Comparisons / DSPy vs LlamaIndex
DSPy vs LlamaIndex: Which Agent Framework to Use?
DSPy replaces hand-written prompts with compiled modules. LlamaIndex started as a RAG framework — connect your data, query it with an LLM. Here is how they compare — paradigm, ecosystem, and the use cases each one is actually built for.
By the numbers
DSPy
33.4k
2.8k
Python
MIT
2023-01-09
Stanford NLP (Omar Khattab)
LlamaIndex
48.3k
7.2k
Python
MIT
2022-11-02
Jerry Liu
GitHub stats as of April 2026. Stars indicate community interest, not necessarily quality or fit for your use case.
| Concept | DSPy | LlamaIndex |
|---|---|---|
| Agent | `dspy.ReAct` module with signature and tools | `AgentRunner` with `AgentWorker`, or `ReActAgent` for tool-calling agents |
| Prompts | `dspy.Signature` defines input/output fields, compiled to optimized prompts | — |
| Optimization | `dspy.BootstrapFewShot`, `MIPROv2` auto-tune prompts against a metric | — |
| Tools | Tools passed to `ReAct` module as callable list | `FunctionTool` for custom tools, `QueryEngineTool` to query an index as a tool |
| Chaining | `dspy.ChainOfThought`, `dspy.Module` with `forward()` composition | — |
| Evaluation | `dspy.Evaluate` with metric functions and dev sets | — |
| Agent Loop | — | `AgentRunner.chat()` manages step-by-step execution via `AgentWorker` tasks |
| RAG Integration | — | `VectorStoreIndex` + `QueryEngineTool` — the agent can query your data as a tool call |
| Memory | — | `ChatMemoryBuffer` with token limit, or custom memory modules |
| Orchestration | — | `AgentRunner` step API for custom control flow, or multi-agent pipelines |
DSPy vs LlamaIndex, head to head
DSPy DSPy replaces hand-written prompts with compiled modules.
LlamaIndex LlamaIndex started as a RAG framework — connect your data, query it with an LLM.
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 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; LlamaIndex would force you to translate.
Pick LlamaIndex if
Pick LlamaIndex if llamaIndex adds genuine value when your agent needs to query structured or unstructured data as part of its reasoning — that's the index-as-tool pattern, and it's well-executed. But if you're building a general-purpose agent that doesn't need RAG, the agent framework is overhead. The plain Python version of the agent loop is the same 60 lines either way. LlamaIndex 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.
What both add
Both DSPy and LlamaIndex 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 DSPy and LlamaIndex 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 →