Why AutoGen Developers Need AgentGenome
AutoGen excels at multi-turn agent conversations. AgentGenome captures conversation flow patterns, loop detection behaviors, and escalation triggers—portable across any provider.
Conversation Loops Lack Exit Metrics
AutoGen agents can loop indefinitely. Without profiling, you can't set data-driven circuit breakers.
Multi-Turn Optimization Is Guesswork
When should conversations escalate? How many turns are optimal? Without behavioral baselines, you're guessing.
Conversation Patterns Are LLM-Specific
Your agents learned conversation timing on GPT. Claude might handle turn-taking differently.
Compliance Requires Consistent Conversations
Regulated industries need identical conversation behaviors across environments. Can you prove consistency?
"Sound familiar?"
AgentGenome Solves Every Problem
Add 3 lines of code. Capture your agent's behavioral DNA. Deploy anywhere.
from autogen import ConversableAgent, UserProxyAgent
from agentgenome import profile_conversation
assistant = ConversableAgent("assistant", llm_config={"model": "gpt-4"})
user_proxy = UserProxyAgent("user_proxy")
@profile_conversation(genome_id="support-conversation")
def handle_support(query: str):
return user_proxy.initiate_chat(
assistant,
message=query,
max_turns=10
)
# Conversation patterns captured automaticallyWhat 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.
Loop profiles work cross-provider. Capture conversation patterns, circuit breakers, and escalation logic—deploy them on any LLM.
Your AutoGen Agents Deserve Freedom
You've invested months optimizing AutoGen 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 Agents, CrewAI, LlamaIndex, or any provider.
- ✓Keep your optimizations. Zero rework.
- ✓95%+ behavioral consistency guaranteed.
- ✓Hours, not weeks. Included in Pro tier.
# Export conversation genome
from agentgenome import genome
# Capture multi-turn patterns
genome.export('support-conversation.genome')
# Deploy on Claude, Gemini, any provider
genome.import_to('support-conversation.genome', provider='anthropic')
# Same conversation flow, new substrateDeploy your AutoGen genome on any supported provider:
Profile once. Deploy anywhere. Never locked in.
Build AutoGen applications Once.
Deploy on Any LLM.
Loop profiles work cross-provider. Capture conversation patterns, circuit breakers, and escalation logic—deploy them on any LLM.
Your Agent Genome
Behavioral DNA captured in universal format
Profile Your AutoGen applications
Add 3 lines of code. Capture behavioral DNA automatically.
from autogen import ConversableAgent, UserProxyAgent
from agentgenome import profile_conversation
assistant = ConversableAgent("assistant", llm_config={"model": "gpt-4"})
user_proxy = UserProxyAgent("user_proxy")
@profile_conversation(genome_id="support-conversation")
def handle_support(query: str):
return user_proxy.initiate_chat(
assistant,
message=query,
max_turns=10
)
# Conversation patterns captured automaticallyYour applications become substrate-independent
Profile today, deploy on any LLM tomorrow. Your optimizations travel with you.
Real Results with AgentGenome
How FinBot Made Multi-Turn Conversations Substrate-Independent
The Challenge
FinBot's multi-agent financial advisor had perfected its conversation flow on GPT-4 over 8 months. Compliance required supporting multiple LLM providers—each conversation needed to work identically regardless of substrate.
The Solution
AgentGenome profiled conversation flow patterns, turn-taking behaviors, escalation triggers, and loop detection. The same genome was deployed to GPT, Claude, and Gemini.
"Our regulators don't care which LLM powers the conversation. They care that behavior is consistent. AgentGenome gives us proof."
— Michael Torres, Founder, FinBot
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 AutoGen | 🔓 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
- AutoGen 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 AutoGen."
When AutoGen raises prices or a better model launches, will you be ready to leave?
What You'll Achieve with AgentGenome
Real metrics from AutoGen 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 AutoGen?
Here's how to escape with your behavioral DNA intact
Profile Your Current Agent
Add 3 lines of code to capture your AutoGen 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 AutoGen.
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 AutoGen 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 AutoGen