Documentation Index
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Use this file to discover all available pages before exploring further.
Python API Reference
cascadeflow exposes a three-tier API for Python. Each tier adds more control.
Quick Start
import cascadeflow
# Tier 1: Global activation
cascadeflow.init(mode="observe")
# Tier 2: Scoped run with constraints
with cascadeflow.run(budget=0.50) as session:
result = await agent.run("Analyze this data")
print(session.summary())
# Tier 3: Per-agent policy
@cascadeflow.agent(budget=0.20, compliance="gdpr")
async def my_agent(query: str):
return await llm.complete(query)
API Surface
| Function | Purpose | Docs |
|---|
cascadeflow.init() | Activate the harness globally | Reference |
cascadeflow.run() | Create a scoped run context with constraints | Reference |
@cascadeflow.agent() | Attach per-agent policy | Reference |
HarnessConfig | Full configuration dataclass | Reference |
HarnessRunContext | Session object with summary() and trace() | Reference |
Install
With framework extras:
pip install "cascadeflow[langchain]"
pip install "cascadeflow[openai-agents]"
pip install "cascadeflow[crewai]"
pip install "cascadeflow[google-adk]"
Modes
| Mode | Behavior |
|---|
off | Disabled — no tracking, no enforcement |
observe | Track all calls, log what would happen, enforce nothing |
enforce | Active control — budget caps, model switching, stop actions |
Actions
In enforce mode, the harness can take four actions at each decision boundary:
| Action | Effect |
|---|
allow | Proceed with the original model |
switch_model | Route to a different model |
deny_tool | Block a tool call |
stop | Halt the run (budget exceeded, policy violation) |
