Comparing 11 Agent Frameworks: DSPy vs Agno

Overview

Agent frameworks in 2026 share a common goal: give developers a structured way to turn large language models into systems that can plan, use tools, and act autonomously. The landscape includes libraries that emphasize graph‑based orchestration, multi‑agent conversation, lightweight tool wrapping, or high‑performance execution. Two frameworks that sit at opposite ends of the spectrum are DSPy, which treats prompting as a programmable optimization problem, and Agno, which focuses on low‑latency agent loops with built‑in toolchains. This review examines what each does, how they work, where they excel, and how they compare to nine other notable frameworks.

DSPy Deep Dive

DSPy (Declarative Self‑improving Python) is a library for composing language model calls into reusable modules and automatically optimizing their prompts or few‑shot examples. Rather than writing hand‑crafted prompts, you define a signature (input → output) and let DSPy search for prompt variations that improve a metric on a validation set. The core abstraction is the Module, which can be stacked into pipelines similar to neural network layers.

Key features

• Signature‑based programming: Declare what a module should do (e.g., class Summarize(dspy.Signature): document -> summary).
• Automatic prompt optimization: Techniques like MIPRO (Minimal Input Prompt Optimization) or BootstrapFewShot generate candidate prompts and select the best based on a supplied metric.
• Compatibility: Works with any LLM provider that offers a completion API (OpenAI, Anthropic, local Hugging Face models via transformers).
• Integration: Can be used inside LangChain chains, AutoGen conversations, or as a standalone Python package.

How it works When you instantiate a module, DSPy initially fills the prompt with a default template. During optimization, it treats the prompt text as a set of editable tokens, proposes mutations (adding few‑shot examples, rephrasing instructions), evaluates each candidate on a validation set, and keeps the variant that improves the metric. The search algorithm is configurable; the default uses a beam search with a temperature schedule.

Example: a simple QA module

import dspy

class QA(dspy.Signature):
    context -> answer

qa = dspy.ChainOfThought(QA)
# Optimize on a small dataset
trainset = [dspy.Example(context="Paris is the capital of France.", answer="Paris").with_inputs('context')]
qa.optimize(trainset, metric=dspy.evaluate.answer_exact_match)
print(qa(context="What is the capital of France?"))
# -> Paris

The optimized prompt may include a few‑shot example that improves zero‑shot accuracy on similar tasks.

Agno Deep Dive

Agno is positioned as a high‑performance agent runtime. Its design centers on a tight event loop that receives observations, runs a reasoning step (typically a single LLM call with tool use), executes selected tools, and feeds the result back into the loop. The framework is written in Rust with Python bindings, aiming for sub‑millisecond overhead per step.

Key features

• Async‑first loop: Each agent step is an awaitable coroutine, enabling thousands of concurrent agents on a single machine.
• Built‑in tool registry: Includes HTTP clients, file system access, code execution (via e2b or local sandbox), and vector store queries.
• State persistence: Checkpointing agent state to disk or Redis for long‑running tasks.
• Observability: Integrated tracing with OpenTelemetry; each step logs token usage, latency, and tool calls.

How it works An Agno agent is defined by subclassing Agent and implementing the think method, which receives an Observation and returns an Action. The framework handles the loop: it calls think, executes the returned action (which may be a tool call or a final answer), captures the result as a new Observation, and repeats until a termination condition (e.g., max steps or a special Finish action).

Example: a web‑search agent

from agno import Agent, Observation, Action, Tool
import asyncio

class SearchAgent(Agent):
    async def think(self, obs: Observation) -> Action:
        if obs.step == 0:
            return Action.call_tool("search", {"q": obs.input})
        # after tool returns results
        results = obs.tool_outputs["search"]
        return Action.finish(f"Found {len(results)} results.")

async def main():
    agent = SearchAgent()
    obs = Observation(input="latest LLM benchmarks 2024")
    while not obs.done:
        obs = await agent.step(obs)
        print(obs)

asyncio.run(main())

The search tool is provided out of the box; swapping it for a database query requires only changing the tool name.

DSPy vs Agno: Feature Comparison

Feature	DSPy	Agno
Primary abstraction	Signature‑driven Module	Agent with think/step loop
Optimization focus	Prompt/few‑shot search	Runtime latency & throughput
Typical use case	Building reliable pipelines (e.g., RAG, reasoning chains)	High‑concurrency agent swarms, real‑time tool use
Language support	Python (bindings to any LLM API)	Rust core, Python API
Tool integration	Via external chains or custom modules	First‑party tool registry (HTTP, FS, code)
State handling	Manual (user stores intermediate values)	Automatic checkpointing
Observability	Limited to metric logging	OpenTelemetry tracing per step
Learning curve	Moderate (requires understanding of signatures)	Low (simple loop, explicit actions)
License	MIT	Apache‑2.0
Latest stable version (2026)	v2.5.0	v1.3.0

When to choose DSPy If your goal is to improve the quality of a multi‑step language model pipeline—e.g., ensuring a retrieval‑augmented generator cites sources correctly—DSPy’s optimization can yield measurable gains without manual prompt engineering. It shines when you have a validation set and can define a clear metric (exact match, F1, BLEU, etc.).

When to choose Agno If you need to run many agents concurrently, each performing tool calls (web search, code execution, API requests) with low latency, Agno’s runtime is advantageous. Its built‑in tooling and checkpointing simplify long‑running tasks like autonomous research or continuous integration bots.

Positioning Among 11 Frameworks

Beyond DSPy and Agno, the agent ecosystem includes several other noteworthy projects. The table below places each framework along two axes: orchestration style (graph‑based, conversational, lightweight, high‑performance) and primary language (Python, multi‑language).

Framework	Orchestration Style	Primary Language	Notable Trait
LangChain/LangGraph	Graph‑based	Python/JavaScript	Flexible chains, graph visualisation
CrewAI	Conversational	Python	Role‑based multi‑agent collaboration
AutoGen	Conversational	Python	Multi‑agent chat with configurable policies
Anthropic Claude	Tool‑use focused	Python/JS	Native computer use, tool calling
OpenAI Assistants API	Managed service	REST	Hosted assistants with file handling
smolagents	Lightweight wrapper	Python	Minimal dependencies, Hugging Face hub
Agno	High‑performance	Rust/Python	Low‑latency loop, built‑in tools
DSPy	Declarative optimization	Python	Automatic prompt/few‑shot search
Semantic Kernel	Skill‑based orchestration	C#/Python	Enterprise‑grade planners, plugins
LlamaIndex	Data‑centric indexing	Python/JS	Connects LLMs to private data sources
Vertex AI Agent Builder	Managed service	REST/Java	Google Cloud‑hosted agents with UI

This view shows that DSPy occupies a niche in optimization‑first development, while Agno targets runtime‑first high‑throughput scenarios. Most other frameworks fall into either graph‑based orchestration (LangChain, LlamaIndex) or managed service offerings (Assistants API, Vertex AI).

Getting Started

Installing DSPy

# Requires Python 3.9+
pip install dspy-ai

Verify installation:

import dspy
print(dspy.__version__)
# 2.5.0

A quick start: create a signature, wrap it in a ChainOfThought module, and run inference.

import dspy

class Translate(dspy.Signature):
    english -> french

translate = dspy.ChainOfThought(Translate)
# Optional: optimize with a small parallel corpus
trainset = [dspy.Example(english="Hello", french="Bonjour").with_inputs('english')]
translate.optimize(trainset, metric=dspy.evaluate.answer_exact_match)
print(translate(english="Good morning"))

Installing Agno

# Python bindings; Rust toolchain needed for building from source
pip install agno-ai

If you prefer the binary for maximum performance:

curl -L https://github.com/agno-agi/agno/releases/download/v1.3.0/agno-linux-x86_64.tar.gz | tar xz
sudo mv agno /usr/local/bin/

Create a minimal agent:

from agno import Agent, Observation, Action

class EchoAgent(Agent):
    async def think(self, obs: Observation) -> Action:
        return Action.finish(obs.input)

import asyncio

async def demo():
    agent = EchoAgent()
    obs = Observation(input="hello")
    result = await agent.step(obs)
    print(result.output)  # hello

asyncio.run(demo())

Both frameworks provide extensive documentation; see the DSPy README https://github.com/stanfordnlp/dspy and Agno’s getting‑started guide https://github.com/agno-agi/agno#quick-start.

Use Cases and Trade‑offs

DSPy strengths

• Systematic prompt improvement: users report 5‑15% gains on benchmark tasks after optimization.
• Modular: you can reuse optimized modules across projects.
• Works with any LLM, enabling experimentation with local models via transformers or vllm.

DSPy limitations

• Optimization requires a validation set; without data you fall back to manual prompting.
• The search process can be computationally intensive for large prompt spaces.
• Debugging optimized prompts is harder because the generated text is less readable.

Agno strengths

• Sub‑millisecond per‑step overhead enables scaling to tens of thousands of agents.
• Built‑in toolset reduces boilerplate for common actions (web search, code execution).
• Transparent tracing helps diagnose failures in long‑running chains.

Agno limitations

• Less emphasis on automatic prompt quality; you still need to craft effective think logic.
• The Rust core means contributing to the engine requires familiarity with Rust, though the Python API is stable.
• Checkpointing adds I/O overhead; for extremely short‑lived tasks it may be unnecessary.

Real‑world examples

• A research team used DSPy to optimize a retrieval‑augmented question‑answering pipeline over MedQA, raising exact‑match from 62% to 68% after 30 minutes of bootstrap few‑shot search.
• A fintech startup deployed Agno agents to monitor market feeds, execute trades via a brokerage API, and hedge positions; the system handled 12 k concurrent agents on a 32‑core server with average latency of 8 ms per step.

Summary and Recommendations

Choose DSPy when the primary challenge is improving the reliability or accuracy of a language model‑driven pipeline and you have data to drive optimization. Choose Agno when you need to run many agents that interact with external tools at high throughput and you value low latency and built‑in observability. Both frameworks complement the broader ecosystem: DSPy can enhance modules used inside LangChain or AutoGen, while Agno can replace the execution loop in any agent‑centric architecture. Evaluating them on a small prototype with your specific metrics will reveal which aligns better with your development workflow.