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Convex Agents Debugging

Convex Agents Debugging

github.com/sstoboTesting & quality24196 installsUpdated 40w ago
Repository

About Convex Agents Debugging

A debugging skill for developers building agents with Convex Agents. It helps inspect model requests, context, messages, and stored data, and covers tool-call tracing and common type issues.

Key features

  1. 01Log model requests and responses
  2. 02Inspect context messages
  3. 03Query agent data tables
  4. 04Fetch context manually
  5. 05Trace tool calls and results
  6. 06Analyze message ordering

Use cases

  1. 01Investigate unexpected agent behavior
  2. 02Check whether context is being used
  3. 03Audit tool calls and results
  4. 04Analyze message order and token usage

Install

Copy the command and run it in a terminal.

$npx skills add https://github.com/sstobo/convex-skills --skill convex-agents-debugging

After installing, type/convex-agents-debuggingin Claude Code to confirm it loaded.

Details

From the project README

Purpose

Debugging tools help understand what's happening inside agents, what the LLM receives, and what's stored. Essential for developing reliable agent applications.

When to Use This Skill

  • Agent behavior is unexpected
  • LLM responses are off-target
  • Investigating why certain context isn't being used
  • Understanding message ordering
  • Checking file storage and references
  • Auditing tool calls and results
  • Profiling token usage

Log Raw LLM Requests and Responses

const myAgent = new Agent(components.agent, {
  name: "My Agent",
  languageModel: openai.chat("gpt-4o-mini"),
  rawRequestResponseHandler: async (ctx, { request, response }) => {
    console.log("LLM Request:", JSON.stringify(request, null, 2));
    console.log("LLM Response:", JSON.stringify(response, null, 2));

    await ctx.runMutation(internal.logging.saveLLMCall, {
      request,
      response,
      timestamp: Date.now(),
    });
  },
});

Log Context Messages

See exactly what context the LLM receives:

const myAgent = new Agent(components.agent, {
  name: "My Agent",
  languageModel: openai.chat("gpt-4o-mini"),
  contextHandler: async (ctx, args) => {
    console.log("Context Messages:", {
      recent: args.recent.length,
      search: args.search.length,
      input: args.inputMessages.length,
    });

    args.allMessages.forEach((msg, i) => {
      console.log(`Message ${i}:`, {
        role: msg.role,
        contentLength: typeof msg.content === "string"
          ? msg.content.length
          : JSON.stringify(msg.content).length,
      });
    });

    return args.allMessages;
  },
});

Inspect Database Tables

Query agent data directly:

export const getThreadMessages = query({
  args: { threadId: v.string() },
  handler: async (ctx, { threadId }) => {
    return await ctx.db
      .query(components.agent.tables.messages)
      .filter((msg) => msg.threadId === threadId)
      .collect();
  },
});

Fetch Context Manually

Inspect what context would be used:

import { fetchContextWithPrompt } from "@convex-dev/agent";

export const inspectContext = action({
  args: { threadId: v.string(), prompt: v.string() },
  handler: async (ctx, { threadId, prompt }) => {
    const { messages } = await fetchContextWithPrompt(ctx, components.agent, {
      threadId,
      prompt,
    });

    return {
      contextMessages: messages.length,
      messages: messages.map((msg) => ({
        role: msg.role,
        contentType: typeof msg.content,
      })),
    };
  },
});

Trace Tool Calls

Log all tool invocations:

export const myTool = createTool({
  description: "My tool",
  args: z.object({ query: z.string() }),
  handler: async (ctx, { query }): Promise<string> => {
    console.log("[TOOL] myTool called with:", query);
    const result = await someOperation(query);
    console.log("[TOOL] myTool returned:", result);
    return result;
  },
});

Fix Type Errors

Common circular reference issue:

// WRONG - no return type
export const myFunction = action({
  args: { prompt: v.string() },
  handler: async (ctx, { prompt }) => {
    return await someLogic();
  },
});

// CORRECT - explicit return type
export const myFunction = action({
  args: { prompt: v.string() },
  returns: v.string(),
  handler: async (ctx, { prompt }): Promise<string> => {
    return await someLogic();
  },
});

Analyze Message Structure

Debug message ordering:

export const analyzeMessages = query({
  args: { threadId: v.string() },
  handler: async (ctx, { threadId }) => {
    const messages = await listMessages(ctx, components.agent, {
      threadId,
      paginationOpts: { cursor: null, numItems: 100 },
    });

    return messages.results.map((msg) => ({
      order: msg.order,
      stepOrder: msg.stepOrder,
      role: msg.message.role,
      status: msg.status,
    }));
  },
});

Key Principles

  • Log early: Capture data while developing
  • Use console for quick checks: Fast iteration
  • Save important events: Archive LLM calls for analysis
  • Explicit return types: Prevents circular references
  • Dashboard inspection: Easiest way to see database state

Next Steps

  • See playground for interactive debugging
  • See fundamentals for agent setup
  • See context for context-aware debugging

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