# Product Requirements Document (PRD)

Version: v0.1 (Open Source Release)  
Status: Implementation Ready  
Target: One-night MVP implementation with a clear architectural foundation for future expansion.

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# 1. Product Overview

Hivemind is an open-source framework for orchestrating distributed swarms of AI agents that collaboratively solve complex tasks.

Traditional LLM systems rely on a single agent or sequential chains of prompts. This approach does not scale well for complex reasoning problems that require decomposition, parallel processing, and structured execution.

Hivemind addresses this limitation by introducing a swarm execution model in which tasks are decomposed into smaller units, executed by multiple agents in parallel, and coordinated through a scheduler and execution graph.

The system provides a minimal yet extensible framework for:

- defining agents
- decomposing tasks into subtasks
- executing subtasks concurrently
- coordinating task dependencies
- logging system behavior
- aggregating outputs into final results

The design prioritizes clarity, modularity, and composability so that developers can experiment with large-scale agent coordination without needing complex infrastructure.

The initial release focuses on a minimal, reliable foundation rather than feature completeness.

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# 2. Product Vision

The long-term vision for Hivemind is to provide the foundational infrastructure layer for distributed AI agent systems.

Instead of treating LLMs as isolated tools, Hivemind treats them as workers within a coordinated computational system.

The framework should make it possible to:

- run large numbers of agents simultaneously
- coordinate complex reasoning workflows
- observe and debug agent behavior
- extend the system with new planning or execution strategies

In its mature form, Hivemind aims to function as a runtime for distributed cognition, where agent swarms collaboratively solve large problems in a structured and observable manner.

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# 3. Problem Statement

Large reasoning tasks are poorly suited to single-pass LLM prompts.

Typical limitations include:

- context window constraints
- sequential execution bottlenecks
- lack of parallelism
- limited transparency into reasoning processes
- inability to coordinate multiple reasoning strategies

While existing frameworks allow chaining or tool usage, they do not provide robust infrastructure for coordinating many agents simultaneously.

Developers who attempt to build swarm-like systems often encounter several challenges:

- scheduling tasks across agents
- managing task dependencies
- handling parallel execution
- tracking system state
- debugging complex workflows

Hivemind provides a lightweight orchestration layer that solves these problems while remaining simple enough to run locally.

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# 4. Target Users

The primary users of Hivemind are developers, researchers, and engineers experimenting with agent-based reasoning systems.

Typical user profiles include:

AI researchers exploring distributed reasoning architectures.

Machine learning engineers building experimental agent systems.

Startup engineers prototyping new AI workflow architectures.

Open-source contributors experimenting with multi-agent coordination.

Advanced users interested in building new planning or scheduling algorithms.

The system is intentionally developer-focused and does not target non-technical users.

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# 5. Core Principles

The design of Hivemind follows several guiding principles.

Minimalism

The framework should contain the smallest possible set of primitives required to build swarm systems.

Extensibility

Developers should be able to replace planners, schedulers, and execution strategies without modifying the core architecture.

Observability

System behavior should be transparent and easy to inspect through structured event logs.

Parallelism

The architecture must enable concurrent agent execution to maximize performance.

Deterministic execution flow

Task scheduling and dependency resolution should be predictable and reproducible.

Local-first design

The initial version must run locally without requiring distributed infrastructure or external services.

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# 6. Product Scope

The v0.1 release includes the following major capabilities:

Agent abstraction

Agents represent individual reasoning workers capable of executing tasks.

Task decomposition

A planner component breaks large tasks into smaller subtasks.

Task graph representation

Subtasks are represented as nodes within a dependency graph.

Parallel execution

Independent tasks are executed concurrently.

Execution orchestration

A swarm controller coordinates the lifecycle of task execution.

Event logging

All system actions are recorded in an append-only event log.

Example workflows

The repository includes several example swarm configurations demonstrating common use cases.

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# 7. Non-Goals

The initial release explicitly excludes the following features.

Distributed cluster execution across multiple machines.

Persistent vector databases.

Long-term memory systems.

Complex agent communication protocols.

Production-level security or authentication.

User interface dashboards.

External orchestration frameworks.

These features may appear in future releases but are not required for the first open-source version.

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# 8. System Architecture

The Hivemind architecture consists of five primary components.

Agents

Agents are the fundamental reasoning units. Each agent receives a task description and produces an output.

Agents are designed to be stateless workers. All coordination occurs outside the agent itself.

Planner

The planner decomposes complex tasks into smaller units of work. The output of the planner is a structured list of subtasks.

Planner behavior may vary depending on the implementation. In the initial release, the planner is LLM-based.

Task Graph

Subtasks are organized into a directed acyclic graph representing dependency relationships.

Tasks may only execute once their dependencies are completed.

Executor

The executor is responsible for scheduling and executing tasks within the graph.

It identifies tasks that are ready to run and assigns them to available workers.

Worker Pool

The worker pool provides concurrency control and manages multiple agents executing tasks simultaneously.

Event Log

All actions performed by the system generate structured events recorded in a persistent log.

The event log allows users to inspect system behavior after execution.

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# 9. Execution Workflow

A typical Hivemind execution follows several stages.

Task submission

The user submits a high-level task description.

Task planning

The planner decomposes the task into subtasks.

Graph construction

Subtasks are organized into a dependency graph.

Task scheduling

The executor identifies tasks whose dependencies have been satisfied.

Parallel execution

Available workers execute tasks concurrently.

Result collection

Task outputs are recorded and passed to dependent tasks if necessary.

Completion

The swarm finishes once all tasks in the graph have completed.

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# 10. Data Model

The system revolves around several core data entities.

Task

Represents a unit of work to be executed by an agent.

Tasks contain a unique identifier, a textual description, dependency information, and an optional result field.

Agent

Represents a reasoning worker capable of executing tasks.

Agents contain configuration information describing how they interact with language models and tools.

Event

Represents a recorded system action.

Events contain a timestamp, an event type, and structured metadata describing the action.

Swarm Configuration

Defines the parameters controlling swarm behavior, such as worker count and planner settings.

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# 11. Event System

The event log is a core observability feature.

Every major system action produces an event.

Examples include:

task_created  
task_started  
agent_invoked  
task_completed  
task_failed  
swarm_started  
swarm_completed

Events are recorded sequentially in an append-only log file.

This structure allows developers to reconstruct swarm execution behavior after the system finishes.

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# 12. Example Use Cases

Research synthesis

A swarm decomposes a complex research topic into smaller questions, assigns them to agents, and aggregates the findings into a coherent report.

Document summarization

Large document collections are divided into segments, summarized in parallel, and combined into a final overview.

Codebase analysis

Agents analyze different parts of a software repository simultaneously and produce architectural summaries.

Information extraction

Agents process large datasets in parallel to extract structured knowledge.

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# 13. Repository Structure

The repository is organized to emphasize clarity and modularity.

Core components are separated by responsibility, with independent modules for agents, swarm orchestration, runtime systems, and utilities.

Example workflows are included to demonstrate system usage and encourage experimentation.

The project structure is designed to remain stable as the system evolves.

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# 14. Success Criteria

The v0.1 release will be considered successful if it satisfies the following criteria.

The system can decompose tasks into subtasks.

Subtasks can execute concurrently across multiple agents.

Task dependencies are respected.

The execution process is observable through structured event logs.

The repository contains working examples demonstrating swarm workflows.

The project can be installed and executed locally with minimal configuration.

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# 15. Future Roadmap

The initial release establishes the architectural foundation for future capabilities.

Potential future directions include:

Distributed cluster execution across multiple machines.

Swarm replay and execution visualization.

Persistent memory systems.

Agent communication protocols.

Tool marketplaces and shared agent capabilities.

Adaptive planning strategies.

Advanced scheduling algorithms.

Long-term swarm knowledge graphs.

These extensions should build on the core primitives defined in the initial architecture.

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# 16. Risks and Challenges

Several technical challenges may emerge during development.

Planner reliability

LLM-based planners may generate inconsistent task decompositions.

Execution coordination

Parallel execution introduces potential complexity in dependency management.

Output aggregation

Combining results from multiple agents into coherent outputs may require additional logic.

Observability

Debugging distributed agent behavior may require more advanced monitoring tools.

These challenges are expected and can be addressed iteratively.

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# 17. Summary

Hivemind introduces a minimal yet powerful architecture for coordinating large numbers of AI agents.

The system focuses on a small set of primitives that enable task decomposition, parallel execution, and structured orchestration.

By emphasizing modular design and observability, Hivemind provides a foundation for experimenting with distributed reasoning systems.

The v0.1 release prioritizes simplicity and clarity, establishing the groundwork for future extensions while remaining approachable for developers.

The ultimate goal is to enable scalable agent swarms capable of solving problems beyond the reach of single-agent systems.