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  3. Multi Research Agents
Multi Research Agents logo
Health: ActiveRecent health check succeeded.Last checked 9/9/2026, 6:01:55 PM

Multi Research Agents

User RatingsBe the first to rate and review this MCP server!
View Repository16 GitHub StarsTotal stargazers on GitHub for the source repository (16 stars).Visit Website
researchweb-searchmulti-agentkotlinmcp

Coordinates parallel web research agents and combines their findings through an MCP DeepResearch tool or REST endpoint.

Quick Install

Automated & IDE Setup

Copy the AI prompt to install this server into Claude Code, Cursor, or another agent β€” or use 1-click editor setup below.

One-click editor setup isn’t available for this listing yet β€” we don’t have a confirmed install command, and we’d rather show nothing than point your editor at the wrong package or host. Follow the project’s own setup instructions, linked above.

Manual Client & Custom JSON ConfigExpand JSON β–Ύ
No confirmed setup config for this listing yet. We only publish a config block when the install details come from the project itself β€” its README, its docs, or a verified owner. We haven’t found those for gemy411/multi-research-agents, and we’d rather show nothing than a guess you’d paste into your client. Follow the project’s own setup instructions for the current steps.
Install Directory Badge Claim listing AlternativesπŸ”Ž More in Search & Data Extraction

Overview

The gemy411/multi-research-agents MCP server coordinates multiple AI agents to investigate complex research questions and synthesize their findings. A lead agent analyzes each query, deploys focused subagents, evaluates intermediate results, and can launch further investigations before producing an answer. Subagents search the web and fetch content through a Sonar Search Manager, while OpenRouter provides access to Gemini and Sonar models. Reach for it when a question spans several domains or requires iterative, multi-perspective web research rather than a single search.

Use cases

β€’Investigate a complex topic across several research domains
β€’Compare developments from multiple organizations
β€’Analyze technical, economic, and social dimensions of a question
β€’Iterate on research after reviewing preliminary findings

Key features

β€’MCP DeepResearch tool
β€’Ktor REST search endpoint
β€’Parallel specialized subagents
β€’Iterative agent deployment
β€’Web search and content fetching
β€’OpenRouter model access

Capabilities & Tool Schemas

Inspect callable tools, capabilities, and parameters exposed to AI agents by Multi Research Agents.

Extracted Tool Capabilities
MCP DeepResearch tool
Ktor REST search endpoint
Parallel specialized subagents
Iterative agent deployment
Web search and content fetching
OpenRouter model access

How Multi Research Agents works

What gemy411/multi-research-agents MCP server does

The gemy411/multi-research-agents MCP server provides a coordinated workflow for complex research questions. It exposes a DeepResearch MCP tool that accepts a natural-language query, and it also provides a REST endpoint at GET /search?query=.... Both interfaces invoke the same lead research agent and return the resulting synthesis.

The system is intended for investigations that benefit from dividing a question into separate areas. Examples in the project include comparing quantum-computing developments and examining the economic, environmental, and social effects of urban vertical farming.

How it works

A lead agent first interprets the incoming question and determines a research plan. It uses a deployAgent capability to create specialized subagents, which can work on different aspects of the query in parallel. Each subagent performs focused web searches, retrieves content from selected URLs, and reports its findings to the lead agent.

The lead agent reviews those intermediate results and may deploy more subagents when the investigation needs additional comparisons, timelines, or technical analysis. It then combines the collected findings into a final response. The lead agent uses a Gemini Pro model and supports up to 50 iterations; subagents use Gemini Flash and support up to 30 iterations. Conversation history can be compressed when it exceeds 20 messages.

Setup and configuration

The project is implemented in Kotlin with Ktor and uses the Koog agent framework. To configure it, copy the example environment file to .env and provide an OpenRouter credential:

bash
cp example.env .env

Set OPEN_ROUTER_API_KEY in that file. PORT can be used to select the server port; the documented default is 8080, while the example configuration uses 4321. Build and start the application from the repository with:

bash
./gradlew build
./gradlew run

The documented startup address is http://0.0.0.0:8080 when the default port is used.

Tools and capabilities

The gemy411/multi-research-agents MCP server exposes the following documented research-facing capability:

  • DeepResearch: accepts a complex research question through MCP and runs the lead-agent workflow.
  • Web search and content retrieval: available to research subagents through the Sonar Search Manager.
  • Parallel subagent execution: allows separate research tasks to proceed simultaneously.
  • Iterative delegation: lets the lead agent add research tasks after reviewing early findings.
  • Result synthesis: combines specialized reports into one response.

OpenRouter is used to access the configured language models, including Sonar by Perplexity for search functionality.

Limitations and notes

The provided material does not document authentication behavior for the MCP transport, supported MCP client applications, deployment instructions, or a project license. Running the application requires an OpenRouter API key, and the available research behavior depends on the configured models and Sonar search access. The REST and MCP interfaces are described as exposing the same underlying functionality; no separate REST-only or MCP-only research features are specified.

Read the full README β†’View source on GitHub β†’

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Adoption & maintenance

Factual signals from GitHub, npm, and our automated checks β€” not a rating.

GitHub stars
16
Stargazers on the source repository.
Last commit
5mo ago
Most recent push to the default branch.
Directory activity
5 views
Config copies, upvotes, and views on AllMCPs.

Reviews

No reviews yet β€” be the first to share how this listing worked for you.

Frequently Asked Questions about Multi Research Agents

Clone the repository, copy the example environment file to `.env`, set `OPEN_ROUTER_API_KEY`, then run `./gradlew build` followed by `./gradlew run`.

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Technical Specs & Signals

CategoryπŸ”ŽSearch & Data Extraction
PricingBring your own API key (usage-based cost)
More technical detailsExpand β–Ύ
AuthAPI key
Last updatedSep 2, 2026
10/10 checks healthy over the last 33d
Views5
Unique ViewsTotal visits recorded for this listing page on AllMCPs.
Installs0
Installs & Copy ActionsTotal times users copied install commands or configuration snippets for this server.
GitHub stars16
GitHub Star CountTotal stargazers on GitHub representing community popularity (16 stars).
Last commit5mo ago
Last Repository CommitThe most recent commit or push recorded for this server's GitHub repository.Last commit on Mar 19, 2026
44Quality signal: Fair Β· 44/100How this signal is calculated β–Ύ
Server availabilityNot measured

Not scored for repo-hosted servers β€” we can't reach the running server, only its GitHub page. Hosted MCP endpoints are health-checked live.

Verified ownership10/20
Documentation & tools19/30
Adoption & activity3/15
Community engagement1/10

A guidance signal from public completeness & health data β€” not a user rating. New listings start lower and rise as they add docs, get verified, and grow adoption. Signals we can't observe for a listing are skipped, not counted against it.

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