IVQ 101-150

Architecture & Design Patterns

  1. How do you modularize LangGraph workflows into reusable components?

  • Encapsulate nodes as independent Python modules/functions.

  • Package reusable node sets (e.g., “summarization chain”, “tool executor”) as importable libs.

  • Compose small graphs → combine in a master graph.

  1. What’s the best way to separate business logic from orchestration?

  • Nodes = pure functions, implement business logic only.

  • The StateGraph = orchestration logic only — connects nodes, conditions, edges.

  1. Can you dynamically reconfigure a LangGraph after deployment?

  • Not directly hot-swappable, but you can:

    • Parametrize edge logic via config/state.

    • Load nodes conditionally at runtime.

    • Redeploy updated graphs with new edges/nodes.

  1. What architectural styles inspire LangGraph?

  • Primarily a hybrid of:

    • DAG (Directed Acyclic Graph) for linear flows.

    • FSM (Finite State Machine) for loops & conditional edges.

    • Some inspiration from Petri Nets for multi-branch parallelism.

  1. How do you version control LangGraph workflows?

  • Use Git for node code + graph config.

  • Store versioned templates for edges/conditions in JSON/YAML if externalized.

  • Tag releases & deploy with explicit version numbers.

  1. What design pattern does LangGraph most closely follow?

  • A State Machine + Pipeline hybrid.

  • Follows the Orchestrator-Worker pattern: the graph decides what runs; nodes do the work.

  1. Can workflows be persisted and resumed from failure?

  • Yes — if you store state externally (e.g., DB, S3) at each step.

  • On failure, reload the last good state and rerun from that node.

  1. How do you organize large-scale projects with dozens of nodes?

  • Group related nodes in packages/modules.

  • Use naming conventions (preprocessing, analysis, llm_tools).

  • Visualize flows with diagrams.

  • Document edges/conditions carefully.

  1. How do you use decorators/wrappers for common behavior?

  • Use decorators for:

    • Logging input/output.

    • Exception capture.

    • Retry/backoff logic. Example:

  1. How do you integrate CI/CD?

  • Use unit tests for nodes.

  • Lint/check graphs for dangling edges.

  • Deploy via GitHub Actions, with container builds & rollout.

  • Automate test runs with each PR.

Observability & Monitoring

  1. How do you log state changes?

  • Use Python logging in each node.

  • Or wrap nodes with a logging decorator.

  • Store logs in files, Loki, or cloud logging (e.g., CloudWatch).

  1. How do you record inputs/outputs of each node?

  • Log them at entry/exit.

  • Store in structured form (JSON).

  • Optionally persist in a DB for audit.

  1. Prometheus, Grafana, others?

  • Yes — expose custom metrics:

    • Node runtime.

    • Success/failure counters.

  • Use Prometheus client to push metrics → visualize in Grafana.

  1. How do you monitor failed executions?

  • Log all exceptions.

  • Push alerts via Prometheus or error tracking (Sentry, Rollbar).

  • Optionally notify via Slack/webhook.

  1. Emit structured logs inside nodes?

  • Use Python logging with json formatter.

  • Include workflow_id, node_name, state snapshot.

  1. Create metrics or custom alerts?

  • Emit metrics (duration, errors).

  • Use Prometheus Alertmanager for thresholds.

    • E.g., “>5 failed runs in 5 min”.

  1. Track memory/token usage per node?

  • Log prompt/response length.

  • Use cost estimation helpers.

  • If using OpenAI, parse usage in API response.

  1. Enable distributed tracing (OpenTelemetry)?

  • Wrap nodes with OpenTelemetry spans:

  1. Perform root-cause analysis?

  • Store state snapshots + execution path.

  • Analyze logs + traces.

  • Link errors to node names.

  1. Tools for dashboards?

  • Grafana (metrics).

  • Kibana/ELK (logs).

  • Sentry (errors).

  • Custom dashboards with Streamlit if needed.

Tooling & Automation

  1. GUI or visual builder?

  • Official LangGraph: not yet mainstream.

  • You can sketch flows in Mermaid/Graphviz/Draw.io.

  1. CLI for build/run/test?

  • Not official yet — wrap in Makefile or custom CLI.

  • Or integrate with invoke or fabric.

  1. Integrate with GitHub Actions?

  • Yes — run tests, lint, container builds.

  1. Validate workflow logic before compile?

  • Write unit tests for edges:

    • Check all condition outputs map to valid nodes.

  • Optionally build a graph validator.

  1. Self-healing/retry?

  • Wrap nodes with retry decorators.

  • Use conditional edges for fallback.

  1. Import/export as JSON/YAML?

  • Not native yet — community patterns exist.

  • You can define graph edges/nodes in config and load dynamically.

  1. Parameterize workflows at runtime?

  • Pass state dict at run() call.

  • Use env vars/config for dynamic parts.

  1. Scaffolding tools?

  • Not yet. Use cookiecutter or custom templates.

  1. Deploy as a server?

  • Yes — wrap with FastAPI → multi-user orchestration.

  1. Automate deployment?

  • Use Docker + Terraform/CloudFormation.

  • Deploy to ECS, GKE, or K8s.

Human-in-the-Loop & Decision Flow

  1. Insert human decision node?

  • Route state to a review node → pause → wait for human update.

  1. Pause/resume workflows?

  • Save state to DB.

  • Resume by loading state and calling graph.run(state).

  1. Integrate Slack/email for decisions?

  • Use API call in the review node to send Slack/email.

  • Wait/poll for response.

  1. Override state manually?

  • Let reviewer edit state fields.

  • Reload state with updated values.

  1. Enforce audit trails?

  • Log every state change + user ID.

  • Save to immutable store if needed.

  1. Simulate user feedback loop?

  • Add feedback node that updates state.

  • Loop back to previous node if changes needed.

  1. Include confidence scores + override?

  • Store LLM scores in state.

  • If below threshold, route to human or backup node.

  1. Involve SMEs for validation?

  • Notify SMEs via review node.

  • Record approvals.

  1. Build approval chains?

  • Chain multiple review nodes: team_lead → manager → legal.

  1. Enable annotation/labeling?

  • Add labeling UI outside LangGraph → write back to state.

Comparisons & Competitive Landscape

  1. LangGraph vs LangChain Expression Language (LCEL)?

  • LCEL = chaining LLM calls & tools declaratively.

  • LangGraph = stateful branching + loops → better for complex agent flows.

  1. LangGraph vs Prefect?

  • Prefect = general-purpose data workflows, scheduling, retries.

  • LangGraph = LLM + agent orchestration, more conversational.

  1. LangGraph vs n8n?

  • n8n = low-code ETL/automation.

  • LangGraph = programmatic, deeply LLM-native.

144. LangGraph vs Semantic Kernel Planner?

  • Similar goal: plan tool calls.

  • LangGraph is Python-native; SK is C#/.NET + plugins.

145. Advantages over Airflow?

  • Airflow = heavy batch ETL, not interactive.

  • LangGraph = fine-grained loops, stateful, conversational agents.

146. Multi-agent reasoning?

  • LangGraph naturally supports agent loops, tool calls, memory passing.

  • Easier than stitching FSM logic by hand.

147. Trade-offs vs custom orchestration?

  • LangGraph = saves time for common agent patterns.

  • Custom = full control, but more boilerplate.

148. Async or sync pipelines?

  • Best with async (FastAPI + async LLM calls).

  • Sync fine for simple local runs.

  1. LangGraph vs Autogen conversational graphs?

  • Similar multi-agent loop ideas.

  • Autogen bakes in specific back-and-forth agent roles.

  • LangGraph = you control nodes, edges, custom states.

150. When choose LangGraph vs plain Python?

  • Choose LangGraph for complex, stateful flows:

    • Multi-step agents

    • Tool + LLM loops

    • Fallbacks, branching.

  • Use plain Python for one-shot LLM calls or simple scripts.

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