Comparisons / Anthropic Agent SDK vs Rasa
Anthropic Agent SDK vs Rasa: Which Agent Framework to Use?
The Anthropic Agent SDK packages Claude Code's agent loop as a library. Rasa is an open-source framework for building conversational AI — chatbots and virtual assistants. Here is how they compare — paradigm, ecosystem, and the use cases each one is actually built for.
By the numbers
Anthropic Agent SDK
3.1k
582
Python
MIT
2023-01-17
Anthropic
Google, Spark Capital
Yes
Rasa
21.1k
4.9k
Python
Apache-2.0
2016-10-14
Rasa Technologies
Rasa Pro / Rasa Cloud
Yes
GitHub stats as of April 2026. Stars indicate community interest, not necessarily quality or fit for your use case.
| Concept | Anthropic Agent SDK | Rasa |
|---|---|---|
| Agent | Claude agent with built-in tools, MCP servers, and system prompt | Rasa agent with NLU pipeline, dialogue policies, and action server |
| Tools | Built-in tools (`bash`, file read/write, web) + MCP server connections | Custom actions running on a separate action server via HTTP |
| Agent Loop | SDK's internal agentic loop with automatic tool dispatch | — |
| Sub-Agents | Agents invoke other agents as tools via the SDK | — |
| Lifecycle Hooks | 18 hook events: pre/post tool call, message, error, etc. | — |
| MCP Integration | One-line MCP server config for Playwright, Slack, GitHub, etc. | — |
| NLU | — | NLU pipeline: tokenizer, featurizer, intent classifier, entity extractor |
| Dialogue | — | Stories/Rules YAML + dialogue policies for conversation flow |
| Slots | — | Typed slots for tracking entities and state across turns |
| CALM | — | LLM for understanding + deterministic `Flows` for business logic |
Anthropic Agent SDK vs Rasa, head to head
Anthropic Agent SDK The Anthropic Agent SDK packages Claude Code's agent loop as a library.
Rasa Rasa is an open-source framework for building conversational AI — chatbots and virtual assistants.
Both wrap the same underlying agent pattern — an LLM call, a tool dispatch, a loop — in different abstractions. The choice between them is mostly about which mental model and ecosystem fits the team you have, not which one is technically more capable.
Pick Anthropic Agent SDK if
Pick Anthropic Agent SDK if the Anthropic Agent SDK's real value is packaging Claude Code's battle-tested agent loop with built-in tools and MCP integration. If you want a production agent that reads files, runs commands, and connects to services, it saves significant plumbing. For understanding how agents work, the plain version is more instructive. Anthropic Agent SDK is the right fit when the tradeoffs in its intro line up with how your team actually wants to work day-to-day; Rasa would force you to translate.
Pick Rasa if
Pick Rasa if rasa is purpose-built for production conversational AI with enterprise requirements — on-premise deployment, regulatory compliance, deterministic business logic. For general-purpose agents or simple chatbots, an LLM with a system prompt and a few tools is faster to build and more flexible. Rasa is the right fit when the tradeoffs in its intro line up with how your team actually wants to work day-to-day; Anthropic Agent SDK would force you to translate.
What both add
Both Anthropic Agent SDK and Rasa pull in a class hierarchy and a dependency tree to wrap what is, at the core, an HTTP POST in a while loop. If your use case is straightforward — one provider, a handful of tools, a single agent — the framework cost may exceed the framework benefit. The lesson below shows the same pattern in ~60 lines without either dependency.
Or build your own in 60 lines
Both Anthropic Agent SDK and Rasa implement the same 8 patterns. An agent is a function. Tools are a dict. The loop is a while loop. The whole thing composes in ~60 lines of Python.
No framework. No dependencies. No opinions. Just the code.
Build it from scratch →