AI Frameworks · victordibia

designing-multiagent-systems

A comprehensive educational repository and accompanying book on building multi-agent systems from first principles. Includes PicoAgents, a fully implemented framework demonstrating agent reasoning, memory, tools, workflows, and orchestration patterns—designed to teach how multi-agent systems work rather than lock you into a single framework.

Talk to DEV.co Discuss Implementation

Source: GitHub — github.com/victordibia/designing-multiagent-systems

694

GitHub stars

181

Forks

Python

Primary language

Apache-2.0

License (OSI-approved)

Key facts

Objective fields from the source. Values we can't verify are shown as “Unknown” rather than guessed.

Field	Value
Repository	victordibia/designing-multiagent-systems
Owner	victordibia
Primary language	Python
License	Apache-2.0 — OSI-approved
Stars	694
Forks	181
Open issues	6
Latest release	v0.4.0 (2026-02-11)
Last updated	2026-03-12
Source	https://github.com/victordibia/designing-multiagent-systems

What designing-multiagent-systems is

Python-based resource covering agent architecture (tools, memory, middleware, observability), computer vision agents, type-safe workflow orchestration, autonomous multi-agent patterns (GroupChat, LLM-driven, plan-based), and production UX considerations. Includes working examples with FastAPI/SSE streaming, real-time observability, and minimal web UI components.

Quickstart

Get the designing-multiagent-systems source

Clone the repository and explore it locally.

terminalbash

1git clone https://github.com/victordibia/designing-multiagent-systems.git2cd designing-multiagent-systems3# follow the project's README for install & configuration

Need it deployed, integrated, or customized instead? DEV.co ships production installs.

Best use cases

Learning & Understanding Multi-Agent Design

Teams wanting to understand multi-agent fundamentals from first principles before adopting production frameworks. The "build from scratch" approach exposes architectural decisions and trade-offs clearly.

Prototyping Agent Workflows & Orchestration

Rapid iteration on coordination patterns (workflows, autonomous orchestration, plan-based routing) with type-safe implementations and built-in observability/streaming for interactive debugging.

Building Interactive Agent UX Applications

Web applications requiring real-time agent interaction with human-in-the-loop approval, streaming responses, and production-grade observability (OTEL) without framework lock-in.

Implementation considerations

No pre-built integrations with LLM providers (OpenAI, Anthropic, etc.); you supply the client and adapt examples to your API.
PicoAgents is lightweight by design; scaling to many concurrent agents or persistent state requires additional infrastructure (databases, message queues, orchestration).
Observability (OTEL) and memory examples are provided but depend on your observability backend setup (Jaeger, Datadog, etc.).
Computer vision agents require image processing dependencies (PIL, vision models); carefully audit model licenses and data handling.
Human-in-the-loop patterns use simple approval mechanisms; real production systems need proper audit trails, consent management, and legal compliance review.

When to avoid it — and what to weigh

Production Deployment Without Customization — This is a teaching repository and framework. Production use requires adaptation, testing, and likely integration with mature monitoring/deployment infrastructure.
Seeking Pre-Built Enterprise Features — PicoAgents is intentionally minimal and didactic. Missing out-of-the-box features like distributed execution, advanced persistence, or multi-tenant isolation found in mature agent frameworks.
Heavy Reliance on Specific LLM Provider APIs — Framework-agnostic approach means you must implement your own LLM client integration; no pre-built abstractions for provider-specific features (vision, structured outputs, tool use variations).
Requirement for Long-Term Stability Guarantees — Repository is actively developed but educational in nature; breaking API changes may occur as patterns evolve. No formal SLA or commercial support.

License & commercial use

Apache License 2.0 (Apache-2.0). Permissive OSI-approved license allowing commercial use, modification, and distribution with attribution and liability/warranty disclaimers.

Apache-2.0 permits commercial use of the code. However, this is an educational repository; commercial applications require your own LLM API integrations (which may have separate commercial terms) and careful review of model/data licensing in your use case (especially computer vision agents). No warranty or commercial support is provided by the repository maintainer.

DEV.co evaluation signals

Editorial assessment — not user reviews. Directional, with an explicit confidence level.

Signal	Assessment
Maintenance	Active
Documentation	Strong
License clarity	Clear
Deployment complexity	Moderate
DEV.co fit	Good
Assessment confidence	High

Security considerations

Standard considerations apply: (1) Tool execution can run arbitrary code—validate and sandbox tool definitions carefully; (2) LLM prompts are subject to injection attacks; use structured outputs and input validation; (3) Computer vision agents process images—audit model behavior and data retention; (4) No built-in authentication or authorization; web UI examples use simple SSE streams with no access control; (5) OTEL traces may contain sensitive data (prompts, tool outputs)—configure observability backend with appropriate access controls and retention policies. No formal security audit data available.

Alternatives to consider

LangGraph (LangChain)

Mature production framework with built-in persistence, streaming, and LLM integrations. More opinionated but less educational; suitable if you want a ready-to-use abstraction rather than understanding internals.

Microsoft Agent Framework / AutoGen

Production-grade agent orchestration with GroupChat patterns, tool calling, and multi-LLM support. More feature-rich but steeper learning curve and stronger framework coupling.

Google Agent Reasoning Architecture (ADK)

Google's structured approach to agent design with native GCP integrations. Comparable in educational value but tied to Google ecosystem; less framework-agnostic.

Software development agency

Build on designing-multiagent-systems with DEV.co software developers

Explore the repository to understand agent architecture, workflows, and orchestration. Grab the book for narrative guidance, or dive into the code examples and run them locally to learn by doing.

Talk to DEV.co

Related open-source tools

Surfaced by semantic similarity across the DEV.co open-source index.

generative-ai OmAgent ai-engineering-hub

Related on DEV.co

Explore the category and the services that help you build with it.

AI Frameworks AI Application Development Web App Development API Development Talk to DEV.co

designing-multiagent-systems FAQ

Can I use PicoAgents in production?

Technically yes (Apache-2.0 allows it), but it is an educational framework. You must add your own LLM client, persistence, observability integration, and security hardening. No warranty or support provided by the maintainer.

Does this work with OpenAI, Anthropic, or other LLM APIs?

The framework is LLM-agnostic. Examples show patterns; you integrate your own LLM client. The repository does not include pre-built adapters for specific providers, though examples can be adapted.

What is the difference between PicoAgents and the book?

The repository is the official code companion to the paid book *Designing Multi-Agent Systems*. The book provides narrative context and deeper explanation; the repo has working, runnable examples.

Is there commercial support or an SLA?

Unknown. This is a single-author educational project. No formal commercial support is advertised. Bug fixes and updates depend on the maintainer's availability; no SLA is provided.

Software development & web development with DEV.co

DEV.co helps companies turn open-source tools like designing-multiagent-systems into production software. Our software development services cover the full lifecycle — architecture, web development, integration, and maintenance — delivered by software developers and web developers who ship. Engage our software development agency to implement or customize it for your ai frameworks stack.

Build Multi-Agent Systems from First Principles

Explore the repository to understand agent architecture, workflows, and orchestration. Grab the book for narrative guidance, or dive into the code examples and run them locally to learn by doing.

Talk to DEV.co Schedule a Consultation