Agentic Research

Name: Agentic Research
Author: TheAiSingularity

by TheAiSingularityversion 0.1.3i★ –iRuns on your computer

Registry nameio.github.TheAiSingularity/agentic-research

Install

Overview

Description from the maker (original English)

Local research agent that verifies its own answers. Runs on Gemma 3 4B + Ollama, $0/query.

PopNx MCP’s three pillars

These three are never merged into one score: opinions, measurements and popularity are different things.

User voiceiNo reviews yetReviews open together with accounts in a later step
Our basic checksi1 of 1 passedChecked from public information; code not scanned yet
UsageiNot measured yet★ –

What you can do with it

We have not written a plain-language summary for this one yet. Below are the facts we read from the registry.

Connection: Runs on your computer
Transport: stdio
Category: Search & AI memory
You need to fill in: OPENAI_API_KEY

Install

Pick the AI app you use, then copy the settings.

Before you install

Basic checks: 1 of 1 passed
Runs on your computer with your user’s permissions (files, network, apps).
You fill in yourself: OPENAI_BASE_URL, OPENAI_API_KEY, MODEL_SYNTHESIZER, MODEL_PLANNER, EMBED_MODEL, SEARXNG_URL, LOCAL_CORPUS_PATH, ENABLE_RERANK, ENABLE_FETCH

Values marked secret are keys to your account. Never share them or paste them into a chat.

You need

PyPIPython’s uv installed (for the uvx command)

Which app do you use?

ClaudeClaude CodeCursorVS CodeChatGPT

Pick your app above. We remember it, so next time the button says “Install in …”.

Open Claude Desktop → Settings → Developer → Edit Config, and paste this into claude_desktop_config.json

{
  "mcpServers": {
    "agentic-research": {
      "command": "uvx",
      "args": [
        "agentic-research-engine"
      ],
      "env": {
        "OPENAI_BASE_URL": "your-value",
        "OPENAI_API_KEY": "your-value",
        "MODEL_SYNTHESIZER": "your-value",
        "MODEL_PLANNER": "your-value",
        "EMBED_MODEL": "your-value",
        "SEARXNG_URL": "your-value",
        "LOCAL_CORPUS_PATH": "your-value",
        "ENABLE_RERANK": "your-value",
        "ENABLE_FETCH": "your-value"
      }
    }
  }
}

Run this in your terminal

claude mcp add agentic-research --env OPENAI_BASE_URL=your-value --env OPENAI_API_KEY=your-value --env MODEL_SYNTHESIZER=your-value --env MODEL_PLANNER=your-value --env EMBED_MODEL=your-value --env SEARXNG_URL=your-value --env LOCAL_CORPUS_PATH=your-value --env ENABLE_RERANK=your-value --env ENABLE_FETCH=your-value -- uvx agentic-research-engine

Fill in the empty values before running it.

Open in Cursor

Or copy the settings yourself

Paste into .cursor/mcp.json in your project, or ~/.cursor/mcp.json

{
  "mcpServers": {
    "agentic-research": {
      "command": "uvx",
      "args": [
        "agentic-research-engine"
      ],
      "env": {
        "OPENAI_BASE_URL": "your-value",
        "OPENAI_API_KEY": "your-value",
        "MODEL_SYNTHESIZER": "your-value",
        "MODEL_PLANNER": "your-value",
        "EMBED_MODEL": "your-value",
        "SEARXNG_URL": "your-value",
        "LOCAL_CORPUS_PATH": "your-value",
        "ENABLE_RERANK": "your-value",
        "ENABLE_FETCH": "your-value"
      }
    }
  }
}

Your browser asks to open the app, then the app shows the settings and asks you to confirm. Key values are left empty for you to fill in.

Open in VS Code

Or copy the settings yourself

Paste into .vscode/mcp.json in your project

{
  "servers": {
    "agentic-research": {
      "type": "stdio",
      "command": "uvx",
      "args": [
        "agentic-research-engine"
      ],
      "env": {
        "OPENAI_BASE_URL": "your-value",
        "OPENAI_API_KEY": "your-value",
        "MODEL_SYNTHESIZER": "your-value",
        "MODEL_PLANNER": "your-value",
        "EMBED_MODEL": "your-value",
        "SEARXNG_URL": "your-value",
        "LOCAL_CORPUS_PATH": "your-value",
        "ENABLE_RERANK": "your-value",
        "ENABLE_FETCH": "your-value"
      }
    }
  }
}

Your browser asks to open the app, then the app shows the settings and asks you to confirm. Key values are left empty for you to fill in.

This one runs on your computer, and ChatGPT can only connect to servers on the internet. Pick another app.

Your choice stays on this device in a cookie; no account needed.

Values you fill in

OPENAI_BASE_URL env
Any OpenAI-compatible endpoint. Default: OpenAI cloud. Use http://localhost:11434/v1 for Ollama.
OPENAI_API_KEY secret env
API key for the endpoint above. Use 'ollama' as a sentinel value when running locally against Ollama.
MODEL_SYNTHESIZER env
Model identifier used for the synthesize node. Defaults to 'gpt-5-mini'; set to 'gemma3:4b' for Mac-local Ollama.
MODEL_PLANNER env
Model for the plan / classify / critic / compress / verify nodes. Defaults to 'gpt-5-nano'.
EMBED_MODEL env
Embedding model identifier (for retrieval + memory). Default 'text-embedding-3-small'; use 'nomic-embed-text' on Ollama.
SEARXNG_URL env
Base URL of the SearXNG meta-search instance. Default http://localhost:8888.
LOCAL_CORPUS_PATH env
Path to an index directory built via scripts/index_corpus.py. When set, local corpus hits augment web search.
ENABLE_RERANK env
Set to '1' to enable the BAAI/bge-reranker-v2-m3 cross-encoder rerank stage. First run downloads ~560MB.
ENABLE_FETCH env
Set to '0' to skip the trafilatura full-page fetch stage. Default '1'.

These settings are generated from the maker’s registry entry. If the README says otherwise, follow the README.

What it can do

Runs on your computer; the tool list appears in your AI app after you install it.

Basic checksi

We check information anyone can see; we have not read the code. Checks that do not apply are left out of the count. · Checked Nov 6, 2026

✓ Pass
Maker matches the repo
The registry name was verified with the GitHub account TheAiSingularity, which owns the repo
… Not checked yet
Still maintained
We have not read this repo from GitHub yet
… Not checked yet
Has a licence
We have not read this repo from GitHub yet
– Not applicable
Endpoint responds
This one runs on your computer; there is no internet endpoint
– Not applicable
Sign-in with OAuth
Runs on your computer and uses local settings instead
… Not checked yet
No hidden instructions in tool descriptions
Runs on your computer; we can only read tool lists from internet endpoints

Compared with others in Search & AI memoryi

More stars than 0% of the other 7234 servers in this category
Published to the registry more recently than 10% of the other 7234 servers in this category
Basic checks equal to or better than 100% of the other 7234 servers in this category

Compared with every active server in this category in the registry. Stars count only when the publisher owns the repo.

Common questions

Is Agentic Research safe?

It passed 1 of 1 applicable basic checks. We have not read the code. Before adding a key or token, see what access the server asks for and use a key with only the permissions you need.

What keys or codes does it need?

You need to fill in: OPENAI_API_KEY

Does it run on my computer or in the cloud?

It runs on your computer. Your AI app starts it when needed, and data does not pass through the maker’s servers (unless it calls an online service itself).

Is it still maintained?

The GitHub repo last changed –, and the latest version (0.1.3) reached the registry 7 months ago.

Which apps can use it?

Apps that support MCP, such as Claude Desktop, Claude Code, Cursor and VS Code. You also need Python (uv) on your computer.

What can I use instead?

In the same category: Context7, Codebase Memory, Exa

Alternatives

Servers in the same category from other makers, sorted by stars.

Reading a card:★ GitHub starsiThree pillars: reviews · checks · usageiUpdated = latest version published to the registryi

Context7

by upstash

Feeds AI the latest docs for libraries and frameworks before it answers, so code it writes does not use outdated, removed functions.

Computer or cloud★ 64,224MITUpdated 3 days ago

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Codebase Memory

by DeusData

Codebase knowledge graph for AI agents — 162 languages, sub-ms queries, 99% fewer tokens.

Runs on your computer★ 48,148MITUpdated 20 days ago

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Exa

by exa-labs

Connect AI agents to Exa for web search, content fetching, and multi-step research.

Runs in the cloud★ 5,213MITUpdated 44 days ago

Install

README from the maker

Open the README

Copied from the maker’s repo as written. PopNx MCP has not reviewed or endorsed it. HTML, images and scripts are removed. Links lead off this site.

agentic-research-engine-oss

The best $0 research agent that runs on a laptop. Open-source end-to-end, reproducible, privacy-preserving. No cloud dependency by default; no telemetry; every LLM call, every source, and every verification decision is visible.

TL;DR
Why use this instead of…
Quickstart — Mac local
Quickstart — no install (Google Colab)
Three ways to drive it
What ships
Domain presets
Bring your own documents
MCP + Claude plugin
Plugin / skill loader
Architecture at a glance
Repo layout
Configuration (env vars)
Testing
Troubleshooting
Honest limits
Status + roadmap
Contributing
License

TL;DR

Local-first research agent that verifies its own answers. Runs on Gemma 3 4B + Ollama (3.3 GB on disk) for $0/query; swaps to any OpenAI-compatible endpoint with one env var.

pip install agentic-research-engine

agentic-research ask "what is Anthropic's contextual retrieval?" --domain papers


Interfaces	CLI · Textual TUI · FastAPI web GUI · MCP server (Claude Desktop / Cursor / Continue)
Pipeline	8-node LangGraph (`classify → plan → search → retrieve → fetch → compress → synthesize → verify`); every node env-toggleable for ablation
Retrieval	SearXNG meta-search + trafilatura fetch + hybrid BM25 / dense / RRF; opt-in `bge-reranker-v2-m3` cross-encoder
Reasoning	HyDE query expansion · FLARE active retrieval · Chain-of-Verification (Dhuliawala et al 2023) · ThinkPRM step critic
Domains	6 presets (`general` · `medical` · `papers` · `financial` · `stock_trading` · `personal_docs`) — write your own in 10 lines of YAML
Plugins	load Claude plugins or `agentskills.io` skills from GitHub or local paths
Memory	opt-in local SQLite trajectory log with semantic retrieval; wipe anytime; no telemetry
Providers	OpenAI · Groq · vLLM · SGLang · Together · Ollama — any OpenAI-compatible endpoint via `OPENAI_BASE_URL`
Quality	137 mocked tests, zero-network · honest live benchmarks published in `RESULTS.md` · MIT end-to-end

Why use this instead of…

you currently use	we give you
Perplexity / ChatGPT Deep Research / Kagi Assistant	the same reasoning-with-citations flow, local and free, with your data never leaving the machine
Perplexica self-hosted	the UX Perplexica has plus a CoVe verifier, FLARE active retrieval, adaptive compute router, and Claude-plugin packaging
Khoj	stronger research-specific reasoning (we're not personal-knowledge-focused), six domain presets, and an MCP server for other agents to call
gpt-researcher	newer pipeline architecture, better small-model handling, observable trace, plugin ecosystem
MiroThinker-H1 / OpenResearcher-30B	they're stronger on BrowseComp; we run on a laptop with no GPU and cost $0
Writing your own LangGraph research agent	save 2-3 months; reuse our 8-node pipeline + 30+ tested env gates + 137 tests

Honest read: on complex multi-hop reasoning benchmarks, Gemma 3 4B sits 15–25% below 30 B+ open models. We don't claim to beat GPT-5.4 Pro. We claim to be the best $0, runs-on-your-laptop, fully-open research agent in April 2026.

Quickstart — Mac local

Option A — PyPI (fastest)

# 1) Local inference (Ollama + Gemma 3 4B + embedding model — 3.6 GB combined)

brew install ollama ollama pull gemma3:4b nomic-embed-text

2) Self-hosted meta-search (Docker; optional but recommended)

docker run -d --name searxng -p 8888:8080 searxng/searxng

3) The engine itself

pip install agentic-research-engine

4) Go

export OPENAI_BASE_URL=http://localhost:11434/v1 OPENAI_API_KEY=ollama export MODEL_SYNTHESIZER=gemma3:4b EMBED_MODEL=nomic-embed-text export SEARXNG_URL=http://localhost:8888 agentic-research ask "what is Anthropic's contextual retrieval?" --domain papers

Option B — from source

# 1) Same local-inference prereqs as Option A (ollama pull + docker run)

2) Clone + install (gives you the CLI, TUI, Web GUI, MCP server, benchmarks, tutorials)

git clone https://github.com/TheAiSingularity/agentic-research-engine-oss cd agentic-research-engine-oss (cd scripts/searxng && docker compose up -d) cd engine && make install make smoke # end-to-end run on the canonical "what is contextual retrieval" question

Expected wall-clock on an M-series Mac: ~45 s for a factoid, ~90 s for multi-hop synthesis. Zero dollars per query.

Higher honesty — cloud-model mode

Gemma 3 4B is surprisingly good at structure (plan, route, verify, compress) but confabulates specific factoids when SearXNG doesn't surface a source containing the right token. Live SimpleQA-mini run on 2026-04-21 (see engine/benchmarks/RESULTS.md) showed gemma3:4b emitting "2023" for *"year Anthropic published Contextual Retrieval"* (gold: 2024) and "LayoutLMv3" for *"which cross-encoder for reranking"* (gold: bge-reranker-v2-m3).

The fix you probably want isn't a smarter synthesizer — it's a more honest one. A 5-question head-to-head on the same retrieval output showed gpt-5-nano + gpt-5-mini refuse to confabulate when evidence was missing (*"The provided evidence does not answer this question"*), where gemma3:4b confidently guessed. Per-claim faithfulness went from 82.9 % → 100 %. Pass rate barely moved (1/5 vs 0/5) because retrieval is the real bottleneck — if SearXNG didn't return a source with the gold token, neither model can produce it.

Swap the whole stack to a cloud endpoint:

# drop the Ollama base URL (fall back to OpenAI cloud)

unset OPENAI_BASE_URL export OPENAI_API_KEY=sk-...

defaults are already cloud-sized: gpt-5-nano for plan/verify, gpt-5-mini for synth.

Explicit override if you want to pin them:

export MODEL_PLANNER=gpt-5-nano export MODEL_SYNTHESIZER=gpt-5-mini # or gpt-5, claude-sonnet-4-5, etc. agentic-research ask "…" --domain papers

Cost is dominated by synthesizer tokens (~5–15 k per query). Full cloud mode with gpt-5-nano + gpt-5-mini runs roughly $0.02–0.05 per research query and is ~2-3× slower than Gemma local (measured: 127 s vs 52 s mean wall on the 5-question subset). Works with any OpenAI-compatible endpoint — Groq, Together, Mistral, DeepSeek, local vLLM — so you can pick a cheap fast model (llama-3.3-70b on Groq ≈ $0.003/query) or a frontier one. Per-node base-URL routing (run gemma3:4b locally for plan/verify AND gpt-5-mini on cloud for synth in the same query) is tracked for 0.2; today the pipeline uses one global OPENAI_BASE_URL.

The bigger accuracy lever is retrieval. Point LOCAL_CORPUS_PATH at an indexed corpus containing your answer and either model will be correct.

Quickstart — no install (Google Colab)

Five runnable notebooks in tutorials/:

01 — Engine API quickstart (mocked, no key) — see how the pipeline works without running inference.
02 — Groq cloud inference (free tier) — real LLM, no local GPU.
03 — Build your own corpus — upload PDFs, index them, query.
04 — MCP server from Python — drive the engine as a tool from another agent.
05 — Domain presets showcase — compare presets on the same question.

Each notebook is self-contained, runs end-to-end on Colab free tier, no credit card required.

Three ways to drive it

CLI

engine ask "what is hybrid retrieval?" --domain papers --memory session

engine reset-memory engine domains list engine version

TUI (Textual — keyboard-driven, SSH-safe)

make tui

Three panes: sources · answer + hallucination flags · trace + memory hits. Press Enter to ask, Ctrl-M to cycle memory mode, Ctrl-L to clear, Ctrl-Q to quit.

Web GUI (FastAPI + HTMX on `localhost:8080`)

make gui

open http://127.0.0.1:8080 in your browser

No auth. No cloud. No analytics. Dark theme. Streams tokens in place.

What ships

`engine/` — the flagship

8-node LangGraph pipeline with 2026-SOTA composition: classify → plan → search → retrieve → fetch_url → compress → synthesize → verify

Every stage is env-toggleable for leave-one-out ablation. Techniques folded in: HyDE, CoVe verification, iterative retrieval, FLARE active retrieval, question classifier router, step critic (ThinkPRM pattern), LongLLMLingua-lite compression, cross-encoder rerank (BAAI/bge-reranker-v2-m3), Anthropic contextual chunking, W6 small- model hardening (three-case synthesize prompt + per-chunk char cap).

`core/rag/` — reusable retrieval primitives (v1 stable)

HybridRetriever (BM25 + dense + RRF) · CrossEncoderReranker · contextualize_chunks (Anthropic pattern) · CorpusIndex (bring- your-own-PDFs). 5 exports, used by the engine and the archived recipes.

`archive/recipes/` — pre-engine reference recipes

research-assistant, trading-copilot, document-qa, rust-mcp-search-tool. All still work; all tests still pass. The research-assistant/production/main.py is a thin shim over engine.core.pipeline so the cookbook framing is preserved.

Domain presets

Six YAML files in engine/domains/:

preset	when to use
`general`	default; anything
`medical`	disease / treatment / drug / trial (PubMed / Cochrane / NEJM bias; no prescriptive advice)
`papers`	academic CS / ML / physics / biology (arXiv + Semantic Scholar + OpenReview)
`financial`	SEC filings, earnings, company fundamentals (dates on every number)
`stock_trading`	technical + news per ticker — hard rule: never recommends buy/sell/hold
`personal_docs`	Q&A over your own corpus, air-gapped (only `corpus://` URLs allowed)

Write your own in ~10 lines of YAML — see docs/domains.md.

Bring your own documents

python scripts/index_corpus.py build ~/papers --out ~/papers.idx

export LOCAL_CORPUS_PATH=~/papers.idx engine ask "what do my papers say about contextual retrieval?" --domain personal_docs

Supported formats: PDF (via pypdf), Markdown, plain text, HTML (via trafilatura). The index persists as a directory with a human-readable manifest.json + a pickled index.pkl. Rebuild anytime the docs change.

Details: docs/self-learning.md covers the trajectory + memory model; docs/plugins-skills.md covers external plugins.

MCP + Claude

Only the beginning is shown; read the rest in the repo

Agentic Research

Overview

PopNx MCP’s three pillars

What you can do with it

Install

Before you install

Values you fill in

What it can do

Basic checksi

Maker matches the repo

Still maintained

Has a licence

Endpoint responds

Sign-in with OAuth

No hidden instructions in tool descriptions

Compared with others in Search & AI memoryi

Common questions

Alternatives

Context7

Codebase Memory

Exa

README from the maker

Table of contents

TL;DR

Why use this instead of…

Quickstart — Mac local

Option A — PyPI (fastest)

2) Self-hosted meta-search (Docker; optional but recommended)

3) The engine itself

4) Go

Option B — from source

2) Clone + install (gives you the CLI, TUI, Web GUI, MCP server, benchmarks, tutorials)

Higher honesty — cloud-model mode

defaults are already cloud-sized: gpt-5-nano for plan/verify, gpt-5-mini for synth.

Explicit override if you want to pin them:

Quickstart — no install (Google Colab)

Three ways to drive it

CLI

TUI (Textual — keyboard-driven, SSH-safe)

Web GUI (FastAPI + HTMX on localhost:8080)

open http://127.0.0.1:8080 in your browser

What ships

engine/ — the flagship

core/rag/ — reusable retrieval primitives (v1 stable)

archive/recipes/ — pre-engine reference recipes

Domain presets

Bring your own documents

MCP + Claude

Web GUI (FastAPI + HTMX on `localhost:8080`)

`engine/` — the flagship

`core/rag/` — reusable retrieval primitives (v1 stable)

`archive/recipes/` — pre-engine reference recipes