# Falcon Deep Literature

> Deep, high-reasoning literature synthesis via FutureHouse's Falcon agent (LITERATURE_HIGH job). Use when the user wants a thematic review, gap analysis, or systematic synthesis across many papers — not a single fact lookup. Costs more credits and takes minutes longer than Crow but produces SOTA-quality scholarly output.

- Skill: `qhjqhj00/falcon-deep-literature` (Agent Skill, multi-file: 5 files)
- Install (CLI): `npx skillmds add qhjqhj00/falcon-deep-literature`
- Raw SKILL.md: https://api.skillmd.com/api/skills/qhjqhj00/falcon-deep-literature/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: AI & ML
- Author: qhjqhj00 (https://skillmd.com/u/qhjqhj00)
- Updated: 2026-09-08
- Page: https://skillmd.com/skills/qhjqhj00/falcon-deep-literature

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# Falcon — Deep Scientific Literature Synthesis (FutureHouse Platform)

Falcon is FutureHouse's high-reasoning literature agent. Same retrieval backbone as Crow (PaperQA2 on Semantic Scholar / Crossref / Unpaywall), but using `LITERATURE_HIGH` mode which enables extended reasoning, broader paper coverage, and produces synthesis-quality answers suitable for review-style writing.

Use Falcon when the user asks for:

- A thematic review of a topic ("Summarize the state of evidence on …")
- Gap analysis ("What's missing from the literature on …")
- Comparison across mechanisms / drugs / methods
- Background section drafting for a paper

For one-shot factual questions, **Crow is faster and cheaper** — only escalate to Falcon when depth matters.

## Prerequisites

- `pip install edison-client`
- `EDISON_API_KEY` from <https://platform.edisonscientific.com/profile>

## Minimal usage

```python
import os
from edison_client import EdisonClient, JobNames

client = EdisonClient(api_key=os.environ["EDISON_API_KEY"])
resp = client.run_tasks_until_done({
    "name": JobNames.LITERATURE_HIGH,
    "query": (
        "Synthesize the current evidence for and against using GLP-1 receptor "
        "agonists in the treatment of non-alcoholic steatohepatitis (NASH). "
        "Cover mechanism, key clinical trials, head-to-head comparisons with "
        "other therapies, and remaining uncertainties."
    ),
})

print(resp.formatted_answer)
```

`formatted_answer` contains the synthesis with inline citations and a sorted reference list. Use it directly as the body of a "Background" or "Related Work" section.

## Recipes

### Multi-section review by parallel queries
Falcon answers one question at a time. For a multi-section review, fire several queries in parallel and stitch:

```python
import asyncio
from edison_client import EdisonClient, JobNames

sections = {
    "Mechanism":   "Summarize the molecular mechanism of GLP-1RA action in hepatocytes.",
    "Trials":      "Review phase 2 and 3 RCTs of GLP-1RAs in NASH.",
    "Comparators": "Compare GLP-1RA efficacy in NASH vs FGF21 analogs and pioglitazone.",
    "Gaps":        "What aspects of GLP-1RA use in NASH remain underexplored?",
}

async def main():
    client = EdisonClient(api_key=os.environ["EDISON_API_KEY"])
    tasks = [{"name": JobNames.LITERATURE_HIGH, "query": q} for q in sections.values()]
    answers = await client.arun_tasks_until_done(tasks)
    for h, a in zip(sections, answers):
        print(f"## {h}\n\n{a.formatted_answer}\n")

asyncio.run(main())
```

### Increase max steps / timeout for very deep synthesis
```python
from edison_client.models.app import TaskRequest

resp = client.run_tasks_until_done(TaskRequest(
    name=JobNames.LITERATURE_HIGH,
    query="...",
    runtime_config={"max_steps": 60, "timeout": 1800},
))
```

### Verbose mode — get retrieved papers + intermediate state
```python
resp = client.run_tasks_until_done(
    {"name": JobNames.LITERATURE_HIGH, "query": "..."},
    verbose=True,
)
# resp.environment_frame contains contexts, paper metadata, scored chunks
papers = resp.environment_frame["docs"]
```

## Cost / latency expectations

- **Latency**: 1–8 minutes per query (vs Crow's 20–90 s)
- **Credits**: roughly 5–10× a Crow call
- **Quality**: SOTA on LitQA2 / WikiCrow benchmarks; outputs are typically usable in published reviews after light editing

## When to fall back to Crow

If your question is fact-shaped ("what dose", "what year", "did paper X show Y"), **call Crow instead** — Falcon's extra reasoning budget is wasted and you'll burn 10× the credits for the same answer.