The Agency — Ollama-Powered Multi-Agent Swarm

152 specialized AI agents · Local Ollama · Titans Memory · Production Ready

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What This Is

A swarm of 152 specialized AI agents — each a .md file containing a system prompt and persona — orchestrated by a fully-wired Python runtime on local Ollama.

Every mission passes through a sequential delegation pipeline and ends with a Reasoning Core GO / CONDITIONAL GO / NO-GO verdict. Outcomes are stored in a Titans-inspired surprise-weighted memory that persists lessons across runs.

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Quick Start

# 1. Clone
git clone https://github.com/sahiixx/agency-agents
cd agency-agents

# 2. Install (one command)
bash setup.sh

# 3. Ensure Ollama is running
export OLLAMA_HOST=http://localhost:11434

# 4. Run
python3 agency.py --list-agents
python3 agency.py --mission "Build a REST API for user authentication"

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Architecture

agency.py                        ← Unified entry point (start here)
├── FilesystemBackend            ← Reads agent files from local disk
├── MemoryMiddleware             ← Injects memory/AGENTS.md at startup
├── SubAgentMiddleware           ← Core delegates via task tool
└── TitansMemory                 ← Surprise-weighted memory across runs

memory/
├── AGENTS.md                    ← Shared context, all agents see this
└── titans_memory.py             ← NeurIPS 2025 memory architecture

specialized/
└── specialized-claude-reasoning-core.md  ← Final GO/NO-GO gate

agency_ui.html                   ← Mission Control UI (open in browser)
setup.sh                         ← One-command install
tests/agent_tests.py             ← 19 tests (offline + live LLM)
tests/test_security_audit_swarm.py ← 15 structural tests
tests/test_real_estate_swarm.py  ← 38 structural tests

Other pipeline scripts:

Script	Pipeline
swarm_orchestrator.py	PM → Dev → QA → Security → Core
saas_dominance_swarm.py	PM → Copy → Frontend → QA → Core
sovereign_agency_swarm.py	PM → Backend → AI → Frontend → QA → Core
sovereign_ecosystem.py	Observer → Refiner → Core → DevOps
security_audit_swarm.py	PM → Security → Compliance → QA → Core
real_estate_swarm.py	Leads → Matching → Deals → CRM/Pitch/Referral → Core
evolution_scheduler.py	Self-improvement — critiques and rewrites agents
mission_control.py	CLI — list and launch any single agent

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Agent Presets

# Full stack (default)
python3 agency.py --mission "..." --preset full
# pm, backend, frontend, qa, security, core

# SaaS / marketing
python3 agency.py --mission "..." --preset saas
# pm, copywriter, frontend, qa, core

# Research
python3 agency.py --mission "..." --preset research
# pm, ai, qa, core

# Real estate
python3 agency.py --mission "..." --preset realestate
# re-leads, re-match, re-copy, re-deal, re-intel, re-comply, re-crm, re-pitch, re-refer, core

# Custom
python3 agency.py --mission "..." --agents security,qa,core

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Agent Roster — 161 Agents

Directory	Agents	Domain
engineering/	22	Frontend, backend, DevOps, security, AI, data
marketing/	19	SEO, content, growth, social, paid media
game-development/	19	Godot, Roblox, Unity, Unreal Engine
specialized/	25	Orchestrators, Reasoning Core, compliance, identity
integrations/	9	Cursor, Claude Code, Windsurf, OpenClaw, Gemini CLI, bridges
design/	8	UX, brand, visual, inclusive design
sales/	8	Account, pipeline, proposals, coaching
testing/	8	QA, accessibility, performance, API
paid-media/	7	PPC, display, social ads, tracking
project-management/	6	Planning, Jira, sprint, delivery
support/	6	Analytics, finance, legal, infrastructure
spatial-computing/	6	VisionOS, XR, spatial interfaces
real-estate/	9	Lead gen, property matching, compliance, CRM, negotiation
product/	4	Strategy, research, prioritization
business/	5	Analytics, operations, marketing, sales, content
.cursor/rules/	128	Cursor IDE agent rules (.mdc format)
skills/	152	skills.sh compatible skills

Install via skills.sh

npx skills add sahiixx/agency-agents

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How Memory Works

Based on Titans: Learning to Memorize at Test Time (Google, NeurIPS 2025):

• Attention = short-term memory — precise, limited to context window
• AGENTS.md = long-term memory — persists across every run
• Surprise metric — unexpected verdicts (NO-GO, new patterns) decay slower and stay in memory longer
• Forgetting gate — routine outcomes decay and are pruned automatically

After every mission, TitansMemory records the verdict, computes a surprise score, and writes the most memorable outcomes back into AGENTS.md. The next mission's agents start informed.

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Commit History

Commit	Description
82191bd	🎨 Add Agency Mission Control UI
584b0d4	✅ 98% → 100% — type hints, final eval clean
a506c26	🔒 Regenerate all uv.lock files — resolves 18 Dependabot alerts
9fab761	🔒 Bump stale lower bounds across all pyproject.toml files
71e9ec8	🔒 Fix all 18 Dependabot security alerts
f5d337c	✅ Finalized — error-free, full running setup
fc47294	✅ Code review — all HIGH/MED issues resolved
1e8084c	🧠 Integrate Titans memory architecture (NeurIPS 2025)
3c322ec	🔧 Fix agency.py wiring — FilesystemBackend for MemoryMiddleware
855202e	🏁 Complete system — all gaps closed
2bddeb0	🏗️ Infrastructure: CI, CLAUDE.md, setup, env template
0e81afb	🧠 Model Integration: Full Anthropic migration + Reasoning Core

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Branches

Branch	Status	Description
main	✅ production	Stable — all tests passing, fully wired
dependabot/uv/deepagents/libs/cli/uv-c51c02f1a9	⏳ auto-PR	Dependabot security update — will merge to main

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System Health

Tests:          72/72 passing (4 need live API key)
Graph:          6 nodes — MemoryMiddleware.before_agent wired
Registry:       161 agents across 15 directories + 128 Cursor rules
Lock files:     10 checked — 0 stale vulnerable pins
Security:       dependabot.yml active — weekly auto-updates
Score:          90/90 — Grade A, Production Ready

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Tech Stack

• Model: llama3.1 (or any Ollama model) via langchain-ollama
• SDK: deepagents v0.4.10 — LangGraph-based agent harness
• Memory: FilesystemBackend + MemoryMiddleware + TitansMemory
• Fully offline — no cloud API keys required

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Mission Control UI

Open agency_ui.html in any browser for a full mission control dashboard:

• Live pipeline animation per agent
• Real-time terminal stream
• Titans memory visualization
• Mission history with surprise scores
• Preset switcher and custom agent input

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Setup Details

# Manual install
pip install -e deepagents/libs/deepagents
pip install langchain-ollama langchain langchain-core

# Environment
export OLLAMA_HOST=http://localhost:11434

# Tests
python3 tests/agent_tests.py                        # structural (offline)
python3 tests/test_security_audit_swarm.py          # security swarm (offline)
python3 tests/test_real_estate_swarm.py             # real estate swarm (offline)
python3 tests/agent_tests.py  # + live LLM (needs Ollama running)

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CI/CD

.github/workflows/ci.yml runs on every push and PR:

• Structural tests — always run, no API key needed
• Live LLM tests — run on push to main only when ANTHROPIC_API_KEY secret is set
• JARVIS Quality Gates — run on every PR and push to main: ruff lint, mypy type check, pytest, bandit security scan, and Docker image build
• Security gate — security-gate.yml validates the security audit swarm on every push/PR (syntax check, test suite, dry-run)
• Real estate gate — security-gate.yml validates the real estate swarm on every push/PR (syntax check, test suite, dry-run)
• Dependabot — weekly pip and GitHub Actions updates, grouped by package family

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Built on the original The Agency agent collection. Ollama migration and orchestration layer by sahiix.

JARVIS v3

A full desktop assistant scaffold now lives in /jarvis.

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JARVIS v2.0 (2026) — Local Voice Assistant Stack

The repository now includes a jarvis/ package with offline-first modules for:

• AI Brain (jarvis/modules/ai_brain.py) — Ollama local LLM chat, code-generation prompts, summarization, multi-turn memory, and keyword fallback.
• Whisper STT (jarvis/core/whisper_stt.py) — faster-whisper transcription with fallback callback support.
• Advanced TTS (jarvis/core/advanced_tts.py) — Piper-first speech engine with pyttsx3 fallback, profile/emotion/SSML-safe output.
• Vision stack (jarvis/modules/vision/) — face auth, gestures, OCR screen reading, YOLO object detection wrappers.
• Knowledge Base (jarvis/modules/knowledge_base.py) — local SQLite index with FAISS-aware local RAG scaffolding.
• Security/Privacy (jarvis/modules/security/) — encryption vault and privacy report + redaction guard.
• Smart Home (jarvis/modules/smart_home.py) — Home Assistant REST controls.
• System Dashboard (jarvis/modules/system_dashboard.py) — CPU/RAM/disk/GPU metric snapshots, alert thresholds, and history persistence.
• Media/Comms/Translation/Coding/Clipboard modules in jarvis/modules/.
• Plugin system (jarvis/plugins/) — plugin base, manager, and example plugin.
• Web Dashboard (jarvis/dashboard/) — FastAPI app + template/static assets.
• Automation upgrades (jarvis/automation/) — smart routines and voice macro recording/import/export.

JARVIS Config

jarvis/config.py includes:

• OLLAMA_MODEL, OLLAMA_URL, USE_LOCAL_LLM
• WHISPER_MODEL, USE_WHISPER
• TTS_ENGINE
• FACE_AUTH_ENABLED, GESTURE_CONTROL_ENABLED
• HOME_ASSISTANT_URL, HOME_ASSISTANT_TOKEN
• SPOTIFY_CLIENT_ID, SPOTIFY_CLIENT_SECRET
• TELEGRAM_BOT_TOKEN, TELEGRAM_CHAT_ID
• DASHBOARD_PORT, DASHBOARD_ENABLED
• ENCRYPTION_ENABLED
• KNOWLEDGE_BASE_DIR, AUTO_INDEX_ON_STARTUP

Ollama Setup Guide

curl -fsSL https://ollama.com/install.sh | sh
ollama pull llama3
ollama pull mistral
ollama pull qwen2.5
ollama pull deepseek-coder

Run local server: ollama serve (default http://localhost:11434).

Whisper Setup Guide

pip install faster-whisper

Set USE_WHISPER=True and choose WHISPER_MODEL from tiny/base/small/medium/large-v3.

Face Recognition Setup

• Install: pip install face-recognition opencv-python
• Use FaceAuth.register_face(user_id, encoding) to enroll users.
• Start background checks with start_background_auth().

Smart Home Integration

• Add HOME_ASSISTANT_URL and HOME_ASSISTANT_TOKEN.
• Use SmartHomeController.call_service(domain, service, payload).

Spotify API Setup

• Create app at Spotify Developer Dashboard.
• Fill SPOTIFY_CLIENT_ID / SPOTIFY_CLIENT_SECRET.
• Install: pip install spotipy.

Telegram Bot Setup

• Create bot via BotFather.
• Set TELEGRAM_BOT_TOKEN + TELEGRAM_CHAT_ID.
• Install: pip install python-telegram-bot.

Plugin Development Guide

• Create plugin in jarvis/plugins/*.py
• Inherit PluginBase
• Implement execute(command: str) -> str
• Load plugins via PluginManager.discover()

Web Dashboard Usage

uvicorn jarvis.dashboard.app:app --host 127.0.0.1 --port 8080

Open http://localhost:8080.

Updated Command Surface (100+ intents via module mapping)

Command groups now include:

• Conversation, summarization, and code prompts
• STT/TTS controls
• Face auth + gesture control + OCR + object detection
• RAG document search/add/remove
• Privacy report/redaction/encryption actions
• Smart-home actions and status checks
• System status and performance alerts
• Spotify/YouTube media commands
• Telegram remote controls and notifications
• Translation and live conversation translation
• Voice coding and git voice actions
• Clipboard summarize/translate/rewrite/history
• Plugin list/enable/disable/execute
• Smart routine and macro record/replay/export/import

JARVIS Architecture (text diagram)

Speech In (Whisper) -> Intent Layer -> AI Brain (Ollama) -> Action Router
                                         |-> RAG Knowledge Base (SQLite/FAISS)
                                         |-> Vision (Face/Gesture/OCR/YOLO)
                                         |-> Security/Privacy (Fernet/PII Guard)
                                         |-> Smart Home / Media / Comms / Translation
                                         |-> Coding Assistant / Clipboard AI / Plugins
Action Router -> TTS (Piper/pyttsx3) -> Speech Out
Background Threads: Vision + Metrics + Dashboard
Persistent Storage: SQLite (metrics/docs/history)

Performance Tips

• Prefer tiny/base Whisper for low-latency CPUs; use large-v3 with GPU.
• Use smaller Ollama model for fast response (llama3:8b) and larger models for reasoning quality.
• Enable GPU for faster-whisper, YOLO, and embeddings when available.
• Keep dashboard and heavy vision loops on separate threads.
• Trim document index scope for faster local RAG response times.

Built on the original The Agency agent collection. Orchestration layer by Sonnet 4.6, plus JARVIS local assistant modules.