Gigaxity Deep Research — Open-source deep research MCP server for Claude Code, Codex, Cursor, Hermes, and any MCP-capable agent

Open-source deep research MCP server for Claude Code, Hermes, Cursor, and any MCP-compatible agent. Qwen3-30B-A3B-Thinking via OpenRouter plus multi-source web synthesis with citations.

Gigaxity Deep Research is a multi-source synthesis pipeline — six MCP tools (two primitives search/research plus four deep-research tools ask/discover/synthesize/reason) with a matching FastAPI REST surface, fronting parallel multi-source search, RRF fusion, citation binding, and contradiction detection. The synthesis stage runs against any OpenAI-compatible chat-completions model; the recommended default is Alibaba's Qwen3-30B-A3B-Thinking, a reasoning-tuned 30B-A3B MoE model, but DeepSeek-R1, Qwen-QwQ, Llama 3.x, and hosted-aggregator endpoints (OpenRouter and the like) all work — pick any model your endpoint serves. The search layer pulls from a "Triple Stack" of complementary MCPs (Ref, Exa, Jina) alongside SearXNG, Tavily, and LinkUp connectors. A bundled gptr-mcp companion — the MCP shim around GPT Researcher — adds Reddit, X, and YouTube as social-first sources.

If you want to run the synthesis model on your own hardware, the local-inference branch swaps OpenRouter for any OpenAI-compatible endpoint (vLLM, SGLang, or llama.cpp). The search-MCP layer is priced separately by each provider. See docs/guides/free-tier-strategy.md for what their free tiers cover and how to wire them up.

Python on FastAPI. MIT License. Runs as an MCP stdio server, FastAPI REST API, or both. Drop-in instructions for the full deep research stack live in CLAUDE.md / AGENTS.md (byte-identical) so any MCP-capable harness — Claude Code, Codex, Cursor, Hermes, etc. — or a standalone agent (system-prompt path) can mirror the configuration.

What it does

Gigaxity Deep Research wires a multi-source search layer (SearXNG, Tavily, LinkUp) to an RRF fusion stage and a synthesis engine with citation binding, then exposes the whole pipeline as six MCP tools — two primitives (search, research) plus four deep-research tools (ask, discover, synthesize, reason) — that Claude Code or any MCP-compatible agent can call. The synthesis stage runs against any OpenAI-compatible chat-completions model; the recommended default is Qwen3-30B-A3B-Thinking, a reasoning-tuned MoE model from Alibaba.

So when an agent hits a question outside its training cutoff, it doesn't hallucinate or shell out to a generic search tool. It calls discover to widen the source set, reads the top hits, and calls synthesize to fold the evidence into a citation-backed answer. Round-trip is typically 5–15 seconds against a hosted reasoning model.

Features

Tools (MCP and REST)

The MCP server exposes two primitives plus four deep-research tools — six tools total. The primitives give you raw search and the simple combined pipeline; the deep-research tools split discovery, synthesis, and reasoning so each step can be driven independently.

Primitives

Deep-research tools

Pipeline

• Multi-source search: parallel queries across SearXNG, Tavily, and LinkUp with graceful degradation if any source is unavailable.

• RRF fusion: Reciprocal Rank Fusion combines and re-ranks results across providers.

• Adaptive routing: query classification picks the right combination of connectors per query.

• Query expansion: HyDE-style variant generation for broader coverage.

• Query decomposition: multi-aspect breakdown for complex queries.

• Quality gate: CRAG-style filtering keeps low-quality sources out of synthesis.

• Contradiction detection: PaperQA2-style disagreement surfacing flags conflicting claims rather than averaging them out.

• Citation binding: VeriCite-style claim-to-evidence mapping in the final answer.

• Outline-guided synthesis: SciRAG-style structured generation for tutorial and academic presets.

• Output verification: post-synthesis hard-gate on empty, truncated, reasoning-only, or uncited synthesis output — failed outputs are flagged with a verdict header and not cached, rather than being relayed as clean successes. Soft conditions (partial citation coverage, surfaced contradictions) append an advisory note.

• Focus modes: general, academic, documentation, comparison, debugging, tutorial, news.

Compatibility

• Reasoning models: works with Qwen3-Thinking, DeepSeek-R1, Qwen-QwQ, and any other OpenAI-compatible chat-completions model.

• Multi-tenant: accepts a per-request X-OpenRouter-Api-Key header so multiple users can share one server instance and bill their own OpenRouter accounts.

• MCP and REST: the same orchestration logic powers both surfaces.

What the full install includes

The Quick Starts below cover the orchestrator MCP — one of seven in the full stack. The complete deep research workflow (automatic per-query routing across the whole stack) comprises four parts:

1. Seven MCPs. This repo's orchestrator (gigaxity-deep-research) plus the Triple Stack (Ref + exa + jina — search/docs/code trio) plus three more (exa-answer, brightdata_fallback, gptr-mcp).

2. Companion projects and dependencies. SearXNG (primary search source, bundled at companions/searxng/) and GPT Researcher (transitive dependency of gptr-mcp); plus the minimal MCP wrappers bundled at companions/exa-answer/ and companions/brightdata-fallback/.

3. The pasteable instruction block in CLAUDE.md — drop into the global CLAUDE.md / AGENTS.md your harness loads (e.g. ~/.claude/CLAUDE.md for Claude Code), or paste into a standalone agent's system prompt. The agent then fires the research workflow on external-knowledge queries and routes each query class to the right MCP.

4. The bundled research-workflow skill — the deep reference for the routing classifier (token costs per tool, presets, fallback chains).

Walk the Setup roadmap below for a stage-by-stage path through all four.

Quick start: MCP install (Claude Code walkthrough)

The example below registers the server with Claude Code via ~/.claude.json. The same MCP stdio config block works for any MCP-capable harness (Codex, Cursor, Hermes, Continue.dev, etc.) — substitute that harness's config file path. For standalone agents that don't have an MCP harness, run the REST API instead (see the next quick start).

# Clone and install
git clone https://github.com/yoloshii/gigaxity-deep-research.git
cd gigaxity-deep-research

python -m venv .venv
source .venv/bin/activate
pip install -e .

# Configure
cp .env.example .env
# Edit .env: set RESEARCH_LLM_API_KEY (https://openrouter.ai/keys)
#            and RESEARCH_SEARXNG_HOST (or leave default)

Add to ~/.claude.json under mcpServers:

"gigaxity-deep-research": {
  "type": "stdio",
  "command": "/path/to/gigaxity-deep-research/.venv/bin/python",
  "args": ["/path/to/gigaxity-deep-research/run_mcp.py"],
  "env": {
    "RESEARCH_LLM_API_BASE": "https://openrouter.ai/api/v1",
    "RESEARCH_LLM_API_KEY": "YOUR_OPENROUTER_API_KEY",
    "RESEARCH_LLM_MODEL": "qwen/qwen3-30b-a3b-thinking-2507"
  }
}

Restart Claude Code. The six tools (search, research, ask, discover, synthesize, reason) become callable as mcp__gigaxity-deep-research__<tool>.

The MCP alone gives you raw access to the six tools. Most of the deep research value — automatic per-query tool routing across the full seven-MCP stack, the social-first layer via gptr-mcp, the routing skill, and the global agent-instruction block — comes from the rest of the staircase. Walk it in Setup roadmap below.

Quick start: REST API for distributed compute

When the model lives on a different machine from the orchestrator (e.g. you self-host the model on a GPU box and want the rest of the pipeline on a CPU-only edge node), run it as a REST API.

docker compose up -d
curl http://localhost:8000/api/v1/health

REST endpoints:

Each endpoint accepts an optional X-OpenRouter-Api-Key header that overrides the env-configured key for that request. Multi-tenant deployments use it to bill each user separately.

Full REST reference: docs/reference/rest-api.md.

Setup roadmap

The Quick Starts above get the orchestrator MCP running against a model and a search source. The full deep research workflow — automatic tool routing across the seven-MCP stack, social-first research via gptr-mcp, the routing skill that classifies queries, plus the agent-instruction block that wires it all into Claude Code — needs the rest of the staircase below.

Each stage has a verify step, so you can stop at any point and know the layer below is solid. Stages 1–4 give you a working orchestrator. Stages 5–7 turn it into the full deep research stack.

Smoke matrix

Run each query in Claude Code (or the agent of your choice) after Stage 7 and confirm the routing matches:

If a query routes somewhere unexpected, the most common cause is the global instruction block from Stage 6 not being pasted into your global CLAUDE.md / AGENTS.md. Without it, the agent has to discover the routing logic on its own per session.

Common pitfalls

• Stage 2 is required, not optional. SearXNG is the only required search connector — Tavily and LinkUp are optional additional connectors that run in parallel and contribute to RRF fusion. Skipping SearXNG leaves the synthesis layer with nothing to fuse unless you configure Tavily or LinkUp as a substitute.

• Verify Stage 4 before adding companions. A failing research call after Stage 5 is hard to debug because the failure could be any of seven MCPs misfiring; confirm the orchestrator alone works first.

• Stage 6 is what makes the agent route automatically. Without the skill plus the instruction block, the seven MCPs are visible but the agent treats them as raw tools, not a stack.

• local-inference branch defaults to http://localhost:8000/v1; main defaults to OpenRouter. Stage 3's verify command is the same either way, but the env var values differ — match them to your branch.

Modes

The local-inference branch currently mirrors main and serves as a placeholder for the planned client-and-default swap. Once the swap lands, that branch will ship with RESEARCH_LLM_API_BASE defaulted to http://localhost:8000/v1 and the OpenRouter client replaced by a generic OpenAI-compatible client. Until then, point RESEARCH_LLM_API_BASE at your local endpoint on either branch — search, fusion, synthesis, and citations behave identically.

Architecture

┌────────────────────── Gigaxity Deep Research ──────────────────────┐
│                                                                    │
│  MCP stdio (run_mcp.py) ──┐               ┌── REST (FastAPI)       │
│                           ▼               ▼                        │
│                      ┌─────────────────────────┐                   │
│                      │      Discovery layer     │                   │
│                      │  routing · expansion ·   │                   │
│                      │  decomposition · focus   │                   │
│                      │  (invokes LLM)           │                   │
│                      └────────────┬────────────┘                   │
│                                   ▼                                │
│  ┌──────── Search aggregator (parallel, fail-graceful) ─────────┐  │
│  │   SearXNG     ·     Tavily     ·     LinkUp                  │  │
│  │       ↓ rank-merged across connectors ↓                      │  │
│  │                      RRF fusion                              │  │
│  └─────────────────────────┬────────────────────────────────────┘  │
│                            ▼                                       │
│                      ┌─────────────────────────┐                   │
│                      │     Synthesis layer      │                   │
│                      │  CRAG quality gate ·     │                   │
│                      │  contradiction detector ·│                   │
│                      │  outline guide · RCS     │                   │
│                      │  (invokes LLM)           │                   │
│                      └────────────┬────────────┘                   │
│                                   ▼                                │
│             Citation-bound markdown answer                         │
│      (+ contradictions, outline when preset enables)               │
│                                                                    │
│  Shared dependency: OpenAI-compatible LLM                          │
│  (OpenRouter or local; Qwen3 30B et al.)                           │
└────────────────────────────────────────────────────────────────────┘

Different tools enter the pipeline at different stages: research() runs the whole flow; discover() stops after the search aggregator (returns ranked sources + gaps); synthesize() and reason() skip Discovery and Search and operate on pre-gathered sources; ask() calls the LLM directly with no search hop; search() returns RRF-fused results without touching the LLM.

The bigger stack

Gigaxity Deep Research is the synthesis MCP in a seven-MCP deep research stack — agent-framework-agnostic, designed to drop into any MCP-capable harness (Claude Code, Codex, Cursor, Hermes, etc.) or a standalone agent that takes a system prompt. The other six MCPs handle search, URL reading, and social discovery:

The bundled research-workflow skill plus the instruction block in CLAUDE.md wire all seven together with a query classifier (quick factual, direct, exploratory, synthesis, social-first), so the agent picks the right tools per query class on its own. Drop the instruction block into your own global CLAUDE.md or AGENTS.md to mirror the setup. All seven sanitized MCP server configs live in docs/reference/mcp-configs.md for one-stop copy-paste.

Documentation

• Introduction: what this is, why it exists, where it fits

• Quickstart: five-minute MCP install

• Concepts: architecture: how the pipeline works

• Concepts: presets: fast, tutorial, academic, comprehensive, contracrow

• Concepts: focus modes

• Concepts: fallback chains: how Brightdata, Jina, and the rest chain on URL/search/synthesis failures

• Guide: MCP setup (Claude Code walkthrough — same MCP block adapts to any MCP-capable harness)

• Guide: REST API setup for distributed compute

• Guide: Local inference (Qwen3 self-host)

• Guide: Bundled companions setup (SearXNG, Exa Answer, Brightdata)

• Guide: Triple Stack — full deep research setup

• Guide: Free-tier strategy: configuring the search MCPs against each provider's free tier

• Reference: MCP tools: input/output reference for the six stdio MCP tools this server exposes

• Reference: MCP configs: sanitized JSON blocks for all seven MCPs in the stack, in one place

• Reference: REST API

• Reference: Configuration: RESEARCH_* env vars for this server

• Troubleshooting

Research foundations

The pipeline implements techniques from the recent literature:

Roadmap

:white_check_mark: Shipped  :construction: Planned  :memo: Exploring

Requirements

• Python 3.11+

• An OpenRouter API key (https://openrouter.ai/keys) for default mode

• A SearXNG instance, self-hosted (https://docs.searxng.org/) or third-party, as the primary search source

• Optional: Tavily API key and/or LinkUp API key — each runs in parallel with SearXNG and contributes to RRF fusion when configured

• Optional: Docker + Docker Compose for REST mode

License

MIT. Copyright (c) 2026 Yoloshii.