From multi-agent experiments to production agents
AutoGen is powerful for research and prototyping. Connic takes agents to production with managed infrastructure, connectors, and observability built-in.
Feature Comparison
See how Connic stacks up against AutoGen across key capabilities.
Development Experience
Agent definition
Connic uses YAML + Python. AutoGen is Python-class based with extensive configuration options.
Simple getting started
Connic: `connic init` and edit YAML. AutoGen requires understanding its agent class hierarchy.
Multi-agent workflows
Connic supports sequential agents. AutoGen excels at conversational multi-agent patterns.
Custom tools
Both support Python functions as tools. AutoGen has more complex registration patterns.
Local testing
Connic offers hot-reload testing. AutoGen runs locally but requires restart for changes.
Production Infrastructure
Managed hosting
Connic deploys to managed infrastructure. AutoGen has no hosting solution.
Git-based deployments
Push to deploy with Connic. AutoGen requires custom CI/CD setup.
Auto-scaling
Connic scales automatically. AutoGen requires building scaling infrastructure.
Environment management
Built-in dev/staging/prod environments. AutoGen needs manual environment handling.
Secrets management
Secure secrets per environment. AutoGen relies on external solutions.
Integrations & Triggers
HTTP webhook triggers
Built-in webhooks with Connic. AutoGen requires building HTTP layer.
Scheduled execution
Native cron connector. AutoGen has no scheduling support.
Message queue integration
Kafka, SQS, Redis built-in. AutoGen requires custom integration.
Database triggers
PostgreSQL change triggers. Not available in AutoGen.
Payment/SaaS events
Stripe connector built-in. AutoGen has no SaaS integrations.
Observability
Run tracing
Automatic in Connic. AutoGen has basic logging, needs custom tracing.
Execution history
Full history in Connic dashboard. AutoGen has no built-in history.
Token usage tracking
Automatic in Connic. AutoGen requires custom implementation.
Debug UI
Web dashboard for debugging. AutoGen is terminal/code-based only.
Agent Capabilities
LLM agents
Both support LLM-powered agents with tool calling.
Agent-to-agent chat
AutoGen excels at conversational multi-agent. Connic supports sequential pipelines.
Human-in-the-loop
AutoGen has strong HITL patterns. Connic supports it via middleware.
Code execution
Both can execute code. AutoGen has Docker-based code execution built-in.
Why teams choose Connic
Key advantages that make Connic the better choice for production AI agents.
Production-Ready Platform
Connic handles hosting, scaling, and operations. AutoGen is a framework you must deploy yourself.
Enterprise Connectors
Webhooks, Kafka, SQS, Email, Stripe, PostgreSQL triggers - all built-in, zero integration code.
Full Observability
Tracing, history, token tracking, cost monitoring in one dashboard. No custom logging needed.
Simpler Configuration
Define agents in YAML, write tools in plain Python. No class hierarchies or decorator patterns.
YAML-First Approach
Version control friendly configs. Review agent changes in PRs like any other code.
Managed Knowledge Base
Built-in RAG with semantic search. No vector database setup required.
The Bottom Line
AutoGen and Connic serve different use cases. Here's when to use each.
Use Connic when
- You need agents running in production, not just experiments
- You want managed infrastructure without DevOps overhead
- You need enterprise integrations (webhooks, queues, databases)
- You prefer simple YAML + Python over complex framework patterns
- You want tracing and observability out of the box
Use AutoGen when
- You're researching multi-agent conversation patterns
- You need complex agent-to-agent negotiations
- Human-in-the-loop is central to your workflow
- You want fine-grained control over agent communication
- You're building academic or research prototypes