, error_type
narrative.llm.analysis_latency	Histogram (s)	story_name

narrative.llm.analysis_latency measures the time between FailureOccurred and LLMAnalysisReady (only recorded when background_analysis=True):

from runtime_narrative import OtelMetricsRenderer

renderer = OtelMetricsRenderer(meter_name="my_app")

PrometheusRenderer ([prometheus] extra)

Exposes four Prometheus metrics:

Metric	Type	Labels
narrative_story_duration_seconds	Histogram	story_name, success
narrative_stage_duration_seconds	Histogram	story_name, stage_name
narrative_story_failures_total	Counter	story_name, error_type
narrative_story_total	Counter	story_name, success

from runtime_narrative import PrometheusRenderer
from prometheus_client import CollectorRegistry, start_http_server

registry = CollectorRegistry()
renderer = PrometheusRenderer(registry=registry)
start_http_server(8000, registry=registry)

Combining renderers

from runtime_narrative import story, JsonRenderer, SqliteStoryRenderer, OtelRenderer

with story("Nightly ETL", renderers=[
    JsonRenderer(),
    SqliteStoryRenderer("stories.db"),
    OtelRenderer(),
]):
    ...

---

Framework integrations

FastAPI / Starlette

RuntimeNarrativeMiddleware wraps every HTTP request in async with story(...). Route handlers only need to declare stages — no story() context required in handlers:

from fastapi import FastAPI
from runtime_narrative import RuntimeNarrativeMiddleware, JsonRenderer, AnthropicFailureAnalyzer

app = FastAPI()
app.add_middleware(
    RuntimeNarrativeMiddleware,
    renderers=[JsonRenderer()],
    failure_analyzer=AnthropicFailureAnalyzer(),
    runtime_environment="production",
)

@app.post("/orders")
async def create_order(payload: OrderIn):
    with stage("Validate Input"):
        validate(payload)
    with stage("Persist Order"):
        order = await db.insert(payload)
    return {"id": order.id}

Each request becomes a story named "METHOD /path" (e.g. "POST /orders"). When renderers is not passed, the middleware auto-selects ConsoleRenderer on a TTY and JsonRenderer otherwise.

When opentelemetry-api is installed, the middleware automatically extracts incoming W3C traceparent/tracestate headers so story spans become children of the upstream trace:

app.add_middleware(
    RuntimeNarrativeMiddleware,
    propagate_trace_context=True,   # default; set False to disable
)

Django

ASGI (async):

# settings.py
MIDDLEWARE = [
    "runtime_narrative.middleware_django.RuntimeNarrativeDjangoMiddleware",
    ...
]

WSGI (sync):

# settings.py
MIDDLEWARE = [
    "runtime_narrative.middleware_django.RuntimeNarrativeDjangoSyncMiddleware",
    ...
]

Story name is "METHOD /path". Requires pip install "runtime-narrative[django]".

Celery

from celery import Celery
from runtime_narrative import NarrativeTask, connect_narrative, JsonRenderer

app = Celery("myapp")

# Option A — per task
@app.task(base=NarrativeTask)
def process_order(order_id: str) -> None:
    with stage("Validate"): validate(order_id)
    with stage("Charge"):   charge(order_id)

# Option B — set defaults for all tasks globally
connect_narrative(
    app,
    renderers=[JsonRenderer()],
    failure_analyzer=AnthropicFailureAnalyzer(),
)

Story name is "<task.name> [task_id=<id>]". Override options per task by setting narrative_* class attributes directly. Requires pip install "runtime-narrative[celery]".

gRPC

import grpc
from runtime_narrative import RuntimeNarrativeAsyncInterceptor, JsonRenderer

# Async server
server = grpc.aio.server(
    interceptors=[RuntimeNarrativeAsyncInterceptor(renderers=[JsonRenderer()])],
)

# Sync server
from runtime_narrative import RuntimeNarrativeInterceptor
server = grpc.server(
    thread_pool,
    interceptors=[RuntimeNarrativeInterceptor(renderers=[JsonRenderer()])],
)

Story name is the full gRPC method path, e.g. "/mypackage.MyService/MyMethod". Requires pip install "runtime-narrative[grpc]".

---

Async task groups

NarrativeTaskGroup runs concurrent asyncio tasks under a shared story. Tasks inherit the parent story context automatically via ContextVar copy, so stage() calls inside tasks are tracked normally:

import asyncio
from runtime_narrative import story, NarrativeTaskGroup, NarrativeTaskGroupError

async def main():
    async with story("Parallel Pipeline"):
        async with NarrativeTaskGroup() as tg:
            tg.create_task(fetch_orders(),    name="Fetch Orders")
            tg.create_task(fetch_inventory(), name="Fetch Inventory")
        # waits for both; stages from both appear in the timeline

asyncio.run(main())

If tasks fail, NarrativeTaskGroupError is raised with failed_tasks: dict[str, BaseException]:

try:
    async with NarrativeTaskGroup() as tg:
        tg.create_task(risky_job(), name="Risky Job")
except NarrativeTaskGroupError as e:
    for task_name, exc in e.failed_tasks.items():
        print(f"{task_name} failed: {exc}")

No extra dependencies. Python 3.9+.

---

Persistence and CLI

SqliteStoryRenderer records all six lifecycle events to a local SQLite database. No extra dependencies:

from runtime_narrative import story, SqliteStoryRenderer

with story("Nightly ETL", renderers=[SqliteStoryRenderer("stories.db")]):
    ...

Schema:

Table	Key columns
stories	story_id, name, success, duration_seconds, started_at, completed_at
stages	story_id, stage_name, stage_index, parent_stage_name, duration_seconds, completed, failed
failures	story_id, stage_name, error_type, error_message, filename, lineno, traceback_text, llm_analysis

LLMAnalysisReady back-fills the llm_analysis column in failures so background analysis results are persisted even when they arrive after StoryCompleted.

CLI (registered as runtime-narrative):

# List the 10 most recent failures
runtime-narrative failures --db stories.db

# Filter by stage name or story name (LIKE pattern)
runtime-narrative failures --last 25 --stage "Insert Records"
runtime-narrative failures --story "Nightly ETL"

# Show the full detail for one story
runtime-narrative story <story_id> --db stories.db

The --db flag defaults to runtime_narrative.db in the current directory.

---

Alert routing

AlertRoutingRenderer fans out FailureOccurred events to webhook destinations concurrently. Destination failures are logged to stderr and swallowed — they never crash the story:

from runtime_narrative import (
    story,
    AlertRoutingRenderer,
    SlackWebhookDestination,
    HttpWebhookDestination,
)

renderer = AlertRoutingRenderer(
    destinations=[
        SlackWebhookDestination("https://hooks.slack.com/services/..."),
        HttpWebhookDestination(
            "https://alerts.example.com/webhook",
            headers={"Authorization": "Bearer ..."},
            timeout=5.0,
        ),
    ],
    only_stories={"Nightly ETL", "Payment Processor"},  # None = all stories
    only_error_types={"ValueError", "TimeoutError"},    # None = all error types
)

async with story("Nightly ETL", renderers=[renderer]):
    ...

SlackWebhookDestination sends a Block Kit message with a header, error detail section, and an optional analysis section when llm_analysis is present.

HttpWebhookDestination POSTs a JSON payload containing: story_id, story_name, stage_name, error_type, error_message, filename, lineno, function, llm_analysis, timestamp.

---

dry_run mode

dry_run=True suppresses exceptions raised inside stage bodies, marks all stages completed, and emits StoryCompleted(success=True). Useful for smoke-testing instrumentation wiring without triggering real side effects:

with story("Nightly ETL", dry_run=True):
    with stage("Load Warehouse"):
        raise IOError("would connect to DB in prod")   # suppressed
    with stage("Transform"):
        raise RuntimeError("would run transforms")     # suppressed
# StoryCompleted(success=True) emitted for all stages

---

Testing utilities

StoryRecorder is a dual sync/async context manager that captures story events for assertions. No output is produced:

from runtime_narrative import stage
from runtime_narrative.testing import StoryRecorder

def test_pipeline_success():
    with StoryRecorder("ETL") as recorder:
        with stage("Load"):   rows = [1, 2, 3]
        with stage("Insert"): db.insert(rows)

    recorder.assert_stages_completed(["Load", "Insert"])
    recorder.assert_no_failure()
    recorder.assert_story_completed(success=True)

def test_invalid_input_fails_at_validate():
    import pytest

    with pytest.raises(ValueError):
        with StoryRecorder("ETL") as recorder:
            with stage("Load"):     pass
            with stage("Validate"): raise ValueError("bad schema")

    recorder.assert_stage_failed("Validate", error_type="ValueError")
    recorder.assert_story_completed(success=False)

Works as async with StoryRecorder(...) too. Pass any story() kwargs including dry_run=True:

with StoryRecorder("ETL", dry_run=True) as recorder:
    run_pipeline()   # side effects suppressed

recorder.assert_stages_completed(["Load", "Validate", "Insert"])
recorder.assert_no_failure()

Assertion methods:

Method	What it checks
assert_stages_completed(names)	All named stages appear in StageCompleted events
assert_no_failure()	No FailureOccurred event was emitted
assert_stage_failed(name, error_type=None)	A FailureOccurred event at that stage name; optionally checks error_type
assert_story_completed(success=None)	A StoryCompleted event was emitted; optionally checks the success flag

---

Custom renderers and analyzers

Custom renderer

Implement handle(self, event: object). Async renderers (async def handle) are awaited inside async with story(...):

class PagerDutyRenderer:
    async def handle(self, event: object) -> None:
        if type(event).__name__ == "FailureOccurred":
            await pagerduty.trigger(
                summary=f"{event.story_name} failed at {event.stage_name}",
                details={"error": event.error_type, "analysis": event.llm_analysis},
            )

async with story("Nightly ETL", renderers=[PagerDutyRenderer()]):
    ...

Six event types are emitted. Key fields on each:

Event	Key fields
StoryStarted	story_id, story_name, timestamp
StageStarted	story_id, stage_name, timestamp, stage_index (0-based), parent_stage_name
StageCompleted	story_id, stage_name, timestamp, duration_seconds, stage_index, parent_stage_name
FailureOccurred	story_id, story_name, stage_name, error_type, error_message, filename, lineno, function, traceback_text, exception_chain, exact_cause, stage_timeline, progress_percent, llm_analysis, diagnostics_mode, stack_frames, source_snippet, compressed_stack_summary, locals_by_frame
StoryCompleted	story_id, story_name, success, progress_percent, completed_stages, total_stages, timestamp
LLMAnalysisReady	story_id, story_name, stage_name, llm_analysis, timestamp

parent_stage_name is None for top-level stages and set to the enclosing stage name for nested stages.

Custom failure analyzer

Implement analyze_failure(...). Add analyze_failure_async(...) for native async — otherwise the sync method is called via asyncio.to_thread:

class MyAnalyzer:
    async def analyze_failure_async(
        self,
        *,
        story_name: str,
        stage_name: str,
        failure,           # FailureSummary: .error_type, .error_message, .filename,
                           #                 .lineno, .function, .source_line,
                           #                 .traceback_text, .exception_chain
        stage_timeline: str,
        progress_percent: int,
    ) -> str | None:
        result = await my_llm_client.complete(build_prompt(failure))
        return result.text

    def analyze_failure(self, *, story_name, stage_name, failure, stage_timeline, progress_percent):
        # sync fallback used when called from sync story()
        return requests.post(...).json()["text"]

with story("Import", failure_analyzer=MyAnalyzer()):
    ...

---

Environment variables

Variable	Values	Default	Effect
RUNTIME_NARRATIVE_ENV	development, production	development	Production caps tracebacks to 8 000 chars and forces lean mode
RUNTIME_NARRATIVE_FAILURE_DIAGNOSTICS	lean, rich	lean	rich captures local variable values at the failing frames. Invalid values raise ValueError at story construction.
RUNTIME_NARRATIVE_ALLOW_RICH_IN_PRODUCTION	1, true	off	Bypass production safeguard; allow rich diagnostics in production
RUNTIME_NARRATIVE_MODEL	model name string	—	Default model for AnthropicFailureAnalyzer; also used by example scripts for OllamaFailureAnalyzer / LLMFailureAnalyzer
ANTHROPIC_API_KEY	API key	—	Required by AnthropicFailureAnalyzer; read automatically if api_key= is not passed

---

Python compatibility

Python 3.9 – 3.13. Zero required dependencies beyond python-dotenv.