Deep Research¶

A multi-hop research agent that searches across your indexed documents, cross-references information, and returns answers with citations. It reasons through complex queries step by step, pulling context from multiple sources in your local knowledge base.

Quickstart (5 minutes)¶

1. Install and initialize¶

git clone https://github.com/open-jarvis/OpenJarvis.git
cd OpenJarvis
uv sync --extra dev
jarvis init --preset deep-research

This writes a pre-configured ~/.openjarvis/config.toml for the deep research agent.

2. Index your documents¶

# Install Ollama: https://ollama.com
ollama pull qwen3.5:9b

# Index a directory of files
jarvis memory index ./docs/
jarvis memory index ~/Documents/papers/

OpenJarvis chunks the content and stores it in a local SQLite/FTS5 database. Supported formats include .txt, .md, .pdf, .py, .json, .csv, and more.

3. Ask a research question¶

jarvis ask "Summarize all documents about transformer architectures"

The deep research agent will:

Search your indexed documents for relevant chunks
Reason across multiple sources (up to 8 hops)
Synthesize a coherent answer with references to source documents

CLI Commands¶

# Ask a question (uses deep_research agent by default with this config)
jarvis ask "What meetings did I have with Alice last month?"

# Explicitly specify the agent
jarvis ask --agent deep_research "Compare the approaches described in paper-a.pdf and paper-b.pdf"

# Index more documents
jarvis memory index ~/Downloads/reports/
jarvis memory index ./notes.md

# Search memory directly
jarvis memory search "project timeline"
jarvis memory search -k 20 "budget estimates"

# Check what's indexed
jarvis memory stats

Configuration Reference¶

The preset writes this to ~/.openjarvis/config.toml:

[engine]
default = "ollama"

[intelligence]
default_model = "qwen3.5:9b"
temperature = 0.3               # Low temperature for factual research

[agent]
default_agent = "deep_research"
max_turns = 8                   # Multi-hop reasoning steps

[tools]
enabled = ["knowledge_search", "knowledge_sql", "scan_chunks", "think", "web_search"]

[tools.storage]
default_backend = "sqlite"

Key settings¶

Setting	Default	Description
`intelligence.default_model`	`qwen3.5:9b`	The model used for reasoning. Larger models (e.g., `qwen3.5:35b`) give better results on complex queries.
`intelligence.temperature`	`0.3`	Low temperature keeps answers factual. Increase for more creative synthesis.
`agent.max_turns`	`8`	Maximum reasoning hops. Increase for deeply nested research tasks.
`tools.enabled`	5 tools	`knowledge_search` (semantic), `knowledge_sql` (structured), `scan_chunks` (browse), `think` (reasoning scratchpad), `web_search` (online fallback).
`tools.storage.default_backend`	`sqlite`	FTS5-backed full-text search. Also supports `faiss`, `colbert`, `bm25`, and `hybrid`.

Example Queries¶

# Summarize across multiple documents
jarvis ask "Summarize all emails about the Q3 budget review"

# Cross-reference sources
jarvis ask "What do papers A and B agree on regarding attention mechanisms?"

# Find specific information
jarvis ask "What meetings did I have with Alice last month?"

# Extract structured data
jarvis ask "List all action items from the meeting notes in ~/Documents/meetings/"

# Research with web fallback
jarvis ask "Compare our internal benchmarks with the latest published results"

Indexing Different Data Sources¶

Local files and directories¶

# Recursively index a directory
jarvis memory index ~/Documents/

# Single file
jarvis memory index ./report.pdf

# Custom chunk size for long documents
jarvis memory index ./paper.pdf --chunk-size 1024 --chunk-overlap 128

PDFs¶

PDFs are automatically extracted and chunked. For best results with scanned PDFs, ensure they have been OCR-processed.

jarvis memory index ~/Papers/*.pdf

Web pages¶

Use the web_search tool (enabled by default in this config) to pull in online sources at query time. For persistent indexing of web content, download pages first:

curl -s https://example.com/article | jarvis memory index --stdin --source "example.com"

Code repositories¶

jarvis memory index ./src/ --chunk-size 256

Smaller chunk sizes work better for code, where each function or class is a natural unit.

Troubleshooting¶

"No results found" -- Make sure you have indexed documents first with jarvis memory index. Check indexed content with jarvis memory stats.

Answers are too vague -- Try increasing max_turns in the config (e.g., 12 or 15) to give the agent more reasoning steps. You can also try a larger model like qwen3.5:35b.

Slow responses -- The agent makes multiple search passes. Each turn involves a model call. Reduce max_turns or use a smaller model (qwen3.5:4b) for faster but less thorough results.

Web search not working -- The web_search tool requires the Tavily API. Install with uv sync --extra tools-search and set TAVILY_API_KEY.

Wrong chunks retrieved -- Try re-indexing with different chunk sizes. For technical documents, smaller chunks (256) often retrieve more precisely. For narrative text, larger chunks (1024) preserve more context.