diff --git a/docs/core-concepts/memory-operations/search.mdx b/docs/core-concepts/memory-operations/search.mdx
index d964066ff..57f3f14e4 100644
--- a/docs/core-concepts/memory-operations/search.mdx
+++ b/docs/core-concepts/memory-operations/search.mdx
@@ -215,7 +215,7 @@ client.search("preferences", filters={
- **Use natural language**: Mem0 understands intent, so describe what you're looking for naturally
- **Scope with user ID**: Always provide `user_id` to scope search to relevant memories
- **Platform API**: Use `filters={"user_id": "alice"}`
- - **OSS**: Use `filters={"user_id": "alice"}`
+ - **OSS**: Use `user_id="alice"` as parameter
- **Combine filters**: Use AND/OR logic to create precise queries (Platform)
- **Consider wildcard filters**: Use wildcard filters (e.g., `run_id: "*"`) for broader matches
- **Tune parameters**: Adjust `top_k` for result count, `threshold` for relevance cutoff
diff --git a/docs/migration/platform-v2-to-v3.mdx b/docs/migration/platform-v2-to-v3.mdx
index dd3b93fa6..fa021aac2 100644
--- a/docs/migration/platform-v2-to-v3.mdx
+++ b/docs/migration/platform-v2-to-v3.mdx
@@ -42,7 +42,7 @@ Previously, when an agent said something like "I've booked your flight for March
### Retrieval is hybrid now
-Search now uses hybrid retrieval, which improves ranking quality — especially for queries involving exact keywords, proper nouns, entities that appear across multiple memories, and time-aware queries. Existing code that reads `results[]` and the top-level `score` continues to work, but V3 can return additional top-level fields when the platform has more structured context available:
+Search now uses hybrid retrieval, which improves ranking quality — especially for queries involving exact keywords, proper nouns, entities that appear across multiple memories, and time-aware queries (via Temporal Reasoning). The response shape is unchanged:
```json
{
@@ -51,11 +51,6 @@ Search now uses hybrid retrieval, which improves ranking quality — especially
"id": "mem-uuid",
"memory": "User moved to San Francisco in January 2026",
"score": 0.82,
- "score_breakdown": {
- "semantic": 0.74,
- "bm25": 0.68,
- "entity": 0.12
- },
"metadata": {},
"categories": ["location"]
}
@@ -63,7 +58,7 @@ Search now uses hybrid retrieval, which improves ranking quality — especially
}
```
-The top-level `score` remains a `[0, 1]` value. Relative ranking between results stays comparable to v2, but absolute numbers shift since the scoring method changed — retune any hard thresholds in your app against representative queries. Temporal signals are used internally for ranking and are not returned as extra client-facing data.
+The top-level `score` remains a `[0, 1]` value. Relative ranking between results stays comparable to v2, but absolute numbers shift since the scoring method changed — retune any hard thresholds in your app against representative queries. Temporal signals are applied internally during ranking and are not returned as extra client-facing fields.
## API Changes
@@ -169,11 +164,6 @@ Poll status via `GET /v1/event/{event_id}/` — status will be `SUCCEEDED` or `F
"id": "mem-uuid",
"memory": "User moved to San Francisco from New York in January 2026",
"score": 0.82,
- "score_breakdown": {
- "semantic": 0.74,
- "bm25": 0.68,
- "entity": 0.12
- },
"metadata": {},
"categories": ["location"],
"created_at": "2026-01-15T10:30:00Z",
diff --git a/docs/platform/features/temporal-reasoning.mdx b/docs/platform/features/temporal-reasoning.mdx
index 144f95044..9df007114 100644
--- a/docs/platform/features/temporal-reasoning.mdx
+++ b/docs/platform/features/temporal-reasoning.mdx
@@ -99,80 +99,6 @@ const results = await client.search("what did I do last week?", {
`reference_date` is especially useful for tests and backfills because it makes relative phrases reproducible.
-## See it in action
-
-### Add a few dated memories
-
-
-```python Python
-client.add(
- [{"role": "user", "content": "I finished the Q1 product review on March 10, 2025."}],
- user_id="jordan",
-)
-
-client.add(
- [{"role": "user", "content": "I have a dentist appointment on April 18, 2025 at 2 PM."}],
- user_id="jordan",
-)
-
-client.add(
- [{"role": "user", "content": "I am the product lead at Acme Corp."}],
- user_id="jordan",
-)
-```
-
-```javascript JavaScript
-await client.add(
- [{ role: "user", content: "I finished the Q1 product review on March 10, 2025." }],
- { userId: "jordan" }
-);
-
-await client.add(
- [{ role: "user", content: "I have a dentist appointment on April 18, 2025 at 2 PM." }],
- { userId: "jordan" }
-);
-
-await client.add(
- [{ role: "user", content: "I am the product lead at Acme Corp." }],
- { userId: "jordan" }
-);
-```
-
-
-
- Platform writes are asynchronous. For quick validation, wait briefly before searching or verify the completed event in the dashboard or CLI.
-
-
-### Search with a temporal query
-
-
-```python Python
-results = client.search(
- "what did I do last week?",
- filters={"user_id": "jordan"},
- reference_date="2025-03-21T00:00:00Z",
-)
-
-for item in results["results"]:
- print(item["memory"], item["score"])
-```
-
-```javascript JavaScript
-const results = await client.search("what did I do last week?", {
- filters: { user_id: "jordan" },
- referenceDate: "2025-03-21T00:00:00Z",
-});
-
-results.results.forEach((item) => {
- console.log(item.memory, item.score);
-});
-```
-
-
-
- Expected behavior: the March 10 review leads this result set, the April 18 appointment does not, and the ongoing role at Acme does not crowd out the dated answer.
-
-
## Supported query patterns