AI agents are moving fast-from “single prompt → single response” workflows to systems that plan, use tools, collaborate, and iterate. But once you go beyond a basic chatbot, two challenges show up immediately:
- Orchestration: How do you reliably manage multi-step agent behavior, branching logic, retries, and human-in-the-loop decisions?
- Observability: When an agent fails (or behaves oddly), how do you trace what happened, evaluate quality, and improve it over time?
That’s where LangGraph and LangSmith come in. In this post, you’ll learn what each tool does, when to use them, and practical patterns to build agent systems that are both powerful and debuggable.
What Are LangGraph and LangSmith?
LangGraph (Orchestration)
LangGraph is designed for building agent workflows as graphs-think nodes (steps) and edges (transitions). Instead of hardcoding agent behavior in linear chains, you model it as a stateful graph that can:
- Branch based on conditions (e.g., “if confidence < threshold → ask clarifying question”)
- Loop until a goal is reached (e.g., “search → read → summarize → verify → repeat”)
- Support multi-agent collaboration (e.g., researcher agent + writer agent + reviewer agent)
- Introduce human approvals at key points (human-in-the-loop)
In short: LangGraph helps you turn “agent vibes” into a structured, controllable system.
LangSmith (Observability + Evaluation)
LangSmith focuses on visibility and quality control for LLM applications. It helps you:
- Trace agent runs end-to-end (prompts, tool calls, intermediate steps, outputs)
- Debug failures and regressions
- Build evaluations (automated or human) for output quality
- Track changes over time as prompts/models/tools evolve
In short: LangSmith helps you understand and improve your AI agents with tracing, testing, and evaluation.
Why Orchestration and Observability Matter for AI Agents
When teams start building agentic systems, the “demo works” phase is easy. The hard part is shipping something reliable in production.
Common problems that appear quickly
- Agents loop forever or “thrash” between tools
- Tool results are inconsistent and the agent doesn’t recover
- The agent makes silent assumptions instead of asking clarifying questions
- Costs spike due to repeated calls and retries
- You can’t reproduce bugs because the system is nondeterministic
LangGraph gives structure to agent behavior. LangSmith gives you the ability to see and measure what the agent did.
When to Use LangGraph vs. When to Use LangSmith
Use LangGraph when you need:
- Multi-step workflows with branching/looping
- Tool-using agents with predictable flow control
- Multi-agent collaboration patterns
- State management (e.g., carrying context across steps)
- Human review points (approval gates)
Use LangSmith when you need:
- Debugging and tracing across prompts + tool calls
- Benchmarking different prompts/models
- Regression testing for agent workflows
- Quality scoring (helpfulness, factuality, completeness, tone)
- Auditability and reproducibility
Best practice: use both together
A strong production setup typically looks like this:
- LangGraph defines the workflow (what happens, in what order, under what conditions)
- LangSmith captures the run and evaluates it (what actually happened, and whether it was good)
Key Concepts in LangGraph (Explained Simply)
1) Nodes = Steps in your agent workflow
Examples of nodes:
- “Parse user request”
- “Plan tasks”
- “Search internal docs”
- “Call tool: database query”
- “Write draft response”
- “Run QA checks”
2) Edges = Transitions between steps
Edges can be unconditional:
- “After planning → run search”
Or conditional:
- “If missing info → ask a question”
- “If result confidence low → try alternative source”
3) State = the shared memory of the graph
State might include:
- User request
- Extracted constraints
- Retrieved documents
- Tool outputs
- Draft answer
- Confidence score
- Evaluation feedback
This makes the workflow easier to reason about than loosely passing variables between functions.
Practical Agent Patterns You Can Build with LangGraph
Pattern 1: Reliable “Plan → Execute → Verify” agent
A common production pattern is to separate planning, execution, and verification:
- Plan: Identify steps and required info
- Execute: Use tools to gather or compute
- Verify: Check for missing pieces, contradictions, or low confidence
- Finalize: Produce the user-facing answer
This reduces hallucinations and prevents the agent from jumping to conclusions.
Pattern 2: Human-in-the-loop approval gates
In regulated or high-risk workflows (finance, healthcare, legal), you can insert a checkpoint:
- Draft response → Human review → Publish/Reject
If rejected, route back to the appropriate node (“re-run research” or “rewrite”).
Pattern 3: Multi-agent “specialist team” workflow
Instead of one agent doing everything, create roles:
- Researcher agent (gathers facts, citations)
- Analyst agent (compares options, detects gaps)
- Writer agent (creates final narrative)
- Reviewer agent (checks tone, policy, formatting)
LangGraph makes this collaboration explicit and repeatable. For more on designing robust collaboration, see agent-to-agent communication patterns and architectures.
Pattern 4: Tool fallback and retry logic
When a tool fails, a robust agent should:
- Retry with backoff (if transient)
- Switch to a fallback tool/provider
- Ask the user for missing permissions/details
- Log the failure for later analysis
This is much easier to implement in a graph with conditional edges than in a single linear chain.
What You Gain with LangSmith (Tracing + Evaluations)
1) Tracing: See the full story behind an agent response
Instead of guessing why an answer is wrong, you can inspect:
- the exact prompt sent
- documents retrieved
- tool inputs/outputs
- intermediate reasoning steps (if you capture them)
- final response and metadata (tokens, latency, cost)
This is essential for debugging production issues. If you’re planning to operationalize tracing across environments, LangSmith for agent governance provides a practical playbook.
2) Evaluations: Measure quality instead of relying on opinions
LLM apps fail quietly: they may sound confident while being incomplete or incorrect. LangSmith-style evaluation helps you create repeatable checks such as:
- Correctness: Is the answer factually right?
- Groundedness: Does it rely on provided sources?
- Completeness: Did it address all requirements?
- Format adherence: Did it follow the schema?
- Safety/compliance: Did it avoid restricted content?
You can run evaluations on:
- a test suite of prompts
- production samples
- edge cases (ambiguous, adversarial, incomplete inputs)
3) Regression testing: Prevent “one small change” from breaking everything
When you change:
- prompts
- tools
- models
- retrieval settings
…you want to know whether quality improved or regressed. Traces + evaluations make that measurable.
A Simple End-to-End Workflow: LangGraph + LangSmith Together
Here’s a practical blueprint you can adapt:
Step 1: Define the agent graph (LangGraph)
- Input → “Clarify request” (if needed)
- “Plan” → “Retrieve info” → “Use tools”
- “Draft response”
- “Self-check” (policy + formatting + confidence)
- Final output
Step 2: Add instrumentation (LangSmith)
Capture:
- each node execution
- tool calls and timing
- prompt versions
- final results
Step 3: Create evaluation criteria
Start with 3–5 scoring dimensions:
- helpfulness
- factuality/groundedness
- completeness
- instruction-following
- tone
Step 4: Iterate with real evidence
Use traces to find failure points, then improve:
- prompts
- routing conditions
- tool schemas
- fallback logic
- retrieval parameters
Common Questions (Featured Snippet-Friendly)
What is LangGraph used for?
LangGraph is used to build stateful, multi-step agent workflows using a graph structure, enabling branching logic, loops, tool orchestration, and human-in-the-loop steps.
What is LangSmith used for?
LangSmith is used for observability and evaluation of LLM applications-capturing traces of agent runs, debugging failures, and measuring output quality with structured evaluations.
Do I need both LangGraph and LangSmith?
Not always-but many production teams use both: LangGraph to control the workflow and LangSmith to trace and evaluate behavior so the system can be improved reliably.
How do LangGraph and LangSmith help reduce hallucinations?
LangGraph helps by enforcing structured steps like “retrieve then verify,” while LangSmith helps by revealing where hallucinations occur and enabling evaluations that detect factual or grounding issues.
Best Practices for Production-Ready AI Agents
Keep state explicit
Avoid hidden variables and implicit context. Store what matters in state:
- user intent
- constraints
- tool outputs
- confidence signals
Add “stop conditions” to prevent endless loops
Define loop limits, timeouts, and fallback behaviors. Agents should fail gracefully with a helpful message.
Treat prompts like versioned code
Track prompt changes and test them like software. Pair new prompts with evaluation runs.
Invest early in observability
If you wait until production failures pile up, debugging becomes expensive. Tracing from day one pays off fast—especially if you set up monitoring agents and flows with Grafana and Sentry alongside your tracing.
Build evaluation sets from real user traffic
Synthetic tests help, but real-world prompts reveal the edge cases that actually matter.
Final Thoughts: Build Agents You Can Trust (and Improve)
AI agents can be genuinely transformative-but only when they’re built with structure and visibility. LangGraph provides the orchestration layer to make agent behavior more deterministic and maintainable. LangSmith provides the observability and evaluation layer to make agent performance measurable and improvable.
If your goal is to move from prototypes to production-without losing control-combining graph-based orchestration with end-to-end tracing and evaluation is one of the most practical paths forward.
