zo-swarm-orchestrator

name	zo-swarm-orchestrator
description	Local-only multi-agent orchestrator with token optimization, 6-signal composite routing, and persistent memory. Routes all tasks through local executor bridges (Claude Code, Hermes, Gemini, Codex) with DAG dependencies, auto-episode creation, and cognitive profiles.
metadata	{"created":"2026-02-08T00:00:00.000Z","updated":"2026-03-27T00:00:00.000Z","version":"5.0.0"}

Swarm Orchestrator Skill v5.0.0

A reusable skill that enables any persona to spawn parallel agent teams, delegate tasks across specialized personas, and synthesize results into coherent output.

Current routing model: All tasks execute locally through bridge scripts (no API calls). 6-signal composite routing distributes work based on capability matching, health signals, complexity fit, execution history, procedural learning, and episodic performance. Auto-creates memory episodes after every swarm run.

---

⚡ Quick Start

# PRIMARY: Bun TS orchestrator (full features - 6-signal routing, stagnation detection, OmniRoute, etc.)
bun Skills/zo-swarm-orchestrator/scripts/orchestrate-v5.ts tasks.json

# FALLBACK: Python orchestrator (lightweight, reliable, basic features)
python3 Skills/zo-swarm-orchestrator/scripts/orchestrate.py tasks.json

# Long campaigns (>5 tasks) — hybrid runner (progress streaming + handoff)
bun Skills/zo-swarm-orchestrator/scripts/swarm-hybrid-runner.ts tasks.json --notify sms

# Cascade mode: use --no-cascade to skip downstream tasks when a root fails
bun Skills/zo-swarm-orchestrator/scripts/orchestrate-v5.ts tasks.json --no-cascade

# Status + health checks
bun Skills/zo-swarm-orchestrator/scripts/orchestrate-v5.ts status <swarm-id>
bun Skills/zo-swarm-orchestrator/scripts/orchestrate-v5.ts doctor

---

MCP Server

The swarm orchestrator can be accessed via MCP (Model Context Protocol) for integration with AI assistants.

Available Tools

Tool	Description
swarm_execute	Execute a swarm campaign with multiple parallel tasks
swarm_status	Check status of a running or completed swarm
swarm_results	Retrieve detailed results from a completed swarm
swarm_benchmark	Run a benchmark comparing memory strategies
swarm_list	List recent swarm campaign runs

Usage

Stdio transport (for Claude Desktop, Cursor):

bun /home/workspace/Skills/zo-swarm-orchestrator/scripts/mcp-server.ts

HTTP transport (for network access):

# Start the server
bun /home/workspace/Skills/zo-swarm-orchestrator/scripts/mcp-server-http.ts

# Or run as a Zo service (recommended)
# Service URL: https://zo-swarm-mcp-marlandoj.zocomputer.io

Example: swarm_execute

{
  "tasks": [
    {
      "task": "Analyze the performance of the database query",
      "priority": "high",
      "timeoutSeconds": 300
    },
    {
      "task": "Review the API endpoint for security issues",
      "priority": "medium"
    }
  ],
  "campaignName": "security-audit",
  "waitForCompletion": true
}

---

Long-Running Swarms (>15 min)

The Problem

Zo's chat interface times out after ~15 minutes of inactivity. For large campaigns (>10 tasks), the swarm continues running in the background, but the chat window loses connection and never sees the final output.

The Solution: Hybrid Runner

Use swarm-hybrid-runner.ts for campaigns that may exceed 15 minutes:

# Hybrid mode with progress streaming + graceful handoff
bun scripts/swarm-hybrid-runner.ts campaign.json --notify sms

# Or with email notification
bun scripts/swarm-hybrid-runner.ts campaign.json --notify email

What happens:

1. ✅ First 13 minutes: Streams progress updates to chat every 10 seconds
2. ⏰ At 13 minutes: Gracefully hands off to background mode
3. 📱 When complete: Sends SMS/email notification with results path
4. 💾 Always: Saves full results to ~/.swarm/results/<swarm-id>.json

Check Status Anytime

# Check progress on a running or completed swarm
bun scripts/orchestrate-v5.ts status <swarm-id>

# Example output:
# 🔍 Swarm Status: swarm_1710284123456
# Status: 🏃 running
# PID: 12345
# Started: 3/12/2026, 2:05:00 PM
# Progress: 8/20 tasks (40%)
# Elapsed: 18m 34s
# Last update: 30s ago
# 📄 Results: ~/.swarm/results/swarm_1710284123456.json

Recommended Workflow

Campaign Size	Recommended Approach	Rationale
1-5 tasks	Direct orchestrator	Completes in <10 min, no timeout risk
6-10 tasks	Direct with --notify email	May approach timeout, get notified when done
10+ tasks	Hybrid runner	Will exceed timeout, need graceful handoff
20+ tasks	Hybrid runner + batch	Consider breaking into multiple smaller campaigns

Background Mode (Manual)

For running swarms completely in the background from the start:

# Start in background with notification
nohup bun scripts/orchestrate-v5.ts campaign.json \
  --notify sms \
  > /tmp/swarm.log 2>&1 &

# Check status
bun scripts/orchestrate-v5.ts status <swarm-id>

# View live logs
tail -f /tmp/swarm.log

Notification Options

# SMS notification (default for hybrid runner)
--notify sms

# Email notification
--notify email

# No notification (results file only)
# (omit --notify flag)

When notifications are sent:

• ✅ Swarm completes successfully
• ❌ Swarm fails (preflight or runtime errors)
• Both cases include: duration, success/failure summary, results file path

---

RAG Integration

The orchestrator automatically enriches task prompts with relevant SDK documentation from the Agentic RAG system. This gives agents instant access to best practices and code patterns across the entire Zouroboros ecosystem.

How It Works

1. Task Analysis: When a task contains keywords like "agent", "API", "database", "workflow", etc., the RAG system queries the knowledge base
2. Semantic Search: Uses Ollama (nomic-embed-text) for local embeddings, Qdrant for vector search — zero API costs
3. Context Injection: Top 3 most relevant SDK patterns are injected into the prompt before the task description

Covered SDKs (19 Total)

Category	SDKs
AI/Agent Frameworks	claude-sdk, langchain, openai-agents, crewai, adk, llamaindex, pydantic-ai, autogen, dspy, instructor, langgraph, semantic-kernel
Zouroboros Stack	hono, mcp-sdk, qdrant, bun, drizzle-orm
Business	stripe, airtable

Example Enriched Prompt

## Relevant SDK Patterns from Zouroboros Knowledge Base

### Pattern 1: crewai — core-concepts/crew
**Relevance:** 68.2% | **Use Case:** Role-based agent teams for project workflows

```python
from crewai import Crew, Process

crew = Crew(
    agents=[researcher, writer],
    tasks=[research_task, write_task],
    process=Process.hierarchical,
    manager_agent=manager
)

Pattern 2: openai-agents — handoffs

Relevance: 64.5% | Use Case: Lightweight multi-agent with handoffs

...

Your Task

Build a multi-agent workflow for content creation...

### Verification

Check logs for RAG enrichment activity:

📚 [task-001] RAG enriched: 3 patterns (1247ms)

If no patterns are found, the task proceeds normally — RAG is additive, not required.

### Configuration

| Variable | Default | Description |
|----------|---------|-------------|
| `QDRANT_URL` | (required) | Qdrant Cloud endpoint |
| `QDRANT_API_KEY` | (optional) | Qdrant API key |
| `OLLAMA_URL` | `http://localhost:11434/api/embeddings` | Ollama endpoint |

### Disabling RAG

RAG is automatic and non-blocking. To disable, set `RAG_ENABLED=false` (future release) or simply ensure QDRANT_URL is unset.

---

## Version Status

| Version | Status | Key Innovation |
|---------|--------|----------------|
| **v5 current** | ✅ **Current** | Dual-engine runtime, 6-signal routing, local bridges, cascade mitigation, memory context injection, hierarchical telemetry |

---

## Architecture: Local-Only Execution

All tasks execute through local executor bridges. There are no remote API calls — no Zo API, no Anthropic Direct, no API credentials needed.

EARLIER ARCHITECTURE CURRENT ARCHITECTURE ─────────────────── ──────────────────── 3 execution paths 1 execution path (local bridges)

• Local executor - Local executor only
• Anthropic Direct API
• Zo API fallback Dual concurrency channels Single concurrency channel
• maxConcurrency (API) - concurrency (local)
• localConcurrency (local) API credentials required No API credentials needed

All tasks must have a matching local executor in the registry. The preflight check validates this before execution starts.

---

## Token-Optimized Hierarchical Memory

Building on persistent memory, the orchestrator uses intelligent memory strategies that automatically manage token budgets to prevent context window exhaustion.

### The Problem It Solves

From February 2026 production failures:
- **Context window exhaustion**: 5 agents × 256k tokens = system failure
- **Unbounded memory growth**: Sequential memory grows forever
- **No token budgeting**: No visibility into prompt token usage

### What It Adds

| Feature | Implementation | Benefit |
|---------|----------------|---------|
| **Token cleaning** | HTML stripping, deduplication, normalization | ~10-15% token reduction |
| **Hierarchical memory** | Working memory + long-term memory retrieval | Bounded context, relevant history |
| **Sliding window** | Fixed-size recent context window | Predictable token usage |
| **Token budgets** | Configurable max tokens per context | Never exceed context limits |
| **Strategy selection** | Choose memory strategy per task | Optimize for cost vs. context |

---

## 6-Signal Composite Routing

The orchestrator scores each executor on six signals to pick the best agent for each task:

| Signal | Weight | What it measures |
|--------|--------|------------------|
| **Capability** | configurable | Task keyword matching against executor expertise |
| **Health** | configurable | Circuit breaker state, recent error rate |
| **Complexity fit** | configurable | Simple tasks → fast executors, deep analysis → thorough ones |
| **History** | configurable | Historical success rate for similar tasks |
| **Procedure** | 0.10 | Learned workflow preference from procedural memory (±0.05) |
| **Temporal** | 0.05 | Recent episodic performance bonus/penalty (±0.025) |

composite = (w.capability × capScore) + (w.health × healthScore) + (w.complexityFit × complexityScore) + (w.history × historyScore) + (0.10 × (procedureScore - 0.5)) + (0.05 × (temporalScore - 0.5))

The routing improves with use — after a few runs, tasks flow to whichever executor handles them best.

### Routing presets

| Preset | Best for |
|--------|----------|
| `balanced` (default) | Equal consideration of all signals |
| `fast` | Speed-optimized, prefer simple executors |
| `reliable` | Maximize uptime, penalize unhealthy executors |
| `explore` | Favor executors with proven track records |

```bash
bun orchestrate-v5.ts tasks.json --routing-strategy reliable

---

Auto-Episodes & Cognitive Profiles

Auto-Episodes

Every swarm run automatically creates an episode in the zo-memory-system with:

• Outcome (success/partial/failure)
• Duration and task count
• Executor list and per-task results
• Entity tags for querying

Query past runs:

bun ~/Skills/zo-memory-system/scripts/memory.ts episodes --entity "swarm.commerce-a" --since "7 days ago"

Cognitive Profiles

Executor history is extended with:

• Recent episode IDs — last 10 episodes for "why did this happen?" queries
• Failure patterns — auto-classified: timeout, mutation_failed, file_not_found, permission_denied
• Entity affinities — per-entity success rates as exponential moving averages

These feed back into the composite router for smarter task assignment.

---

Memory Strategies

Comparison

Strategy	Token Efficiency	Context Quality	Best For
none	⭐⭐⭐⭐⭐	⭐☆☆☆☆	Independent tasks, cost-sensitive
sliding	⭐⭐⭐⭐☆	⭐⭐⭐☆☆	Recent context matters
hierarchical	⭐⭐⭐⭐☆	⭐⭐⭐⭐⭐	Complex multi-step workflows
sequential	⭐☆☆☆☆	⭐⭐⭐⭐⭐	Debugging, small swarms only

Benchmark Results

Strategy       Avg Prompt    Memory    Token
               Tokens        Items     Savings
───────────────────────────────────────────────
none           26            0         -93%
sliding        356           3         -8%
hierarchical   416           4         -8%
sequential     386           5         0%

---

CLI Options

Option	Description	Default
--swarm-id <id>	Unique swarm identifier	auto-generated
--strategy <type>	Memory strategy	hierarchical
--max-tokens <n>	Token budget for context	8000
--concurrency <n>	Max parallel local executors	4
--timeout <seconds>	Per-task timeout	300
--dag-mode <mode>	streaming or waves	streaming
--routing-strategy <s>	fast, reliable, balanced, explore	balanced
--no-memory	Disable all memory	false
--notify <channel>	Send completion notification: sms or email	none (file always written)

---

Task File Format

[
  {
    "id": "task-1",
    "persona": "frontend-developer",
    "task": "Analyze UI/UX for accessibility issues",
    "priority": "high",
    "memoryStrategy": "hierarchical",
    "outputToMemory": true,
    "memoryMetadata": {
      "category": "ui-analysis",
      "priority": "high",
      "tags": ["accessibility", "responsive"]
    }
  }
]

---

Files Reference

File	Purpose
orchestrate-v5.ts	Main orchestrator with 6-signal routing
token-optimizer.ts	Token cleaning + hierarchical memory
swarm-memory.ts	SQLite persistence + inter-agent messaging
swarm-config.ts	Configuration management CLI
benchmark.ts	Memory strategy benchmarking
test-orchestrator.ts	Test suite
performance-test.ts	Baseline vs enhanced performance test
inter-agent-comms.ts	Inter-agent communication system

---

Local Executors

All tasks are routed to local executor bridges. The bridge scripts, registry, and tooling live in the companion skill zo-swarm-executors:

Executor	Bridge	Speed	Strengths
claude-code	claude-code-bridge.sh	~25-120s	Complex multi-file changes, codebase-aware analysis, git operations
hermes	hermes-bridge.sh	~15-60s	Web research, security audits, multi-tool investigation, data gathering
gemini	gemini-bridge.sh	~2-12s (daemon)	Large-context analysis (1M+ tokens), multimodal, cross-validation
codex	codex-bridge.sh	~3s	Fast code generation, shell commands, rapid prototyping

Skills/zo-swarm-executors/
├── bridges/          # claude-code, hermes, gemini, codex bridge scripts
├── registry/         # executor-registry.json
├── scripts/          # doctor.ts, register.ts, test-harness.ts, gemini-daemon.ts
└── docs/             # BRIDGE_PROTOCOL.md, identity references

The orchestrator discovers executors via executor-registry.json. All task personas must have a matching local executor — the preflight check validates this before execution starts.

---

Utilities

# Benchmark all memory strategies
bun benchmark.ts

# Test orchestrator
bun test-orchestrator.ts

# Memory management
bun swarm-memory.ts stats
bun swarm-memory.ts list-sessions
bun swarm-memory.ts cleanup 30

---

Configuration

You can configure the orchestrator using either a config file or environment variables.

Option 1: Config File (Recommended)

Create config.json in the skill root:

{
  "localConcurrency": 4,
  "timeoutSeconds": 300,
  "maxRetries": 3,
  "memory": {
    "enable": true,
    "workingMemorySize": 2,
    "longTermMemorySize": 3,
    "enableDeduplication": true,
    "enableHTMLStripping": true,
    "maxTokens": 8000
  }
}

Priority: CLI flags > Config file > Environment variables > Defaults

Option 2: Environment Variables

Variable	Description	Default
SWARM_WORKSPACE	Root for deployment resources	/home/workspace
SWARM_LOCAL_CONCURRENCY	Max parallel local executors	4
SWARM_TIMEOUT_SECONDS	Per-task timeout	300
SWARM_MAX_RETRIES	Retry attempts	3
SWARM_IDENTITY_DIR	Persona identity files directory	$SWARM_WORKSPACE/IDENTITY
SWARM_SOUL_FILE	Path to constitution file	$SWARM_WORKSPACE/SOUL.md
SWARM_PERSONA_MEMORY_DIR	Persona-specific memory files	$SWARM_WORKSPACE/.zo/memory/personas
SWARM_MEMORY_SCRIPT	Memory search script path	$SWARM_WORKSPACE/Skills/zo-memory-system/scripts/memory.ts
SWARM_EXECUTOR_REGISTRY	Local executor registry JSON path	Skills/zo-swarm-executors/registry/executor-registry.json

---

Roadmap

Planned optimizations from production profiling (see README.md for full details):

Phase	Optimizations	Expected Savings
Phase 1 — Quick Wins	O3: Pre-warm cache TTL, O4: Prompt format constraints, O6: Circuit breaker tuning	5-13%
Phase 2 — Execution Engine	O2: DAG streaming improvements, O1: Request batching	+13-30%
Phase 3 — Refinements	O5: Early filtering, O7: Output deduplication, O8: Concurrent caching	+2-5%

---

References

• Prompt-Refiner: https://github.com/JacobHuang91/prompt-refiner
• Agent-Memory-Playground: https://github.com/AIAnytime/Agent-Memory-Playground