🦉 ResearchOwl

Exhaustive research engine with Telegram interface.

Recursively discovers, scrapes, and processes sources from across the web, then generates podcast scripts, blog posts, reports, or social threads using Ollama.

Architecture

Telegram (/research <topic>)
    ↓
ExhaustiveScraper
    ├── DuckDuckGo (8 queries × 5 results)
    ├── Wikipedia + recursive internal links
    ├── Reddit (top posts + top comments)
    ├── YouTube (transcripts)
    ├── PDFs (public documents)
    └── Web scraping (trafilatura)
         ↓ recursive expansion (depth 1-3)
ContentProcessor (Ollama qwen2.5:7b + bge-m3 embeddings)
    ├── Chunking (800 token chunks, 100 overlap)
    ├── Quality scoring (0-10 per chunk)
    ├── Embeddings (cosine similarity RAG)
    └── Deduplication
         ↓
OutputGenerator (Ollama)
    ├── 🎙️ Podcast script (20-30 min)
    ├── 📝 Blog post (1500-2500 words)
    ├── 📊 Research report (structured)
    └── 🐦 Social thread (15-25 tweets)

Telegram Commands

Command	Description
/research <topic>	Start exhaustive research
/status	Check progress
/finish	Stop early, proceed to generation
/generate podcast|blog|report|thread	Generate output
/generate short_en	Vertical Short: shot spec → grounding check → MP4
/short_spec	Last shot spec as a JSON file, to hand-edit and re-render
/sources	List all sources found
/cancel	Cancel current research

Shorts (/generate short_en)

Claude writes a shot spec — typed JSON, not prose — which shortsmith renders into a 1080×1920 MP4. The bot sends the video and, in a separate message, a claims report.

/research JAL 1628 Alaska 1986 …
/generate blog en          → Ghost draft, article URL stored on the output row
/generate short_en         → spec → grounding → render → video + claims report
                             (YouTube upload is deliberately manual)

Three things make this different from generating text, and each has its own mitigation:

• It is a contract, not prose. The template schemas are fetched live from GET /templates and never copied here, so a template added to shortsmith is available immediately. A spec is validated locally against those schemas before anything renders, and the exact error paths (shots.0.radar_sweep.props.sweeeps) go back to the model verbatim — up to 3 attempts.
• It contains figures and quotes. grounding.py extracts every quote, figure, date and proper noun and checks it against the exact chunks the model was given. No LLM in that path: normalisation plus substring, deterministic and free. Whatever is not in the chunks is checked against the worked example that travels in the prompt, so a figure lifted from it is reported as a prompt leak, not as an invention — different diagnosis, different fix. Neither ever blocks the render: both are surfaced next to the video and a human decides.
• It becomes a published video. Nothing is uploaded anywhere. The MP4 lands in Telegram for review, and in /data/shorts/{session_id}.mp4.

Fallbacks hold throughout: if shortsmith is unreachable, the job errors, or the spec never validates, the spec JSON comes back as a file. The expensive part is the generation, not the render.

Full spec of the phase: docs/shortsmith-phase2-spec.md.

Local Development

# 1. Clone and setup
git clone https://git.chemavx.xyz/chemavx/researchowl
cd researchowl

# 2. Create virtualenv
python3 -m venv venv && source venv/bin/activate
pip install -r requirements.txt

# 3. Configure
cp .env.example .env
# Edit .env with your values

# 4. Run
python main.py

Deploy to k3s

# 1. Create namespace and secrets
kubectl create namespace researchowl
kubectl create secret generic researchowl-secrets \
  --from-literal=telegram-bot-token=YOUR_TOKEN \
  --from-literal=telegram-allowed-users=YOUR_USER_ID \
  -n researchowl

# 2. Copy manifests to your k8s-manifests repo
cp k8s/*.yaml /path/to/k8s-manifests/researchowl/

# 3. Apply ArgoCD app
kubectl apply -f k8s/argocd-app.yaml

# 4. Push to Gitea → Gitea Actions builds → ArgoCD deploys
git add . && git commit -m "feat: add researchowl" && git push

Tuning

Variable	Default	Description
MAX_SOURCES	150	Hard cap on sources
MAX_DEPTH	3	Link recursion depth
QUALITY_THRESHOLD	0.4	Min chunk quality (0-1)
REQUEST_DELAY	1.0s	Delay between requests

Want more thoroughness?

• Increase MAX_SOURCES to 300+
• Increase MAX_DEPTH to 4-5
• Lower QUALITY_THRESHOLD to 0.3

Want faster results?

• Lower MAX_SOURCES to 50
• Set MAX_DEPTH to 1-2
• Higher QUALITY_THRESHOLD to 0.6

Bot avatar

The profile picture of @chemavx_researchowl_bot is not an opaque binary checked into the repo: assets/make_avatar.py draws it with PIL at 4× and scales it down, so the emblem can be retouched without hunting for an original. Telegram crops avatars to a circle, so everything that matters lives inside the inscribed circle; verified legible at 48 px.

python3 assets/make_avatar.py     # writes assets/avatar.png

It is applied over the API with the token from the secret, no BotFather. Watch out for setMyProfilePhoto: its photo parameter is not the file, it is an InputProfilePhoto object pointing at the attachment. Posting the file on its own gets you a baffling photo isn't specified.

TOK=$(kubectl get secret researchowl-secrets-infisical -n researchowl \
        -o jsonpath='{.data.telegram-bot-token}' | base64 -d)
curl -s -F 'photo={"type":"static","photo":"attach://av"}' \
     -F "av=@assets/avatar.png" \
     "https://api.telegram.org/bot$TOK/setMyProfilePhoto"
unset TOK

Notes

• Uses qwen2.5:7b (scoring) and bge-m3 (embeddings) on your existing Ollama — zero API cost
• Optionally add ANTHROPIC_API_KEY for Claude fallback on generation
• SQLite database stored in /data/researchowl.db
• All outputs saved to DB and available via /outputs