Why LlamaIndex Developers Need AgentGenome
LlamaIndex powers sophisticated RAG applications. AgentGenome profiles how your agents retrieve, rank, and synthesize—making these behaviors portable across any retrieval stack.
RAG Pipelines Lack Quality Metrics
Your index retrieves documents, your agent synthesizes. But how well? Without profiling, RAG quality is invisible.
Retrieval vs. Generation Balance Is Unknown
Is your agent using retrieved context well? Too little reliance = hallucination. Too much = parrot. Where's the balance?
Upgrading Embeddings Can Break Everything
New embedding model might improve retrieval but break generation patterns. Without behavioral baselines, you'll discover issues in production.
Vector DB Migrations Are Risky
Switching from Pinecone to Weaviate shouldn't change agent behavior—but does it? AgentGenome validates consistency.
"Sound familiar?"
AgentGenome Solves Every Problem
Add 3 lines of code. Capture your agent's behavioral DNA. Deploy anywhere.
from llama_index.core import VectorStoreIndex
from llama_index.llms.openai import OpenAI
from agentgenome import profile
index = VectorStoreIndex.from_documents(documents)
query_engine = index.as_query_engine(llm=OpenAI(model="gpt-4"))
@profile(genome_id="rag-agent")
def query(question: str):
response = query_engine.query(question)
return response.response
# Retrieval + generation patterns capturedWhat 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.
RAG genomes deploy anywhere. Capture how your agent uses retrieved context and maintain that behavior across embedding models, vector DBs, or LLMs.
Your LlamaIndex Agents Deserve Freedom
You've invested months optimizing LlamaIndex 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 LangChain RAG, Haystack, Cohere RAG, or any provider.
- ✓Keep your optimizations. Zero rework.
- ✓95%+ behavioral consistency guaranteed.
- ✓Hours, not weeks. Included in Pro tier.
# Export RAG behavioral genome
from agentgenome import genome
# Capture LlamaIndex RAG patterns
genome.export('rag-agent.genome')
# Upgrade embeddings with confidence
genome.import_to('rag-agent.genome', config={'embeddings': 'new-model'})
# Or switch LLM entirely
genome.import_to('rag-agent.genome', provider='anthropic')Deploy your LlamaIndex genome on any supported provider:
Profile once. Deploy anywhere. Never locked in.
Build LlamaIndex applications Once.
Deploy on Any LLM.
RAG genomes deploy anywhere. Capture how your agent uses retrieved context and maintain that behavior across embedding models, vector DBs, or LLMs.
Your Agent Genome
Behavioral DNA captured in universal format
Profile Your LlamaIndex applications
Add 3 lines of code. Capture behavioral DNA automatically.
from llama_index.core import VectorStoreIndex
from llama_index.llms.openai import OpenAI
from agentgenome import profile
index = VectorStoreIndex.from_documents(documents)
query_engine = index.as_query_engine(llm=OpenAI(model="gpt-4"))
@profile(genome_id="rag-agent")
def query(question: str):
response = query_engine.query(question)
return response.response
# Retrieval + generation patterns capturedYour applications become substrate-independent
Profile today, deploy on any LLM tomorrow. Your optimizations travel with you.
Real Results with AgentGenome
How DocuQuery Made RAG Behaviors Portable
The Challenge
DocuQuery's document Q&A agent used LlamaIndex with Pinecone. When switching to a more cost-effective vector DB, they needed to ensure RAG behaviors stayed consistent—retrieval relevance, synthesis quality, citation accuracy.
The Solution
AgentGenome profiled RAG behaviors before migration—how documents were used, citation patterns, synthesis methods. After migration, comparison confirmed behavioral consistency.
"We proved our RAG agent behaves the same on cheaper infrastructure. That's not a gamble, that's data."
— Emily Tran, CTO, DocuQuery
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 LlamaIndex | 🔓 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
- LlamaIndex 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 LlamaIndex."
When LlamaIndex raises prices or a better model launches, will you be ready to leave?
What You'll Achieve with AgentGenome
Real metrics from LlamaIndex 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 LlamaIndex?
Here's how to escape with your behavioral DNA intact
Profile Your Current Agent
Add 3 lines of code to capture your LlamaIndex 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 LlamaIndex.
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 LlamaIndex developers 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 LlamaIndex