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.
---
## 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 Enter to ask, Ctrl-M to cycle memory mode,
Ctrl-L to clear, Ctrl-Q 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 ` | uv's venv isn't auto-activated by your shell | use `.venv/bin/ …`, `uv run …`, 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 /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
. The line below is the
ownership marker the registry validates — **do not remove** when
editing this README.
mcp-name: io.github.TheAiSingularity/agentic-research