robust-llm-chain 0.4.1

Production-grade cross-vendor failover for LLM APIs

robust-llm-chain

🇰🇷 한국어 문서: README_KO.md · ARCHITECTURE_KO.md · CONTRIBUTING_KO.md · SECURITY_KO.md · CODE_OF_CONDUCT_KO.md. 원본 (English) 이 정본.

Production-grade cross-vendor failover for LLM APIs. When your provider hits 529 / pending / throttle, automatically retry on the next vendor — same request, sub-second detection, worker-coordinated round-robin.

robust-llm-chain is a small, focused Python library that adds cross-vendor failover to LLM API calls. It implements LangChain's Runnable interface, so it drops into existing chains, while exposing a richer acall() API for operational metadata (attempts, cost, usage).

It does one thing well: when Anthropic Direct returns 529 or stalls before the first token, the library transparently re-issues the same request to OpenRouter (or any other configured provider) — within seconds, not minutes.

---

Why this exists

Two pains that off-the-shelf libraries address only partially:

1. Anthropic 529 / Overloaded

Anthropic Direct periodically returns 529 Overloaded during demand spikes. A single retry against the same endpoint usually fails the same way. The right fix is cross-vendor failover — Claude is also reachable through Bedrock and OpenRouter — but most LLM client libraries only retry against the same provider.

2. Streaming "pending" provider

A provider can accept your request, hold the connection open, and never send the first token. With a 60-second total timeout, you wait the full minute before failing. With a 30-second timeout, you misclassify slow-but-real responses as failures.

robust-llm-chain separates the two:

• first_token_timeout (default 15s) — if no token arrives in this window, give up on this provider and try the next one. Fallback happens before the user notices a delay.
• per_provider_timeout (default 60s) — total response budget, applied after the first token has streamed.
• total_timeout — wall-clock cap across all attempts.

These two timeouts are the core differentiator: most libraries only have a single overall timeout, so a pending provider burns 30–60 seconds before fallback even starts.

---

Quickstart

Install:

pip install "robust-llm-chain[anthropic,openrouter]"

Set two environment variables (ANTHROPIC_API_KEY, OPENROUTER_API_KEY), then:

import asyncio
import os
from robust_llm_chain import RobustChain

chain = (
    RobustChain.builder()
    .add_provider(
        type="anthropic",
        model="claude-haiku-4-5-20251001",
        api_key=os.environ["ANTHROPIC_API_KEY"],
        priority=0,                                # preferred fallback target
    )
    .add_provider(
        type="openrouter",
        model="anthropic/claude-haiku-4.5",
        api_key=os.environ["OPENROUTER_API_KEY"],
        priority=1,                                # lower fallback preference; still RR-selected on alternate calls
    )
    .build()
)
# acall: convenience method that returns a ChainResult with operational metadata
result = asyncio.run(chain.acall("두 줄로 자기소개 해줘."))
print(result.output.content)                                       # BaseMessage.content
print(f"used: {result.provider_used.id} | tokens: {result.usage}") # metadata

The standard Runnable ainvoke() returns just a BaseMessage (for LangChain composition). To get attempts, cost, and usage in one call, use acall() or read chain.last_result.

What happens:

• Two providers configured via the fluent builder: Anthropic Direct and OpenRouter — both active failover paths. Round-robin distributes the first attempt of each call across them (call 1 starts on Anthropic, call 2 on OpenRouter, …). Priority decides the fallback order after the first provider fails — priority=0 (Anthropic) is tried before priority=1 (OpenRouter). See Provider configuration for the two-role table.
• Credentials are passed as values (api_key=...). Where the value comes from — env var, secrets manager, Vault — is your call. The builder never reads os.environ on your behalf, so the source is explicit at the call site.
• If the first-attempt provider returns 529 / overloaded / pending, the request transparently fails over to the next provider in the priority-ordered fallback sequence (lowest priority first, regardless of which provider was attempted first). No additional configuration.
• Missing env var → os.environ["..."] raises KeyError with the exact var name (Python's standard fail-fast).

Defaults: single-worker / pricing=None / backend=LocalBackend(). For multi-worker round-robin, cost computation, or multi-key / multi-region patterns, see Provider configuration and Advanced usage below.

Three configuration paths are available — from_env (env-driven dict, single-per-type), builder (fluent, multi-key OK, fail-fast — used here), and explicit providers=[ProviderSpec(...)] list. See the comparison matrix in Provider configuration.

---

Anatomy of a result

acall() returns ChainResult — eight fields with everything you need to log, audit, and observe a call:

Field	Type	What it carries
output	BaseMessage	The model's response (output.content is the text)
input	list[BaseMessage]	The normalized prompt actually sent (after ChatPromptTemplate rendering)
usage	TokenUsage	input_tokens / output_tokens / cache_read_tokens / cache_write_tokens / total_tokens
cost	CostEstimate | None	USD per category — None when no PricingSpec is attached (cost tracking is opt-in)
provider_used	ProviderSpec	The provider that actually returned the response (the last attempt). Credentials are masked in repr
model_used	ModelSpec	The model spec of the successful provider
attempts	list[AttemptRecord]	Every provider attempt — successful and failed — in order. See below
elapsed_ms	float	End-to-end wall clock time

Happy path — single provider succeeds

result = await chain.acall("두 줄로 자기소개 해줘.")

result.output.content              # → "안녕하세요. 저는 Claude 입니다. 두 줄로 자기소개 해 드릴게요."
result.usage                        # → TokenUsage(input_tokens=18, output_tokens=27, total_tokens=45, ...)
result.cost                         # → None  (no PricingSpec attached)
result.provider_used.id             # → "anthropic-direct"
result.provider_used.type           # → "anthropic"
result.model_used.model_id          # → "claude-haiku-4-5-20251001"
result.elapsed_ms                   # → 845.2
result.attempts                     # → [
                                    #     AttemptRecord(provider_id="anthropic-direct",
                                    #                   phase="model_creation", elapsed_ms=12,
                                    #                   error_type=None, fallback_eligible=False, ...),
                                    #     AttemptRecord(provider_id="anthropic-direct",
                                    #                   phase="first_token", elapsed_ms=320,
                                    #                   error_type=None, fallback_eligible=False, ...),
                                    #   ]

Failover path — primary throttles, fallback succeeds

result = await chain.acall("...")

result.output.content               # → response from OpenRouter
result.provider_used.id             # → "openrouter-claude"  (the one that succeeded)
result.attempts                     # → [
                                    #     AttemptRecord(provider_id="anthropic-direct",
                                    #                   phase="first_token", elapsed_ms=412,
                                    #                   error_type="OverloadedError",
                                    #                   error_message="529: Overloaded",
                                    #                   fallback_eligible=True, ...),
                                    #     AttemptRecord(provider_id="openrouter-claude",
                                    #                   phase="model_creation", elapsed_ms=8,
                                    #                   error_type=None, fallback_eligible=False, ...),
                                    #     AttemptRecord(provider_id="openrouter-claude",
                                    #                   phase="first_token", elapsed_ms=290,
                                    #                   error_type=None, fallback_eligible=False, ...),
                                    #   ]

AttemptRecord.error_message is already sanitized via _security.sanitize_message — provider key prefixes are masked and the string is truncated to 200 chars. Safe to log directly.

chain.last_result (contextvars-scoped) and aggregates

Property	What it carries
chain.last_result	The most recent ChainResult for this asyncio task only (contextvars-isolated, so concurrent asyncio.gather(chain.acall(...), chain.acall(...)) calls don't see each other's results)
chain.total_token_usage	Cumulative TokenUsage across every successful call on this RobustChain instance (lock-protected)
chain.total_cost	Cumulative CostEstimate across every successful call (None until first call with pricing)

The standard Runnable ainvoke() returns just a BaseMessage. To inspect attempts / cost / usage after ainvoke or astream, read chain.last_result.

---

Logging

The library emits structured WARN/ERROR-only logs through Python's standard logging module. There is no DEBUG/INFO chatter, and prompt or response text is never logged — that is the application's responsibility (see SECURITY.md hardening #3).

Logger names

Logger	Source	When it fires
robust_llm_chain.chain	RobustChain instance + from_env	provider build failures, fallback attempts, unknown provider type warnings
robust_llm_chain.observability.langsmith	cleanup_run	LangSmith outage (timeout / generic exception), backpressure drops

Both honor whatever handler / formatter / level you configure on the root logger or these specific names. To silence one, logging.getLogger("robust_llm_chain.chain").setLevel(logging.ERROR) etc.

Structured fields (the extra payload)

Every WARN/ERROR record carries extra fields you can route in JSON formatters or aggregators (Datadog, Splunk, Loki, …):

Event	Fields
langsmith_cleanup_timeout	run_id
langsmith_cleanup_fail	run_id, error_type
langsmith_cleanup_drop	max_inflight

Custom logger inject: RobustChain(providers=..., logger=my_logger) — wire your own logger if you want a per-chain stream.

What is NOT logged (by design)

• Prompt text (input) and response text (output.content) — application's ChainResult.input / ChainResult.output to persist if needed
• API keys / AWS credentials — ProviderSpec.__repr__ masks them; AttemptRecord.error_message is sanitized via _security.sanitize_message before being stored
• Per-attempt success debug info — only WARN on failure / fallback events. Production-grade, low-cardinality

---

Installation & Extras

What gets pulled in by default: langchain-core>=0.3 (transitive — provides Runnable / BaseChatModel / BaseMessage / PromptValue / ChatPromptTemplate). The umbrella langchain package is intentionally NOT a dependency — this library uses only the core abstractions, keeping the dependency footprint minimal. Provider SDKs (langchain-anthropic / langchain-openai / langchain-aws) and backends (aiomcache) are opt-in extras below.

Command	What's included
pip install robust-llm-chain	Core only — langchain-core auto-pulled. No provider adapters, so from_env() raises NoProvidersConfigured until you add at least one extra
pip install "robust-llm-chain[anthropic]"	+ langchain-anthropic (Anthropic Direct)
pip install "robust-llm-chain[openrouter]"	+ langchain-openai (OpenRouter — OpenAI-compatible API)
pip install "robust-llm-chain[openai]"	+ langchain-openai (OpenAI Direct)
pip install "robust-llm-chain[bedrock]"	+ langchain-aws (AWS Bedrock — Claude / Llama / Nova / etc.)
pip install "robust-llm-chain[memcached]"	+ aiomcache (async client for worker-coordinated round-robin)
pip install "robust-llm-chain[anthropic,openrouter,bedrock,memcached]"	Recommended production combo (3-way Claude failover)
pip install "robust-llm-chain[all]"	Every adapter and backend currently shipped

A redis backend extra is planned for a future release — not yet shippable, so the extra is intentionally absent from the list above.

The library does not depend on python-dotenv. Loading .env files is up to your application.

---

Provider configuration — three paths

There are three ways to tell RobustChain which providers to use. They differ in what they can express and how concise the call site is:

Capability	RobustChain.from_env(model_ids={...})	RobustChain.builder().add_provider(...).add_bedrock(...).build()	RobustChain(providers=[ProviderSpec(...)])
Source of credentials	env vars (auto-read, dict key = type)	values passed via api_key= (read from anywhere — env, vault, secrets manager)	values passed via api_key=
Source of model_id	dict value	model="..." keyword arg	ModelSpec(model_id=...) field
One provider per type	✅	✅	✅
Multiple keys for the same type (e.g. anthropic-1 + anthropic-2 for rate-limit headroom)	❌ — dict key is unique	✅ — call add_provider(type="anthropic", ...) twice with distinct api_key= / id=	✅ — same type, distinct id
Multi-region (Bedrock east + west)	❌ — single AWS_REGION env	✅ — explicit region= per add_bedrock(...)	✅ — explicit per-spec region
Different model_ids on the same type	❌ — dict key is unique	✅ — different model= per call	✅ — different model.model_id per spec
Per-spec priority ordering	❌ — uniform default 0	✅ — priority= keyword	✅ — explicit ordering primary→fallback
Missing API_KEY behavior	silent skip → that provider is dropped, others still build	depends on caller — os.environ["..."] raises KeyError, vault libs raise their own errors	n/a (you supplied the key explicitly)
Mental model	12-factor / env-driven	fluent, credentials-as-values	code-as-config
Use when	Dev, single-vendor-per-type production, env-driven deploys	Most production use cases — multi-key / multi-region / cross-vendor with credentials sourced from anywhere	When you already have ProviderSpec instances from elsewhere (config loader, orchestrator, etc.)

Quick decision tree

• "Just want one Claude + one OpenAI from env vars, simplest possible" → from_env. Done.
• "Need multi-key / multi-region / cross-vendor / explicit priority" → RobustChain.builder() (recommended for most production). See examples/builder.py.
• "Already constructing ProviderSpec instances elsewhere in code (config loader, orchestrator)" → explicit providers=[ProviderSpec(...)] list. See the inline code in Advanced usage below.

Two-role traffic model (v0.4.0+):

Role	What it controls	When it kicks in
Round-robin	Which provider this call attempts first (over user-listed order)	Call start, every call
Priority	Order of fallback attempts after the first provider fails (lower wins)	Only when first attempt fails

priority= lower value wins (DNS MX / cron / Linux nice convention); ties preserve user-listed order. Example with [A(p=0), B(p=1), C(p=2)]: call 1 = A→B→C, call 2 = B→A→C, call 3 = C→A→B. RR distributes initial-attempt load; priority decides who picks up after a failure.

Recognized environment variables (for from_env)

Variable	Provider	Active	Notes
ANTHROPIC_API_KEY	anthropic	✅	Anthropic Direct
OPENROUTER_API_KEY	openrouter	✅	OpenRouter (any vendor's model)
OPENAI_API_KEY	openai	✅	OpenAI Direct (gpt-*, o1-*, etc.)
AWS_ACCESS_KEY_ID + AWS_SECRET_ACCESS_KEY + AWS_REGION	bedrock	✅	All three required; missing any one → provider skipped

---

Default Behavior

Setting	Default	Meaning
backend	LocalBackend() (asyncio.Lock)	Single-worker safe round-robin
per_provider_timeout	60s	Total response budget per provider
first_token_timeout	15s	Fallback if first chunk doesn't arrive in this window
total_timeout	per_provider × N + 60s buffer, capped at 360s	Wall-clock cap across all attempts
stream_cleanup_timeout	2s	aclose() budget when falling back during streaming
temperature	0.1	Per-call override available
max_output_tokens	ModelSpec.max_output_tokens or 4096	Per-call override available
pricing	None → result.cost = None	Cost computation skipped without pricing
Logger name	"robust_llm_chain"	Hierarchical (e.g. robust_llm_chain.stream)
Logger level	WARNING	Set to INFO/DEBUG for fallback diagnostics
Type hints	py.typed marker shipped	mypy/pyright recognize types out of the box
chain.invoke() (sync)	not implemented	Wrap with asyncio.run()

Philosophy: zero environment variables, zero external files required. RobustChain(...) runs immediately.

---

Three things that make this different

1. Streaming first-token timeout for pending detection. Most libraries only have an overall timeout. A pending provider burns the full window before fallback. This library measures the first chunk arrival separately (default 15s) and falls over the moment that budget elapses.

2. Worker-coordinated round-robin. (Memcached today; pluggable IndexBackend for Redis or your own) In a multi-worker deployment (gunicorn × 8, etc.), most OSS libraries hold the round-robin index per process. With 8 workers that means 8 simultaneous requests can land on the same provider. This library shares the index through a backend (Memcached or your own implementation of IndexBackend) so the load actually spreads.

3. Cross-vendor (and cross-model) failover. Same prompt, multiple paths. Active providers: Anthropic Direct + OpenRouter + OpenAI Direct + AWS Bedrock. Common patterns:

   • Same-model 3-way failover for Claude — Anthropic Direct ↔ Bedrock (us-east-1) ↔ OpenRouter
   • Cross-region within Bedrock — id="bedrock-east" (us-east-1) ↔ id="bedrock-west" (us-west-2)
   • Cross-vendor cross-model — Claude on Anthropic ↔ GPT on OpenAI when "we just need some answer"
   • Multi-key per vendor — id="anthropic-primary" ↔ id="anthropic-backup" for tenant isolation or rate-limit headroom

---

Who is this for

• Long-running multi-worker Python services (FastAPI + gunicorn, Django, Celery)
• Teams running Claude across multiple paths (Anthropic Direct + Bedrock + OpenRouter), or mixing Claude + GPT for survivability
• Anyone who has actually been paged at 3am because of 529 Overloaded or stalled streams
• Existing LangChain Runnable users — drop-in compatible

Not for: serverless / Edge runtimes, single-provider stacks, multimodal-first workloads.

---

Compared to other libraries

Library	What it does	What this library adds on top
litellm	Comprehensive multi-provider router with weighted / cost-based routing	Narrower scope: cross-vendor failover, first-token timeout, worker-coordinated round-robin
LangChain Runnable.with_fallbacks	Sequential exception-based fallback inside one Runnable	Adds first-token timeout (sub-second pending detection) + inter-worker round-robin via shared backend
Vercel AI SDK	TypeScript/edge-first SDK with streaming UX	This is async Python for long-running multi-worker servers — different runtime target

For most users the answer is "use both": this library handles the cross-vendor failover layer, while litellm handles broader routing if you have it. They compose — robust-llm-chain is a single Runnable you can plug anywhere.

---

Advanced usage

Runnable examples: all four patterns below — multi-key, 3-way Claude failover, cross-vendor (Claude → GPT), Bedrock multi-region — are runnable scripts in examples/builder.py (using RobustChain.builder()). Try with uv run python examples/builder.py multikey (or 3way / xvendor / multiregion). The inline code blocks below show the same patterns expressed via explicit providers=[ProviderSpec(...)] for use cases where you already have spec instances from a config loader.

Multi-worker production (Memcached-coordinated round-robin)

import aiomcache
from robust_llm_chain import RobustChain
from robust_llm_chain.backends import MemcachedBackend

memcached = aiomcache.Client("memcached.internal", 11211)
chain = RobustChain.from_env(
    model_ids={
        "anthropic":  "claude-haiku-4-5-20251001",
        "openrouter": "anthropic/claude-haiku-4.5",
    },
    backend=MemcachedBackend(client=memcached, key_prefix="myapp:rr"),
)

Memcached failure semantics: fail-closed. If Memcached is unreachable, the library raises BackendUnavailable rather than silently falling back to a local index. The whole point of the worker-coordinated round-robin is consistency across workers; an automatic fallback would silently break that. Catch the error in your app and decide explicitly (healthcheck-then-rebuild-chain pattern recommended).

Explicit ProviderSpec (when env-based config isn't enough)

import os
from robust_llm_chain import RobustChain, ProviderSpec, ModelSpec, PricingSpec, TimeoutConfig

chain = RobustChain(
    providers=[
        ProviderSpec(
            id="anthropic-direct",
            type="anthropic",
            api_key=os.environ["ANTHROPIC_API_KEY"],
            model=ModelSpec(
                model_id="claude-haiku-4-5-20251001",
                pricing=PricingSpec(input_per_1m=0.80, output_per_1m=4.00),
                max_output_tokens=8192,
            ),
        ),
        ProviderSpec(
            id="openrouter",
            type="openrouter",
            api_key=os.environ["OPENROUTER_API_KEY"],
            model=ModelSpec(
                model_id="anthropic/claude-haiku-4.5",
                pricing=PricingSpec(input_per_1m=1.00, output_per_1m=5.00),
            ),
        ),
    ],
    timeouts=TimeoutConfig(per_provider=60.0, first_token=15.0),
)

Multiple keys per vendor

import os
from robust_llm_chain import RobustChain, ProviderSpec, ModelSpec

# Two Anthropic API keys — round-robin between them, fall over if one rate-limits.
# Same shape works for any single-key provider (OPENAI_API_KEY_1 / _2, etc.).
# Naming is your call (_1/_2, _PRIMARY/_BACKUP, _TEAM_A/_TEAM_B, …).
chain = RobustChain(providers=[
    ProviderSpec(
        id="anthropic-1",
        type="anthropic",
        api_key=os.environ["ANTHROPIC_API_KEY_1"],
        model=ModelSpec(model_id="claude-haiku-4-5-20251001"),
    ),
    ProviderSpec(
        id="anthropic-2",
        type="anthropic",
        api_key=os.environ["ANTHROPIC_API_KEY_2"],
        model=ModelSpec(model_id="claude-haiku-4-5-20251001"),
    ),
])

Bedrock cross-region failover (us-east-1 ↔ us-west-2)

import os
from robust_llm_chain import RobustChain, ProviderSpec, ModelSpec

chain = RobustChain(providers=[
    ProviderSpec(
        id="bedrock-east",
        type="bedrock",
        aws_access_key_id=os.environ["AWS_ACCESS_KEY_ID"],
        aws_secret_access_key=os.environ["AWS_SECRET_ACCESS_KEY"],
        region="us-east-1",
        model=ModelSpec(model_id="anthropic.claude-haiku-4-5-20251001-v1:0"),
    ),
    ProviderSpec(
        id="bedrock-west",
        type="bedrock",
        aws_access_key_id=os.environ["AWS_ACCESS_KEY_ID"],
        aws_secret_access_key=os.environ["AWS_SECRET_ACCESS_KEY"],
        region="us-west-2",
        model=ModelSpec(model_id="anthropic.claude-haiku-4-5-20251001-v1:0"),
    ),
])

Cross-vendor same-model: 3-way Claude (Anthropic + Bedrock + OpenRouter)

chain = RobustChain.from_env(model_ids={
    "anthropic":  "claude-haiku-4-5-20251001",
    "bedrock":    "anthropic.claude-haiku-4-5-20251001-v1:0",
    "openrouter": "anthropic/claude-haiku-4.5",
})
# Round-robin between three paths to Claude. If Anthropic 529s, fall to
# Bedrock or OpenRouter automatically.

Cross-vendor cross-model: Claude → GPT

chain = RobustChain.from_env(model_ids={
    "anthropic": "claude-haiku-4-5-20251001",
    "openai":    "gpt-4o-mini",
})
# When "we just need some answer" matters more than "exactly the same model".

Streaming

async for chunk in chain.astream("Tell me a joke."):
    print(chunk.content, end="", flush=True)

# After completion, metadata is available
print(chain.last_result.attempts, chain.last_result.cost)

Error handling

from robust_llm_chain.errors import (
    AllProvidersFailed, ProviderTimeout, FallbackNotApplicable, BackendUnavailable,
    ProviderInactive, ProviderModelCreationFailed,
)

try:
    result = await chain.acall("...")
except BackendUnavailable as e:
    # Memcached down — switch to LocalBackend explicitly or fail the request
    log.error("backend unavailable", extra={"error": str(e)})
except ProviderInactive:
    # Adapter extras not installed (e.g. `pip install robust-llm-chain[anthropic]`
    # missing) — environment problem, not a transient error. fail-fast.
    raise
except FallbackNotApplicable:
    # Auth error or parser failure — no point retrying
    raise
except AllProvidersFailed as e:
    for attempt in e.attempts:
        log.error("provider failed", extra={"provider": attempt.provider_id, "error": attempt.error_type})
except ProviderTimeout as e:
    log.error(f"total timeout in phase={e.phase}")

Adapter build errors (ProviderModelCreationFailed, v0.4.1+): any raw SDK / config exception raised by adapter.build() (e.g. ValueError("model id wrong"), botocore.errorfactory.ValidationException) is wrapped into ProviderModelCreationFailed so external callers see a single typed contract instead of vendor-specific exceptions. The original raw exception is preserved on __cause__. Wrapped errors are fallback-eligible — multi-provider fault tolerance treats one vendor's config error as "try the next one". A persistently-broken provider therefore fails silently as long as another succeeds; monitor ChainResult.attempts for phase == "model_creation" to detect chronic config drift. All providers failing surfaces as AllProvidersFailed.

---

Architecture

Module structure, dependency graph, call lifecycle (acall / ainvoke / astream), error flow, and extension points (custom ProviderAdapter / IndexBackend) are documented in ARCHITECTURE.md. Read that before opening a PR or wiring a custom adapter.

---

Status

v0.4.x in pre-1.0 active development. CI matrix: Python 3.11 / 3.12 / 3.13. Public API may break before 1.0; all changes are documented in CHANGELOG.md (v0.3 and v0.4 each shipped a BREAKING failover-semantic change — see migration notes there).

As-Is — no support guarantee. Provided under MIT license; no SLA, no issue-response timeline, no feature-request commitment. Bugs are fixed when convenient. If something doesn't work for your use case → fork it. PRs welcome but not depended on. This is a personal project optimized for the maintainer's own dogfooding.

⚠️ Upgrading from v0.3.x? v0.4.0 splits round-robin and priority into two distinct roles: RR picks the first provider this call attempts (over user-listed order); priority orders the fallback sequence after that first provider fails. v0.3 used a single priority-sorted rotation, so fallback order shifted every call. v0.4 makes fallback always honor priority. Attempt sequences differ from v0.3 whenever your user-listed order does not match priority-sorted order (and even when it does, fallback order changes for any call where the first provider fails). The only no-op case is n=1 (one provider). See CHANGELOG [0.4.0] for the migration table. Verify your traffic and fallback ordering before upgrading, regardless of N.

⚠️ Upgrading from v0.2.x? v0.3.0 flipped priority= semantic to lower-value-wins (DNS MX / cron convention) AND consolidated 4 typed add_* builder methods to add_provider(type=…) + add_bedrock(...). If you copy-pasted v0.2 README's priority=0 (labeled primary) — your traffic was hitting fallback first. v0.3 makes it actually go to primary. Verify your traffic distribution before/after upgrade. Full migration in CHANGELOG.md [0.3.0].

---

License

MIT. See LICENSE.