Comparisons / Anthropic Agent SDK vs DSPy
Anthropic Agent SDK vs DSPy: Which Agent Framework to Use?
The Anthropic Agent SDK packages Claude Code's agent loop as a library. DSPy replaces hand-written prompts with compiled modules. 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
DSPy
33.4k
2.8k
Python
MIT
2023-01-09
Stanford NLP (Omar Khattab)
GitHub stats as of April 2026. Stars indicate community interest, not necessarily quality or fit for your use case.
| Concept | Anthropic Agent SDK | DSPy |
|---|---|---|
| Agent | Claude agent with built-in tools, MCP servers, and system prompt | `dspy.ReAct` module with signature and tools |
| Tools | Built-in tools (`bash`, file read/write, web) + MCP server connections | Tools passed to `ReAct` module as callable list |
| 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. | — |
| Prompts | — | `dspy.Signature` defines input/output fields, compiled to optimized prompts |
| Optimization | — | `dspy.BootstrapFewShot`, `MIPROv2` auto-tune prompts against a metric |
| Chaining | — | `dspy.ChainOfThought`, `dspy.Module` with `forward()` composition |
| Evaluation | — | `dspy.Evaluate` with metric functions and dev sets |
Anthropic Agent SDK vs DSPy, head to head
Anthropic Agent SDK The Anthropic Agent SDK packages Claude Code's agent loop as a library.
DSPy DSPy replaces hand-written prompts with compiled modules.
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; DSPy would force you to translate.
Pick DSPy if
Pick DSPy if dSPy's real innovation is automated prompt optimization — replacing manual prompt engineering with algorithmic tuning. This is genuinely novel and valuable for production systems where prompt quality matters at scale. For simple agents or learning, hand-written prompts are easier to understand and modify. DSPy 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 DSPy 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 DSPy 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 →