Ganglion is a domain-specific execution engine designed to orchestrate autonomous mining agents within Bittensor subnets.
Install & Download
1. ClawHub CLI
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npx clawhub@latest install codebase-guide
2. Manual Installation
Copy the skill folder to one of these locations
~/.openclaw/skills/ <project>/skills/ Priority: Workspace > Local > Bundled
3. Prompt Installation
Copy this prompt to OpenClaw to install it automatically.
Help me install codebase-guide using Clawhub. If Clawhub is not installed, install it first (npm i -g clawhub).
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Get the raw skill files in a ZIP archive.
What is Ganglion: Bittensor Mining Execution Engine?
Ganglion serves as a robust search infrastructure for Bittensor subnet mining, providing a sophisticated pipeline framework to manage autonomous agents. Instead of defining the models themselves, it focuses on the search process, coordinating how agents discover optimal configurations. As a key component in the ecosystem of Openclaw Skills, it offers a flexible architecture through its CLI, Python library, and HTTP bridge API.
The framework is built for durability and growth, featuring a cross-run strategic memory that compounds over time. By allowing agents to access shared knowledge of successful patterns and failed anti-patterns, it enables a level of emergent cooperation essential for competitive mining environments.
Ganglion: Bittensor Mining Execution Engine Use Cases
- Orchestrating autonomous mining agents for Bittensor subnet model search.
- Scaling mining operations across multiple bots using a shared knowledge pool.
- Automating complex multi-stage pipelines with built-in retry and escalation policies.
- Integrating external tools via MCP to expand agent capabilities at runtime.
- Auditing and rolling back configuration changes in remote mining environments.
How Ganglion: Bittensor Mining Execution Engine Works
- The user initializes a project directory containing a config.py that defines the subnet configuration and pipeline structure.
- The engine detects the operating mode (Local or Remote) based on environment variables like GANGLION_URL or GANGLION_PROJECT.
- The orchestrator executes defined pipeline stages in dependency order, utilizing specific agents and registered tools.
- Strategic memory is captured in a cross-run knowledge store, allowing agents to learn from successful patterns and previous failures.
- The system continuously applies retry policies and manages state mutations, which can be audited or rolled back via the API for high reliability.
Ganglion: Bittensor Mining Execution Engine Setup
Ensure you have Python 3.11+ and your OPENAI_API_KEY configured. Install the package and initialize your project to begin using Openclaw Skills:
pip install ganglion
ganglion init ./my-subnet --subnet sn9 --netuid 9
To run in remote mode, start the HTTP bridge:
ganglion serve ./my-subnet --bot-id alpha --port 8899
export GANGLION_URL=http://127.0.0.1:8899
Ganglion: Bittensor Mining Execution Engine Data Schema & Taxonomy
Ganglion organizes data within a project directory structure. The primary configuration resides in config.py, while operational state is tracked through an audit log and knowledge store.
| Component | Description |
|---|---|
| config.py | Defines SubnetConfig and PipelineDef for the engine. |
| tools/ | Directory containing local tool implementations. |
| agents/ | Directory for agent logic and prompt templates. |
| knowledge/ | Cross-run memory store indexed by capability and bot ID. |
| audit log | Records all mutations for validation and multi-step rollback. |
Ganglion: Bittensor Mining Execution Engine Advanced Features
- Multi-Bot Workflows: Share strategic knowledge across multiple Openclaw Skills sessions using unique bot identifiers for cooperative mining.
- MCP Integration: Connect to external Model Context Protocol servers to dynamically inject new tools into the agent repertoire at runtime.
- Runtime Mutation: Patch pipelines, register agents, or swap retry policies via API without stopping the execution engine.
- Model Escalation: Automatically switch to more powerful LLMs or climb a cost ladder when cheaper models fail to make progress.
- Standardized Response Envelope: High-level error handling and data extraction using a consistent success/error JSON structure.
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