> ## Documentation Index
> Fetch the complete documentation index at: https://docs.xpander.ai/llms.txt
> Use this file to discover all available pages before exploring further.
# Frameworks
> Pick a framework and see what xpander wires up for you
Xpander supplies your agent's identity (instructions, tools, knowledge bases, memory, deployment target) and routes tasks to your handler.
The **framework** you choose is the library that runs the LLM loop in your process: the thing with an `Agent` class, a tool-calling interface, and an `arun()` method. xpander does not replace your framework. It plugs into it.
Selection lives in `xpander_config.json`, written by `xpander agent new`. The SDK reads `framework` from that file and wires up accordingly:
```json xpander_config.json theme={"dark"}
{
"agent_id": "agt_01H...",
"organization_id": "org_01H...",
"api_key": "xpd_...",
"framework": "agno" // or: "open-ai-agents", "langchain", "strands-agents"
}
```
Right now, the SDK supports 4 frameworks:
## Comparison
| Capability | Agno | OpenAI Agents SDK | LangChain | AWS Strands |
| ------------------------------------------------- | ----------------------- | ---------------------------------------------- | ----------------------------------------- | ----------------------------------------- |
| Pre-built connectors (2,000+) | Auto-included | `agent.openai_agents_sdk_tools` | `agent.tools.functions` | `agent.strands_tools` |
| Custom `@register_tool` functions | Auto-included | Auto-included | Auto-included | Auto-included |
| Instructions (role, goal, general) | Auto-attached | `agent.instructions.full` | `agent.instructions.full` | `agent.instructions.full` |
| Model + provider credentials | Auto-attached | `agent.model_name` (you build the client) | `agent.model_name` (you build the client) | `agent.model_name` (you build the client) |
| Knowledge-base retriever | Auto-attached | Manual via `agent.knowledge_bases_retriever()` | Manual | Manual |
| Session storage (Postgres) | Auto-wired | Manual | Manual | Manual |
| User and agent memory | Auto-wired | Manual | Manual | Manual |
| Context optimization (toon encoding, compaction) | Auto-wired | Not available | Not available | Not available |
| Guardrails (PII, prompt injection, moderation) | Auto-wired | Manual | Manual | Manual |
| Multi-agent teams | Auto-wired (`AgnoTeam`) | Manual | Manual | Manual |
| Session helpers (`agent.get_user_sessions`, etc.) | Yes | Raises `NotImplementedError` | Raises `NotImplementedError` | Raises `NotImplementedError` |
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 connectors and custom `@register_tool` functions reach every framework.** 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 other frameworks, 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 framework.
* **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).
See the framework's dedicated page for the full guide.
## How to choose
Default to Agno unless you have a reason not to. Pick a non-Agno framework 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.
* **Framework-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 tools to a service that already runs one of these frameworks. Example: a FastAPI worker that already imports `agents.Runner`.
You're not locked in. The agent's identity lives in xpander's control plane, so you can swap frameworks later by editing `framework` in `xpander_config.json` and rewriting your handler.
## Next steps
The recommended path. What `Backend.aget_args()` actually wires up.
Manual wiring with `agent.openai_agents_sdk_tools`.
Manual wiring with `agent.tools.functions` and `create_react_agent`.
Manual wiring with `agent.strands_tools` on AWS-native orchestration.
The SDK class names mapped onto agents, tasks, threads, tools, and memory.
10-minute scaffold-to-deploy walkthrough on the default Agno path.