LangChain

Composable Chains, Prompts, and Tools

Create modular chains that combine template prompts, structured parsers, and tool calls into repeatable steps. Swap components without rewiring everything when specs or models change mid-project. Type-safe inputs and outputs keep contracts clear, and prompt templates render variables consistently across locales and channels. Output parsers validate JSON and schema shapes, retries add fallbacks on malformed responses, and tool adapters hide provider quirks. Examples cover chat, extraction, and classification to standardize patterns.

Agents, Graphs, and Control Flow

Build agents that choose tools dynamically, or define deterministic graphs for critical flows. Handle retries, branches, and guards so production routes stay stable while research remains flexible. State containers persist memory between steps, and declarative edges make logic readable in code reviews and design docs. LangGraph models flows as state machines with guarded transitions, human-in-the-loop pauses, and resumable checkpoints for long tasks. Timeouts and concurrency limits protect backends, and diagrams explain paths clearly.

Retrieval, Indexes, and RAG

Load documents from file systems, cloud storage, and APIs; chunk and embed text; and query vector stores with hybrid retrievers. Ground answers in sources for citations and better factuality that stakeholders can verify later. Pipelines support filters, rerankers, and metadata routing so results stay relevant, reducing hallucinations in sensitive workflows. Loaders handle PDFs, HTML, slides, and code; caching speeds hot queries; and multi-vector retrievers blend keyword and semantic recall. Chunkers preserve headings for faithful cites.

Observability, Testing, and Evaluation

Capture traces, spans, and inputs to compare prompts, models, and parameters over time. Run offline evals on datasets and track quality, cost, and latency together so tradeoffs are explicit. Regression checks prevent silent degradations, and feedback signals become training labels for ongoing improvement. LangSmith stores traces and datasets, collects feedback via APIs, and runs evals for faithfulness, toxicity, and task success. Comparisons quantify cost versus quality, and canary checks catch regressions before wide release.

Deployment, Serving, and Integrations

Expose chains as APIs, schedule jobs, or run serverless for scale. Adapters connect to vector databases, orchestration tools, and secret stores so ops stays simple as traffic grows. CI examples and reference projects shorten ramp-up, and dependency pins keep environments reproducible across teams and regions. LangServe exposes routes with streaming events; secrets integrate with cloud vaults; deploy templates target major clouds. End-to-end tests validate critical paths, and adapters evolve without rewriting core logic.