LangChain Development Problems

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Here’s a structured list of 50 different types of development-related problems or bugs you might encounter when working with LangChain, LangGraph, or Pipedream. I grouped them into categories so you can see patterns of issues across the stack.

  1. Incorrect LLM prompt formatting (f-strings misused, missing input variables).
  2. Token limit exceeded due to lack of chunking in TextSplitter.
  3. Infinite recursion in RecursiveCharacterTextSplitter.
  4. Wrong embedding model dimensions vs vector database schema mismatch.
  5. Memory leaks from improperly scoped ConversationBufferMemory.
  6. API key not loaded correctly in OpenAI() constructor.
  7. Failure to handle RateLimitError exceptions gracefully.
  8. Broken retriever chain when VectorStoreRetriever returns None.
  9. Serialization errors when persisting chains with custom tools.
  10. Stale cache in LLMCache leading to outdated responses.
  11. Misconfigured AgentExecutor causing tool loop (hallucinated tool calls).
  12. Incorrect tool return type (string vs dict) breaking agent parsing.
  13. Document loader inconsistencies (e.g., PyPDFLoader fails on malformed PDFs).
  14. Output parser errors (e.g., PydanticOutputParser fails on JSON with trailing commas).
  15. Streaming outputs not flushed correctly in async mode.

LangGraph Development Problems

  1. Deadlocks in graph execution due to cyclic dependencies.
  2. Mis-declared node inputs/outputs causing broken DAG wiring.
  3. State mutation inside async nodes causing race conditions.
  4. Wrong checkpoint serialization leading to resume failure.
  5. Graph execution not resuming after system crash (corrupt checkpoint).
  6. Node retries not handled (exception propagates instead of retry).
  7. Incorrect type coercion when passing outputs between nodes.
  8. Missing edge in graph definition leading to partial execution.
  9. Version mismatch of LangGraph schema vs runtime execution engine.
  10. Graph visualizer showing incorrect node dependencies due to stale cache.
  11. Event-driven triggers not firing in subscription nodes.
  12. Improper backpressure handling in streaming nodes.
  13. Infinite loop if graph recursion is not bounded.
  14. Checkpoint store backend (SQLite/Postgres) schema migration failure.
  15. Graph cancellation not releasing resources (e.g., hanging threads).

Pipedream Development Problems

  1. Incorrect OAuth2 credential refresh flow (Google, Slack, etc.).
  2. Missing environment variable injection at runtime.
  3. Input schema validation errors when webhook payload changes.
  4. Broken connector action due to upstream API deprecation.
  5. Timeout in long-running steps exceeding 300s execution limit.
  6. File uploads exceeding platform size limits.
  7. Race conditions between parallel steps writing to same datastore.
  8. Step retries causing duplicate API calls (idempotency failure).
  9. Memory limit exceeded (512MB cap) in Node.js steps with large payloads.
  10. Silent failure when using unsupported npm packages.
  11. Rate-limiting not respected in loops (API bans account).
  12. Cron scheduling drift (job not firing at exact time).
  13. Event-driven trigger (e.g., Stripe webhook) dropped due to transient outage.
  14. Permissions misconfigured when accessing Pipedream connected apps.
  15. Workflow export/import losing custom credentials mapping.

Cross-Cutting Issues (LangChain / LangGraph / Pipedream)

  1. Dependency version conflicts (e.g., openai>=1.0 vs LangChain requiring older).
  2. Inconsistent async vs sync function usage across modules.
  3. Poor error logging (stack trace swallowed by framework).
  4. Misconfigured secrets management across multiple environments.
  5. Hard-to-reproduce bugs due to nondeterminism in LLM outputs.

✅ These 50 problems span design-level, runtime, integration, and platform-specific issues. They’re the kinds of things developers encounter while building RAG systems, orchestration pipelines, or workflow automations with LangChain, LangGraph, and Pipedream.

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