deep_agents/SHARE_SUMMARY.md
A shareable summary of the new Deep Agents example and its benchmark results. Full details: example, BENCHMARK.md.
We plugged GPT Researcher into LangChain's
Deep Agents as
the research engine: a chief-editor deep agent plans a report outline with
write_todos, spawns parallel researcher subagents, and each subagent
calls GPT Researcher's full pipeline (deep_research) to produce a cited
section draft. The harness handles orchestration; GPT Researcher handles
evidence. It reproduces the STORM-style pipeline of our 8-agent LangGraph
example in a fraction of the code, and works over the web, local documents
(PDF/DOCX/md), or both.
The obvious question: does swapping the quickstart's raw search tool for
GPT Researcher actually make the agent's research better? We ran both on
DeepResearch Bench
(RACE report quality + FACT citation verification, 10 topic-stratified
tasks), keeping everything else identical - same create_deep_agent
harness, same gpt-5.4 model, same system prompt, same step budget. The
only variable was the research tooling.
| Metric | Deep agent + raw Tavily search | Deep agent + GPT Researcher |
|---|---|---|
| RACE report quality | 0.503 | 0.512 |
| Citations per report | 34.5 | 62.9 |
| Effective (verified) citations per report | 18.6 | 35.2 (+89%) |
| Citation precision | 53.9% | 56.0% |
| Per-task wins (verified citations) | 3/10 | 7/10 |
Every citation was verified by the official FACT pipeline: extract the claim, fetch the cited page, judge whether the page supports the claim. So this isn't "more links" - it's ~2x the claims a reader can actually verify, at equal-or-better report quality and higher precision. For calibration, 35 effective citations per report is in the range DeepResearch Bench reports for dedicated deep research products (OpenAI Deep Research ~40), while plain LLM + search-tool setups typically land at 5-15.
hybrid mode, the same
agent researches private documents alongside the web. On a due-diligence
task over a realistic fictional internal corpus (27 files: PDFs, DOCX,
distractors, stale archived vintages - no leakage from the public web
possible), it recovered up to 94% of internal-document facts vs 0%
for the raw-search agent. Even the DIY alternative - mounting the corpus
on the deep agent's own file tools - averages only 62.5%: it has to
guess which files to read, cannot parse DOCX, and sometimes trusts an
archived vintage. GPT Researcher's format-aware parsing and embedding
retrieval make it both higher and consistent, at equal public-web fact
coverage.A raw search tool returns snippets, so the writing model cites whatever the search API surfaced and fills gaps from memory. GPT Researcher plans sub-queries, scrapes and reads the full pages, filters content by relevance, and hands the agent pre-cited synthesis - so what reaches the final report is grounded in sources that were actually read.
Everything ships in the repo: the agent builders, a generation script that emits DeepResearch Bench-format reports for both setups, and a small patch adding an OpenAI judge backend to the official scoring harness. See BENCHMARK.md for the three commands.