Why Phidata Developers Need AgentGenome
Phidata creates assistants with persistent memory and knowledge. AgentGenome profiles memory usage, retrieval patterns, and knowledge application—ensuring your assistant's "memory" works on any LLM.
Assistants Lack Memory Profiling
Your Phidata assistant remembers context. But how well? Without profiling, memory effectiveness is invisible.
Knowledge Retrieval Is Opaque
When does your assistant use stored knowledge vs. generate fresh? AgentGenome captures retrieval patterns.
Memory Patterns Vary By LLM
Different LLMs utilize context memory differently. Can you maintain consistent memory usage?
Long-Term Context Is Critical
Persistent memory is your assistant's differentiator. Profiling ensures it works across providers.
"Sound familiar?"
AgentGenome Solves Every Problem
Add 3 lines of code. Capture your agent's behavioral DNA. Deploy anywhere.
from phi.assistant import Assistant
from phi.llm.openai import OpenAIChat
from agentgenome import profile_memory
assistant = Assistant(
llm=OpenAIChat(model="gpt-4"),
memory=True,
)
@profile_memory(genome_id="memory-assistant")
def chat(message: str):
return assistant.run(message)
# Memory usage and retrieval 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.
Memory states migrate with agents. Capture how your assistant uses memory—deploy consistent memory behavior on any LLM.
Your Phidata Agents Deserve Freedom
You've invested months optimizing Phidata 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, LlamaIndex, Semantic Kernel, or any provider.
- ✓Keep your optimizations. Zero rework.
- ✓95%+ behavioral consistency guaranteed.
- ✓Hours, not weeks. Included in Pro tier.
# Export memory genome
from agentgenome import genome
# Capture memory state patterns
genome.export('memory-assistant.genome')
# Deploy on Claude with same memory behavior
genome.import_to('memory-assistant.genome', provider='anthropic')
# Same memory utilization, new substrateDeploy your Phidata genome on any supported provider:
Profile once. Deploy anywhere. Never locked in.
Build Phidata applications Once.
Deploy on Any LLM.
Memory states migrate with agents. Capture how your assistant uses memory—deploy consistent memory behavior on any LLM.
Your Agent Genome
Behavioral DNA captured in universal format
Profile Your Phidata applications
Add 3 lines of code. Capture behavioral DNA automatically.
from phi.assistant import Assistant
from phi.llm.openai import OpenAIChat
from agentgenome import profile_memory
assistant = Assistant(
llm=OpenAIChat(model="gpt-4"),
memory=True,
)
@profile_memory(genome_id="memory-assistant")
def chat(message: str):
return assistant.run(message)
# Memory usage and retrieval patterns capturedYour applications become substrate-independent
Profile today, deploy on any LLM tomorrow. Your optimizations travel with you.
Real Results with AgentGenome
How MemoryAI Made Assistant Memory Portable
The Challenge
MemoryAI's Phidata assistant had developed excellent memory utilization on GPT-4. When exploring Claude for its conversational quality, they needed memory patterns to survive migration.
The Solution
AgentGenome profiled memory retrieval, context utilization, and knowledge application patterns. The memory genome was deployed on Claude.
"Memory is what makes our assistant valuable. Genome profiling proved we could keep it on any LLM."
— Emily Wright, Founder, MemoryAI
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 Phidata | 🔓 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
- Phidata 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 Phidata."
When Phidata raises prices or a better model launches, will you be ready to leave?
What You'll Achieve with AgentGenome
Real metrics from Phidata 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 Phidata?
Here's how to escape with your behavioral DNA intact
Profile Your Current Agent
Add 3 lines of code to capture your Phidata 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 Phidata.
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 Phidata 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 Phidata