cascadeflow

OpenClaw-native domain cascading. Use when users need cost/latency reduction via cascading, domain-aware model assignment, OpenClaw-native event handling, and command setup including /model cflow and optional /cascade stats commands.

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Install skill "cascadeflow" with this command: npx skills add saschabuehrle/cascadeflow

CascadeFlow: Cost + Latency Reduction | 17+ Domain-Aware Models + OpenClaw-Native Events

Use CascadeFlow as an OpenClaw provider to lower cost and latency via cascading. Assign up to 17 domain-specific models (for coding, web search, reasoning, and more), including OpenClaw-native event handling, and cascade between them (small model first, verifier when needed). Keep setup minimal, then verify with one health check and one chat call.

Why Use It

  • Reduce spend with drafter/verifier cascading.
  • Run 17+ domain-aware model assignments (code, reasoning, web-search, and more).
  • Support cascading with streaming and multi-step agent loops.
  • Handle OpenClaw-native event/domain signals for smarter model selection.

Security Defaults

  • Install from PyPI and verify package artifact before first run.
  • Keep the server bound to localhost by default.
  • Use explicit auth tokens for chat and stats endpoints (recommended for production).
  • Expose remote access only behind TLS/reverse proxy with strong tokens.
  • Use least-privilege provider keys (separate test keys from production keys).

How It Works

  1. OpenClaw sends requests to CascadeFlow through OpenAI-compatible /v1/chat/completions.
  2. CascadeFlow reads prompt context plus OpenClaw-native event/domain metadata (for example metadata.method, metadata.event, and channel/category hints).
  3. CascadeFlow selects a domain-aware drafter/verifier pair (small model first).
  4. If quality passes threshold, drafter answer is returned (cost/latency advantage).
  5. If quality fails threshold, verifier runs and final answer is upgraded.
  6. The same cascading behavior is supported for streaming and multi-step agent loops.

Advantages

  • Lower average cost by avoiding verifier calls when not needed.
  • Lower average latency for simple and medium tasks.
  • Better quality on hard tasks through verifier fallback.
  • Better operational handling through OpenClaw-native event/domain understanding.

Quick Start

Or ask your OpenClaw agent to set it up for you as an OpenClaw custom provider with OpenClaw-native events and domain understanding.

  1. Install and verify package source:
python3 -m venv .venv
source .venv/bin/activate
python -m pip install --upgrade "cascadeflow[openclaw]>=0.7,<0.8"
python -m pip show cascadeflow
python -m pip download --no-deps "cascadeflow[openclaw]>=0.7,<0.8" -d /tmp/cascadeflow_pkg
python -m pip hash /tmp/cascadeflow_pkg/cascadeflow-*.whl

Optional variants:

python -m pip install --upgrade "cascadeflow[openclaw,anthropic]>=0.7,<0.8"   # Anthropic-only preset
python -m pip install --upgrade "cascadeflow[openclaw,openai]>=0.7,<0.8"      # OpenAI-only preset
python -m pip install --upgrade "cascadeflow[openclaw,providers]>=0.7,<0.8"   # Mixed preset
  1. Pick preset + credentials:
  • Presets: examples/configs/anthropic-only.yaml, examples/configs/openai-only.yaml, examples/configs/mixed-anthropic-openai.yaml
  • Provider key(s): ANTHROPIC_API_KEY=... and/or OPENAI_API_KEY=... (required based on selected preset)
  • Service tokens: --auth-token ... and --stats-auth-token ... (recommended for production; use long random values)
  1. Start server (safe local default):
set -a; source .env; set +a
python3 -m cascadeflow.integrations.openclaw.openai_server \
  --host 127.0.0.1 --port 8084 \
  --config examples/configs/anthropic-only.yaml \
  --auth-token local-openclaw-token \
  --stats-auth-token local-stats-token

Optional harness activation (runtime in-loop policy controls):

# Observe first (recommended): log decisions, no blocking
python3 -m cascadeflow.integrations.openclaw.openai_server \
  --host 127.0.0.1 --port 8084 \
  --config examples/configs/anthropic-only.yaml \
  --harness-mode observe

# Enforce mode with limits
python3 -m cascadeflow.integrations.openclaw.openai_server \
  --host 127.0.0.1 --port 8084 \
  --config examples/configs/anthropic-only.yaml \
  --harness-mode enforce \
  --harness-budget 1.0 \
  --harness-max-tool-calls 12 \
  --harness-max-latency-ms 3500 \
  --harness-compliance strict
  1. Configure OpenClaw provider:
  • baseUrl: http://<cascadeflow-host>:8084/v1 (local default: http://127.0.0.1:8084/v1)
  • If remote: http://<server-ip>:8084/v1 or https://<domain>/v1 (TLS/reverse proxy)
  • api: openai-completions
  • model: cascadeflow
  • apiKey: same value as your --auth-token

Commands

  • /model cflow: default OpenClaw model switch using alias cflow.
  • /cascade: optional custom command (if configured in OpenClaw).
  • /cascade savings: optional custom subcommand for cost stats.
  • /cascade health: optional custom subcommand for service status.

Links

  • Full setup + configs: references/clawhub_publish_pack.md
  • Listing strategy: references/market_positioning.md
  • Official docs: https://github.com/lemony-ai/cascadeflow/blob/main/docs/guides/openclaw_provider.md
  • GitHub repository: https://github.com/lemony-ai/cascadeflow

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