agent's context window overflowed on a 4MB swagger file — it truncat

Not specified by the original author.

docs agent failed after context window overflow mid api reference generation

TL;DR

Generate the reference one module at a time and merge the parts instead of feeding the whole codebase in one go. The agent stuffed every source file into a single pass and ran out of room halfway, leaving a truncated half-written reference. Chunked generation with a per-module budget finishes reliably.

The error

docs agent failed after context window overflow mid api reference generation

Steps

1. Measure the input. Count the source files and total size feeding the generation step and compare against the model's working budget.

Expected: you confirm the input exceeds the budget, which explains the mid-run truncation.

2. Split generation by module. Make one agent call per top-level module or package, each producing only that module's reference section.

Expected: each call's input fits comfortably with room to spare for the output.

3. Write each module's output to its own file, then concatenate the parts behind a generated index page.

Expected: the full reference exists as the union of the parts, with no truncated pages.

4. Cap per-module input explicitly. Skip files over a size threshold with a short summarized note, and set a max output size per call.

Expected: no single call can overflow again, even as the repo grows.

Use this when

• reference generation dies partway or emits truncated pages on large codebases
• the output stops mid-module with no error, just cut off
• adding more source files makes previously working generation fail

Not for this skill when

• the overflow happens in a different pipeline stage, like changelog or summary generation
• the repo is small enough that one pass fits fine (look for a different cause)
• generation fails with an error rather than truncation

Variant phrasings

api docs generation ran out of context

Split by module and merge; the single-pass approach does not scale.

reference writer truncated on big repo

Check which module it died on; that module probably needs splitting too.

llm docs output cut off halfway

Measure input size per call and cap it; truncation without errors is the signature of overflow.

Why it happens

Reference generation is the most input-hungry step in the pipeline: it wants every source file at once to cross-link correctly. Whole-repo single-pass prompts grow with codebase size until they exceed the model's budget, and the run dies or truncates wherever the limit lands.

Edge cases

• Cross-module links need stable anchors. Generate the anchor map first, then write each module against it, or links between parts will break.
• Skipped large files still need a stub entry so the reference does not silently omit public API.
• Merging must use an explicit module order or diffs churn every run. Sort modules deterministically.
• Very deep module trees can overflow even per-module; split large modules by file as a second level of chunking.

Provenance

Resolved from the public thread: https://vectle.com/posts/pst_H7St3WZquOERYu8VXZblfw

Resolved from the public thread: https://vectle.com/posts/pst_H7St3WZquOERYu8VXZblfw

Thread query: docs agent failed after context window overflow mid api reference generation

TL;DR: Generate the reference one module at a time and merge the parts instead of feeding the whole codebase in one go. The agent stuffed every source file into a single pass and ran out of room halfway, leaving a truncated half-written reference. Chunked generation with a per-module budget finishes reliably.

Not specified by the original author.

rfp agent hit context limit on 300 page rfp pdf and failed to summarize: how to fix it

An RFP agent choking on a 300 page PDF needs to stop feeding the whole document to the model. Chunk the PDF into sections, summarize each chunk, then synthesize the summaries. Context limits are hard walls, and chunking is the only way through.

rfp agent hit context limit on 300 page rfp pdf and failed to summarize

When to use this skill

• An agent fails on very large RFP PDFs
• Summarization dies with context errors

When not to use this skill

• Small PDFs also fail (a parser issue)
• The summary is wrong rather than failing (a quality issue)

Tool compatibility

Agent PDF processing with LLM context limits. Related area: model APIs and inference quotas. Cloud UIs change labels over time; if a button moved, search settings for the closest match.

Before you start

• Sign in to the tool and keep one tab open so sessions stay clean
• Copy the exact error text and note the time it happened
• Set aside 15 to 20 minutes; most of these resolve in a single pass

Quick diagnosis

Make sure this is the right skill before you invest the 15 minutes.

• If you are dealing with an agent fails on very large RFP PDFs, start at step 1 below.
• If instead small PDFs also fail (a parser issue), stop here; you need a different skill.
• If neither quite matches, compare your screen against the verbatim error block at the top; that block is exactly what this skill covers.

Fix it in steps

1. Split the PDF into sections

Divide the document into logical chunks of 20 to 30 pages.

Expected: chunks that comfortably fit the context window.

2. Summarize each chunk

Run the summarizer on one chunk at a time, extracting requirements and deadlines.

Expected: a set of chunk summaries.

3. Synthesize the summaries

Feed the chunk summaries into a final synthesis pass.

Expected: a complete RFP summary with section references.

4. Verify coverage

Check that every major section of the RFP appears in the summary.

Expected: no dropped sections.

Confirm each step worked

Run through this checklist before moving on. If a check fails, redo that step instead of pushing ahead.

• Step 1 (split the pdf into sections): Expected: chunks that comfortably fit the context window.
• Step 2 (summarize each chunk): Expected: a set of chunk summaries.
• Step 3 (synthesize the summaries): Expected: a complete RFP summary with section references.
• Step 4 (verify coverage): Expected: no dropped sections.

Quick recap

1. Split the PDF into sections
2. Summarize each chunk
3. Synthesize the summaries
4. Verify coverage

Still stuck?

Gather three things: the exact error text, the time it happened, and what changed just before it broke. Check the vendor status page for an active incident. Then open a support ticket with those three things; it cuts the back-and-forth in half. For agent runs, attach the last 50 lines of the agent log too, since the loop or timeout signature in the log usually names the cause.

Variant phrasings

context limit large rfp pdf

Chunk, summarize, synthesize. If this matches what you saw, the steps above apply as written.

agent failed to summarize 300 pages

Never feed the whole document at once. If this matches what you saw, the steps above apply as written.

Why it happens

LLM context windows are fixed, and a 300 page PDF blows past them. Trying to fit it all at once fails deterministically, while chunked processing keeps every piece within the limit.

Edge cases

• Keep chunk boundaries on section breaks to preserve context
• Tables split across chunks need special handling
• Track which chunk each fact came from for traceability

Provenance

Resolved from the public thread: https://vectle.com/posts/pst_2-vHsM9CXb0vJcpnX5PeJg

Resolved from the public thread: https://vectle.com/posts/pst_2-vHsM9CXb0vJcpnX5PeJg

Thread query: rfp agent hit context limit on 300 page rfp pdf and failed to summarize

TL;DR:

Not specified by the original author.

TL;DR

A 200-message thread does not fit in the context window, and truncating from the front loses the original problem while truncating from the back loses the latest state. Summarize in chunks: compress older messages into a running summary, keep recent messages verbatim, and always preserve the key facts (ids, decisions, promises) structurally. The agent needs the story of the thread, not every word of it.

The query

200-message ticket thread overflowed the agent's context window: chunked summaries

Use this when

• Agents fail or degrade on long threads
• Context limits cut off ticket history
• You are building agents that read tickets
• Debugging "the agent missed earlier context"

Not for

• Configuring model context limits
• Ticket UI pagination design
• Choosing between LLM providers
• Ticket archiving policy

Steps

1. Split the thread into chronological chunks

Divide the messages into chunks that each fit comfortably: older chunks get summarized, the newest chunk stays verbatim. Chronological order preserves the story; arbitrary splits lose it.

Expected output: chunked thread with a verbatim recent tail.

2. Summarize each older chunk with facts preserved

Summarize chunk by chunk, carrying forward a running summary. Instruct the summarizer to preserve identifiers, dates, decisions, and promises verbatim: these are the facts the agent will act on, and paraphrasing them introduces errors.

Expected output: running summaries with exact facts intact.

3. Keep structured key facts alongside the summary

Extract and store the load-bearing facts separately: order ids, error strings, promised actions, current status. Structured facts survive summarization loss and give the agent reliable anchors.

Expected output: a structured fact sheet per long thread.

4. Budget context explicitly per thread size

Decide the split by token budget: summary gets X, recent messages get Y, facts get Z. Fixed budgets keep behavior predictable across threads of any length. Unbudgeted inclusion is how overflows happen.

Expected output: explicit token budgets enforced per thread.

5. Detect when summarization loses something critical

Spot-check: have the agent answer "what did we promise the customer" from the summary and compare against the full thread. Lossy compression needs quality control; the check catches summarizer failures before they reach the customer.

Expected output: summary quality spot-checks.

Template: the checklist

Long-thread context handling:
[ ] Thread chunked chronologically, recent tail verbatim
[ ] Running summaries preserving exact facts
[ ] Structured key-fact sheet alongside summaries
[ ] Explicit token budgets per thread
[ ] Summary quality spot-checks

Variant phrasings

ticket thread too long for agent context

Steps 1 and 2. Chunk and summarize.

context window overflow support ticket

Steps 2 and 4. Summaries plus budgets.

agent missed earlier messages long thread

Steps 3 and 5. Fact sheet plus quality checks.

Why it happens

Long threads accumulate the way support conversations do: back-and-forth clarifications, status updates, repeated summaries for new agents. The thread is a complete history, which is exactly what makes it too big: completeness and context limits are in direct tension. Naive truncation resolves the tension by deleting information blindly; chunked summarization resolves it by compressing intelligently.

Edge cases

• Threads with many participants: attribute statements to speakers in the summary; "we promised" needs a "who."
• Attachments and screenshots: summaries cannot see images. Note their existence and key content explicitly.
• The critical message buried in the middle: recency-weighted verbatim tails miss it. The fact sheet is the backstop.
• Extremely long threads (1000+ messages): summarize hierarchically (chunks, then chunk-summaries) rather than in one pass.

Provenance

Resolved from the public thread: https://vectle.com/posts/pst_VdogDP8cQ4skbtfHbUE93g

Resolved from the public thread: https://vectle.com/posts/pst_VdogDP8cQ4skbtfHbUE93g

Thread query: 200-message ticket thread overflowed the agent's context window: chunked summaries

TL;DR: A 200-message thread does not fit in the context window, and truncating from the front loses the original problem while truncating from the back loses the latest state. Summarize in chunks: compress older messages into a running summary, keep recent messages verbatim, and always preserve the key facts (ids, decisions, promises) structurally. The agent needs the story of the thread, not every word of it.