open-research-agent 0.2.0

Open Research Agent - multi-agent research with adversarial review

Open Research Agent (ORA)

Open Research Agent (ORA) is an open-source multi-agent research CLI. ORA plans research, searches and scrapes web sources, synthesizes findings, and optionally uses an adversarial reviewer for higher-intensity research.

Current release: 0.1.0

What ORA does

ORA turns a research question into a sourced markdown report:

1. A supervisor drafts a research plan.
2. The researcher searches and scrapes web sources.
3. The writer synthesizes findings into a report.
4. For intensity levels 3 and above, an adversarial reviewer audits the draft.

Current backend support

ORA 0.1.0 currently supports one LLM backend:

• LLM backend: DeepSeek API
• Search and scraping backend: Firecrawl

The default model names are deepseek-v4-flash for research and writing, and deepseek-v4-pro for planning and review. Other LLM backends are not currently supported.

Installation

Install from PyPI:

pip install open-research-agent

The primary CLI command is open-research-agent. The shorter ora command is also installed as a convenience alias.

Install from source for development:

git clone https://github.com/cameronmpalmer/open-research-agent.git
cd open-research-agent
python3 -m venv .venv
source .venv/bin/activate
pip install -e ".[dev]"

Configuration

Set the required API keys:

export DEEPSEEK_API_KEY="your-deepseek-api-key"
export FIRECRAWL_API_KEY="your-firecrawl-api-key"

Create a default config file:

open-research-agent config --init

Show the active configuration and intensity levels:

open-research-agent config --show

The config file is stored at:

~/.ora/config.yaml

Quick start

Preview a research plan without running the full pipeline:

open-research-agent plan "What are the tradeoffs between Rust and Go for backend services?"

Run a standard research task:

open-research-agent research "What are the tradeoffs between Rust and Go for backend services?" --intensity 2

Run deeper research with adversarial review:

open-research-agent research "What are the tradeoffs between Rust and Go for backend services?" --intensity 4

Save to an explicit file:

open-research-agent research "AI memory systems" --output ai-memory-systems.md

Print only to stdout and do not save a report file:

open-research-agent research "AI memory systems" --no-save

Intensity levels

ORA supports five research intensity levels:

Level	Label	Minimum sources	Max rounds (safety cap)	Reviewer
1	Quick	3	5	No
2	Standard	8	5	No
3	Thorough	15	7	Yes
4	Deep	50	10	Yes
5	Exhaustive	100	10	Yes

Levels 3, 4, and 5 use the adversarial reviewer by default.

CLI flags

Flag	Description
-i, --intensity 1-5	Research intensity level (default: 2)
-o, --output PATH	Save report to a specific path
--no-save	Print to stdout without saving a file
--stdout	Print to stdout (report is still saved)
-m, --model NAME	Override the LLM model for research and writing
-r, --reviewer-model NAME	Override the LLM model for planning and review
-y, --auto-approve	Skip the interactive plan approval prompt
--no-review	Disable adversarial reviewer (even at intensity 3+)
--max-revisions N	Cap reviewer revision rounds (default: 3)
--quiet	Suppress progress output, show only the final report

Output files

By default, open-research-agent research saves a timestamped markdown report in the current directory. Generated research reports are local outputs and should not be committed to the repository.

Use --output to choose a specific path, or --no-save to print the report without writing a file.

Development

Install development dependencies:

pip install -e ".[dev]"

Run tests:

pytest

Run the CLI locally:

open-research-agent --help
ora --help
python -m ora --help

See CONTRIBUTING.md for contributor setup and repository hygiene expectations.

License

MIT License. See LICENSE.