An open-source Deep Agent assistant for biologists, bioinformaticians, and clinical researchers.

Quickstart · Capabilities · Data sources

Capabilities

• Literature question-answering - scan hundreds of papers and trial records at once to perform deep literature searches

• Data analysis via code execution - generate and execute code in a safely contained LangSmith Sandbox to perform almost any data analysis

• File and figure generation - create CSV and Excel files of data, Word docs such as clinical or lab protocols, and data visualizations and plots

Data sources

• PubMed - over 29 million scientific abstracts

• PMC full texts - full text of over 8 million open-access papers

• ClinicalTrials.gov - records from over 600,000 trials

• Web search - agentic search over the entire open web

• File upload - attach a spreadsheet, a reference-manager export (.nbib/.ris/.bib), a PDF, a compound set (.sdf/.smi), FASTA/GenBank, or a figure. Tables get analysed; a bibliography becomes a corpus the agent hydrates from PubMed and reads across

Quickstart

1. Get and configure LangSmith

You need a LangSmith account. Setup will prompt you to add your LANGSMITH_API_KEY.

LangSmith Sandboxes must also be enabled from the Sandboxes tab in your LangSmith console. On a personal account on the free Developer tier you will need to add a credit card to use sandboxes, but you get free 5 LangSmith Compute Units (LCUs) per month, enough for ~650 agent runs.

2. Get the code

In the terminal:

git clone https://github.com/langchain-samples/deep-life-sci.git

cd deep-life-sci

Needs git, which setup also uses to fetch the chat UI.

3. Get uv

uv is a widely-used package manager for Python that allows the setup script to install the necessary libraries.

On macOS / Linux:

curl -LsSf https://astral.sh/uv/install.sh | sh

On Windows:

irm https://astral.sh/uv/install.ps1 | iex

4. Run the setup script

uv run scripts/setup.py

The chat UI needs Node.js 20.9 or newer. If yours is missing or older, setup installs a private copy inside the repo; nothing else on your machine changes.

5. Configure your models

Model calls go through the LangSmith LLM gateway, so your workspace also needs the provider key behind them, added once under Settings → Integrations → Provider Secrets as OPENAI_API_KEY and/or ANTHROPIC_API_KEY. Add whichever providers the models you run use. For Amazon Bedrock, add AWS_BEARER_TOKEN_BEDROCK (a Bedrock API key) and prefix model ids with bedrock/; see the note at the top of models.yaml.

Then, in the models.yaml file, configure the models you want to use for the agent. Defaults are OpenAI; recommended Anthropic alternatives are shown in comments.

6. Run the agent

uv run scripts/dev.py                        # opens the chat UI in your browser (recommended)

# or

uv run agent "which papers base-edit PCSK9?" # runs headlessly in CLI

Ctrl-C to stop the running server.

Coming soon

• LangSmith cloud deployments with user authentication
• Additional scientific data sources

Disclaimer

This is a demonstration project, intended for research and educational use. Its answers are generated by language models from published literature and trial registries, and may be incomplete, outdated, or wrong. It is not medical advice, and must not be used for clinical decision-making, diagnosis, or treatment.

Notes

Models: Deep Life Sci runs on GPT-5.6 Terra with High effort by default. To change the model or effort for the main agent, subagents or web search, edit models.yaml; changes apply to your next message. To add a provider, including a custom OpenAI-compatible endpoint, configure it in LangSmith under LLM Gateway. The wrench under the chat box shows what each role is running.

The UI is a modified clone of agent-chat-ui in .chat-ui/.

Full text journal articles are only available if present in PMC's open-access subset. Only abstracts are available for paywalled papers.

MIT Licensed