The Staging Environment for Distribution
You wouldn't push code to production without staging. Why would you push your GTM?
NeoSim is a CLI-first simulation engine that lets founders stress-test their Go-To-Market strategy before spending a dollar on execution.
# Install
pip install neosim
# Or install from source
pip install -e .
# Set your API key (choose one based on your provider)
export ANTHROPIC_API_KEY=sk-ant-... # Claude
# OR
export OPENAI_API_KEY=sk-... # GPT-4
# OR
export GROQ_API_KEY=gsk_... # Groq (fast inference)
# OR run locally with Ollama (no key needed)
# Initialize project
neosim init
# Run simulation
neosim simNeoSim deploys LLM-powered agents to simulate:
- Buyer Agents - ICP personas making buy/pass/object decisions
- Competitor Agents - Predicting competitive responses
- Channel Agents - Projecting CAC, reach, and ROI per channel
- Advisor Agent - Synthesizing insights and flagging risks
Each simulation produces:
- Projected metrics with confidence intervals (CAC, conversion, time-to-breakeven)
- Top buyer objections with suggested counters
- Channel rankings by expected ROI
- Competitive risk assessment
- Strategic recommendations
neosim init # Interactive project setup
neosim sim # Run GTM simulation
neosim sim -n 30 # Run with 30 cycles
neosim compare a.yaml b.yaml # A/B test strategies
neosim report results.json # Generate shareable HTML report
neosim execute results.json # Generate execution plan
neosim distribute results.json # Generate platform-ready contentNeoSim is configured via neosim.yaml:
version: "1.0"
project_name: "My Startup"
# LLM Configuration - choose your provider
llm_provider: "anthropic" # anthropic, openai, google, groq, together, ollama
llm_model: "claude-sonnet-4-20250514"
product:
name: "AwesomeApp"
description: "AI-powered productivity tool"
category: "SaaS"
stage: "pre-launch"
unique_value_prop: "10x faster with AI assistance"
icp_personas:
- name: "Technical Founder"
role: "Founder/CTO"
company_size: "startup"
pain_points:
- "Manual repetitive tasks"
- "Scaling bottlenecks"
goals:
- "Ship faster"
- "Reduce ops overhead"
pricing:
model: "freemium"
tiers:
- name: "Free"
price: 0
- name: "Pro"
price: 29
channels:
- name: "organic-social"
priority: 5
- name: "community"
priority: 4
competitors:
- name: "BigCorp"
positioning: "Enterprise solution"
market_share: "dominant"
simulation:
cycles: 30
buyer_agents: 20╭──────────────────────────────────────────────────────────────╮
│ NeoSim - Simulation Complete │
│ ID: a1b2c3d4 | Duration: 45.2s │
╰──────────────────────────────────────────────────────────────╯
Overall Assessment: PROMISING
Confidence Score: 72%
┏━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━┳━━━━━━━━┳━━━━━━━━┳━━━━━━━━━━━━┓
┃ Metric ┃ Low ┃ Mid ┃ High ┃ Confidence ┃
┡━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━╇━━━━━━━━╇━━━━━━━━╇━━━━━━━━━━━━┩
│ CAC │ $32 │ $48 │ $71 │ medium │
│ Conversion Rate │ 4.2% │ 6.8% │ 9.1% │ medium │
│ Time to Breakeven │ 3mo │ 5mo │ 8mo │ low │
└────────────────────┴────────┴────────┴────────┴────────────┘
Top Objections:
- Price (12x): Emphasize ROI and time savings
- Trust (8x): Lead with social proof and case studies
Channel Rankings:
1. community: Score 8.2 (ROI 3.1x at $28 CAC)
2. organic-social: Score 6.5 (ROI 2.4x at $42 CAC)
Strategic Recommendations:
- Focus on community-led growth before paid acquisition
- Address pricing objections with clear ROI calculator
- Build trust through beta user case studies
After running a simulation, use the Distribution Bridge to generate ready-to-post content for your launch:
# Generate full distribution kit
neosim distribute results.json
# Output: distribution_kit.json| Platform | Content |
|---|---|
| Twitter/X | 8-tweet launch thread + 30 days of daily content + engagement hooks |
| Launch post (Show & Tell format) + value posts + comment strategy | |
| Founder journey posts + thought leadership + connection templates | |
| Product Hunt | Tagline, description, first comment, media checklist, hunter outreach |
| Carousel posts + Reel concepts with scripts + Story templates + bio optimization | |
| TikTok | Video hooks (first 3 sec) + full scripts + trend adaptations + format templates |
Plus:
- 30-day content calendar with optimal posting times
- Launch day playbook (T-24h → first 24h action items)
- Engagement templates for replies and objection handling
# Full distribution kit (text platforms)
neosim distribute results.json
# Include video platforms (Instagram + TikTok)
neosim distribute results.json --platforms twitter,reddit,linkedin,producthunt,instagram,tiktok
# Specific platforms only
neosim distribute results.json --platforms twitter,producthunt
# Custom output location
neosim distribute results.json -o my_launch_kit.json
# Set calendar start date
neosim distribute results.json --calendar-start 2024-03-15
# Export calendar to CSV (for import to scheduling tools)
neosim distribute results.json --calendar-csv calendar.csv
# Polish content with Claude API (costs API credits)
neosim distribute results.json --enhance{
"metadata": {
"simulation_id": "a1b2c3d4",
"product_name": "MyApp",
"generated_at": "2024-02-15T10:00:00Z"
},
"platforms": {
"twitter": {
"launch_thread": ["Tweet 1...", "Tweet 2..."],
"daily_content": [...],
"engagement_hooks": [...]
},
"reddit": {
"posts": [...],
},
"linkedin": {
"posts": [...]
},
"product_hunt": {
"tagline": "60 char tagline",
"description": "260 char description",
"first_comment": "Maker story..."
}
},
"content_calendar": [
{"day": 1, "platform": "producthunt", "time": "12:01am PST", "priority": "critical"},
{"day": 1, "platform": "twitter", "time": "6:00am EST", "priority": "critical"}
],
"launch_playbook": {
"t_minus_24h": ["Warm up audience..."],
"launch_hour": ["Post PH", "Tweet thread"],
"first_24h": ["Reply to all comments"]
}
}1. neosim init → Create config
2. neosim sim -o results.json → Run simulation
3. neosim distribute results.json → Generate content
4. Review & customize → Replace [PLACEHOLDERS]
5. Schedule & launch → Use your favorite tools
NeoSim is LLM-agnostic - choose the provider that fits your needs:
| Provider | Env Variable | Default Model | Notes |
|---|---|---|---|
anthropic |
ANTHROPIC_API_KEY |
claude-sonnet-4-20250514 | Recommended for quality |
openai |
OPENAI_API_KEY |
gpt-4o | GPT-4 family |
google |
GOOGLE_API_KEY |
gemini-1.5-pro | Gemini models |
groq |
GROQ_API_KEY |
llama-3.3-70b-versatile | Fast inference |
together |
TOGETHER_API_KEY |
Llama-3.3-70B-Instruct | Open models |
ollama |
(none - local) | llama3.2 | Run locally, free |
Set your provider in neosim.yaml:
llm_provider: "groq" # Fast and cheap
llm_model: "llama-3.3-70b-versatile"Then set the corresponding API key:
export GROQ_API_KEY=gsk_...For Ollama (local), just ensure Ollama is running - no API key needed.
# Clone
git clone https://github.com/nodeops/neosim
cd neosim
# Install dev dependencies
pip install -e ".[dev]"
# Run tests
pytest
# Format
black neosim/
ruff check neosim/- CLI scaffold, config schema, agent prompts
- Simulation engine, Rich terminal output
- HTML reports, benchmark replays
- A/B compare mode, JSON export
- Distribution Bridge - Platform content generation
- Twitter/X generator (threads + daily content)
- Reddit generator (launch posts + strategy)
- LinkedIn generator (founder posts + templates)
- Product Hunt generator (complete launch kit)
- Instagram generator (carousels + reels + stories)
- TikTok generator (hooks + scripts + trends)
- 30-day content calendar
- Launch day playbook
- LLM content enhancement (
--enhanceflag)
- Multi-LLM Support - Provider agnostic
- Anthropic (Claude)
- OpenAI (GPT-4)
- Google (Gemini)
- Groq (fast inference)
- Together AI (open models)
- Ollama (local/free)
- Lifetime pricing model
- CreateOS integration
- Beta launch
MIT