Skip to main content
xpander supplies your agent’s identity (instructions, skills, knowledge bases, memory, deployment target) and delivers each task to your handler. In the SDK, skills are exposed as tools, the name agent loops use for them. Agents created in Xpander Chat or through the API run on Claude Code, Codex or OpenCode by default. The SDK integrations on these pages are optional: you pick them when you want to run the LLM loop in your own process. The agent loop you choose is the library that does that: the thing with an Agent class, a function-calling interface, and an arun() method. xpander does not replace your agent loop; it supplies the agent definition and runs every skill call. Selection lives in xpander_config.json, written by xpander agent new. The SDK reads the framework field from that file and wires up accordingly:
xpander_config.json
Right now, the SDK integrates with four agent loops:
Agno

Agno

OpenAI Agents SDK

OpenAI Agents SDK

LangChain + LangGraph

LangChain

AWS Strands

AWS Strands

​
Comparison

A few takeaways worth calling out:
  • Agno is the only path with automatic wiring. Backend.aget_args() dispatches on agent.framework. On the other three, you load the agent through Agents().aget(...) and read fields off it yourself.
  • Pre-built skills and custom @register_tool functions reach every agent loop. What changes is the property name you read them from: agent.tools.functions for LangChain, agent.openai_agents_sdk_tools for the OpenAI Agents SDK, agent.strands_tools for AWS Strands.
  • Memory, knowledge retrieval, and guardrails are Agno-only auto-wired. On the other agent loops, xpander gives you the data (agent.knowledge_bases_retriever(), session metadata, the agent’s memory config) but your code is responsible for plugging it into the loop.
  • Agno-exclusive features: context optimization (toon encoding, runtime compaction), AgnoTeam-based multi-agent coordination, and the session helpers (agent.get_user_sessions, agent.get_session, agent.delete_session, which raise NotImplementedError outside Agno).

​
How to choose

Within the SDK path, start with Agno unless you have a reason not to. Pick a non-Agno agent loop when:
  • Existing investment. Your team already builds on it and switching cost is real. Example: a LangGraph workflow that’s been in production for six months.
  • Loop-specific feature. You need something Agno doesn’t have. Example: LangGraph’s stateful multi-step workflows, the OpenAI Agents SDK’s Runner ergonomics, Strands’ AWS-native primitives.
  • Embedded in an existing app. You’re adding xpander skills to a service that already runs one of these agent loops. Example: a FastAPI service that already imports agents.Runner.
You’re not locked in. The agent’s identity lives in xpander’s control plane, so you can swap agent loops later by editing framework in xpander_config.json and rewriting your handler.

​
Next steps

Agno

The recommended path. What Backend.aget_args() actually wires up.

OpenAI Agents SDK

Manual wiring with agent.openai_agents_sdk_tools.

LangChain + LangGraph

Manual wiring with agent.tools.functions and create_react_agent.

AWS Strands

Manual wiring with agent.strands_tools on AWS-native orchestration.

Core Concepts

The SDK class names mapped onto agents, tasks, threads, skills, and memory.

Quickstart

10-minute scaffold-to-deploy walkthrough on the Agno path.