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Portable multi-agent AI developer setup for Claude Code + Ollama. Role-based local LLM orchestration via Bash — plan, co
README · Architecture · Agents · Skills & Commands · Plugins
TypeScript + Bash orchestration that runs AI agents — Ollama for code generation, Claude for planning and triage — 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
scripts/run_pipeline.sh wraps steps 1–3 into a single non-interactive script. Pass a task description and it runs triage, parallel planning, and the TS orchestrator in sequence without any Claude interaction required.
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
BuildChecker.ts Runs npx tsc --noEmit; returns pass/fail with stderr
DiffCompressor.ts Strips lock files, collapses blanks, truncates long hunks
FileWriter.ts Parses %%FILE...%%ENDFILE blocks from Ollama output; writes to disk
TriageRouter.ts Reads triage_ts.md and extracts the chosen TriageRoute
cli/
commit.ts npm run ao-commit — calls local-commit.sh via spawn
review.ts npm run ao-review — runs reviewer agent on current diff
stats.ts npm run ao-stats — prints token savings summary
update.ts npm run ao-update — pulls latest orchestrator version
mcp/
server.ts MCP HTTP server (default port 3456); exposes get_stats,
triage_task, and run_ollama tools to Claude
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.
tsxjqgraphify package (optional, for knowledge graph updates)git clone https://github.com/Mybono/ai-orchestrator ~/Projects/ai-orchestrator
cd ~/Projects/ai-orchestrator
./scripts/install.sh
Or with curl:
curl -sSL https://raw.githubusercontent.com/Mybono/ai-orchestrator/main/scripts/install.sh | bash
install.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": "qwen3:32b-q4_K_M",
"reviewer": "qwen3:32b-q4_K_M",
"pre-reviewer": "qwen3:8b",
"debugger": "qwen3:32b-q4_K_M",
"devops": "qwen3:8b",
"quick-coder": "qwen3:8b",
"commit": "qwen2.5-coder:7b",
"triage": "qwen3:8b",
"embedding": "mxbai-embed-large"
},
"free_api_url": "http://localhost:3001/v1/chat/completions",
"free_api": {
"planner": "qwen/qwen3-32b",
"coder": "qwen3-coder-next",
"reviewer": "auto",
"pre-reviewer": "auto",
"debugger": "qwen3-coder-next",
"devops": "auto",
"quick-coder": "qwen3-coder-next",
"commit": "auto",
"triage": "auto"
},
"cerebras_api": {
"coder": "gpt-oss-120b",
"reviewer": "gpt-oss-120b",
"pre-reviewer": "gpt-oss-120b",
"debugger": "gpt-oss-120b",
"devops": "gpt-oss-120b",
"quick-coder": "gpt-oss-120b",
"commit": "gpt-oss-120b",
"triage": "gpt-oss-120b"
},
"fallback": {
"coder": "claude-sonnet-4-6",
"reviewer": "claude-sonnet-4-6",
"pre-reviewer": "claude-haiku-4-5-20251001",
"debugger": "claude-sonnet-4-6",
"devops": "claude-haiku-4-5-20251001",
"quick-coder": "claude-haiku-4-5-20251001",
"commit": "claude-haiku-4-5-20251001",
"triage": "claude-haiku-4-5-20251001",
"embedding": ""
},
"cloud_first_roles": ["planner", "reviewer", "debugger"]
}
Routing follows a 4-tier provider chain. Roles listed in cloud_first_roles (planner, reviewer, debugger) try Cerebras first, then FreeLLM, then Ollama, then Claude. All other roles start with Ollama, then fall through FreeLLM, Cerebras, and finally Claude. Changing a model name takes effect immediately — 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 standalone
npm run ao-commit # generate commit message via Ollama and commit
npm run ao-review # review current diff with reviewer agent
npm run ao-stats # print token savings (day/week/month)
npm run ao-update # pull latest orchestrator version
npm run ao-mcp # start MCP server on port 3456
bash scripts/run_pipeline.sh "<task description>" # run full pipeline autonomously
MIT
README · Architecture · Agents · Skills & Commands · Plugins
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