axonflow-litellm 1.0.0

AxonFlow governance integration for LiteLLM

axonflow-litellm

AxonFlow governance integration for LiteLLM. Enforce policies, audit LLM calls, and gate high-risk requests behind human approval — all through a drop-in wrapper around litellm.completion().

Installation

pip install axonflow-litellm

Quick Start

from axonflow_litellm import AxonFlowLogger, AxonFlowLoggerConfig, PolicyDeniedError

logger = AxonFlowLogger(AxonFlowLoggerConfig(
    endpoint="http://localhost:8080",
    client_id="my-app",
    client_secret="...",
))

try:
    response = logger.completion(
        model="gpt-4o",
        messages=[{"role": "user", "content": "Summarize quarterly earnings"}],
    )
    print(response.choices[0].message.content)
except PolicyDeniedError as e:
    print(f"Blocked: {e.reason}")

How It Works

AxonFlowLogger provides two integration modes:

Governance Mode (recommended)

Use logger.completion() or logger.acompletion() as drop-in replacements for litellm.completion() / litellm.acompletion():

1. Pre-check — sends the prompt to AxonFlow for policy evaluation
2. HITL — if the policy returns require_approval, creates a human-in-the-loop review request and polls until approved, rejected, or timed out
3. LLM call — delegates to LiteLLM (all providers supported)
4. Audit — records the response to AxonFlow for observability

# Async (recommended for production)
response = await logger.acompletion(
    model="claude-sonnet-4-6",
    messages=[{"role": "user", "content": "..."}],
    user_token="jwt-from-your-auth",
)

Audit-Only Mode

Register as a LiteLLM callback for observability without blocking:

import litellm

litellm.callbacks = [logger]
response = litellm.acompletion(model="gpt-4o", messages=[...])

In this mode, every LLM call is recorded to AxonFlow for audit trail. Policy denials are logged as warnings but cannot block the request (a LiteLLM SDK limitation — callback exceptions are silently swallowed).

Configuration

Parameter	Default	Description
endpoint	(required)	AxonFlow agent URL
client_id	(required)	AxonFlow client identifier
client_secret	""	AxonFlow client secret
default_user_token	"anonymous"	Token for policy evaluation when none provided
tenant_id	None	AxonFlow tenant identifier
fail_open	True	Allow LLM calls when AxonFlow is unreachable
call_timeout_seconds	5.0	Per-hook timeout for AxonFlow API calls
breaker_failure_threshold	5	Consecutive failures before circuit opens
breaker_recovery_seconds	30.0	Wait before attempting recovery probe
enable_hitl_polling	True	Enable HITL approval flow for require_approval
approval_poll_interval_seconds	2.0	Polling interval for HITL status
approval_max_wait_seconds	300.0	Maximum wait for HITL decision
extra_context	{}	Additional context sent with every pre-check

Fail-Open vs. Fail-Closed

By default, fail_open=True: if AxonFlow is unreachable or times out, the LLM call proceeds normally. This ensures an AxonFlow outage does not break your application.

For high-stakes workloads where unapproved LLM calls must never proceed:

config = AxonFlowLoggerConfig(
    endpoint="http://localhost:8080",
    client_id="payments-service",
    client_secret="...",
    fail_open=False,
)

Exceptions

Exception	When
PolicyDeniedError	Policy denied the request
ApprovalRejected	HITL approval was rejected
ApprovalTimeout	HITL approval timed out

All exceptions carry .reason (string) and .policies (list of policy IDs).

MCP Governance

LiteLLM is LLM-completion-focused. For MCP tool governance, use AxonFlow's MCP server directly.

Requirements

• Python >= 3.10
• litellm >= 1.40
• axonflow >= 8.2.0

License

MIT