If you’ve built workflows that process large blocks of text—scraping content, generating documents, pulling API responses, or working with LLM outputs—you’ve probably hit this wall: your execution results get cut off. TaskAGI now stores 5 times more data in workflow execution results before truncation kicks in, jumping from 20,000 to 100,000 characters. That means less data loss, better debugging, and workflows that actually capture what they produce.
Featured Answer: TaskAGI increased workflow execution result storage from 20K to 100K characters, allowing agents to retain substantially larger text outputs and API responses without truncation. This benefits workflows handling document generation, web scraping, content extraction, and AI model outputs where complete data preservation matters for debugging and downstream operations.
Why This Matters for Your Workflows
Text gets cut off for a reason—servers have limits. But those limits often hit exactly when you need the full picture. You’re debugging a workflow, the execution log shows [truncated], and you can’t see what actually went wrong. Or you’re processing a scraped article, and the result stops mid-sentence. Or an AI model generates a detailed response that gets clipped.
The old 20K character limit was tight for real-world scenarios:
- A typical blog post runs 1,500–3,000 words (roughly 9,000–18,000 characters). You could barely fit one.
- LLM outputs from models like GPT-4 or Claude often exceed 5,000 words for detailed tasks.
- Web scraping multiple elements, API responses with nested data, or multi-step document processing easily spilled over.
- Debugging became guesswork when you couldn’t see the full execution result.
The new 100K limit changes that equation. Now you can:
- Store 4–5 complete blog posts or articles in a single execution result
- Capture full LLM responses without artificial truncation
- Debug workflows with complete context—no more mystery truncations
- Process larger batches of scraped or extracted data without losing pieces
Who Benefits Most
This update directly helps builders working with:
- Content workflows: Scraping articles, generating long-form content, processing documents
- AI agent outputs: Tasks requiring detailed LLM responses, multi-step reasoning, or comprehensive reports
- Data extraction: Web scrapers pulling multiple fields, API integrations returning nested data
- Document processing: Workflows converting, summarizing, or analyzing PDFs, emails, or text files
- Debugging complex agents: Seeing full execution logs without guessing what happened behind the truncation
If your workflows have hit the 20K ceiling, this removes that constraint entirely for most real-world use cases.
How It Works
You don’t need to do anything. The increase is automatic. When you run workflows in TaskAGI, execution results now store up to 100,000 characters by default. If a result exceeds that, it still truncates (servers have limits), but you get 5x the breathing room.
This applies to:
- AI model outputs (text generation, analysis, summaries)
- Web scraper results
- API response bodies
- Text processing node outputs
- Any workflow step that produces text
When you review execution results in the TaskAGI dashboard or via the API, you’ll see the fuller picture. For workflows that previously hit the limit, you might suddenly see data you’ve never seen before—that’s the improvement working.
Practical Examples
Example 1: Content Scraping Workflow
You build an agent that scrapes 10 blog posts and stores the content. With 20K characters, you could fit maybe 2 posts before hitting the wall. Now you fit 10–12 complete articles in the execution result. You can review the full output, debug if something went wrong, or pass the complete results to the next step in your workflow.
Example 2: AI-Powered Report Generation
Your workflow uses an LLM to generate a detailed report (3,000+ words). Previously, the execution result cut off halfway through. Now the complete report stays intact, letting you:
- See the full output without gaps
- Pass it to downstream steps (saving to a database, sending via email, posting to a CMS)
- Debug if the LLM response wasn’t what you expected
Example 3: Debugging a Multi-Step Workflow
You have a 5-step workflow: fetch data → parse it → enrich it → transform it → store it. Step 3 fails. You check the execution result from step 2 to understand what went wrong. With the full 100K characters available, you see exactly what data was passed forward instead of a truncated snippet.
When You Might Still Hit Limits
The 100K character limit handles most workflows, but edge cases exist:
- Bulk data processing: If you’re processing 100+ documents in a single step, you might exceed 100K
- Highly nested JSON: Complex API responses with deep nesting can be verbose
- Large file processing: Converting or analyzing files with hundreds of thousands of words
If you consistently hit the new limit, split your workflow into smaller batches or use external storage (database, cloud storage) for intermediate results instead of relying on execution results.
Best Practices with Larger Execution Results
1. Use execution results for debugging, not long-term storage — They’re meant to show you what happened, not replace a database. For persistent data, save to your CRM, database, or cloud storage.
2. Monitor your workflow performance — Larger execution results take slightly longer to store and retrieve. If you’re running hundreds of workflows daily, watch for any performance changes.
3. Structure your data thoughtfully — Just because you can store 100K characters doesn’t mean you should dump everything. Keep execution results clean and focused on what matters for debugging.
4. Test with real data — If you’re building a workflow that processes large text, test it with actual data sizes to ensure it behaves as expected.
Common Questions
Does this affect workflow performance?
Minimally. Larger execution results take marginally longer to write and retrieve, but for most workflows, the difference is imperceptible. You won’t notice a slowdown unless you’re running thousands of simultaneous executions.
What happens if I exceed 100K characters?
The result still truncates, just at a much higher threshold. Most real-world workflows won’t hit it. If you do, consider breaking your workflow into smaller steps or storing large data externally.
Does this apply to all workflow nodes?
Yes. Any node that produces text output (AI models, scrapers, API calls, text processing) benefits from the increased limit.
Can I configure this per workflow?
Not currently. The 100K limit applies platform-wide. If you need custom limits or external storage integration, reach out to the TaskAGI team.
What This Means for Your Builds
If you’ve worked around the 20K limit by splitting workflows, storing intermediate results externally, or accepting truncated outputs, you have more options now. You can build simpler workflows without those workarounds. For new builds, you can process larger batches of text-based data without hitting constraints.
It’s a quiet improvement, but it removes real friction from building text-heavy AI agents. Start using it in your next workflow—you’ll notice the difference when you need to debug or handle substantial content.
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