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    LLM API Integration Debugger

    1

    Technical debugger for LLM API integrations, SDK mismatches, and response parsing errors.

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    LLM API Integration Debugger

    LLM API Integration Debugger

    Example session with this skill installed

    My OpenAI call fails with 'TypeError: 'ChatCompletion' object is not subscriptable'. I'm using 'print(response["choices"][0]["message"]["content"])'. How do I fix this?

    • Read your context and instructions
    • Compiled the llm api integration

    The SDK is returning a Pydantic object, but you are attempting to access it as a Python dictionary.

    LLM Integration Debug Report

    ComponentStatusFinding
    ProviderOpenAIv1.0.0+ SDK detected based on response type.
    ErrorTypeErrorSubscript access [] is not supported for this object.
    Root CauseSyntax MismatchThe library transitioned from dict-like responses to object-oriented attributes.

    Corrected Implementation

    
# Problematic Pattern
# print(response["choices"][0]["message"]["content"])

# Corrected Pattern (OpenAI SDK v1+)
print(response.choices[0].message.content)

# Alternative (convert to dict if preferred)
response_dict = response.model_dump()
print(response_dict["choices"][0]["message"]["content"])

    Next steps

    1. Update the accessor syntax to use dot notation for all ChatCompletion attributes.
    2. Verify the SDK version in your requirements.txt matches the implementation.
    3. Run a test call to ensure response.choices[0].message.content returns the expected string.

    Connects securely to your tools. The creator never sees your data.

    About this skill

    The problem

    LLM API integrations often fail with cryptic errors, mismatched SDK versions, or incorrect response parsing. Developers waste hours tracing whether a 401 is a malformed header or a deployment secret issue.

    What it does

    • Analyzes code, payloads, and error logs to identify the exact point of failure in the request lifecycle.
    • Diagnoses authentication, serialization, streaming, and provider-specific parameter mismatches.
    • Maps HTTP status codes to specific integration fixes rather than generic causes.
    • Detects response parsing errors where code expects a different JSON structure than the provider returns.

    Frameworks & tools

    Supports all major LLM providers including OpenAI, Anthropic, and Google Gemini. Works with Python, JavaScript/TypeScript, and raw HTTP clients like cURL or Axios.

    Why this beats prompting it yourself

    General-purpose models often hallucinate parameters or recommend outdated SDK syntax. This skill follows a strict INSPECT to VALIDATE workflow, ensuring diagnoses are backed by technical evidence rather than guesswork.

    Use cases

    • Fixing 'object is not subscriptable' errors in SDK response parsing.
    • Debugging streaming chunks that break application logic.
    • Resolving authentication failures that only appear in CI/CD or production environments.
    • Correcting malformed tool-calling and function-calling payloads.

    Known limitations

    Does not execute live API calls. Requires provided code snippets or error logs to function.

    Cannot execute live network requests. Requires manual input of error logs or code snippets. Does not provide live API keys.

    How to use

    1. 1

      Connect your agent to the Agensi MCP

      Copy the prompt and paste it into Claude, ChatGPT, Cursor or any agent that supports MCP. It sets up the connection for you. Manual setup

    2. 2

      Sign in to Agensi

      Your agent shows a sign-in link. Sign in and click Allow.

    3. 3

      Ask for what you need

      Your agent loads this skill and uses it on your task. It always asks before any payment.

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    Listed1 month ago

    What's inside

    Frequently Asked Questions

    This skill specializes in diagnosing cryptic failures in LLM workflows, such as serialization errors, streaming breaks, and tool-calling payload mismatches that general-purpose AI models often misinterpret.

    It is fully compatible with OpenAI, Anthropic, and Google Gemini integrations, and supports code written in Python, JavaScript, TypeScript, and raw HTTP requests.

    For security reasons, the debugger does not execute live API calls; it functions by analyzing your provided code snippets, payload structures, and error logs to find the root cause.

    This skill is a logical plugin for your existing AI agent; once added to your workspace, you can immediately begin feeding it error logs or integration code for analysis.

    Yes, the debugger is updated regularly to reflect the latest SDK changes and API version updates from major providers to ensure syntax recommendations remain accurate.

    It is ideal for fixing 'object is not subscriptable' errors, resolving production-only authentication failures, and debugging malformed JSON in complex function-calling scenarios.

    New in Testing & Debugging