Route Prompts to the Right Venice Model with venice-text-routing

Where this fits

You're running Claude Code locally and a user asks for a real-time stock chart analysis. Your local model can't see images or search the web. You need to escalate to a Venice model, but which one? Venice offers multiple tiers—anonymized, private, TEE, E2EE—each with different capabilities and costs. Manually mapping each prompt to the right model is error-prone and slow. venice-text-routing encodes that decision logic so your agent can automatically pick the cheapest Venice model that meets the prompt's privacy, modality, and capability requirements—sitting between model discovery (venice-models) and the call surface (venice-chat).

Why agents benefit

• Automatic privacy tier selection: The skill reads the prompt's sensitivity and routes to anonymized, private, TEE, or E2EE models without hardcoding rules.
• Capability-aware routing: It checks for reasoning, code, function calling, web search, large context, and structured output—so you don't call a vision model for a text-only task.
• Cost optimization: Among models that satisfy requirements, it picks the cheapest option, cutting API spend on routine escalations.
• Modality detection: Handles vision, audio, and video inputs, ensuring the chosen model supports the media type before you make the call.
• Local-first decision: Helps your agent decide whether to handle the prompt locally or escalate, reducing unnecessary round-trips.

Practical scenarios

Scenario 1: Web search on a private prompt

A user asks your local agent to research a competitor's pricing page. The prompt contains internal company context. The skill routes to a private-tier Venice model that supports web search, avoiding both the anonymized tier (leaks context) and the TEE tier (overkill for non-secret data).

Scenario 2: Image analysis with structured output

A developer sends a screenshot of an error log and wants a JSON summary. The skill sees the image modality and the structured output requirement, then selects a vision-capable model that also supports JSON mode—while choosing the cheapest option in that category.

Scenario 3: Long-context code review

Your agent receives a 50k-token codebase fragment for review. The local model's context window is too small. The skill checks for large-context capability and routes to a Venice model with a 128k token window, picking a non-reasoning model to keep costs low for pure review.

Add it to your agent workflow

Install via npx:

npx skills add veniceai/skills --skill venice-text-routing

Then, in your agent's routing logic, import the skill and call it with the prompt and available models:

import { routeVeniceText } from 'venice-text-routing';

const decision = routeVeniceText({
  prompt: 'Summarize this PDF and extract the key metrics',
  localCapabilities: ['text'],
  availableModels: await fetchVeniceModels(),
});

// decision → { escalate: true, model: 'venice/private-vision', reason: 'vision+structured' }

For a deeper look at how this skill fits into the broader agent ecosystem, check out its listing on OpenAgentSkill.

Compare before adopting

Before wiring this into production, compare:

• Quality signals: Check the skill's GitHub stars (currently 139), recent commits, and whether the routing matrix is updated as Venice releases new models.
• Maintenance freshness: The README mentions a snapshot file (snapshots/text-routing.json)—verify it's current and regenerated regularly.
• Alternatives: You could hardcode routing rules in your agent, but you'd miss cost optimization and privacy nuances. Compare with other routing skills like venice-models + custom logic.
• Workflow fit: Does your agent already use venice-chat? If not, you'll need to add the call surface too. The skill assumes you have a local agent that can handle some prompts natively.

Why it is worth tracking

This skill solves a real pain point for agent developers: the explosion of model choices. As Venice expands its model lineup and adds new privacy tiers, manual routing becomes untenable. The skill's 139 stars and active maintenance signal community trust. Evaluate it when you need to scale agent escalations beyond a single model—especially if you handle sensitive data or have cost constraints. Track it to stay ahead of the routing logic that will soon be standard in every local-first agent.