Miroflow

Miroflow is an agent orchestration framework designed to coordinate multiple large language models and autonomous agents to perform complex research and reasoning tasks. It functions as a hierarchical workflow manager that distributes workloads across specialized agents using intent recognition and structured planning to gather deep information and solve challenging queries.

The system distinguishes itself through a multi-model integration gateway and a provider-agnostic interface, allowing it to unify various language model providers. It extends these models via a tool-augmented framework that connects agents to external functional tools such as web search and code execution.

The platform covers broader capabilities in automated web research, structured document analysis for files like PDFs and spreadsheets, and concurrent task execution. These processes are managed through an intent-based planning engine and a tool-call execution loop to handle high-volume workflows.

Features

Multi-Agent Orchestration Frameworks - Provides a framework to coordinate multiple specialized autonomous agents for executing complex collaborative research and reasoning tasks.

Autonomous Research Agents - Provides an orchestration layer for agents to perform multi-step research via web searches and document analysis.

Hierarchical Task Delegation - Allows a coordinator agent to spawn and coordinate specialized sub-agents for complex task execution.

Agent Delegation Frameworks - Distributes complex research workloads among specialized sub-agents to optimize task execution.

Tool Call Execution Loops - Implements a cycle where agents request tools, execute them, and feed observations back into the reasoning loop.

Agentic Workflow Automation - Coordinates complex research workflows through a sequence of planning and delegation across specialized agents.

Autonomous Web Research Agents - Employs agents to crawl, synthesize, and summarize information from online sources to solve challenging queries.

External Tool Integrations - Connects autonomous agents to external functional tools including web search and code execution.

Hierarchical Agent Orchestration - Implements a manager-worker structural hierarchy to coordinate sub-agents for complex research and internet searches.

Tool-Augmented Language Models - Extends language model capabilities by integrating external tools like web search and code execution for active task performance.

LLM Gateways - Implements a centralized interface that aggregates multiple LLM providers into a single provider-agnostic API.

LLM Provider Interfaces - Provides a unified interface for communicating with various large language model providers to power reasoning tasks.

Multi-Agent Orchestrators - Coordinates teams of specialized AI agents across different LLM providers to solve complex reasoning tasks.

Provider-Agnostic Model Interfaces - Implements an abstraction layer that standardizes inputs and outputs across multiple LLM providers.

Research Workflows - Provides a structured sequence of intent recognition, planning, and delegation for the research-to-finalization lifecycle.

Document Structure Analysis - Uses AI-driven extraction to process text and tables from files such as Excel and PDF.

Real-Time Web Search Integrations - Integrates live internet search capabilities to ground agent reasoning with current information for complex query resolution.

Concurrent Task Execution - Executes high-volume research workflows simultaneously using a fault-tolerant system to manage rate limits and network instability.

Multi-Format Document Parsing - Extracts structured data from various file formats like PDFs and spreadsheets for analysis by LLMs.

Intent-Based Workflow Determination - Analyzes user input to determine whether to trigger a complex agent workflow or a specific prompt.

Concurrent Research Pipelines - Runs multiple research pipelines in parallel using a fault-tolerant system to manage rate limits and network failures.

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Features

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