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46 lines
2.2 KiB
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46 lines
2.2 KiB
Markdown
---
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title: "Gemini A2A Bridge Setup"
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description: "Documentation on setting up an Agent-to-Agent bridge to offload Gemini CLI tasks to a local Ollama instance."
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author: wompmacho
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date: 2026-04-12T12:00:00-04:00
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lastmod: 2026-04-12
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tags:
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- homelab
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- ai
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- gemini-cli
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- ollama
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---
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# Gemini CLI Local A2A Setup
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Setting up an Agent-to-Agent (A2A) bridge allows Gemini CLI to delegate computationally expensive tasks to a local GPU, significantly reducing API token consumption while leveraging high-performance local hardware.
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## Goal
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The primary objective is to offload heavy lifting, such as complex code generation and deep architectural analysis, to a local RTX 4080 GPU located at `10.0.0.109`. By routing these requests through a local instance of Ollama, we maintain high inference speed for large models without incurring external API costs.
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## Bridge Script
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A Node.js bridge script acts as the intermediary between Gemini CLI and Ollama. This script is located at `/home/wompmacho/a2a-ollama-bridge.js` and runs on `localhost:8080`. It performs the following functions:
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1. **Agent Card Hosting:** Serves the required `agent.json` metadata at `/.well-known/agent.json` so Gemini CLI can discover the sub-agent's capabilities.
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2. **Request Translation:** Receives task payloads from Gemini CLI, extracts the prompt, and forwards it to the Ollama API.
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3. **Response Handling:** Captures the local model's output and returns it to Gemini CLI in the expected A2A format.
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## Sub-agent Configuration
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The sub-agent is integrated into Gemini CLI via a configuration file located at `/home/wompmacho/.gemini/agents/local-gemma.md`. This file defines the sub-agent as a `remote` kind and points to the bridge's metadata endpoint:
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```yaml
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---
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kind: remote
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name: local-gemma
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description: "A powerful local sub-agent running Gemma 26B on an RTX 4080."
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agent_card_url: http://127.0.0.1:8080/.well-known/agent.json
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---
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```
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## Model Details
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The bridge is configured to utilize the **gemma4:26b** model. This 26-billion parameter model is optimized for the local RTX 4080, providing a balance of reasoning capability and performance that rivals many cloud-based alternatives for specialized technical tasks.
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