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firepulse-multi-model-ai-orchestrator

Route CLI queries across xAI, OpenRouter, Mistral, and DeepSeek LLM providers.

4|Updated May 16, 2026
Search Tags:#routing#cli#llm#xai#multi-model#deepseek#Mistral
One-click install
npx skills add https://github.com/reason-machines/devtools-skills --skill firepulse-multi-model-ai-orchestrator-reason-machines
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: firepulse-multi-model-ai-orchestrator
Source: https://github.com/reason-machines/devtools-skills/tree/main/skills/firepulse-multi-model-ai-orchestrator
Command: npx skills add https://github.com/reason-machines/devtools-skills --skill firepulse-multi-model-ai-orchestrator-reason-machines

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve? Managing multiple LLM providers requires juggling separate API keys, SDKs, and pricing models. This Skill provides a single command-line gateway that routes queries to the optimal model based on context length, latency, and cost, with streaming responses and persistent session memory. ## Core Features & Use Cases - Smart Multi-Provider Routing: Automatically selects between xAI, OpenRouter, Mistral, and DeepSeek based on configurable strategies (smart, cost, speed, quality) with fallback support. - Streaming and Session Memory: Streams responses token-by-token and maintains persistent conversation history across sessions with configurable context limits. - Cost and Performance Controls: Enforces budget thresholds per query, tracks latency metrics, and supports response caching and parallel provider interrogation. - Use Case: A developer wants code generation from DeepSeek but needs automatic fallback to Mistral when DeepSeek times out, all while keeping each query under $0.01. ## Quick Start Set up FirePulse with my xAI and Mistral API keys and run a single query asking it to explain quantum computing using cost-optimized routing.

Frequently Asked Questions about firepulse-multi-model-ai-orchestrator

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I use multiple LLM providers from the command line?▼

FirePulse connects xAI, OpenRouter, Mistral, and DeepSeek through one CLI. Set your API keys as environment variables, run the bootstrap script, then start an interactive session or pass a single query with the --query flag.

How to route queries to the cheapest LLM automatically?▼

Set the routing strategy to cost with --strategy cost or in config.yaml, and define a budget_threshold in USD per query. The router filters providers by cost per token and falls back if the context window does not fit.

Does FirePulse support streaming responses from LLM APIs?▼

Yes, FirePulse supports streaming responses token-by-token in both CLI and library mode. In Python, iterate over orchestrator.stream() to print chunks as they arrive; Go integration exposes a channel-based stream.

Why does my LLM query fail instead of falling back to another provider?▼

Fallback fails when fallback_enabled is not set to true in the routing section of config.yaml. You can also define a fallback_order list and configure retry_on_failure to attempt multiple providers before erroring.

Can I keep conversation context across multiple LLM queries?▼

Yes, create a session with orchestrator.create_session() to maintain message history across queries. Sessions support configurable max_context_messages, auto-summarization when limits are reached, and saving to JSON files.

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