Rezunate LLM SDK

Unified Python SDK for chat completions, prompt management, and PII guardrails across OpenAI, Anthropic, Google Gemini, Grok (xAI), DeepSeek, Llama (Meta), and Qwen (Alibaba). All requests and responses use the OpenAI format, regardless of provider — streaming included.

Get an API key and learn more at rezunatellm.com.

What's Included

Feature	Cost	Requires
Unified chat completions across seven providers, streaming or not	Free	Your own provider API key
Local regex guardrails (block / flag PII in inputs and outputs)	Free	Nothing — runs client-side
Server-side PII detection	Hosted	Rezunate API key
Prompt management with versioning	Hosted	Rezunate API key
rezunate-guard — PII redaction for Claude Code, no code required	Hosted	Rezunate API key

You only need a Rezunate API key for the hosted features.

Installation

pip install rezunate-llm-sdk

Quickstart

from rezunate_llm_sdk import ChatCompletionRequest, Gateway, Message

gateway = Gateway(
    default_provider="anthropic",
    default_api_key="your-provider-api-key",
)

response = gateway.chat_complete(
    ChatCompletionRequest(
        model="claude-sonnet-4-5",
        messages=[Message(role="user", content="Hello!")],
        max_tokens=100,
    )
)

print(response.choices[0].message.content)

The stateless form is also available when you don't want a long-lived gateway:

from rezunate_llm_sdk import ChatCompletionRequest, Message, chat_complete

response = chat_complete(
    provider="openai",
    api_key="your-openai-api-key",
    request=ChatCompletionRequest(
        model="gpt-4o",
        messages=[Message(role="user", content="Hello!")],
    ),
)

Responses always come back in OpenAI format (response.choices[0].message.content, response.usage.total_tokens, etc.) — whichever provider served the call.

Providers

Seven providers ship with the SDK, all behind the same request and response shape:

Provider	provider=	Wire protocol
OpenAI	openai	OpenAI Chat Completions
Anthropic	anthropic	Anthropic Messages
Google Gemini	google	Gemini generateContent
Grok (xAI)	grok	OpenAI-compatible
DeepSeek	deepseek	OpenAI-compatible
Llama (Meta)	llama	Meta native
Qwen (Alibaba)	qwen	DashScope native

The SDK ships with a factory that creates provider instances on demand. You bring your own API key per provider — Rezunate doesn't proxy or charge for these calls.

from rezunate_llm_sdk import get_available_providers, get_provider

# List the providers the SDK supports
print(get_available_providers())
# [Provider.OPENAI, Provider.ANTHROPIC, Provider.GOOGLE,
#  Provider.GROK, Provider.LLAMA, Provider.DEEPSEEK, Provider.QWEN]

# Build a provider instance directly (skips the Gateway/chat_complete facade)
provider = get_provider("anthropic", api_key="your-anthropic-key")
response = provider.chat_complete(
    ChatCompletionRequest(
        model="claude-sonnet-4-5",
        messages=[Message(role="user", content="Hi!")],
    )
)

You can also switch providers at call time on a single Gateway:

gateway = Gateway()

gateway.chat_complete(req, provider="openai", api_key=openai_key)
gateway.chat_complete(req, provider="anthropic", api_key=anthropic_key)
gateway.chat_complete(req, provider="google", api_key=google_key)

Request Parameters

ChatCompletionRequest follows the OpenAI schema and supports the common knobs:

ChatCompletionRequest(
    model="gpt-4o",
    messages=[
        Message(role="system", content="You are concise."),
        Message(role="user", content="Summarize the last commit."),
    ],
    temperature=0.2,
    max_tokens=500,
    top_p=0.9,
    frequency_penalty=0.0,
    presence_penalty=0.0,
    stop=["END"],
)

Provider-specific arguments (e.g. Google top_k, safety_settings) are accepted as extra fields and forwarded by each provider's transformer.

Streaming

Set stream=True and the call returns an iterator of ChatCompletionChunk objects instead of a single response. Every provider streams, and every one yields the same OpenAI chunk shape.

req = ChatCompletionRequest(
    model="claude-sonnet-4-5",
    messages=[Message(role="user", content="Write a haiku about latency.")],
    max_tokens=200,
    stream=True,
)

for chunk in gateway.chat_complete(req):
    if chunk.error:
        print("stream failed:", chunk.error.message)
        break
    for choice in chunk.choices:
        if choice.delta.content:
            print(choice.delta.content, end="", flush=True)

delta.role is set on the first chunk only, finish_reason on the last. A provider or transport failure does not raise — the iterator yields a single chunk with error populated and stops, so check chunk.error in the loop.

Guardrails behave differently on a stream: local regex block rules still raise, but local redact rules only log (deltas are not rewritten). Hosted guardrails buffer output to a sentence boundary, scan, and emit redacted text — use server_guardrails=True when redacting streamed output matters.

Tool Calling

Define tools in OpenAI format and the SDK translates them to each provider's native shape (Anthropic tool_use, Google functionCall) and normalizes the response back to OpenAI tool_calls.

Tool support by provider: OpenAI, Grok, and DeepSeek pass tools through natively; Anthropic and Google are translated both ways; Llama forwards tools to Meta's native field. Qwen's DashScope transformer does not carry tools — they are dropped on that provider.

from rezunate_llm_sdk import ChatCompletionRequest, Gateway, Message

gateway = Gateway(default_provider="anthropic", default_api_key="your-anthropic-key")

tools = [
    {
        "type": "function",
        "function": {
            "name": "get_weather",
            "description": "Look up the current weather for a city",
            "parameters": {
                "type": "object",
                "properties": {"city": {"type": "string"}},
                "required": ["city"],
            },
        },
    }
]

messages = [Message(role="user", content="What's the weather in Tokyo?")]

response = gateway.chat_complete(
    ChatCompletionRequest(
        model="claude-sonnet-4-5",
        messages=messages,
        tools=tools,
        tool_choice="auto",  # "auto" | "required" | "none" | {"type": "function", "function": {"name": "get_weather"}}
        max_tokens=300,
    )
)

choice = response.choices[0]
if choice.finish_reason == "tool_calls":
    call = choice.message.tool_calls[0]
    print(call.function.name)  # "get_weather"
    print(call.function.arguments)  # '{"city": "Tokyo"}'  (a JSON string)

Run the tool, then send the result back as a tool message to get the final answer:

import json

args = json.loads(call.function.arguments)
result = f"Sunny, 22C in {args['city']}"  # your real tool goes here

messages += [
    Message(role="assistant", tool_calls=choice.message.tool_calls),
    Message(role="tool", content=result, tool_call_id=call.id),
    # For Google, also set name=call.function.name on the tool message.
]

final = gateway.chat_complete(
    ChatCompletionRequest(model="claude-sonnet-4-5", messages=messages, tools=tools, max_tokens=300)
)
print(final.choices[0].message.content)

Local Regex Guardrails (Free)

Define regex patterns in YAML to block, flag, or redact sensitive content (PII or anything custom) in both user inputs and model outputs. Everything is free and runs client-side — no RezunateLLM account needed, no data leaves your machine.

Create a config file:

# guardrails.yaml
guardrails:
  - name: block-ssn
    pattern: '\b\d{3}-\d{2}-\d{4}\b'
    description: "Block Social Security Numbers"
    action: block

  # Redact PII instead of blocking the whole request/response.
  - name: redact-email
    pattern: '\b[A-Za-z0-9._%+-]+@[A-Za-z0-9.-]+\.[A-Z|a-z]{2,}\b'
    description: "Redact email addresses"
    action: redact
    replacement: "[EMAIL]"

  - name: flag-credit-card
    pattern: '\b(?:\d[ -]*?){13,16}\b'
    description: "Flag potential credit card numbers"
    action: flag

Each rule needs:

• name — identifier
• pattern — regex to match
• description — human-readable description
• action — one of:
  • block — raises GuardrailsError, stopping the request (input) or response (output)
  • flag — records a violation and logs it, but lets the content through unchanged
  • redact — replaces every match with replacement (defaults to [REDACTED]) in both the prompt sent to the provider and the model's response
• replacement — text substituted for each match when action: redact (optional; defaults to [REDACTED])

Use it in two ways:

Pass it explicitly to a Gateway:

from rezunate_llm_sdk import Gateway, load_guardrails

config = load_guardrails("guardrails.yaml")
gateway = Gateway(
    default_provider="anthropic",
    default_api_key="your-anthropic-key",
    guardrails_config=config,
)

# Both input and output are checked on every chat_complete call.
gateway.chat_complete(request)

Or enable automatic loading via env var:

export GUARDRAILS_FILE_PATH=path/to/guardrails.yaml

When set, the SDK loads the file once and applies the rules on every chat_complete call without explicit wiring.

You can also call the checker directly. check_guardrails returns a (redacted_text, violations) tuple and raises GuardrailsError on a block rule:

from rezunate_llm_sdk import GuardrailsError, check_guardrails, load_guardrails
from rezunate_llm_sdk.models import GuardrailDirection

config = load_guardrails("guardrails.yaml")  # with a redact rule for emails
try:
    redacted, violations = check_guardrails(
        "Email me at alex@example.com", config, GuardrailDirection.OUTPUT
    )
    print(redacted)  # -> "Email me at [EMAIL]"  (redact rules applied)
    # 'flag' rules show up in `violations`; 'block' rules raise GuardrailsError
except GuardrailsError as e:
    print(f"Blocked by rule '{e.rule_name}' on {e.direction.name}")

Hosted Features

These call the Rezunate API and require REZUNATE_LLM_API_KEY (passed to Gateway or set in the env).

Server-side PII Detection (Premium Feature)

Detect PII entities using the workspace guardrail config managed in RezunateLLM. Returns model-detected entities, with labels, scores, and offsets.

gateway = Gateway(REZUNATE_LLM_API_KEY="your-rezunate-api-key")

result = gateway.guardrails.scan("My SSN is 123-45-6789 and my email is alex@example.com.")

for entity in result.entities:
    print(f"{entity.label}: {entity.text!r} (score={entity.score:.2f})")

print("action:", result.action)  # action taken per workspace config
print("blocked:", result.blocked)  # whether the request was blocked
print("text:", result.text)  # processed text (e.g. with PII redacted)

Automatic PII guardrails on input and output

Set server_guardrails=True and the Gateway scans every chat_complete call with the hosted PII service, applying the workspace config automatically:

• if the scan blocks the content → raises ServerGuardrailsError
• if the scan redacts it → the content is replaced with the server's redacted text (the prompt before it's sent, or the response after)
• detected entities are logged either way

from rezunate_llm_sdk import Gateway, ServerGuardrailsError

gateway = Gateway(
    default_provider="openai",
    default_api_key="your-openai-key",
    server_guardrails=True,  # scan input AND output via hosted PII service
    REZUNATE_LLM_API_KEY="your-rezunate-api-key",
)

try:
    response = gateway.chat_complete(request)
    print(response.choices[0].message.content)  # PII redacted by the server
except ServerGuardrailsError as e:
    print(f"Blocked on {e.direction.name} — detected {[ent.label for ent in e.entities]}")

Choosing which side to scan. True scans both the prompt (input) and the model response (output). To scan only one side, pass a ServerGuardrailsConfig with directions:

from rezunate_llm_sdk import Gateway, ServerGuardrailsConfig

gateway = Gateway(
    default_provider="openai",
    default_api_key="your-openai-key",
    server_guardrails=ServerGuardrailsConfig(directions=("output",)),  # output only
    REZUNATE_LLM_API_KEY="your-rezunate-api-key",
)

server_guardrails value	Scans
True	input and output
ServerGuardrailsConfig(directions=("input",))	input only
ServerGuardrailsConfig(directions=("output",))	output only
False (default)	nothing

You can also override it per call: gateway.chat_complete(request, server_guardrails=...). This runs alongside any local regex guardrails.

Prompt Management

Fetch and render prompts stored on Rezunate LLM. Pin to a specific version, or omit version to use the current one. Variables are interpolated into the template before returning.

gateway = Gateway(
    default_provider="anthropic",
    default_api_key="your-anthropic-api-key",
    REZUNATE_LLM_API_KEY="your-rezunate-api-key",
)

system_prompt = gateway.get_prompt(
    slug_id="customer-support-greeting",
    variables={"name": "Alex", "tier": "premium"},
    version=3,  # optional — defaults to current version
)

response = gateway.chat_complete(
    ChatCompletionRequest(
        model="claude-sonnet-4-5",
        messages=[
            Message(role="system", content=system_prompt),
            Message(role="user", content="Where's my order?"),
        ],
    )
)

Claude Code PII Guard

rezunate-guard redacts PII from your files before Claude Code sends them to the model. It needs no code — you point it at the folders holding personal data and it works from then on.

cd your-project
rezunate-guard install     # register the hooks with Claude Code
rezunate-guard login       # save your Rezunate API key
rezunate-guard init        # write .rezunate-guard.yaml
rezunate-guard status      # confirm protection is actually on

Then list the folders to protect:

# .rezunate-guard.yaml
scan:
  - clients
  - data/patients
  - /mnt/share/case-files    # absolute paths work too

Everything inside a listed folder is protected, at any depth. When Claude reads one of those files, the personal data your workspace is configured to detect is replaced with placeholders before the model sees them:

Ali Hassan emailed Sara Khan, and Sara Khan replied to Ali Hassan
[PERSON_NAME_a786] [PERSON_NAME_f54e] emailed [PERSON_NAME_3225] [PERSON_NAME_f4f5], and [PERSON_NAME_3225] [PERSON_NAME_f4f5] replied to [PERSON_NAME_a786] [PERSON_NAME_f54e]

The same value always gets the same placeholder, across files and sessions, so the text still reads sensibly and Claude can tell the two people apart. It matches on the value, not the person — Ali Hassan and Hassan are two different values and get two different placeholders. Placeholders come from a key that never leaves your machine.

Only the entity types enabled for your workspace are redacted. If phone numbers are off in your guardrail settings, a phone number in a protected file reaches the model in the clear. Check what is enabled before relying on this.

Command	What it does
rezunate-guard install	register the hooks in Claude Code's settings.json
rezunate-guard login	save your API key to ~/.rezunate/credentials
rezunate-guard init	write a .rezunate-guard.yaml template here
rezunate-guard uninstall	remove them again, leaving other hooks alone
rezunate-guard status	report whether anything is actually being protected
rezunate-guard check <paths>	show whether given paths would be scanned, and why

Nothing is scanned unless you name it, and each scanned file is a billable API call, so list only what holds personal data. If the config cannot be read — a typo in the YAML, a folder that isn't there — the guard protects nothing and writes the reason to ~/.rezunate/guard.log. rezunate-guard status reports the same thing and exits non-zero, so check it after editing the config.

Environment Variables

Variable	Purpose
REZUNATE_LLM_API_KEY	Rezunate API key — required for hosted features (prompts, server-side scan)
OPENAI_API_KEY	OpenAI provider key — used by your application code
ANTHROPIC_AI_API_KEY	Anthropic provider key — used by your application code
GOOGLE_API_KEY	Google Gemini provider key — used by your application code
GUARDRAILS_FILE_PATH	Optional path to a local guardrails YAML config; loaded automatically when set
REZUNATE_LLM_BASE_URL	Rezunate API base URL (default https://rezunatellm.com)
REZUNATE_HOME	Where rezunate-guard keeps its key and log (default ~/.rezunate)
CLAUDE_CONFIG_DIR	Where rezunate-guard install writes hook settings (default ~/.claude)