LangChain engagement report
@LangChain - 269K followers on X
Measured over 62 original posts from a 30-day window, last computed on September 24, 2026.
Engagement
A typical post picks up 33 interactions against 269K followers, an engagement rate of 0.012%. Measured over 62 original posts, its engagement rate beats 28% of 15,519 tracked accounts of a similar size, which puts it in the middle of its size range rather than at either end. Posts are seen about 8.9K times each, and 0.371% of those impressions turn into an interaction. That is about 3.31% of the follower count, which is the gap between an audience on paper and an audience in a timeline. Posting runs at about 4.5 posts a day over the last 30 days, with activity on roughly 50% of days. Most posts go out around 15:00 UTC, and Friday is the busiest day of the week. Of the 62 posts sampled, 71% carry an image or video, 23% are part of a thread and 56% link out. The account's strongest tracked post pulled 3.7K interactions, about 112x its own typical post.
Measured over 62 original posts from a 30-day window, last computed on September 24, 2026.
Compared with accounts its own size
LangChain's engagement rate beats 28% of the tracked X accounts closest to it in follower count (15,519 accounts, accounts of similar size (decile 9 of 10)). A percentile is spread evenly by construction, so 50 really is the middle of that group and 90 really is its top tenth.
On engagement per impression rather than per follower it beats 22% of the same group. When those two numbers disagree, the gap is about how far its posts travel rather than how people react to them.
Where this sits in the catalog
At 0.012%, LangChain sits above the 10th percentile of the 157,515 accounts in this comparison. That places it in the bottom 25% band, which runs below 0.022%.
Show the percentile table
| Percentile | Engagement rate |
|---|---|
| 10th percentile | 0.003% |
| 25th percentile | 0.022% |
| 50th percentile | 0.128% |
| 75th percentile | 0.604% |
| 90th percentile | 2.32% |
| 99th percentile | 83.3% |
This ruler is the whole measured catalog, not a size-matched group: it shows where the raw rate falls across every account we can measure, all of which are large. For a like-for-like comparison, read the size-band percentile above instead. See how the bands are built
Posting timing
This account posts most often around 15:00 UTC, and Friday is its busiest day of the week. The bars below are the catalog-wide pattern, with this account's own busiest slot marked. They do not show how this account performs at each hour: we keep one aggregate per account, not one per hour, so that measurement does not exist in our data.
Show engagement by hour posted, utc as a table
| Hour (UTC) | Vs author median | Posts |
|---|---|---|
| 00:00 UTC | -1% | 89K |
| 01:00 UTC | -2% | 90K |
| 02:00 UTC | -3% | 88K |
| 03:00 UTC | -4% | 94K |
| 04:00 UTC | -5% | 76K |
| 05:00 UTC | -4% | 75K |
| 06:00 UTC | -5% | 86K |
| 07:00 UTC | -5% | 93K |
| 08:00 UTC | -4% | 108K |
| 09:00 UTC | -4% | 124K |
| 10:00 UTC | -3% | 129K |
| 11:00 UTC | -3% | 141K |
| 12:00 UTC | -3% | 154K |
| 13:00 UTC | -3% | 167K |
| 14:00 UTC | -4% | 173K |
| 15:00 UTC | -2% | 176K |
| 16:00 UTC | -3% | 171K |
| 17:00 UTC | -3% | 159K |
| 18:00 UTC | -2% | 149K |
| 19:00 UTC | -2% | 141K |
| 20:00 UTC | -1% | 131K |
| 21:00 UTC | 0% | 116K |
| 22:00 UTC | -2% | 100K |
| 23:00 UTC | -1% | 90K |
Show engagement by day of week as a table
| Day | Vs author median | Posts |
|---|---|---|
| Sunday | +5% | 393K |
| Monday | +1% | 483K |
| Tuesday | -2% | 520K |
| Wednesday | -3% | 472K |
| Thursday | -2% | 430K |
| Friday | -3% | 447K |
| Saturday | +2% | 393K |
Formats this account uses
Its own posting mix on the left, and what each of those formats does across every account we track on the right. Only formats where the effect clears our publish test appear here, so an empty row is a format we could not measure rather than one that does nothing.
| Format | This account | Catalog effect | 95% interval | Accounts behind it |
|---|---|---|---|---|
| Image or video | 71% of posts | +111% | +108% to +115% | 34K |
| Outbound link | 56% of posts | -41% | -42% to -40% | 32K |
| Typical length | - | no effect | -2% to -1% | 42K |
- 71% of this account's sampled posts carry an image or video. Across the catalog, posts with an image or video run 111% above the same accounts' other posts.
- 56% of its posts carry a link off X. Across the catalog, posts with an outbound link run 41% below the same accounts' other posts, so a large share of this account's output sits in the weakest bucket we measure.
- Its average post runs 266 characters, which falls in the 180 - 280 characters band. Across the catalog, posts of 180 to 280 characters match the same accounts' other posts almost exactly.
These are catalog-wide differences applied to this account's own posting mix, not a measurement of how each format performs for this account specifically. We keep one median per account, not one per format per account, so the second thing is not something this data can tell you.
Best tweets
- Sep 19, 2026112x their median
We tested Jev against LLM judges on accuracy, repeatability, latency, and cost to see whether System One models could offer a new approach to agent evaluation. https://t.co/hrqdNpm0g8
- Sep 21, 202617x their median
here's a quick breakdown of @typesafeai's new jev model: what it is, and how to plug it into your agents! https://t.co/SgHYWdn51d
- Sep 2, 202615x their median
Introducing Gemini 3.8 Flash, bringing significant improvements in agentic + coding capabilities from 3.7 Flash, available at the same price. https://t.co/Y8QVKzGgFF
- Aug 31, 202611x their median
Next to evals, harness engineering is quickly becoming one of the most important skills for AI engineers to have today.
- Sep 18, 20269.0x their median
instead of generating text, jev from @typesafeai generates structured output this makes it great for classification tasks like model routing, tool selection/search, and guardrails of many forms! it's also ridiculously fast and cheap compared to LLMs doing the same tasks https://t.co/59a8PxBuQ6
- Sep 21, 20268.3x their median
Jev-as-a-judge is now available in LangSmith. ✅ Score every production trace instead of a sample. ✅ Check more criteria per trace without the cost climbing. ✅ Catch safety or security issues fast enough to trigger an automated response. Give it a try and let us know what you think! https://t.co/YDsbxCiGK7
- Sep 18, 20265.9x their median
Hot topic livestream: Learn about Jev A buzzy new model Jev, by @typesafeai, reports up to 200x faster inference and 400x lower cost than comparable LLMs on classification tasks. It's aiming to solve the problem of slow and costly agent loops. Next Tuesday, we’re bringing @sydneyrunkle from our engineering team at @LangChain, for a live convo on how TypeSafe AI’s model makes fast, structured decisions, where it fits in the agent loop, and how to build a harness with Jev. Join here: https://t.co/swJMbfGzQQ
- Sep 1, 20264.8x their median
🧵[1/3] openwiki v0.5.0 is out 🎉 q: what's worse than long running jobs that fail? a: long running jobs that fail and take all progress with it. wiki generation takes time and tokens. openwiki respects your time and tokens. the new architecture makes init and update durable and resumable: ➡️ completed pages are checkpointed as they finish ➡️ interrupted runs resume instead of restarting ➡️ partial progress survives ci failures ➡️ native openwiki and coding agent integrations all use the same resumable lifecycle
- Sep 21, 20264.0x their median
Happening tomorrow! @LangChain_OSS team x @TypeSafeAI livestream. Come learn about Jev with @sydneyrunkle, @huntlovell, and @allietheicon. RSVP: https://t.co/cRuzHGl5Wh https://t.co/PFmRfSaGVZ
- Sep 21, 20263.8x their median
why is jev called a "system one" model? this actually comes from @kahneman_daniel's book "thinking, fast and slow" that outlines two ways of thinking: system one: fast, cheap, almost automatic (jev) system two: slow, deliberate, and analytical (llms) https://t.co/aWYqRpApkX
Ranked by total interactions across everything we have tracked for this account, which is a longer history than the 30-day window the rates above use. The multiple compares each post to this account's own median.
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Reading these numbers
A typical post picks up 33 interactions against 269K followers, an engagement rate of 0.012%. Measured over 62 original posts, its engagement rate beats 28% of 15,519 tracked accounts of a similar size, which puts it in the middle of its size range rather than at either end. Posts are seen about 8.9K times each, and 0.371% of those impressions turn into an interaction. That is about 3.31% of the follower count, which is the gap between an audience on paper and an audience in a timeline. Posting runs at about 4.5 posts a day over the last 30 days, with activity on roughly 50% of days. Most posts go out around 15:00 UTC, and Friday is the busiest day of the week. Of the 62 posts sampled, 71% carry an image or video, 23% are part of a thread and 56% link out. The account's strongest tracked post pulled 3.7K interactions, about 112x its own typical post.
- What is LangChain's engagement rate on X?
- LangChain (@LangChain) has an engagement rate of 0.012%, based on the median interactions across 62 original posts from the last 30 days against 268,811 followers. Replies, reposts and quote-posts of other people are excluded from that sample.
- Is that a good engagement rate?
- At 0.012%, LangChain sits above the 10th percentile of the 157,515 accounts in this comparison. Those comparison accounts are all large ones, because our scanning cadence is weighted towards big accounts, so this is a ranking among peers of similar scale rather than a ranking across X.
- Does @LangChain have real engagement?
- Its engagement rate beats 28% of the tracked X accounts closest to it in follower count (15,519 accounts), which puts it in the middle of its size range group. Ranking inside a size band matters because engagement rate falls as accounts grow, so a raw rate would mostly re-measure the follower count. It is a starting point for a look at follower quality, not a verdict on it.
- When does @LangChain post?
- Most posts go out around 15:00 UTC, and Friday is its busiest day, at roughly 4.47 posts per day across the measured window.