nodus-llm 0.1.0

Multi-credential LLM client with automatic failover and provider abstraction

nodus-llm

Multi-credential LLM client with automatic failover and provider abstraction.

Rotates through ordered credential profiles when an LLM provider fails — handling rate limits, auth errors, billing limits, and context overflow — with exponential per-credential backoff (5m→10m→20m→40m→1h). No required external dependencies; provider SDKs are optional extras.

Status: v0.1.0 — prepared, not yet published.

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Install

pip install nodus-llm

# With OpenAI support:
pip install "nodus-llm[openai]"

# With Anthropic support:
pip install "nodus-llm[anthropic]"

# Both:
pip install "nodus-llm[all]"

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What it provides

Component	Purpose
CredentialProfile	One API key + provider + model with cooldown state
CredentialStore	Ordered profile list with availability tracking
FailoverClient	Rotates profiles on failure with exponential backoff
FailoverError	Raised when all profiles are exhausted
FailoverReason	RATE_LIMIT | AUTH | BILLING | CONTEXT_OVERFLOW | EXHAUSTED
context_window_for(model)	Token limit for a known model
would_overflow(messages, model)	Context window check before sending
CONTEXT_WINDOWS	Dict of model name → token limit

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Quick start

from nodus_llm import CredentialProfile, CredentialStore, FailoverClient
from nodus_llm.providers.openai import OpenAIProvider

profiles = [
    CredentialProfile(api_key="sk-primary", provider="openai", model="gpt-4o"),
    CredentialProfile(api_key="sk-backup",  provider="openai", model="gpt-4o-mini"),
]
store = CredentialStore(profiles=profiles)

client = FailoverClient(store, provider_fn=OpenAIProvider)
reply = client.chat([{"role": "user", "content": "Hello!"}])

---

CredentialProfile and CredentialStore

from nodus_llm import CredentialProfile, CredentialStore

profile = CredentialProfile(
    api_key="sk-abc123",
    provider="openai",     # "openai" | "anthropic" | custom string
    model="gpt-4o",
    max_tokens=4096,       # optional per-profile override
)

store = CredentialStore(profiles=[profile])

available = store.available_profiles()   # profiles not in cooldown
store.mark_failed(profile)               # starts exponential cooldown
store.mark_success(profile)              # clears cooldown

Cooldown schedule: 5m → 10m → 20m → 40m → 1h (exponential, capped at 1h).

---

FailoverClient

from nodus_llm import FailoverClient, FailoverError, FailoverReason

client = FailoverClient(
    store,
    provider_fn=OpenAIProvider,   # factory: (profile) → LLMClient
)

try:
    reply = client.chat(
        messages=[{"role": "user", "content": "Hello!"}],
        max_tokens=256,
    )
except FailoverError as exc:
    print(exc.reason)   # FailoverReason.EXHAUSTED

FailoverClient tries profiles in order. On failure it:

1. Classifies the error → FailoverReason
2. Marks the profile failed (starts cooldown)
3. Tries the next available profile
4. Raises FailoverError if all profiles are exhausted

---

Providers

OpenAI

from nodus_llm.providers.openai import OpenAIProvider

client = FailoverClient(store, provider_fn=OpenAIProvider)

Requires pip install "nodus-llm[openai]".

Anthropic

from nodus_llm.providers.anthropic import AnthropicProvider

client = FailoverClient(store, provider_fn=AnthropicProvider)

Requires pip install "nodus-llm[anthropic]".

OpenAI-compatible endpoints

from nodus_llm.providers.compat import OpenAICompatProvider

profile = CredentialProfile(
    api_key="local-key",
    provider="local",
    model="llama-3.1-8b",
    base_url="http://localhost:11434/v1",
)
client = FailoverClient(store, provider_fn=OpenAICompatProvider)

---

Context window utilities

from nodus_llm import context_window_for, would_overflow, CONTEXT_WINDOWS

limit = context_window_for("gpt-4o")   # 128000 | None (unknown model)

messages = [{"role": "user", "content": "..." * 10000}]
if would_overflow(messages, "gpt-4o-mini"):
    # truncate or summarise before sending
    ...

# All known limits
print(CONTEXT_WINDOWS)  # {"gpt-4o": 128000, "claude-3-5-sonnet-20241022": 200000, ...}

---

Custom provider

Any callable (CredentialProfile) -> LLMClient works as provider_fn. LLMClient is a structural protocol — any object with chat(messages, **kwargs) -> str satisfies it.

class MyProvider:
    def __init__(self, profile): self.profile = profile
    def chat(self, messages, **kwargs): return "custom response"

client = FailoverClient(store, provider_fn=MyProvider)

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Design

• No required dependencies. Core credential management and failover are pure stdlib. Provider SDKs are opt-in extras.
• nodus-circuit-breaker is type-check only. The LLMClient protocol is referenced via TYPE_CHECKING — no runtime import.
• Exponential backoff per credential. Each profile has independent cooldown state; a failed profile is skipped until its cooldown expires.

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Development

pip install -e ".[dev]"
pytest tests/ -q

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License

MIT — see LICENSE.