ao-kernel 2.0.0

Governed AI orchestration runtime — policy-driven, fail-closed, evidence-trail

ao-kernel

Governed AI orchestration runtime — policy-driven, fail-closed, evidence-trail.

ao-kernel is not a general-purpose agent framework. It is a governed runtime that enforces policies, records evidence, and provides deterministic LLM routing for production Python teams.

Installation

pip install ao-kernel                # Core (only jsonschema dependency)
pip install ao-kernel[llm]           # LLM modules (tenacity + tiktoken)
pip install ao-kernel[mcp]           # MCP server support
pip install ao-kernel[otel]          # OpenTelemetry instrumentation
pip install ao-kernel[llm,mcp,otel]  # Everything

Requires Python 3.11+.

Quick Start

# Create workspace
ao-kernel init

# Check health
ao-kernel doctor

# Library mode (no workspace required)
from ao_kernel.config import load_default
policy = load_default("policies", "policy_autonomy.v1.json")

# LLM routing
from ao_kernel.llm import build_request, normalize_response

request = build_request(
    provider_id="openai",
    model="gpt-4",
    messages=[{"role": "user", "content": "Hello"}],
    base_url="https://api.openai.com/v1/chat/completions",
    api_key="sk-...",
)

# Streaming
from ao_kernel.llm import build_request as build_req

stream_request = build_req(
    provider_id="claude",
    model="claude-sonnet-4-20250514",
    messages=[{"role": "user", "content": "Hello"}],
    base_url="https://api.anthropic.com/v1/messages",
    api_key="sk-ant-...",
    stream=True,
)

CLI Reference

Command	Description
ao-kernel init	Create .ao/ workspace
ao-kernel doctor	Workspace health check (8 checks)
ao-kernel migrate [--dry-run] [--backup]	Version migration
ao-kernel version	Print version
ao-kernel mcp serve	Start MCP server (stdio)

Python API

ao_kernel.config

Function	Description
workspace_root(override=None)	Resolve workspace (returns None in library mode)
load_default(resource_type, filename)	Load bundled JSON default
load_with_override(resource_type, filename, workspace)	Workspace override > bundled default

ao_kernel.llm

Function	Description
resolve_route(intent, ...)	Deterministic LLM routing
build_request(provider_id, model, messages, ...)	Provider-native HTTP request
normalize_response(resp_bytes, provider_id)	Extract text + usage + tool_calls
extract_text(resp_bytes)	Extract text from response
execute_request(url, headers, body_bytes, ...)	HTTP with retry + circuit breaker
stream_request(url, headers, ...)	SSE streaming with OK/PARTIAL/FAIL
get_circuit_breaker(provider_id)	Per-provider circuit breaker
count_tokens(messages, provider_id, model)	Token counting

Supported providers: Claude, OpenAI, Google Gemini, DeepSeek, Qwen, xAI.

MCP Server

ao-kernel runs as an MCP (Model Context Protocol) server, exposing governance tools:

ao-kernel mcp serve  # stdio transport

Tools:

• ao_policy_check — Validate action against policy (allow/deny)
• ao_llm_route — Resolve provider/model for intent
• ao_quality_gate — Check output quality
• ao_workspace_status — Workspace health

Resources:

• ao://policies/{name} — Policy JSON
• ao://schemas/{name} — Schema JSON
• ao://registry/{name} — Registry JSON

Context Management

Governed context loop — decisions extracted, scored, and injected automatically.

from ao_kernel.context import start_session, process_turn, compile_context, end_session

# Start session
ctx = start_session(workspace_root=".", session_id="my-session")

# After each LLM turn — automatic extraction + compaction
ctx = process_turn(llm_output, ctx, workspace_root=".", request_id="req-1")

# Compile context for next LLM call (relevance-scored, budget-aware)
compiled = compile_context(ctx, profile="TASK_EXECUTION", max_tokens=4000)
# compiled.preamble → inject into system prompt

# End session — compact + distill + promote
end_session(ctx, workspace_root=".")

SDK Hooks (multi-agent):

from ao_kernel.context.agent_coordination import record_decision, query_memory

record_decision(ws, key="arch.pattern", value="microservices", confidence=0.9)
items = query_memory(ws, key_pattern="arch.*")

Profiles: STARTUP (minimal), TASK_EXECUTION (full), REVIEW (quality focus)

What Makes ao-kernel Different

	ao-kernel	LangGraph	CrewAI	Pydantic AI
Policy engine	96 policies	No	No	No
Fail-closed	Yes	No	No	No
Evidence trail	Self-hosted JSONL	LangSmith SaaS	No	No
Migration CLI	Yes	No	No	No
Doctor	Yes	No	No	No
MCP server	Yes	No	No	No
Streaming	SSE (6 providers)	Yes	Yes	Yes

Architecture

ao_kernel/          <- Public facade (clean API)
  cli.py            <- CLI commands
  config.py         <- Workspace + defaults resolver
  llm.py            <- LLM routing, building, normalization
  mcp_server.py     <- MCP server (4 tools, 3 resources)
  telemetry.py      <- OpenTelemetry (lazy no-op fallback)
  defaults/         <- 338 bundled JSON (policies, schemas, registry, extensions, ops)

src/                <- Compat shim (deprecated, use ao_kernel.*)

License

MIT