docs(articles): VC investment memo agent with LangGraph + Perplexity Agent API#74
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An auditable, citation-grounded VC research agent built with LangGraph and the Perplexity Agent API (built-in web_search/finance_search), plus a LangSmith eval harness comparing search providers. Self-contained code under scripts/memo/.
…tool-loop shim The client-side tool round-trip shipped upstream in langchain-ai/langchain#37934 (langchain-perplexity 1.4.0), so the _compat.py shim is no longer needed. Section 2 now binds search tools with the standard bind_tools API, and the Perplexity tool gets an explicit parameter schema so model tool calls stay well-formed.
- build_graph() snippet now matches scripts/memo/graph.py - synthesizer section states the 7-sections-from-4-nodes citation model (sections 1-6 cite, Thesis is analysis-only) - results paragraph states the run's numbers and invites re-scoring - rename Code explanation -> Directory structure, trim keywords
- no em dashes; feature/limitation bullets use the blog's bolded-lead style - synthesizer, wiring, and limitations wording now identical to the blog - remove undefined 'Section 2' references (notebook-speak); say 'provider comparison' - add the blog's source-availability limitation for parity
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Closing in favor of ppl-ai/api-docs#451 — per discussion with Albert, new cookbook content goes directly into the api-docs repo ( |
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Description
Adds an Articles integration guide: an auditable VC investment-memo agent built with LangGraph + the Perplexity Agent API (built-in
web_search/finance_search), plus a LangSmith eval harness that compares search providers. Self-contained runnable code underscripts/memo/.The design highlight: four research nodes fan out in parallel, and a tool-less synthesizer can only cite evidence the research nodes gathered — a structural guard against fabricated citations, fully auditable in LangSmith.
Type of Contribution
Checklist
Project Details
What problem does this solve?
A first-pass VC memo takes an analyst 4–8 hours across Crunchbase, PitchBook, IR pages, and news, with every number hand-checked. This agent produces a seven-section, primary-source-cited memo in ~90s for ~$0.40.
What makes this contribution valuable to other developers?
It demonstrates the Agent API's built-in tools end-to-end, a reusable reliability pattern (split search from synthesis → auditable citation chain), and a LangSmith workflow for turning "which search provider?" into a measured decision.
External Links (if applicable):
Testing
Ran the agent end-to-end (
python -m memo --company "Anthropic") and the LangSmith comparison across 10 companies onopenai/gpt-5.5. MDX validated withscripts/validate-mdx.js(all files pass).Additional Notes
Companion to an upcoming Perplexity × LangChain blog post; the article URL is referenced there, so this is ready for review and merge.
Recent updates:
langchain-perplexity1.4.0 shipped client-side tool calling (langchain-ai/langchain#37934), so the provider comparison now uses the standardbind_toolsAPI and the temporary_compat.pyshim is gone.Note on CI: the
mdx-validationred X is the workflow failing at setup (org policy requires SHA-pinned actions;pr-validation.ymlonmainuses@v6tags), so it fails for every PR in this repo. All three of its checks pass locally on this branch:scripts/validate-mdx.js(42/42 valid), no localhost links, frontmatter present.