AI Toolchain — OpenCode + wiki-brain
AI-assisted development ecosystem: multi-agent orchestrator, persistent knowledge wiki, and infrastructure automation
Problem
Complex software development demands time and context that chat histories cannot retain. Without a structured system, knowledge is lost between sessions.
Solution
An AI-augmented development ecosystem where I design the architecture and OpenCode orchestrates specialized agents to implement it. Wiki-brain maintains persistent knowledge across sessions with full traceability. Bash/Podman scripts automate deployment.
Key takeaways
- Multi-agent orchestrator with 7 specialists (oracle, librarian, explorer, designer, fixer, observer, council), each running the optimal model for its task
- wiki-brain MCP: a server that exposes an Obsidian vault as search, synthesis, and linting tools for any AI assistant
- LLM Wiki Pattern: knowledge is never lost in chat histories — it is compiled into persistent wiki pages with traceability
- Auto-update with locking, inter-container health checks, and automatic rollback for the entire container fleet
- Full infrastructure defined in YAML (docker-compose) and idempotent Bash scripts
Philosophy
This project is my AI working methodology. It is not a product or a service — it is the tool I use to accelerate my development when it makes sense. The AI does not define who I am; it is an accelerator under my direction.
My role is designer/orchestrator: I design the solution, make the technical decisions, and validate every outcome. The AI executes the implementation when the task allows it.
Note: not all of my code is written with AI. My embedded systems projects (C, C++, firmware) are built the traditional way. This ecosystem is complementary, not a replacement.
Components
OpenCode + oh-my-opencode-slim
An orchestrator that coordinates 7 specialized agents, each with a different AI model optimized for its function:
| Agent | Model | Role |
|---|---|---|
| oracle | deepseek-v4-pro | Architectural decisions, complex debugging, code review |
| librarian | minimax-m2.7 | Documentation and API research |
| explorer | minimax-m2.7 | Fast source code search |
| designer | kimi-k2.6 | UI/UX, visual design |
| fixer | deepseek-v4-flash-free | Fast implementation of bounded tasks |
| observer | kimi-k2.6 | Monitoring and analysis |
| council | deepseek-v4-pro | Strategic advisory |
wiki-brain MCP
A server that implements Karpathy’s LLM Wiki Pattern: the AI assistant writes a persistent wiki and the human reads and directs it. It exposes 9 MCP tools:
- search/read/list — wiki navigation
- query/compile/crystallize — knowledge synthesis with [[wikilinks]] citations
- lint/rebuild_index — consistency maintenance
- ingest_knowledge — incorporation of external research
Infrastructure
30+ Podman containers managed with idempotent Bash automation:
- an orchestration script with stack discovery
- an automatic update script with locking and rollback
- a post-deploy connectivity verification script
- a proactive storage monitoring script
AI Ecosystem Stack
This ecosystem is built with tools that are part of my broader stack:
- 🐳 Docker/Podman — Containerization, networks, volumes, systemd quadlets
- 🐍 Python — Agent logic, MCP servers, automation scripts
- 📝 YAML — Infrastructure configuration, docker-compose, agent definitions
- 🔧 Bash — Orchestration scripts, health checks, automatic recovery
My full stack also includes C, C++, embedded firmware, and microcontrollers — visible in my electronics systems projects.