Nested chains hide causality
A bad output may originate in a retriever, prompt template, parser, tool, memory write, or retry branch several steps earlier.
← Opswald home
LangChain debugging
Debug LangChain agents beyond callback logs and final answers.
Opswald gives teams a practical way to inspect LangChain agent runs across prompts, chain steps, retrievers, tools, memory, retries, and outputs—then replay the failure with evidence pinned.
agent-run.trace
01Prompt + retrieved context captured
02Planner chose tool with incomplete state
03Tool output contradicted the next decision
04Replay pins the first divergent step
What breaks
Agent failures are rarely a single stack trace.
They happen across prompts, memory, retrieved documents, tool schemas, model choices, retries, and side effects. Opswald is built to make that chain inspectable instead of asking engineers to reconstruct it from logs.
Callbacks are fragmented
Callbacks emit useful events, but teams still need a coherent story of why the LangChain agent chose a path.
Tools fail semantically
A tool can return 200 OK while giving stale, partial, or business-invalid data to the next chain step.
Memory changes behavior
Conversation memory and persisted state can make a local rerun behave differently from the production incident.
How to debug a LangChain agent run
Map LangChain events into an agent-level decision graph. The goal is to connect chain inputs, retrieved context, tool calls, parser behavior, and memory writes to the final failure.
- Trace eventsCapture chain starts, prompts, LLM outputs, parser errors, retriever documents, tool calls, and memory writes.
- Group by intentOrganize events around the plan and the decision each step was trying to support.
- Replay safelyPin documents and tool outputs while stubbing external mutations to reproduce the exact bad path.
- Fix the boundaryAdjust prompts, output parsers, retrieval filters, tool schemas, or memory policies where the trace first diverged.
langchain-debug-notes.txt
chain: support_refund_agent retriever: returned outdated policy chunk tool: refund_lookup returned partial customer state memory: previous failed refund persisted as success fix: retrieval filter + schema guard + memory write receipt
Practical debugging
LangChain failures Opswald helps isolate
Retriever mismatch
See which documents entered the prompt and whether they justified the chosen action.
Parser and schema drift
Catch output parser failures and tool argument mismatches before they become silent agent behavior.
Retry and memory bugs
Follow retries, fallback chains, and memory writes that change later decisions.
Keep reading
Related Opswald guides
Investigate agent failure step by stepA framework-neutral incident workflow that works well for LangChain agents.AI agent replayReplay LangChain failures with pinned retrieved context and tool outputs.Debug tool calling failuresPractical checks for LangChain tool schemas, arguments, outputs, and side effects.DocsRead Opswald concepts and integration details.
FAQ
Questions teams ask before instrumenting agents
Does this replace LangSmith or callbacks?
No. Opswald focuses on production debugging and replay around agent decisions, tools, and root cause. It can complement framework-native telemetry.
What should LangChain teams instrument first?
Start with prompts, retrieved documents, tool arguments and outputs, parser results, memory writes, retries, and final user-visible responses.
Debug the next failed agent run with evidence.
Opswald is in early access for teams shipping AI agents that call tools, use MCP servers, or run multi-step workflows in production.
Request Early Access →