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TypeScript + Bash orchestration that runs AI agents โ Ollama for code generation, Claude for planning and triage โ in parallel, in dependency order.
/implement triggers a multi-step pipeline. Claude handles triage and planning; the TypeScript orchestrator runs Ollama agents in dependency order; Claude applies the generated output.
Step 0 Triage Claude reads graph.json (BFS depth=2), writes triage_ts.md
Step 1 Plan Parallel Claude planners write task_context_<domain>.md
Step 1.5 Orchestrate npm start runs Ollama agents in dependency order,
writes ollama_output_<domain>.md
Step 2 Code Parallel Claude coders apply ollama_output_<domain>.md,
write coder_output_<domain>.md
Step 2.5 Pre-review Standards compliance check
Step 3 Build npx tsc --noEmit
Step 4 Review Fast review per file; deep review for flagged files
Step 5 Fix loop Max 3 rounds, circuit breaker on repeat errors
Step 6 Finalize git diff + track savings
Agents communicate through files in .claude/context/. Each step reads file paths from the previous step, not the full content.
src/
types/index.ts AgentDomain, KNOWN_DOMAINS, Role, AgentTask,
AgentResult (done|skipped|failed|blocked), TriageResult
agents/
AgentRunner.ts Wraps call_ollama.sh via spawn; 5 min timeout; 10 MB output limit
TriageAgent.ts BFS depth=2 on graph.json; writes triage_ts.md; CLI via import.meta.url
core/
DependencyGraph.ts Kahn topological sort; duplicate domain detection
Orchestrator.ts Reads task_context_<domain>.md; circuit breaker for failed deps;
reviews ollama_output_<domain>.md after all domains complete
index.ts CLI entry point
| Domain | Depends on |
|---|---|
coder |
(none) |
unit-tester |
coder |
doc-writer |
coder |
devops |
coder, unit-tester, doc-writer |
Domains within the same dependency level run concurrently. If a domain fails, its dependents are marked blocked and skipped.
- Node.js 20+ with
tsx - Claude Code CLI
- Ollama installed and running
jq- Python 3 with
graphifypackage (optional, for knowledge graph updates)
git clone https://github.com/Mybono/ai-orchestrator ~/Projects/ai-orchestrator
cd ~/Projects/ai-orchestrator
./scripts/install.shOr with curl:
curl -sSL https://raw.githubusercontent.com/Mybono/ai-orchestrator/main/scripts/install.sh | bashinstall.sh creates symlinks from ~/.claude/ into the repo. A git pull in the repo directory updates all tooling immediately.
Model routing is controlled by llm-config.json in the repo root:
{
"models": {
"coder": "hf.co/bartowski/Qwen2.5-Coder-14B-Instruct-GGUF:IQ4_XS",
"reviewer": "qwen2.5-coder:7b",
"pre-reviewer": "qwen2.5-coder:7b",
"quick-coder": "qwen2.5-coder:7b",
"commit": "qwen2.5-coder:7b",
"triage": "llama3.1:8b",
"embedding": "mxbai-embed-large"
}
}Changing a model name takes effect immediately โ no restart needed. See Architecture for details.
npm run build # compile TypeScript
npm run typecheck # tsc --noEmit, no output files
npm start "coder,unit-tester" # run TS orchestrator for given domains
npx tsx src/agents/TriageAgent.ts "<task>" # run triage standalonelocal-commit generates a commit message via Ollama and calls scripts/graphify-update.sh before staging, so the updated graph.json is included in the same commit.
MIT
README ยท Architecture ยท Agents ยท Skills & Commands ยท Plugins