AutoGen Alternative for
Enterprise AI Agents
AutoGen pioneered multi-agent code execution -- but it is now in maintenance mode with no commercial support. See where it still fits, where enterprise teams outgrow it, and what a governed migration path looks like.
Read the VerdictQuick Verdict
AutoGen is an open-source multi-agent framework from Microsoft Research that introduced Docker-sandboxed code execution and conversable agents. Its v0.4 redesign (January 2025) moved to an event-driven actor model with layered Core/AgentChat/Extensions APIs and Python/.NET interop via gRPC.
However, AutoGen entered maintenance mode in late 2025. Microsoft has converged AutoGen and Semantic Kernel into the new Microsoft Agent Framework (MIT, GA April 2026). AutoGen still receives security updates but no new features.
VDF AI is an enterprise AI orchestration platform with multi-provider agent execution, spec-driven DAG orchestration, pre-built integrations, governance, and cloud/hybrid/on-prem deployment -- backed by commercial SLAs.
Pricing, Self-Host & Enterprise Support
AutoGen is free and open-source. But free software still costs money to run, secure, and support.
AutoGen
AutoGen is genuinely free to use. Your real cost is the engineering time to deploy, maintain, and govern it -- plus infrastructure and LLM API spend you manage yourself.
VDF AI
Flat per-seat pricing includes the platform, governance, integrations, and support. No hidden infra costs -- cloud, hybrid, or on-prem deployment included.
Self-Hosting
AutoGen requires you to provision and manage your own infrastructure: compute, Docker hosts for sandboxed execution, networking, and security. VDF AI offers managed cloud, hybrid, and full on-premise deployment -- your team focuses on agents, not infrastructure.
Enterprise Support
AutoGen's community support runs through GitHub Discussions and Discord -- no guaranteed response times. VDF AI provides commercial SLAs with dedicated support engineers, onboarding assistance, and escalation paths for production incidents.
Governance & Auditability
Enterprise AI deployments require audit trails, access controls, and compliance tooling. Here is how each platform stacks up.
Access Control
Audit Trail
Data Residency
Cost & Energy Analytics
Code Execution & Sandboxing
AutoGen's Docker-sandboxed code execution loop is one of its genuine differentiators. Here is what each platform offers.
AutoGen Code Execution
- Docker Sandboxing -- agents execute code in isolated Docker containers with configurable resource limits
- Execution Loop -- ConversableAgent proposes code, executes it, and iterates based on output until the task completes
- Multi-Language -- supports Python and shell execution out of the box within containers
- Human-in-the-Loop -- configurable approval gates before code execution
- No Managed Infra -- you provision and maintain Docker hosts, networking, and security yourself
- No Audit Trail -- execution logs are local; no built-in persistent record store
VDF AI Tool Execution
- MCP Server -- enterprise-grade tool execution with managed sandboxing and security policies
- MCP Tool Registry -- centralized catalog of tools with versioning, permissions, and usage tracking
- Enterprise Connectors -- pre-built integrations for Jira, Confluence, GitHub, Google Workspace, M365, Slack, Zoom
- Audit Trail -- every tool invocation logged in Vault with encrypted run records
- No Docker Ops -- managed infrastructure means your team focuses on agent logic, not container orchestration
- RBAC & Approval Flows -- role-based permissions and configurable human approval gates
Multi-Agent Orchestration
Both platforms support multi-agent workflows. The difference is how you define, deploy, and govern them.
AutoGen
- ConversableAgent -- base agent class for talking to other agents, tools, and code execution
- GroupChat & Manager -- RoundRobin, Selector, sequential, and hierarchical orchestration patterns
- Actor Runtime -- event-driven async messaging with distributed gRPC (v0.4+)
- OpenTelemetry -- built-in tracing for agent interactions and tool calls
- AutoGen Studio -- no-code GUI for prototyping (explicitly not production-ready)
AutoGen's v0.4 redesign introduced the layered Core/AgentChat/Extensions API with an event-driven actor model. Strong for research and prototyping; orchestration is defined in Python code.
VDF AI
- Agent Hub -- 6-step builder with multi-provider routing and MCP tool registry
- Networks v3 -- spec-driven DAG orchestration with intent decomposition
- SEEMR -- Self-Evolving Model Router for dynamic provider selection
- MCP Server -- enterprise tool execution with managed sandboxing and connectors
- Portal -- production Angular admin UI with RBAC and lifecycle management
VDF AI separates agent definition from orchestration logic. Networks v3 uses spec-driven DAGs -- no Python required. Teams define workflows visually or via API, with built-in governance at every step.
Deployment Ownership
Who runs the infrastructure, and who is responsible when something breaks?
| Concern | AutoGen | VDF AI |
|---|---|---|
| Hosting | Self-host only; no managed option | Cloud, hybrid, or full on-prem |
| Scaling | Manual; you manage compute and Docker hosts | Platform-managed auto-scaling |
| Updates | Security patches only (maintenance mode) | Regular releases with managed upgrades |
| Monitoring | OpenTelemetry + your own observability stack | Built-in dashboards, energy and cost analytics |
| Security | Your responsibility; Docker isolation for code execution | Platform-managed with SSO, RBAC, encrypted Vault |
| Compliance | DIY; no built-in residency or compliance controls | EU data residency, audit trails, governance tooling |
When AutoGen Is the Right Choice
We believe in honest comparisons. AutoGen has genuine strengths in specific scenarios.
AutoGen Strengths
AutoGen's code execution loop is purpose-built for iterative code generation and execution. If your primary use case is LLM-driven code execution with isolation, AutoGen's approach is battle-tested.
AutoGen's Python-native API and academic roots make it excellent for multi-agent research, experimentation, and rapid prototyping of conversational agent patterns.
MIT-licensed with full source access. The v0.4 actor model and gRPC runtime let advanced teams build custom distributed agent architectures without vendor constraints.
If your team has the engineering capacity to self-host, maintain, and govern the platform, AutoGen's $0 license cost makes it an attractive starting point for budget-conscious teams.
If your organisation is already invested in Azure and .NET, AutoGen's migration path to the Microsoft Agent Framework (GA April 2026) provides continuity within the Microsoft ecosystem.
When to Graduate to VDF AI
These signals suggest your team has outgrown what AutoGen can sustainably deliver.
Graduation Signals
- Maintenance mode risk -- your team is concerned about building on a framework that is no longer actively developed
- No commercial SLA -- production incidents require guaranteed response times, not Discord channels
- Governance gaps -- you need audit trails, RBAC, and compliance controls that AutoGen does not provide
- Infrastructure burden -- managing Docker hosts, networking, and security for agent execution consumes too much engineering time
- Non-Python teams -- your organisation includes teams that cannot or should not write Python to define agent workflows
- Multi-provider routing -- you need dynamic model selection across providers, not hardcoded LLM connections
What VDF AI Adds
- Active platform -- regular releases, roadmap transparency, and a dedicated engineering team
- Commercial SLAs -- guaranteed response times, dedicated support engineers, and escalation paths
- Vault governance -- encrypted run records, RBAC, and complete audit trails for every agent interaction
- Managed infrastructure -- cloud, hybrid, or on-prem deployment without your team managing containers
- Language-agnostic -- Portal UI, API, and MCP protocol mean no Python dependency
- SEEMR routing -- Self-Evolving Model Router dynamically selects the best provider per task
Migration Path
Moving from AutoGen to VDF AI does not mean throwing away what works. Here is a phased approach.
Audit Agent Definitions
Map your existing ConversableAgent configurations, tool registrations, and GroupChat patterns. Document which agents execute code, which use external tools, and how they communicate.
Replicate Agents in Agent Hub
Use VDF AI's 6-step agent builder to recreate your agents with multi-provider routing. Configure MCP tool connections to replace custom tool registrations and Docker-based execution.
Rebuild Orchestration in Networks
Translate GroupChat and sequential patterns into Networks v3 spec-driven DAGs. Add intent decomposition for complex workflows that AutoGen handled with nested conversations.
Connect Enterprise Integrations
Replace custom API integrations with pre-built connectors for Jira, Confluence, GitHub, Google Workspace, M365, Slack, and Zoom. Configure RBAC, audit policies, and data residency in Vault.
Validate & Go Live
Run parallel execution to compare outputs. Validate governance controls, audit trails, and performance. Cut over when confidence is established -- VDF AI's support team assists throughout.
Full Comparison Table
| Feature | AutoGen | VDF AI |
|---|---|---|
| Project Status | Maintenance mode (late 2025); security patches only | Active development with regular releases |
| License | MIT & CC-BY-4.0 | Commercial (free Starter tier) |
| Pricing | Free; you pay for infra and LLM APIs | Flat per-seat; infra included |
| Enterprise Support | Community only (GitHub, Discord) | Commercial SLAs, dedicated support |
| Agent Builder | Python API (ConversableAgent) | 6-step visual builder (Agent Hub) |
| Orchestration | GroupChat, RoundRobin, Selector, hierarchical | Networks v3: spec-driven DAG with intent decomposition |
| Code Execution | Docker-sandboxed execution loop | MCP Server with managed sandboxing |
| Model Routing | Manual LLM configuration per agent | SEEMR: dynamic multi-provider routing |
| Tool Integration | Custom Python functions; manual registration | MCP Tool Registry with enterprise connectors |
| Pre-Built Integrations | None; build your own | Jira, Confluence, GitHub, Google Workspace, M365, Slack, Zoom |
| Admin UI | AutoGen Studio (not production-ready) | Portal: production Angular admin UI |
| Observability | OpenTelemetry tracing (v0.4+) | Built-in dashboards, energy & cost analytics |
| Audit Trail | No built-in persistent audit store | Vault: encrypted run records |
| Access Control | No built-in RBAC | RBAC with SSO integration |
| Deployment | Self-host only | Cloud, hybrid, on-prem, EU residency |
| Language | Python primary; .NET via gRPC | Language-agnostic (UI, API, MCP protocol) |
| Runtime Architecture | Event-driven actor model with gRPC (v0.4+) | Managed platform with auto-scaling |
| Successor | Microsoft Agent Framework (GA April 2026) | Continuous platform evolution |
Frequently Asked Questions
Related Resources
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You Have Questions
Tell us what you’re trying to achieve—governed AI Networks, enterprise RAG, deep integrations, or on‑premise deployment. We’ll help you map the right architecture, security posture, and rollout path. If you’re moving beyond AI pilots and need scalable, auditable execution, reach out—our team is ready to help.