Three Agno Multi-Agent Patterns, From Technical Writing to Candidate Screening

A practical map for multi-agent application design

Many agent projects begin with a vague goal: use several language-model-powered workers to handle a task that is too broad for one prompt. The hard part is turning that goal into an understandable workflow. Which agent owns research? How is information handed to a writer? Where do validation, memory, tools, and structured scoring fit?

Agno Multi-Agent Examples addresses that builder need with three Python examples built around the Agno framework. Rather than presenting a single generic agent, the repository organizes distinct patterns around concrete workloads: generating technical blog posts, producing Agno agent packages from natural-language requests, and screening job candidates.

That makes the repository most useful as a set of implementation starting points for developers who want to compare orchestration shapes. The examples are not presented as one unified product. They are separate systems with their own directories and instructions, each demonstrating a different way to divide work among agents.

Three workflows, three ways to distribute work

The first example, TechBlog AI Writer Team, uses a four-agent team in Agno's coordinate mode. Its stated outcome is a technical blog post of 2,000 or more words. The team divides responsibilities among a Technical Researcher that uses the GitHub API, a News & Trends Agent that uses DuckDuckGo, a Code Example Generator, and a Technical Writer.

The key design idea is specialization paired with shared context. Research, trend discovery, code examples, and final prose are separate jobs, while the README describes team memory and context sharing. For a builder, this is a useful model when the final output needs several kinds of input and a single agent would otherwise need to research, synthesize, write code, and edit all at once.

The second example, Agent Builder Team, uses a three-agent sequential pipeline. It takes a natural-language request and is intended to generate another Agno agent. The Requirements Analyst interprets the request, the Agno Docs Expert consults a knowledge base, and the Code Generator writes the resulting code.

This is a meta-agent pattern: a system that helps create other agent systems. The repository says it produces a complete package of four files and includes self-validating code generation. Its sequence illustrates a practical separation: convert a human request into requirements first, ground implementation in documentation next, then generate code. That ordering can make the handoffs clearer than asking a code generator to infer every constraint directly from the original request.

The third example, AI Candidate Screening System, is another sequential design with four stages. A Resume Parser extracts structured data, a Skills Matcher compares that data with job requirements, an Experience Evaluator examines relevance, and a Scorer & Ranker produces recommendations. The README identifies this example as built for TalentPerformer.

Here, the workflow shows how a broad evaluative task can be made inspectable. Parsing, matching, experience analysis, and ranking are distinguishable steps instead of a single opaque judgment. The project also describes a customizable weighted scoring system and detailed screening reports. Builders working on HR automation should still treat evaluation criteria, recommendations, and any claims of bias reduction as areas requiring careful review and validation in their own deployment. The repository describes the system as bias-free, but the supplied evidence does not document a method or evaluation supporting that outcome.

What the stack contributes

The repository is written in Python and lists Python 3.8 or later as a prerequisite. Its core framework is Agno, listed at version 1.1.0 or later. Agno supplies the agent and team abstractions behind the coordinate and sequential workflows described in the README.

The examples use OpenAI GPT-4o or GPT-4o-mini as their listed LLM options, and the setup requires an OpenAI API key. That requirement is important operationally: these examples depend on an external model service and its associated access and usage costs.

The documentation also names SQLite for agent memory. In the first example, memory and context sharing are part of the team design, which suggests a useful distinction for builders: the model call produces an answer, while persistent storage can retain context across work in the system.

The agent-builder example additionally lists LanceDB as its vector database. A vector database stores numerical representations of content so an application can retrieve material by semantic similarity. In this repository, LanceDB is associated with the Agno documentation knowledge base, giving the documentation-focused agent a defined source of context during the code-generation pipeline.

Start with the coordination model, not the agent count

A useful lesson across the three examples is that the coordination pattern should follow the dependency pattern of the work. The technical writing team uses coordinate mode because research, trend gathering, examples, and writing can be treated as complementary responsibilities. The agent builder and screening systems use sequential pipelines because each later stage depends on an earlier transformation or analysis.

The repository's learning path reflects this progression. It suggests starting with candidate screening for a clearer use case, moving to the coordinated technical-writing team, then exploring the agent builder for the more advanced combination of meta-programming and knowledge-base use.

There are also practical boundaries to keep in mind. Each example needs separate setup inside its own directory, including dependency installation and environment configuration. The README's reported time and cost figures are project claims, not independently verified benchmarks in the supplied evidence. Treat them as rough documentation, not planning guarantees.

For builders looking to study how agent responsibilities, tools, memory, and retrieval can be assembled into targeted workflows, inspect the examples in the verified MuLIAICHI/agno-multi-agent-examples repository.