A
licenseNot graded
qualityD
maintenanceEnables iterative deep research by integrating AI agents with search engines, web scraping, and large language models for efficient data gathering and comprehensive reporting.
20
321
MIT
agentic-research
by abhilash2429
Click on "Install Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@agentic-researchSummarize the latest research on Alzheimer's treatments."
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
agentic-research
An agentic web research pipeline. Ask a question in natural language; it searches the web, fetches candidate pages, chunks and embeds them, retrieves only the relevant spans, and returns an answer with citations that point at the exact source passage.
Built to be called by an AI agent as much as by a human. The MCP server exposes it as a
deep_research tool, so a coding agent can reach for current, cited information instead of
guessing from training data.
Why a retrieval layer
The usual agent approach to the web is: search, fetch the full page, put the whole thing in the context window, answer. That caps you at a handful of sources and charges full token price for pages that turn out to be mostly irrelevant.
typical: search -> fetch full page -> stuff into context -> answer
~5 sources, full token price per page, no span-level citation
here: search -> fetch N pages -> parent/child chunk -> embed
-> retrieve only relevant children -> expand parents on demand
-> compress -> answer
~30 sources, lower token cost, structural citationsRelated MCP server: Deep Research MCP Server
Status
Work in progress, built in phases. See docs/ for a note on each layer.
Phase | Layer | State |
0 | Foundation, shared contracts | done |
1 | Config, role-based LLM factory | done |
2 | Fetch and extract | done |
3 | Hierarchical chunking | done |
4 | Index and rerank | in progress |
5 | Search providers | done |
6 | Single researcher loop | todo |
7 | Full graph: fan-out, clarification, compression | todo |
8 | Eval harness | todo |
9 | Service layer and API | todo |
10 | MCP server | todo |
11 | Web UI | todo |
Setup
python -m venv .venv
.venv/Scripts/python -m pip install -e ".[dev,api,mcp]"
cp .env.example .env # fill in LiteLLM and search keys
docker compose up -d qdrant
ares doctorCLI
One command per layer, so you can see what each one does on its own.
ares doctor # proxy, models and Qdrant reachable
ares fetch <url> --show # raw HTML to clean markdown
ares chunk <url> # the parent/child tree
ares index <url> # embed and store, or report a cache hit
ares search "..." --no-rerank # retrieval without the reranker
ares search "..." --rerank # and with it
ares websearch "..." # provider results
ares research "..." # the whole loop, cited answer
ares eval # scorecard against the committed baselineLicense
MIT
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