Overview

Crowe Architecture Deep Dive

This guide presents an in-depth look at the Crowe protocol, explaining how its components fit together — from the foundation of individual agent classes to the construction of coordinated multi-agent systems. It also highlights the core modules and directories inside the crowe/ package.

Crowe is built with three principles at its core: extensibility, modularity, and production-grade reliability. Its design makes it possible to coordinate intelligent agents, integrate specialized tools, manage persistent memory, and run complex collaborative workflows at scale.

As a high-performance orchestration framework, Crowe empowers teams and organizations to create, deploy, and operate agents that can think, cooperate, and tackle sophisticated problems — either independently or as part of larger groups. Every part of the system can be adapted or swapped out, giving developers freedom to shape solutions for both small automation tasks and advanced, multi-step reasoning pipelines.

Whether you need a single lightweight automation or an intricate web of interacting agents with long-term memory and tool integration, Crowe provides a structured foundation to make it possible. For setup instructions and a broader introduction, visit the Crowe Documentation Home.

Core Architectural Flow

Within Crowe, the architecture is arranged into distinct functional layers, each addressing a specific role in the system’s overall operation. At a high level, the flow begins with the most fundamental element and builds upward:

Agent Module

Agents are the fundamental units of intelligence in Crowe. Each agent combines logic, internal state, and defined behaviors to perform its role.

They can be as lightweight as a stateless utility agent or as sophisticated as a stateful reasoning entity with memory and advanced decision-making capabilities.

Agents are adaptable — they can be tailored for focused responsibilities such as analytical reasoning, specialized tool execution, or evaluation/judging of other agents’ outputs.

Example:

A ReasoningAgent that processes complex data and determines optimal actions.
A ToolAgent that serves as an interface to an external API.

Agent Tools and Context Management (`crowe/tools`, `crowe/utils`)

Tools in Crowe are self-contained, pluggable modules that extend an agent’s abilities — enabling them to connect with external systems, run computations, or retrieve information from resources such as APIs, databases, and file storage.

Alongside tools, the framework provides memory components and utility helpers that allow agents to:

Preserve contextual information across multiple interactions.
Cache results for efficiency.
Manage state in complex, multi-step workflows.

Example Use Cases:

A connector for querying an LLM service.
A persistent memory store for conversation history.
A parser/formatter that processes raw data into structured output.

Cognitive & Specialized Agents (`crowe/agents`)

Built on top of the core aent class, these advanced agents incorporate higher-order reasoning, self-verification, and task-specific intelligence. They are designed to handle complex processes such as strategic planning, evaluation of results, and multi-stage workflows.

Within this category are agents capable of:

Reflecting on their own outputs to refine accuracy.
Iteratively improving solutions through multiple reasoning cycles.
Applying deep domain expertise to specialized problem spaces.

Example Roles:

SelfConsistencyAgent — combines multiple reasoning paths to reach the most reliable conclusion.
JudgeAgent — critically assesses and scores the work of other agents.

Multi-Agent Structures (crowe/structs)

Agents are composed into higher-order structures for collaboration, voting, parallelism, and workflow orchestration.

Includes crowes for majority voting, round-robin execution, hierarchical delegation, and more.

Example: A MajorityVotingCrowe that aggregates outputs from several agents, or a HierarchicalCrowe that delegates tasks to sub-agents.

Multi-Agent Architectures Overview MajorityVotingCrowe HierarchicalCrowe Sequential Workflow Concurrent Workflow

Supporting Components

Communication (crowe/communication): Provides wrappers for inter-agent communication, database access, message passing, and integration with external systems (e.g., Redis, DuckDB, Pulsar). See Communication Structure
Artifacts (crowe/artifacts): Manages the creation, storage, and retrieval of artifacts (outputs, files, logs) generated by agents and crowes.
Prompts (crowe/prompts): Houses prompt templates, system prompts, and agent-specific prompts for LLM-based agents. See Prompts Management
Telemetry (crowe/telemetry): Handles logging, monitoring, and bootup routines for observability and debugging.
Schemas (crowe/schemas): Defines data schemas for agents, tools, completions, and communication protocols, ensuring type safety and consistency.
CLI (crowe/cli): Provides command-line utilities for agent creation, management, and orchestration. See CLI Documentation

Crowe Improvement Proposals (CIPs) — Driving Major Enhancements

For substantial updates — such as introducing new agent types, evolving Crowe’s coordination models, or adding advanced capabilities — the framework follows a structured review process known as Crowe Improvement Proposals (CIPs).

A CIP is a formal design document that outlines a proposed feature, architectural change, or integration. Its purpose is to ensure that impactful modifications are well-specified, openly discussed, and community-reviewed before implementation.

Typical scenarios for a CIP:

Designing new agent classes, collaboration patterns, or orchestration strategies.
Making core framework changes or introducing breaking updates.
Adding major integrations (LLM providers, tools, external systems).
Implementing multi-component or high-complexity features.

📄 For detailed steps and templates, refer to the full CIP Guidelines.

Architecture Diagram Overview

The diagram below illustrates how data and control flow through the Crowe protocol, along with the relationships between its primary modules inside the crowe/ package.

Top-Level Structure:

crowe/agents — Core agent classes forming the foundation of the framework.
crowe/tools, crowe/utils — Tooling components and memory/context management utilities.
crowe/agents — Advanced reasoning agents and specialized intelligence modules.
crowe/structs — Multi-agent orchestration structures.
Communication, Artifacts, Prompts, Telemetry, Schemas, CLI — Supporting systems for integration, monitoring, and operational control.

Module-by-Module Overview

1. `agents/` — Core & Specialized Agents

Houses every agent class in the framework — from lightweight base agents to advanced reasoning units, tool wrappers, evaluators, and judges. Highlights:

Extensible, modular design for easy customization.
YAML-driven configuration for quick setup (see YAML Agent Creation).
Includes agents for self-consistency, evaluation, and domain-specific expertise.

2. `tools/` — Tooling & Integration Layer

Contains all logic related to tools, from registration and invocation to schema definitions and third-party service integration.

Highlights:

Dynamic tool registration and execution by agents.
Supports OpenAI function calling, Cohere, and fully custom tool schemas.
Includes utilities for parsing inputs, formatting outputs, and running tool calls efficiently.

3. `structs/` — Multi-Agent Orchestration

Implements the core coordination layer for Crowe, including multi-agent structures, workflow engines, routing mechanisms, and registries.

Highlights:

Provides Crowe structures for majority voting, round-robin execution, hierarchical delegation, spreadsheet-style processing, and more.
Supports multiple orchestration modes — sequential, concurrent, and graph-based workflows.

Supporting & Operational Modules

utils/ — Utilities & Memory Management Common helper functions, memory handling, caching mechanisms, and wrappers used throughout the framework.
telemetry/ — Observability & Runtime Tracking Centralized logging, telemetry collection, performance monitoring, and bootup routines to ensure smooth operation.
schemas/ — Data Models & Validation Structured schemas for agents, tools, completions, and communication protocols, providing type safety and consistency.
prompts/ — Prompt Management Stores system prompts, task-specific prompts, and agent-level templates for LLM workflows, with support for multi-modal and domain-focused configurations.
artifacts/ — Artifact Storage & Retrieval Manages creation, persistence, and retrieval of artifacts (files, logs, outputs) generated during Crowe operations.
communication/ — Communication & Integration Layer Abstractions for inter-agent messaging, database connections, and integrations with external systems like Redis, DuckDB, and Pulsar.
cli/ — Command-Line Interface Tools Provides CLI utilities for agent creation, management, and orchestration of Crowe workflows.

How the Components Work Together

Crowe’s architecture is built with composability at its core. Individual agents can be developed and configured in isolation, then assembled into larger, coordinated structures (crowes) to support either collaborative or competitive task execution.

Tools and memory modules are integrated into agents only when needed, giving them the ability to handle complex processes, retain context, and optimize results over time. Multi-agent orchestration layers manage the flow of information, coordinate decision-making, and ensure that the right agent takes the right action at the right moment.

The surrounding infrastructure — communication channels, telemetry services, artifact storage, and more — provides the robustness, transparency, and scalability required for production-grade deployments.

Example Workflow:

Define a group of specialized agents (e.g., a market analyst, a summarizer, and a judge).
Connect tools such as LLM APIs, database queries, or web search, along with memory components for context retention.
Assemble the agents into a MajorityVotingCrowe to make collective decisions.
Use communication layers to exchange data between agents and integrate with external systems.
Log all activity and outputs for traceability, analytics, and debugging.

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