agentic-research-engine-oss

<p align="center"> <strong>agentic-research-engine-oss</strong> </p> <p align="center"> <a href="LICENSE"><img src="https://img.shields.io/badge/license-MIT-blue.svg" alt="License"></a> <img src="https://img.shields.io/pypi/v/agentic-research-engine?color=blue&label=pypi" alt="PyPI"> <img src="https://img.shields.io/badge/version-0.1.3--alpha-orange.svg" alt="Version"> <img src="https://img.shields.io/badge/default-gemma%203%204B%20local-green.svg" alt="Default"> <img src="https://img.shields.io/badge/tests-137%2F137-brightgreen.svg" alt="Tests"> <img src="https://img.shields.io/badge/interfaces-CLI%20%7C%20TUI%20%7C%20web-blue.svg" alt="Interfaces"> <img src="https://img.shields.io/badge/MCP-python%20server%20%2B%20claude%20plugin-6aa3ff.svg" alt="MCP"> </p> **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. --- ## Table of contents - [TL;DR](#tldr) - [Why use this instead of…](#why-use-this-instead-of) - [Quickstart — Mac local](#quickstart--mac-local) - [Quickstart — no install (Google Colab)](#quickstart--no-install-google-colab) - [Three ways to drive it](#three-ways-to-drive-it) - [What ships](#what-ships) - [Domain presets](#domain-presets) - [Bring your own documents](#bring-your-own-documents) - [MCP + Claude plugin](#mcp--claude-plugin) - [Plugin / skill loader](#plugin--skill-loader) - [Architecture at a glance](#architecture-at-a-glance) - [Repo layout](#repo-layout) - [Configuration (env vars)](#configuration-env-vars) - [Testing](#testing) - [Troubleshooting](#troubleshooting) - [Honest limits](#honest-limits) - [Status + roadmap](#status--roadmap) - [Contributing](#contributing) - [License](#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. ```bash 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`](engine/benchmarks/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) ```bash # 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 ```bash # 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`](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: ```bash # 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/`](tutorials/): 1. [**01 — Engine API quickstart** (mocked, no key)](tutorials/01_engine_api_quickstart.ipynb) — see how the pipeline works without running inference. 2. [**02 — Groq cloud inference** (free tier)](tutorials/02_groq_cloud_inference.ipynb) — real LLM, no local GPU. 3. [**03 — Build your own corpus**](tutorials/03_build_your_own_corpus.ipynb) — upload PDFs, index them, query. 4. [**04 — MCP server from Python**](tutorials/04_mcp_server_from_python.ipynb) — drive the engine as a tool from another agent. 5. [**05 — Domain presets showcase**](tutorials/05_domain_presets_showcase.ipynb) — 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 ```bash engine ask "what is hybrid retrieval?" --domain papers --memory session engine reset-memory engine domains list engine version ``` ### TUI (Textual — keyboard-driven, SSH-safe) ```bash make tui ``` Three panes: sources · answer + hallucination flags · trace + memory hits. Press <kbd>Enter</kbd> to ask, <kbd>Ctrl-M</kbd> to cycle memory mode, <kbd>Ctrl-L</kbd> to clear, <kbd>Ctrl-Q</kbd> to quit. ### Web GUI (FastAPI + HTMX on `localhost:8080`) ```bash 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`](docs/domains.md). --- ## Bring your own documents ```bash 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`](docs/self-learning.md) covers the trajectory + memory model; [`docs/plugins-skills.md`](docs/plugins-skills.md) covers external plugins. --- ## MCP + Claude plugin `engine/mcp/server.py` is a Python MCP server exposing: - `research(question, domain?, memory?)` → structured `{answer, verified_claims, unverified_claims, sources, trace, totals, memory_hits}` - `reset_memory()` - `memory_count()` Bundled Claude plugin at `engine/mcp/claude_plugin/` — four skills (`/research`, `/cite-sources`, `/verify-claim`, `/set-domain`), ready to submit to the Anthropic marketplace. Register in Claude Desktop: ```jsonc // ~/Library/Application Support/Claude/claude_desktop_config.json { "mcpServers": { "engine": { "command": "python", "args": ["-m", "engine.mcp.server"], "env": { "OPENAI_BASE_URL": "http://localhost:11434/v1", "OPENAI_API_KEY": "ollama", "MODEL_SYNTHESIZER": "gemma3:4b", "SEARXNG_URL": "http://localhost:8888" } } } } ``` --- ## Plugin / skill loader Install third-party Claude plugins or Hermes (`agentskills.io`) skills: ```bash engine plugins install gh:owner/some-research-plugin@v1 engine plugins install file:./my-local-plugin engine plugins install https://example.com/marketplace.json engine plugins list engine plugins uninstall some-plugin ``` Safety: every install runs a forbidden-symbols scan (`eval(`, `exec(`, `os.system(`, …) — rejects plugins that would execute arbitrary code. Registry lives at `~/.agentic-research/plugins/`, fully inspectable, wipable. Full docs: [`docs/plugins-skills.md`](docs/plugins-skills.md). --- ## Architecture at a glance ``` ┌─────────────┐ │ question │ └──────┬──────┘ ▼ ┌─────────────────────────┐ T4.3 router — route by question type │ classify │ └──────────┬──────────────┘ ▼ ┌─────────────────────────┐ T1 decompose · T2 HyDE · T4.1 critic │ plan │ T4.5 refine-on-reject └──────────┬──────────────┘ ▼ ┌─────────────────────────┐ SearXNG parallel × N │ search │ + W5 local corpus (optional) │ (+ T4.1 critic) │ + T4.1 coverage critic └──────────┬──────────────┘ ▼ ┌─────────────────────────┐ T1 hybrid BM25 + dense + RRF │ retrieve │ W4.1 cross-encoder rerank (opt-in) │ (+ W4.1 rerank) │ └──────────┬──────────────┘ ▼ ┌─────────────────────────┐ W4.2 trafilatura clean-text │ fetch_url │ skips corpus:// URLs └──────────┬──────────────┘ ▼ ┌─────────────────────────┐ T4.4 LLM distillation │ compress │ + W6.2 per-chunk char cap │ (+ W6.2 cap) │ └──────────┬──────────────┘ ▼ ┌─────────────────────────┐ T2 synth · T4.2 FLARE on hedges │ synthesize │ W6.1 three-case anti-hallucinate │ (+ FLARE + stream) │ W7 streaming └──────────┬──────────────┘ ▼ ┌─────────────────────────┐ T2 CoVe — decompose + verify │ verify │ └────────┬────────────────┘ │ verified? ── yes ──▶ END │ no │ ◀────── re-search unverified claims ──── loop (bounded by MAX_ITERATIONS) ``` Every stage has an `ENABLE_*` flag so you can leave-one-out ablate. Deep spec: [`docs/architecture.md`](docs/architecture.md). --- ## Repo layout ``` agentic-research-engine-oss/ ├── engine/ the flagship research engine │ ├── core/ pipeline · models · trace · memory │ │ ├── pipeline.py · compaction · domains · plugins │ │ ├── models.py │ │ ├── trace.py │ │ ├── memory.py │ │ ├── compaction.py │ │ ├── domains.py │ │ └── plugins.py │ ├── interfaces/ │ │ ├── cli.py rich stdout CLI with subcommands │ │ ├── tui.py Textual TUI │ │ └── web/ FastAPI + HTMX localhost GUI │ ├── mcp/ │ │ ├── server.py Python FastMCP server │ │ └── claude_plugin/ submittable Claude plugin bundle │ ├── domains/ 6 YAML presets │ ├── examples/ 5 worked research examples │ ├── benchmarks/ mini SimpleQA + BrowseComp fixtures + runner │ └── tests/ pytest suite (all mocked, zero-network) ├── core/rag/ shared retrieval primitives (stable v1) ├── archive/ pre-engine recipes (kept for reference) ├── tutorials/ 5 Google Colab notebooks │ ├── 01_engine_api_quickstart.ipynb │ ├── 02_groq_cloud_inference.ipynb │ ├── 03_build_your_own_corpus.ipynb │ ├── 04_mcp_server_from_python.ipynb │ └── 05_domain_presets_showcase.ipynb ├── scripts/ │ ├── searxng/ self-hosted meta-search (docker-compose) │ ├── setup-local-mac.sh Ollama + Docker + SearXNG one-liner │ ├── setup-vm-gpu.sh Linux + vLLM/SGLang setup │ └── index_corpus.py build a CorpusIndex from PDFs/md/txt ├── docs/ │ ├── architecture.md deep technical spec │ ├── plugins-skills.md write + install plugins │ ├── domains.md write a new preset │ ├── self-learning.md trajectory logging + memory │ ├── progress.md wave-by-wave build log │ ├── how-it-works.md elevator pitches + SOTA comparison │ ├── launch-checklist.md go-live sequence │ └── launch-copy.md drafted HN / Reddit / Twitter copy ├── .github/ │ ├── workflows/ │ │ └── engine-tests.yml CI: mocked suite on every PR │ ├── ISSUE_TEMPLATE/ │ └── PULL_REQUEST_TEMPLATE.md ├── CONTRIBUTING.md ├── CHANGELOG.md ├── CODE_OF_CONDUCT.md ├── LICENSE MIT └── README.md you're reading it ``` --- ## Configuration (env vars) Full list in `engine/core/pipeline.py` header. Most-common knobs: | var | default | purpose | |---|---|---| | `OPENAI_BASE_URL` | unset (cloud OpenAI) | route to Ollama / vLLM / Groq / etc. | | `OPENAI_API_KEY` | `ollama` | sentinel for local; real key for cloud | | `MODEL_SYNTHESIZER` | `gpt-5-mini` (cloud) or `gemma3:4b` (Mac-local path) | final-answer model. Swap to `gpt-5`, `claude-sonnet-4-5`, `llama-3.3-70b` on Groq, etc., for higher factoid accuracy while keeping the rest of the pipeline local. | | `TOP_K_EVIDENCE` | auto (5 for small, 8 for large models) | retrieval budget | | `ENABLE_RERANK` | `0` | opt-in; first run downloads bge-reranker-v2-m3 (~560 MB) | | `ENABLE_FETCH` | `1` | trafilatura full-page fetch | | `ENABLE_STREAM` | `1` | stream synthesis tokens to stdout | | `ENABLE_TRACE` | `1` | per-call observability + summary at CLI end | | `LOCAL_CORPUS_PATH` | unset | set to an index dir to augment search with your docs | | `MEMORY_DB_PATH` | `~/.agentic-research/memory.db` | SQLite trajectory store | Full list: [`docs/architecture.md`](docs/architecture.md) env-vars section. --- ## Testing ```bash cd engine && make test # 120+ mocked tests in engine/tests/ # or repo-wide: PYTHONPATH=$(pwd) .venv/bin/python -m pytest core/rag recipes engine/tests -q ``` All tests are mocked — no network, no API key, no model downloads. Live integration smokes are separate (`make smoke`). CI runs on every push / PR touching engine / core / recipes — see [`.github/workflows/engine-tests.yml`](.github/workflows/engine-tests.yml). --- ## Troubleshooting | symptom | likely cause | fix | |---|---|---| | `ModuleNotFoundError: No module named 'engine'` | `PYTHONPATH` missing the repo root | `export PYTHONPATH=$(pwd)` from the repo root | | CLI answer is empty + fast | Ollama not running | `ollama serve` in another terminal, or `ollama list` to check | | `Connection refused on :8888` | SearXNG not up | `cd scripts/searxng && docker compose up -d` | | `Connection refused on :11434` | Ollama not running | `ollama serve`, or let the system service start it | | First `make smoke` hangs ~20 s before output | Model warming up on first request | normal; subsequent queries are faster | | `ENABLE_RERANK=1` stalls on first run | 560 MB bge-reranker download | wait it out once; cached after | | `[corpus] LOAD BROKEN` | corrupt or wrong-version index | delete + rebuild via `scripts/index_corpus.py` | | TUI shows gibberish over SSH | terminal too narrow | resize to ≥ 100 cols; Textual needs space for the 3-pane layout | | Web GUI shows `Invalid memory mode` | malformed POST | use the form UI; values validated against `off/session/persistent` | | Streaming cuts off mid-answer | flaky backend | re-run; batched fallback kicks in on next attempt. Set `ENABLE_STREAM=0` if it persists | | `zsh: command not found: twine` (or similar) after `uv pip install <pkg>` | uv's venv isn't auto-activated by your shell | use `.venv/bin/<cmd> …`, `uv run <cmd> …`, or `source .venv/bin/activate` before running | | `bad interpreter: .../python3: no such file or directory` after moving or renaming the repo dir | venv shebangs are absolute paths tied to the dir the venv was created in | recreate: `rm -rf .venv && uv venv && uv pip install -e .` (or re-install whatever you had) | | `make test` says 0 tests collected | wrong CWD | run from the `engine/` dir or set `PYTHONPATH` | | Claude Desktop doesn't see the plugin | plugin.json in wrong path | `/plugin marketplace add <absolute-path-to>/engine/mcp/claude_plugin` | Still stuck? Open an issue with the [`bug_report`](.github/ISSUE_TEMPLATE/bug_report.md) template — include `ollama list`, `engine version`, and the error. --- ## Honest limits - **Gemma 4B ≠ GPT-5.4 Pro.** 15–25 % below 30 B+ open models on hard multi-hop. We position as "best $0 local", not "SOTA." - **Gemma 3 4B confabulates specific factoids** when SearXNG doesn't return a source that contains the right token. Measured on SimpleQA-mini: 0/20 strict pass rate (see [`engine/benchmarks/RESULTS.md`](engine/benchmarks/RESULTS.md) — `verified_ratio` 85.5 %, zero `must_not_contain` hits; the model isn't emitting *banned* strings, it's picking wrong ones). Mitigations: (a) swap the whole stack to a cloud endpoint (see "Higher factoid accuracy" above — `$0.02–0.05/query` with `gpt-5-nano` + `gpt-5-mini`), (b) give the engine a `LOCAL_CORPUS_PATH` so your own docs become retrieval targets, (c) set `ENABLE_RERANK=1` to bias retrieval toward the right sources. - **CoVe confirms internal consistency, not ground truth.** Every synthesized claim is checked against retrieved evidence; claims don't get verified *by the world*. If retrieval misses, CoVe will still happily verify a confidently-wrong answer. The engine will never fabricate citations, but it can confidently repeat wrong information that was in its evidence pool. - **No LoRA fine-tuning in v1.** Trajectory data is collected; actual model training deferred until GPU access + data volume. - **No hosted SaaS.** Local-first is the entire v1 positioning. - **Team / multi-user features.** Out of scope for v1. - **General web crawler / own search index.** Not shipping. SearXNG stays. A curated research-focused index may land in v2. - **Mobile.** Not in scope. --- ## Status + roadmap - **0.1.3 — public alpha** (current). Features listed above; on PyPI + the official MCP registry + the Anthropic plugin marketplace. See [`CHANGELOG.md`](CHANGELOG.md). - **0.2** — specialist tool wiring (`tools_enabled` field in presets finally activates), first LoRA run if GPU arrives, plugin catalog in `docs/`. - **0.3** — team-collab features (shared memory, PR-driven domain presets), desktop app packaging via Tauri. - **0.4+** — open-work tracked in [GitHub Issues](https://github.com/TheAiSingularity/agentic-research-engine-oss/issues). --- ## Contributing Good first issues: [`CONTRIBUTING.md`](CONTRIBUTING.md). RFCs for anything pipeline-scope. Plugin + domain-preset submissions welcome. No Co-Authored-By trailers; author-as-written-by. --- ## License MIT. See [`LICENSE`](LICENSE). ### Related (sibling projects) - [HermesClaw](https://github.com/TheAiSingularity/hermesclaw) — the secure runtime these recipes can run inside - [NVIDIA/OpenShell](https://github.com/NVIDIA/OpenShell) — kernel-level agent sandbox - [NousResearch/hermes-agent](https://github.com/NousResearch/hermes-agent) — self-improving agent (whose `agentskills.io` skill format we interoperate with) --- ### MCP registry ownership This PyPI package is the official source of the MCP server registered at <https://registry.modelcontextprotocol.io>. The line below is the ownership marker the registry validates — **do not remove** when editing this README. mcp-name: io.github.TheAiSingularity/agentic-research