Comparisons / Agno vs DSPy
Agno vs DSPy: Which Agent Framework to Use?
Agno (formerly Phidata) is a lightweight Python framework for building agents. 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
Agno
39.2k
5.2k
Python
Apache-2.0
2022-05-04
Agno (formerly Phidata)
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 | Agno | DSPy |
|---|---|---|
| Agent | `Agent(model=OpenAIChat(), instructions=[...])` class with `run()` method | `dspy.ReAct` module with signature and tools |
| Tools | Function tools via `@tool` decorator or built-in toolkits (web search, SQL, etc.) | Tools passed to `ReAct` module as callable list |
| Agent Loop | `Agent.run()` handles tool dispatch internally, configurable via `show_tool_calls` | — |
| Memory / Knowledge | Knowledge bases (PDF, URL, vector DB) injected via `knowledge` param + built-in memory | — |
| Multi-Agent (Teams) | `Team` class with `agents` list, `mode` (sequential, parallel, coordinate), and shared memory | — |
| Storage | `SqlAgentStorage`, `PostgresAgentStorage` for persisting sessions and state | — |
| 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 |
Agno vs DSPy, head to head
Agno Agno (formerly Phidata) is a lightweight Python framework for building agents.
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 Agno if
Pick Agno if agno adds value when you want a batteries-included agent with minimal boilerplate — especially for multi-modal agents or team orchestration. But each of its abstractions maps to a small piece of plain Python. If your agent is straightforward, writing it directly gives you full control with zero framework overhead. Agno 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; Agno would force you to translate.
What both add
Both Agno 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 Agno 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 →