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

2. 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)

3. 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

4. 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