AgentGenome for Llama Users
Profile Your Llama Agents. From Open-Source to Enterprise-Ready.
Capture open-source behaviors for commercial deployment
2,000+ developers • Portable across 12+ LLMs • No credit card required
Why Llama Users Need AgentGenome
Llama gives you open-source freedom. AgentGenome adds enterprise observability. Profile your Llama agents locally, then deploy commercially with documented behavioral profiles.
Open-Source Freedom, Zero Observability
Llama is free and flexible, but without profiling tools, you have no visibility into what your agents are actually doing. AgentGenome brings enterprise observability to open-source.
Commercial Deployment Needs Documentation
Moving from Llama experiments to production requires behavioral documentation. How do you prove consistency, safety, and reliability to stakeholders?
Fine-Tuned Models Diverge Unpredictably
Your fine-tuned Llama might drift from baseline behavior. Without profiling, you'll only discover issues when users complain.
Switching Hosting Providers Means Uncertainty
Moving your Llama deployment between hosting providers (Together, Anyscale, self-hosted) shouldn't change behavior—but does it? AgentGenome proves consistency.
"Sound familiar?"
AgentGenome Solves Every Problem
Add 3 lines of code. Capture your agent's behavioral DNA. Deploy anywhere.
from llama_cpp import Llama
from agentgenome import profile
llm = Llama(model_path="./models/llama-3-8b.gguf")
@profile(genome_id="local-assistant")
def chat(prompt: str):
response = llm(
prompt,
max_tokens=512,
temperature=0.7
)
return response['choices'][0]['text']
# Profile locally, deploy anywhereWhat You Get
- Behavioral profiling without code changes
- 35% average token savings
- Real-time drift detection
- Substrate-independent genome export
- Multi-provider deployment ready
Profile Once. Deploy Anywhere.
Profile open-source, deploy commercial. Capture your Llama agent's genome locally, then deploy via any hosting provider—or switch to commercial LLMs—with behavioral guarantees.
Your Llama Agents Deserve Freedom
You've invested months optimizing Llama prompts. What happens when costs rise, performance drops, or a better model launches?
✗Without AgentGenome
- •Start over. Rebuild every prompt from scratch.
- •Lose months of behavioral optimization.
- •4-6 weeks of engineering per migration.
- •$47K+ average migration cost.
- •40%+ behavioral drift during migration.
With AgentGenome
- ✓Export your behavioral genome in one click.
- ✓Import to GPT, Claude, Mistral, or any provider.
- ✓Keep your optimizations. Zero rework.
- ✓95%+ behavioral consistency guaranteed.
- ✓Hours, not weeks. Included in Pro tier.
# Export Llama behavioral genome
from agentgenome import genome
# Capture local Llama behaviors
genome.export('local-assistant.genome')
# Deploy commercially via Together AI
genome.import_to('local-assistant.genome', provider='together')
# Or switch to GPT-4 if needed
genome.import_to('local-assistant.genome', provider='openai')Deploy your Llama genome on any supported provider:
Profile once. Deploy anywhere. Never locked in.
Build Llama agents Once.
Deploy on Any LLM.
Profile open-source, deploy commercial. Capture your Llama agent's genome locally, then deploy via any hosting provider—or switch to commercial LLMs—with behavioral guarantees.
Your Agent Genome
Behavioral DNA captured in universal format
Profile Your Llama agents
Add 3 lines of code. Capture behavioral DNA automatically.
from llama_cpp import Llama
from agentgenome import profile
llm = Llama(model_path="./models/llama-3-8b.gguf")
@profile(genome_id="local-assistant")
def chat(prompt: str):
response = llm(
prompt,
max_tokens=512,
temperature=0.7
)
return response['choices'][0]['text']
# Profile locally, deploy anywhereYour agents become substrate-independent
Profile today, deploy on any LLM tomorrow. Your optimizations travel with you.
Real Results with AgentGenome
How DataFlow Went from Llama Experiments to Enterprise Deployment
The Challenge
DataFlow built a data analysis agent on Llama 3 locally. When enterprise clients required SOC 2 compliance and behavioral documentation, they faced a choice: rebuild on a commercial LLM or somehow document their open-source agent's behaviors.
The Solution
AgentGenome profiled the local Llama agent, capturing behavioral genomes with full audit trails. These profiles documented consistency for compliance and enabled commercial hosting deployment.
"AgentGenome turned our scrappy Llama prototype into an enterprise-grade product. Same agent, now with proof it works consistently."
— David Park, CTO, DataFlow Analytics
Without vs With AgentGenome
| Aspect | Without AgentGenome | With AgentGenome |
|---|---|---|
| Debugging Time | 4+ hours per incident | 52 minutes average (-78%) |
| Token Efficiency | Unknown waste | 35% average savings |
| Behavioral Visibility | Black box | Full trait analysis |
| Drift Detection | Discover in production | Catch before deployment |
| Agent Portability | 🔒 Locked to Llama | 🔓 Deploy on any LLM |
| Migration Time | 4-6 weeks per provider | Hours with genome export |
| Migration Cost | $47K+ engineering | Included in Pro tier |
| Multi-Provider Strategy | Rebuild for each | One genome, all providers |
| Future-Proofing | Start over when models change | Take your genome with you |
| Vendor Negotiation | No leverage (locked in) | Full leverage (can leave) |
The Cost of Waiting
💸 Financial Lock-In
- Llama pricing has increased multiple times since launch
- Without portable profiles, you pay whatever they charge
- Migration estimate without AgentGenome: $47K and 8 weeks
⚠️ Strategic Lock-In
- Better alternatives might exist—but can you actually switch?
- Your competitors are profiling for portability right now
- When you need to migrate, will you be ready?
🔒 The Vendor Lock-In Tax
- 4-6 weeks of engineering to migrate unprofiled agents
- 40%+ behavioral drift during manual migration
- Zero leverage in pricing negotiations
📉 Competitive Disadvantage
- Competitors with portable profiles ship 80% faster
- They negotiate contracts with leverage—you don't
- They test new models in hours; you take months
"Every day without profiling locks you deeper into Llama."
When Llama raises prices or a better model launches, will you be ready to leave?
What You'll Achieve with AgentGenome
Real metrics from Llama users who profiled their agents
Before AgentGenome
- • Debugging: 4+ hours per incident
- • Migration: 4-6 weeks per provider
- • Token waste: Unknown
- • Drift detection: In production
- • Vendor leverage: None
After AgentGenome
- • Debugging: 52 minutes average
- • Migration: Hours with genome export
- • Token savings: 35% average
- • Drift detection: Before deployment
- • Vendor leverage: Full (can leave anytime)
Already Locked Into Llama?
Here's how to escape with your behavioral DNA intact
Profile Your Current Agent
Add 3 lines of code to capture your Llama agent's behavioral DNA. No changes to your existing logic.
Export Your Genome
One command exports your substrate-independent genome. It works on any LLM provider, not just Llama.
Deploy Anywhere
Import your genome to Claude, Gemini, Llama, or any provider. 95%+ behavioral consistency, zero rework.
Zero-Downtime Migration Promise
AgentGenome's migration assistant guides you through the process. Profile your current agent while it's running, export the genome, and deploy to a new provider—all without touching your production system until you're ready.
Start Free. Unlock Portability with Pro.
Most Llama users choose Pro for multi-provider genome sync. Start free and upgrade when you need portability.
| Portability Features | Free | Pro | Enterprise |
|---|---|---|---|
| Genome Export | JSON only | JSON + YAML | All formats |
| Multi-Provider Sync | — | ✓ | ✓ + Custom |
| Migration Assistant | — | ✓ | ✓ + SLA |
| Custom Substrate Adapters | — | — | ✓ |
Frequently Asked Questions
Everything you need to know about AgentGenome for Llama