AutoGen Agencies for Document Processing

LangChain's document loaders and extraction chains are well-suited for straightforward extraction tasks where a single LLM call with a structured output schema produces reliable results. AutoGen adds value when documents are highly variable in format, when extraction requires iterative refinement, or when you need a validation layer that checks the extracted output before it enters a downstream system. The code-writing capability is the key differentiator: AutoGen can write a custom PDF table parser for a specific invoice format, test it against sample documents, iterate on failures, and produce a validated extraction script — a workflow that LangChain would require significant custom tooling to replicate.

Accuracy depends heavily on document type and extraction complexity. For well-structured documents like standard invoices, purchase orders, or forms, AutoGen-generated extractors reach 92–97% field-level accuracy, comparable to dedicated document AI platforms. For highly variable or unstructured documents — handwritten forms, non-standard contracts, complex tables — AutoGen's iterative code-improvement loop often closes the gap with specialized tools by generating document-specific parsing logic rather than applying a one-size-fits-all model. The practical advantage over specialized tools is adaptability: when a new document format appears, AutoGen agents can write a new extractor without requiring model retraining.

Prompt-based extraction (sending a document to an LLM and asking it to return JSON) is fast to implement but brittle at scale: output format varies, confidence is implicit, and there is no executable artifact to audit or version-control. Code-generated extractors are deterministic once written — the same Python script produces the same output for the same input every time, making them testable and deployable as standard software. The hybrid approach AutoGen enables is optimal: use the LLM to write the extractor code, validate it against ground-truth samples, and then deploy the generated code as a static pipeline stage. You get LLM flexibility in development and code reliability in production.

LLM costs split between extraction and validation. A typical document processing run — Extractor generating and refining a script, Validator checking output — consumes 3,000–8,000 tokens per document type on first run. Once a script is generated and validated, subsequent documents of the same type can be processed by the script alone without LLM calls, dropping per-document cost to near zero. For mixed document types where classification is required before extraction, add 500–1,000 tokens per document for the Router classification step. At volume, the economics strongly favor AutoGen: the LLM cost is front-loaded in script generation, and the marginal cost per document processed by a validated script is effectively infrastructure-only.