Supported Frameworks
AgentVisor™ is designed as a framework-agnostic runtime that confines AI agents. Whether you're building conversational assistants, research agents, or complex multi-agent systems, AgentVisor provides the security isolation, policy enforcement, and durable execution your agents need.
Framework Support
| Framework | Status | Notes |
|---|---|---|
| LangGraph | ✅ Supported | Full integration with checkpointer, graph discovery, and streaming |
| CrewAI | ✅ Supported | Multi-agent crews with crewai.yaml discovery and MCP tool integration |
| Interactive | ✅ Supported | Direct command execution for agentvisor exec — shells, Claude Code, Aider |
| Google ADK | ✅ Supported | Session-scoped state with Thread State API; agent.yaml discovery; AgentVisorSessionService automatically wired |
| Strands Agents | ✅ Supported | AWS's code-first agent SDK; snapshot-based checkpointing; native MCP tool adapter; explicit framework.provider: "strands" selection (no discovery file) |
| Pydantic AI | 🗓️ Under evaluation | Native A2A + MCP support |
| OpenAI Agents SDK | 🗓️ Under evaluation | Native MCP support |
| Agno | 🗓️ Under evaluation | Native MCP + A2A + AG-UI support |
| LlamaIndex | 🗓️ Under evaluation | Strong RAG story |
| Smolagents | 🗓️ Under evaluation | HuggingFace ecosystem; code-agent paradigm well-suited to gVisor sandbox (strongest syscall isolation); native MCP support |
| Microsoft Agent Framework | 🗓️ Under evaluation | Successor to AutoGen and Semantic Kernel; native MCP + A2A support |
| Haystack | 🗓️ Under evaluation | Pipeline-based model; YAML-defined pipelines for agent discovery |
Framework Provider Architecture
AgentVisor uses a pluggable framework provider system. Each provider knows how to discover agent schemas and build the command to start the agent process inside the sandbox. The built-in providers are:
| Provider | Discovery File | Description |
|---|---|---|
| langgraph | langgraph.json | LangGraph Python agents with graph discovery and checkpointing |
| crewai | crewai.yaml | CrewAI multi-agent crews with @CrewBase pattern |
| interactive | (none) | Direct command execution for agentvisor exec (shells, Claude Code, etc.) — selected via explicit framework.provider: interactive, or automatically as a fallback when agentvisor exec finds no other discovery file |
| adk | agent.yaml | Google ADK agents with session state backed by Thread State API |
| strands | (none — explicit framework.provider: strands required) | AWS Strands Agents with snapshot-based checkpointing and native MCP tool adapter |
Because the sandbox is a Linux environment, any agent that can run on Linux can be supported by a framework provider. The provider handles schema discovery and command construction — the security, policy, and observability layers apply regardless of what runs inside.
See the Configuration Reference for framework configuration details.
Choosing a Framework
LangGraph is recommended when you need:
- Stateful conversation threads with durable checkpointing via Temporal
- Graph-based control flow with conditional edges and branching
- Human-in-the-loop patterns with
interrupt()/resume - LangGraph Agent Protocol API compatibility
CrewAI is recommended when you need:
- Multiple specialized agents collaborating on a task
- Role-based agent design with
@CrewBase,@agent,@taskdecorators - Sequential or hierarchical task pipelines
- Simple, declarative multi-agent workflows
Interactive is recommended for:
- Running existing tools under confinement (Claude Code, Aider, shells)
- Wrapping CLI tools that aren't Python agents
- Interactive terminal sessions via
agentvisor exec
Google ADK is recommended when you need:
- Gemini-native agents with ADK's built-in tool and session model
- Session-scoped state automatically persisted across conversation turns
- Fine-grained state scoping (session / user / app / temp) within one agent
Strands Agents is recommended when you need:
- A code-first agent SDK (no YAML/JSON discovery file) with a broad choice of model providers
- Native MCP tool support built into the framework itself
- Multi-agent Graphs (DAG) or Swarms (handoff-based) alongside single agents
- A confirmed production path to Bedrock via LiteLLM
- Native human-in-the-loop interrupts
Under Evaluation
The frameworks above marked "Under evaluation" are candidates for future integration, not committed work. Any framework we do integrate will maintain the same security guarantees — sandbox isolation, policy enforcement, and credential brokering — adapted to that framework's execution model.
If you're interested in a specific framework, contact us at support@manetu.com to discuss your use case.
Next Steps
- Getting Started — Install AgentVisor and run your first agent
- Tutorial — Step-by-step guide using LangGraph
- LangGraph Agents Guide — Deep dive into LangGraph integration
- CrewAI Agents Guide — Deep dive into CrewAI integration
- Google ADK Agents Guide — Deep dive into ADK integration
- Strands Agents Guide — Deep dive into Strands integration