Summarize Long WhatsApp Chat Messages with AI | ThreadRecap
Your work group chat has 8,000 messages over four months. You paste it into ChatGPT and it chokes. You try splitting it into four chunks and the summary loses the thread. You try just summarising the last week and miss the context that explains what is happening now.
Long chats are a real problem for AI tools that were not designed for them. They are not a problem for tools that were.
This page is the working playbook for recapping WhatsApp threads at 5k, 10k, and 50k message scale, with the trade-offs each export size forces.
Why long chats break general AI tools
Every consumer-grade large language model has a fixed context window measured in tokens. A token is roughly three quarters of an English word, so a 5,000-message WhatsApp chat (averaging maybe 12 words per message) is somewhere around 75,000 tokens of text, and that is before you count voice notes, system messages, or formatting. Most consumer ChatGPT tiers hit the wall at this scale or below.
Truncate. Cut the chat to fit. You lose the early messages, which usually means losing the context that explains why the recent decisions were made.
Chunk. Break the chat into pieces and summarise each. You lose coherence between sections, because the model cannot reference a decision from chunk one while it is processing chunk three.
Neither produces a useful recap. The model returns something that looks like a summary and reads like a summary but quietly omits or misrepresents whatever sat on the wrong side of the cut.
How ThreadRecap handles long chats
The WhatsApp chat summariser is built around the assumption that real conversations are long. The pipeline ingests the full export, up to 60,000+ messages and 2 GB ZIP files, without truncation, and maintains conversation context across the entire thread.
This means a 10,000-message chat receives the same analytical quality as a 200-message one. The same goes for 30,000-message chats and 60,000-message chats; the upper bound is set by ZIP size and processing time, not by an arbitrary context-window cliff.
For comparison: a 5,000-message chat exceeds most consumer ChatGPT context windows. ThreadRecap processes that as a normal-sized job. A 50,000-message chat is the upper end of what WhatsApp will even export without truncation, and ThreadRecap takes it.
Strategies for very long chats
Even with a tool that handles length, you can sharpen the result by being deliberate about what you ask for.
1. Use date ranges
If you only care about last week's planning discussion, set the date range accordingly. This is not about technical limits, it is about focus. A summary of "everything since January" is rarely as useful as a summary of "this week's launch coordination". Date filtering also reduces credit cost on a per-run basis, because the analysis runs over fewer messages.
Practical pattern for an active project chat:
This week: Action Items goal, current commitments.
This month: Meeting Recap goal, decisions and progress.
This quarter: Summary goal, major phase changes.
Full history: Relationship Insights or major-phase-only Summary.
2. Filter participants
In group chats, not every participant is relevant to every analysis. If you want to know what the project leads discussed, filter to those people only. Reactions, jokes, side conversations, and acknowledgements from the rest of the group fall out of the analysis automatically.
A 12-person work group chat usually has three to five people doing 80% of the substantive talking. Run analysis on those participants alone and the recap is sharper, the credit cost drops, and you do not lose detail in a sea of "ok!" and "👍". Teams that do this every week tend to follow the community-manager workflow once the routine is set.
3. Run multiple passes
For a chat spanning months, layered analysis beats a single mega-summary:
First pass: overview. Run a Summary goal across the full period to identify the major phases. The output is a high-level timeline: phase one (kickoff, weeks 1–3), phase two (build, weeks 4–10), phase three (launch prep, weeks 11–14).
Second pass: phase detail. For each phase identified in pass one, run Meeting Recap on the relevant date range. Decisions, action items, owners, open questions for that phase only.
Third pass: current state. Action Items goal, restricted to the most recent two weeks. What is outstanding right now.
Each pass is independently cheap and useful. The layered output is dramatically more useful than a single 30,000-word recap that nobody reads.
4. Include voice notes
In long chats, voice notes often carry the most substantive content. A 3-minute voice note typically covers more ground than 50 text messages, especially in workflows where senior people prefer to dictate context rather than type it.
If you export without media, those voice notes are gone from the analysis. The recap will look complete because the chat log shows "audio omitted" lines, but those are exactly the moments where decisions, rationale, and action items most often live.
If your chat exceeds these limits, WhatsApp truncates from the oldest end. You receive the most recent messages, which is usually what you want.
For very old conversations, the practical pattern is to run two exports:
Export without media for full historical coverage (up to 40,000 messages).
Export with media for the recent period (up to 10,000 messages, including voice notes).
Both `.zip` files can be uploaded to ThreadRecap and analysed independently. The historical export gives you long-arc context; the media export gives you current voice content.
Concrete scenarios
Work group chat, active for 4 months, 8,000 messages
Export with media (recent 10,000 messages, covers the full chat).
Voice notes included so audio decisions are captured.
Multi-pass: Summary on full period for phase identification, then Meeting Recap on the current sprint's date range, then Action Items on this week.
Filter participants to project leads (3–4 people) for the Meeting Recap and Action Items passes.
Family group chat spanning 3 years, 50,000 messages
WhatsApp will truncate to roughly the last 40,000 messages on a no-media export, dropping the oldest 10,000.
For "what did we plan for the trip last summer" type questions, run a date-range filter on the relevant months.
For "major events of the past year" type questions, run a Summary goal on a 12-month range.
Voice notes optional. Family chats often have a lot of audio that is more emotional than transactional, so the value depends on what you are recapping.
Ongoing client communication, 12,000 messages over 18 months
Export without media for the historical record, with media for the current month.
Run Meeting Recap or Decisions on each month-long range to build a project timeline.
Run Action Items on the recent two weeks for current commitments.
Filter to the client primary contact and your team's primary contact to drop ambient noise.
Disagreement or conflict spanning weeks, 3,000 messages
Export with media (well under the 10,000 cap).
Run Conflict Resolution goal on the relevant date range.
Output: root cause, each side's perspective, misunderstandings, resolution status, next steps.
Voice notes critical here, because emotional content tends to land in audio.
How costs scale
ThreadRecap charges per usage, so cost scales linearly with chat size:
1 credit per 1,000 messages (rounded up).
1 credit per 10 minutes of audio (rounded up).
Modifiers for group analysis (+2), custom prompts (+3), translation (+1).
Practical cost shapes:
Chat size
Voice notes
Approximate credits
5,000 messages, no audio
None
5
5,000 messages, 30 min audio
30 min
8
10,000 messages, 60 min audio
60 min
16
30,000 messages, 90 min audio
90 min
39
Group analysis adds 2 credits per run regardless of chat size. Running multiple goals on the same upload reuses the parse and transcription work, so additional goals cost only the per-message credit again, not the audio transcription credit.
The key insight
Long chats are not a problem to solve, they are an asset. A 6-month WhatsApp thread contains a complete record of a project, relationship, conflict, or decision process. Most of that content is not stored anywhere else.
The right tool turns that record into structured, searchable knowledge: decisions with owners and dates, action items with deadlines, open questions with context, conflicts with resolutions, and a timeline that lets you find the moment a specific commitment was made.
The wrong tool gives you a wall of text that misses half the conversation by design. If the long chat is a weekly project sync, the output can land directly as meeting minutes from a WhatsApp chat.
