Manus AI File Limit: 512 MB Task Uploads, File Size Caps, and Document Attachments

Dedicated File Upload Pipeline Versus Inline Task Attachments

The Manus API provides two ways to attach files to tasks, each with distinct size ceilings and operational flows:

1. The dedicated file upload pipeline: Intended for individual file upload assets up to 512 MB. This flow uses an asynchronous two-step process. First, an API client sends a POST request to https://api.manus.ai/v2/file.upload specifying the filename. The API creates a file record in pending status and returns a presigned Amazon S3 URL. Second, the client uploads the raw binary bytes directly to that URL via an HTTP PUT request. Once uploaded, the client polls GET /v2/file.detail until the status updates to uploaded, then attaches the resulting file_id to task.create or task.sendMessage.
2. Inline task message attachments: Intended for small snippets, immediate images, or brief prompts passed directly inside task.create or task.sendMessage. When passing files inline, developers supply either a direct file_url or base64-encoded file_data. The Manus API documentation explicitly restricts inline message attachments and decoded payload data to 20 MB each.

Attempting to send a file larger than 20 MB through the inline message payload fails immediately. For any payload between 20 MB and 512 MB, the individual file upload limit pipeline is required.

Storage Pools, Expiration Windows, and Error Handling

Beyond individual file thresholds, Manus enforces account-level capacity rules and ephemeral lifecycle policies:

• Total account storage pool: Total files across an account cannot exceed 10 GB capacity at any single point in time. If an upload causes cumulative account storage capacity to exceed 10 GB, the API rejects the request with a QuotaExceededError.
• Presigned URL validity: The presigned S3 upload URL returned by file.upload remains valid for 3 minutes (180 seconds). The binary upload must complete before this window expires, or the S3 endpoint rejects the incoming stream.
• 48-hour retention window: Uploaded files stored within the Manus infrastructure are ephemeral. The API automatically deletes uploaded files 48 hours after upload. Tasks requiring long-term data continuity cannot rely on Manus as a persistent document repository.
• Rate limits: The file.upload endpoint enforces a rate limit of 40 requests per minute per user. Polling upload status via file.detail supports frequent status queries, while task.create enforces a tighter request ceiling to prevent API throttling.

Programmatic File Upload Implementation via API

Uploading files to Manus programmatically requires staging the file record before initiating task execution. The following Python example demonstrates the two-step presigned URL flow:

import requests

MANUS_API_KEY = "your_manus_api_key"
headers = {
    "Authorization": f"Bearer {MANUS_API_KEY}",
    "Content-Type": "application/json",
}

"""Initialize file record and obtain presigned upload URL."""
init_response = requests.post(
    "https://api.manus.ai/v2/file.upload",
    headers=headers,
    json={"filename": "quarterly_earnings_report.pdf"},
)
upload_data = init_response.json()
file_id = upload_data["file"]["id"]
upload_url = upload_data["upload_url"]

"""Stream binary data directly to the presigned URL via HTTP PUT."""
with open("quarterly_earnings_report.pdf", "rb") as file_payload:
    requests.put(upload_url, data=file_payload)

"""Reference the verified file_id when creating the autonomous task."""
task_payload = {
    "message": {
        "content": [
            {
                "type": "text",
                "text": "Analyze the financial statements and extract EBITDA margins.",
            },
            {
                "type": "file",
                "file_id": file_id,
            },
        ]
    }
}
task_response = requests.post(
    "https://api.manus.ai/v2/task.create",
    headers=headers,
    json=task_payload,
)

File Payload Limits Versus Context Window Saturation

A critical distinction in autonomous agent workflows is the difference between filesystem storage capacity and model context capacity.

When you upload a 300 MB CSV or a 400-page scanned PDF to Manus, the platform transfers the file into the agent’s virtualized Linux container (/home/ubuntu). The agent can inspect the file on disk using shell utilities like head, grep, or custom Python scripts without ingesting the entire document into the underlying language model’s prompt context.

In contrast, conversational assistants like Claude or ChatGPT parse uploaded files directly into tokens that occupy the context window. In Claude Projects, Anthropic sets an individual file cap of 30 MB, and while the file count is unlimited, the total volume of text must fit within the model context window.

In Manus, multi-file research tasks slow down when heavy binaries or large PDFs are directly attached. If an agent attempts to read a massive document in full or converts gigabytes of data into raw prompt tokens, the context window saturates. This leads to attention degradation, elevated inference costs, and slower execution steps.

Connecting Indexed Fast.io Workspaces via MCP

In intelligent workspaces on Fast.io, teams maintain their persistent document collections, project archives, and technical specifications in shared, organization-owned storage. Instead of attaching heavy binaries directly to Manus task runs, the agent queries the workspace dynamically.

Cloud Sync connects Google Drive, Dropbox, Box, and OneDrive folders to a workspace, one-way or two-way, on a schedule or on demand. When documents arrive in a workspace, Intelligence Mode automatically indexes their contents for hybrid search, combining full-text search, semantic vector retrieval, and search-by-metadata-value. Teams can review Fast.io AI capabilities to explore how automated indexing structures unstructured content.

Fast.io provides remote MCP access over Streamable HTTP. For general personal-agent runtimes, Claude applications, and Manus, the server address is https://mcp.fast.io/mcp/tools.

Connecting indexed Fast.io workspaces via MCP enables Manus to retrieve exact excerpts rather than downloading entire document packages. When Manus investigates a regulatory question across ten thousand pages of filings, it searches the workspace via MCP:

Query: "capital adequacy disclosure requirements for tier two instruments"
Retrieved Excerpt: 2 sections from Filing_2025_Q4.pdf (420 tokens)

Instead of downloading a massive annual report into the sandbox and reading thousands of pages into terminal memory, Manus receives the exact excerpt containing the governing clause. The task finishes in seconds, sandbox storage remains empty, and the agent preserves context for complex multi-step reasoning.

Fast.io workspaces provide per-file version history, a detailed activity log of changes, and role-based permissions set per organization, workspace, and share. Monthly plans start with a 14-day free trial that requires a credit card. Teams can review tier options on the Fast.io pricing page:

Extracting Structured Document Data with Metadata Views

When analyzing large collections of contracts, invoices, financial disclosures, or real estate packets, agents frequently require specific field values rather than general text summaries.

Metadata Views turn documents into a live, queryable database. Users describe the fields they want extracted in natural language, and the system designs a typed schema across Text, Integer, Decimal, Boolean, URL, JSON, and Date & Time formats. The feature matches files in the workspace and populates a structured, filterable spreadsheet without requiring custom OCR rules or manual scripting.

Metadata Views operate across PDFs, scanned documents, Word files, spreadsheets, and presentations. Teams can add new columns at any time without reprocessing existing documents. Autonomous agents connecting via MCP can inspect these structured views directly, extracting contract dates, counterparties, policy numbers, or line-item totals in a single structured query. This allows Manus to perform quantitative comparisons across thousands of documents without opening or parsing individual files inside its task sandbox.

Handling Scanned Documents, Tables, and Complex PDFs

Complex documents present distinct challenges for autonomous agent runtimes. A 100-page scanned legal PDF containing bitmap images requires optical character recognition before any textual analysis can occur.

Inside the Manus virtual machine, the agent chooses its extraction strategy based on the file structure:

• Native digital PDFs: If the PDF contains a clean digital text layer, Manus extracts text streams programmatically. This process takes seconds and produces clean text output for downstream reasoning.
• Scanned PDFs and image-heavy reports: If the PDF consists of scanned pages or non-selectable text, the agent must either run OCR utilities within the container or feed page renders into multimodal vision endpoints. This increases task duration and token overhead.
• Complex spreadsheets: For spreadsheets with multiple tabs, merged cells, or macro-enabled workbooks (.xlsm), parsing errors can occur if formulas fail to evaluate outside native desktop office suites. Instructing the agent to inspect specific sheets or converting files to clean CSV exports beforehand improves reliability.

To prevent upload rejections, developers must remember that executable binaries and script files, such as .exe, .sh, .bat, .cmd, and .dmg, cannot be uploaded via file.upload or inline attachments.

Resolving Output Artifacts and Download URLs

In addition to ingesting input files, Manus generates output artifacts during task execution. These outputs include written research reports, spreadsheets, presentations, code files, and deployed websites.

When retrieving task messages via the API endpoint task.listMessages, the assistant message payload contains an attachments array. Each attachment object includes metadata describing the generated asset:

{
  "type": "file",
  "filename": "quarterly_analysis.pdf",
  "content_type": "application/pdf",
  "path": "/home/ubuntu/quarterly_analysis.pdf",
  "url": "https://private.manuscdn.com/...",
  "file_uid": "FILE_UID_EXAMPLE",
  "version_uid": "VERSION_UID_EXAMPLE"
}

Understanding these fields is essential for building automated integrations:

• The path field indicates the internal execution environment path on the Ubuntu sandbox (such as /home/ubuntu/quarterly_analysis.pdf). Applications should not request path directly, as it is inaccessible from the public internet.
• The url field provides a signed, temporary HTTPS link on the Manus content delivery network (private.manuscdn.com).
• Download URLs are temporary. If an application attempts to access a signed URL after it expires, the download request fails. Production applications must retrieve the current URL by polling task.listMessages or download the generated bytes immediately and transfer them into persistent storage.
• For website generation tasks, Manus returns checkpoint references using the custom URI scheme manus-webdev://<version_id>. Applications resolve these checkpoints using website.listCheckpoints and website.status rather than file download endpoints.

Troubleshooting QuotaExceededError and Upload Timeouts

When automating file uploads to Manus, developers occasionally encounter upload failures. Diagnosing the root cause quickly restores pipeline reliability:

• QuotaExceededError on inline message: This error occurs when file_url or base64 file_data exceeds the documented 20 MB inline message attachment limit. Resolve this by switching to the file.upload endpoint, uploading the file to S3, and referencing the resulting file_id.
• QuotaExceededError on file.upload: This error indicates that an asset exceeds the individual 512 MB file upload limit or cumulative 10 GB account capacity has been reached. Check total active files using file.list and purge stale assets using file.delete.
• S3 PUT request 403 Forbidden or timeout: The presigned S3 URL generated by file.upload expires after 3 minutes (180 seconds). If network latency or massive file sizes delay upload initiation beyond this window, the S3 endpoint rejects the request. Generate a fresh presigned URL immediately before initiating the binary stream.
• 429 Rate Limited: The file.upload endpoint enforces a rate limit of 40 requests per minute per user. If batching uploads for large task sets, throttle client requests with exponential backoff to remain within rate limits.