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Building AI Agents for Injective

Learners build from OpenRouter and Open WebUI into MCP tools, skills, harnesses, and a safe AI Injective Market Research Copilot that returns observe-only briefs with explicit evidence boundaries.

Building AI Agents for Injective

Language

Solidity

Total Length

1.7h

Part of Learning Track

Build on Injective AI + 1

What You’ll Learn

How to route model calls through OpenRouter and inspect the returned JSON response.

How to run Open WebUI with OpenRouter as an OpenAI-compatible provider.

How MCP tools, skills, and harnesses let an AI app use external documentation and market-data sources.

How to use Injective documentation and read-only market/context tools with explicit source labels.

How to build an observe-only Injective Market Research Copilot that refuses signed or fund-moving actions.

A concrete AI stack model: OpenRouter as model gateway, Open WebUI as chat interface, MCP servers as tools, skills as procedures, and harnesses as the runtime that coordinates the agent.
A concrete Injective model: docs search, market data, native USDC context, and perpetual-market concepts as the finance-native application environment.
A capstone artifact: a local AI Injective Market Research Copilot that turns natural language into a structured research brief.
A safety and evidence habit: every final brief separates docs_tool, injective_mcp_tool, user_input, model_inference, and missing_metric fields.

Syllabus