AxonFlow Python SDK

Enterprise AI Governance in 3 Lines of Code.

Installation

pip install axonflow

With LLM provider support:

pip install axonflow[openai]      # OpenAI integration
pip install axonflow[anthropic]   # Anthropic integration
pip install axonflow[all]         # All integrations

Quick Start

Async Usage (Recommended)

import asyncio
from axonflow import AxonFlow

async def main():
    async with AxonFlow(
        endpoint="https://your-agent.axonflow.com",
        client_id="your-client-id",
        client_secret="your-client-secret"
    ) as client:
        # Execute a governed query
        response = await client.execute_query(
            user_token="user-jwt-token",
            query="What is AI governance?",
            request_type="chat"
        )
        print(response.data)

asyncio.run(main())

Sync Usage

from axonflow import AxonFlow

with AxonFlow.sync(
    endpoint="https://your-agent.axonflow.com",
    client_id="your-client-id",
    client_secret="your-client-secret"
) as client:
    response = client.execute_query(
        user_token="user-jwt-token",
        query="What is AI governance?",
        request_type="chat"
    )
    print(response.data)

Features

Gateway Mode

For lowest-latency LLM calls with full governance and audit compliance:

from axonflow import AxonFlow, TokenUsage

async with AxonFlow(...) as client:
    # 1. Pre-check: Get policy approval
    ctx = await client.get_policy_approved_context(
        user_token="user-jwt",
        query="Find patient records",
        data_sources=["postgres"]
    )

    if not ctx.approved:
        raise Exception(f"Blocked: {ctx.block_reason}")

    # 2. Make LLM call directly (your code)
    llm_response = await openai.chat.completions.create(
        model="gpt-4",
        messages=[{"role": "user", "content": str(ctx.approved_data)}]
    )

    # 3. Audit the call
    await client.audit_llm_call(
        context_id=ctx.context_id,
        response_summary=llm_response.choices[0].message.content[:100],
        provider="openai",
        model="gpt-4",
        token_usage=TokenUsage(
            prompt_tokens=llm_response.usage.prompt_tokens,
            completion_tokens=llm_response.usage.completion_tokens,
            total_tokens=llm_response.usage.total_tokens
        ),
        latency_ms=250
    )

OpenAI Integration

Transparent governance for existing OpenAI code:

from openai import OpenAI
from axonflow import AxonFlow
from axonflow.interceptors.openai import wrap_openai_client

openai = OpenAI()
axonflow = AxonFlow(...)

# Wrap client - governance is now automatic
wrapped = wrap_openai_client(openai, axonflow, user_token="user-123")

# Use as normal
response = wrapped.chat.completions.create(
    model="gpt-4",
    messages=[{"role": "user", "content": "Hello!"}]
)

MCP Connectors

Query data through MCP connectors:

# List available connectors
connectors = await client.list_connectors()

# Query a connector
result = await client.query_connector(
    user_token="user-jwt",
    connector_name="postgres",
    operation="query",
    params={"sql": "SELECT * FROM users LIMIT 10"}
)

Multi-Agent Planning

Generate and execute multi-agent plans:

# Generate a plan
plan = await client.generate_plan(
    query="Book a flight and hotel for my trip to Paris",
    domain="travel"
)

print(f"Plan has {len(plan.steps)} steps")

# Execute the plan
result = await client.execute_plan(plan.plan_id)
print(f"Result: {result.result}")

Configuration

from axonflow import AxonFlow, Mode, RetryConfig

client = AxonFlow(
    endpoint="https://your-agent.axonflow.com",
    client_id="your-client-id",               # Required for enterprise features
    client_secret="your-client-secret",       # Required for enterprise features
    mode=Mode.PRODUCTION,                     # or Mode.SANDBOX
    debug=True,                               # Enable debug logging
    timeout=60.0,                             # Request timeout in seconds
    retry_config=RetryConfig(                 # Retry configuration
        enabled=True,
        max_attempts=3,
        initial_delay=1.0,
        max_delay=30.0,
    ),
    cache_enabled=True,                       # Enable response caching
    cache_ttl=60.0,                           # Cache TTL in seconds
)

Error Handling

from axonflow.exceptions import (
    AxonFlowError,
    PolicyViolationError,
    AuthenticationError,
    RateLimitError,
    TimeoutError,
)

try:
    response = await client.execute_query(...)
except PolicyViolationError as e:
    print(f"Blocked by policy: {e.block_reason}")
except RateLimitError as e:
    print(f"Rate limited: {e.limit}/{e.remaining}, resets at {e.reset_at}")
except AuthenticationError:
    print("Invalid credentials")
except TimeoutError:
    print("Request timed out")
except AxonFlowError as e:
    print(f"AxonFlow error: {e.message}")

Response Types

All responses are Pydantic models with full type hints:

from axonflow import (
    ClientResponse,
    PolicyApprovalResult,
    PlanResponse,
    ConnectorResponse,
)

# Full autocomplete and type checking support
response: ClientResponse = await client.execute_query(...)
print(response.success)
print(response.data)
print(response.policy_info.policies_evaluated)

Development

# Install dev dependencies
pip install -e ".[dev]"

# Run tests
pytest

# Run linting
ruff check .
ruff format .

# Run type checking
mypy axonflow

Examples

Complete working examples for all features are available in the examples folder.

Community Features

# PII Detection - Automatically detect sensitive data
result = await client.get_policy_approved_context(
    user_token="user-123",
    query="My SSN is 123-45-6789"
)
# result.approved = True, result.requires_redaction = True (SSN detected)

# SQL Injection Detection - Block malicious queries
result = await client.get_policy_approved_context(
    user_token="user-123",
    query="SELECT * FROM users; DROP TABLE users;"
)
# result.approved = False, result.block_reason = "SQL injection detected"

# Static Policies - List and manage built-in policies
policies = await client.list_policies()
# Returns: [Policy(name="pii-detection", enabled=True), ...]

# Dynamic Policies - Create runtime policies
await client.create_dynamic_policy(
    name="block-competitor-queries",
    conditions={"contains": ["competitor", "pricing"]},
    action="block"
)

# MCP Connectors - Query external data sources
resp = await client.query_connector(
    user_token="user-123",
    connector_name="postgres-db",
    operation="query",
    params={"sql": "SELECT name FROM customers"}
)

# Multi-Agent Planning - Orchestrate complex workflows
plan = await client.generate_plan(
    query="Research AI governance regulations",
    domain="legal"
)
result = await client.execute_plan(plan.plan_id)

# Audit Logging - Track all LLM interactions
await client.audit_llm_call(
    context_id=ctx.context_id,
    response_summary="AI response summary",
    provider="openai",
    model="gpt-4",
    token_usage=TokenUsage(prompt_tokens=100, completion_tokens=200, total_tokens=300),
    latency_ms=450
)

Enterprise Features

These features require an AxonFlow Enterprise license:

# Code Governance - Automated PR reviews with AI
pr_result = await client.review_pull_request(
    repo_owner="your-org",
    repo_name="your-repo",
    pr_number=123,
    check_types=["security", "style", "performance"]
)

# Cost Controls - Budget management for LLM usage
budget = await client.get_budget("team-engineering")
# Returns: Budget(limit=1000.00, used=234.56, remaining=765.44)

# MCP Policy Enforcement - Automatic PII redaction in connector responses
resp = await client.query_connector("user", "postgres", "SELECT * FROM customers", {})
# resp.policy_info.redacted = True
# resp.policy_info.redacted_fields = ["ssn", "credit_card"]

For enterprise features, contact sales@getaxonflow.com.

Documentation

• Getting Started
• Gateway Mode Guide

Support

• Documentation: https://docs.getaxonflow.com
• Issues: https://github.com/getaxonflow/axonflow-sdk-python/issues
• Email: dev@getaxonflow.com

License

MIT - See LICENSE for details.