Start here · Researcher

Start as a researcher

Run research workflows that search, cite and export their findings, review every step they took, and keep what matters in a memory that lasts. The gateway ships two research workflows ready to run.

Install, try it, make it yours

Install and choose a strong model 15 min

curl -LsSf https://raw.githubusercontent.com/lpalbou/AbstractFramework/main/scripts/install.sh | sh

Research workflows make many LLM calls, so pick the strongest model your machine fits in the console’s Models tab, or add a cloud provider under Providers.

Run deep-research 20 min

deep-research investigates a request, runs rounds of adversarial review, keeps a verified source ledger and exports Markdown, PDF and DOCX. Its inputs are the request, a viewpoint, and an effort of quick, standard or thorough. It serves the plain chat interface, so AbstractCode can run it directly:

abstractcode login --token <admin token>   # once; the installer’s summary prints this line
abstractcode --workflow deep-research

Or open the Observer, choose Launch and run it once. Its research agents use a read-only evidence allowlist (web search, fetch, skim, read file); only the export step writes files.

Follow every step 10 min

In the Observer, open the run and replay its ledger: each investigation round, every search and fetch, the reviewer’s guidance and the export manifest with file paths and hashes. Nothing is summarized away.

Observer Ledger tab of a completed run: ledger records node-2::done, node-2::reason (llm_call, completed and started) and ANSWERING (on_flow_end) with their JSON payloads and Unfold / Copy buttons, plus Steps / Cycles / Condensed / Copy JSONL controls
The Observer’s Ledger tab: every record of a run, with its payload.

Generate hypotheses with co-scientist 15 min

co-scientist grounds itself in the literature through the deep-research investigation flows, then cycles through generation, reflection, Elo-ranked pairwise debate and evolution, and ends with a reviewed research overview. Launch it from the Observer.

Then make it yours

TRY 1

A weekly literature watch

Schedule a growing automation every 7 days: "Find new papers on <your topic> since the previous run and summarize them with links." Each run sees the previous ones and reports what is new.

Automations

TRY 2

Your own research flow

The shipped research workflows are authored as Flow graphs, and their editable sources are published. Start from one, change the review rounds or the export, and publish your variant under a new name.

Workflow sources

TRY 3

A lasting memory

Give an agent a memory that forms records, recalls them by cue and strengthens what it actually used, with AbstractMemory; or create an entity that keeps its own memory and diary in AbstractEntity.

AbstractMemory · AbstractEntity

Worth knowing

Deadlines are guidance. deep-research derives a deadline and a source budget from the effort and carries them into its prompts; they do not stop a provider call already in flight. A thorough run on a local model can take a long time.
Long conversations keep whole messages. Chats and growing automations replay the newest whole messages up to 50,000 tokens; nothing is cut mid-message, and each run records how many earlier messages were left out.
Check the sources. The source ledger records where each claim came from; read the cited pages before you rely on a finding.