multimind 0.2.1

Local-first reasoning pipeline wrapper for Ollama and LM Studio

🧠 MultiMind AI

A local-first web UI that adds sequential reasoning pipelines and parallel expert councils on top of small local models.

MultiMind AI bridges the gap between small local models and complex reasoning. It effortlessly auto-discovers endpoints like Ollama and LM Studio (OpenAI-compatible) and lets you deploy three reasoning architectures: a sequential Thinking Pipeline (Planning, Execution, Critique), a parallel Agent Council (Expert Advisors & Lead Judge), or a hierarchical Organisation Mode (CEO → Departments → Employees → Synthesis).

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✨ Features

• 🧠 Thinking Pipeline: Elevate smaller models with dedicated Planning, Execution, and Critique phases.
• 🏛 Agent Council: Deploy a committee of expert models in parallel. Several 'Advisors' provide independent perspectives, synthesized by a Lead Judge into a single superior response.
• 🏢 Organisation Mode: Run a hierarchical multi-agent workflow where a CEO decomposes the request, department heads delegate work to specialist roles, and the CEO synthesizes all outputs into one final answer.
• 🔌 Zero-Config Auto-Discovery:
  • Automatically hooks into local Ollama endpoints (http://127.0.0.1:11434).
  • Supports optional discovery for LM Studio (http://127.0.0.1:1234).
• 🎯 Precision Model Mapping: Assign distinct models to handle the different stages of thought or council roles.
• 💬 Immersive UI: Enjoy a streaming timeline interface with collapsible "thought blocks" to keep your UI clean while the AI thinks.
• 📝 Native Markdown & Math Support: Final outputs are rendered as HTML, with math equations typeset using a bundled local KaTeX build.
• ⚡ Frictionless Setup: Purely in-memory settings. Zero .env setup required for your first run.

🚀 Quick Start

Get up and running in your local environment in seconds:

# 1. Install the package via pip
pip install multimind

# 2. Launch the application
multimind

🛠 Setting up for Development / Source Install

# 1. Clone the repository
git clone https://github.com/JitseLambrichts/MultiMind-AI.git
cd MultiMind-AI

# 2. Create a virtual environment
python3 -m venv .venv

# 3. Activate the virtual environment
source .venv/bin/activate  # On Windows: .venv\Scripts\activate

# 4. Install the package in editable mode
pip install -e .

# 5. Launch the application
multimind

Next: Open your browser and navigate to http://127.0.0.1:8000 🎉

🔌 Supported Backends

MultiMind AI works seamlessly with standard local APIs:

• Ollama: Connects via /api/chat and /api/tags
• OpenAI-Compatible Servers (e.g., LM Studio): Connects via /v1/chat/completions and /v1/models

If no provider is automatically detected, you can easily point the backend to your local OpenAI-compatible endpoint using the application's settings panel.

🧠 Thinking Pipeline (Sequential Reasoning)

The sequential reasoning pipeline elevates the capabilities of standard models by splitting inference into modular steps:

1. Plan: Formulates a detailed technical roadmap to solve the user's request.
2. Execute: Implements the primary solution based on the established plan.
3. Critique (Hard Mode): Rigorously audits the implementation for errors or omissions, delivering a refined, superior final answer.

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🏛 Agent Council (Parallel Expert Consensus)

For complex tasks requiring multiple perspectives, MultiMind AI offers the Agent Council. This architecture emphasizes parallel expertise over sequential steps:

1. Parallel Advisors: Multiple models process the user's request independently, providing diverse expert viewpoints.
2. Diverse Perspectives: Each advisor follows expert-level system prompts to ensure accurate, independent technical analysis.
3. The Judge: A final 'Lead Synthesizer' model reviews all advisor outputs, cross-examines their findings, resolves conflicts, and merges the best elements into a single definitive response.

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🏢 Organisation Mode (Hierarchical Multi-Agent Workflow)

For tasks that benefit from structured delegation, MultiMind AI includes Organisation Mode. Instead of parallel peers only, this mode simulates an org chart with explicit delegation layers:

1. CEO Planning: A CEO agent analyzes the user request and splits it into department-level sub-tasks.
2. Department Delegation: Each department head converts its sub-task into role-specific assignments.
3. Employee Execution: Specialist employee agents execute their assigned tasks and stream their outputs.
4. CEO Synthesis: The CEO consolidates all department/employee results into a single cohesive final response.

In the UI, this appears as an interactive organisation chart with expandable nodes and streaming outputs per role, while still ending with one final answer block.

⚙️ Configuration: Organisation mode uses one selected model for all agents in the hierarchy (configurable via the Organisation settings panel).

📝 Note: Chat history is intentionally in-memory only for the current MVP.

📊 Benchmarks

We evaluated the performance of MultiMind AI's reasoning pipeline using a subset of 20 questions from the GSM8K dataset. The results demonstrate a clear improvement in model accuracy when utilizing the different reasoning modes.