docs(integrations): add engram integration

packages.yml

@@ -100,6 +100,10 @@ packages:
   js: "@langchain/deepseek"
   downloads: 697000
   downloads_updated_at: "2026-05-18T00:23:32.797657+00:00"
+- name: langchain-engram
+  repo: Harshitk-cp/langchain-engram
+  downloads: 0
+  downloads_updated_at: "2026-05-29T00:00:00.000000+00:00"
 - name: langchain-exa
   repo: langchain-ai/langchain
   path: libs/partners/exa

src/oss/python/integrations/providers/all_providers.mdx

@@ -417,6 +417,14 @@ Browse the complete collection of integrations available for Python. LangChain P
         Cloud content collaboration and AI-powered search platform.
     </Card>
 
+    <Card
+        title="Engram"
+        href="/oss/integrations/providers/engram"
+        icon="link"
+    >
+        Cognitive memory infrastructure for AI agents — confidence scoring, contradiction detection, and memory lifecycle.
+    </Card>
+
     <Card
         title="Elasticsearch"
         href="/oss/integrations/providers/elasticsearch"

src/oss/python/integrations/providers/engram.mdx

@@ -0,0 +1,41 @@
+---
+title: "Engram integrations"
+description: "Integrate with Engram using LangChain Python."
+---
+
+[Engram](https://engram.ai) is cognitive memory infrastructure for AI agents.
+Engram tracks **confidence scores**, detects **contradictions**, and manages
+**memory lifecycle**—so agents know what they know and when to doubt it.
+
+Key capabilities:
+- 4-type cognitive model: semantic, episodic, procedural, and working memory
+- Calibrated confidence scoring with Bayesian log-odds updates
+- Contradiction detection with automatic belief reconciliation
+- Hybrid vector + knowledge-graph retrieval (pgvector + graph traversal)
+- Memory decay: stale beliefs lose confidence over time
+
+## Installation and setup
+
+<CodeGroup>
+```bash pip
+pip install langchain-engram
+```
+
+```bash uv
+uv add langchain-engram
+```
+</CodeGroup>
+
+Set environment variables:
+
+```bash
+export ENGRAM_BASE_URL=http://localhost:8080
+export ENGRAM_API_KEY=mk_...
+```
+
+See the [Engram quickstart](https://engram.ai#quickstart) for server setup.
+
+## Retrievers
+
+See the [EngramRetriever documentation](/oss/integrations/retrievers/engram) for
+memory-backed retrieval with confidence filtering and hybrid recall.

src/oss/python/integrations/retrievers/engram.mdx

@@ -0,0 +1,118 @@
+---
+title: "Engram retriever"
+description: "Integrate with the Engram retriever using LangChain Python."
+---
+
+# EngramRetriever
+
+This will help you get started with the Engram [retriever](/oss/langchain/retrieval).
+
+Engram is cognitive memory infrastructure for AI agents—tracking not just what
+agents know, but how confident they should be, and when to doubt it. Each retrieved
+memory is a `Document` carrying confidence score, memory tier, and relevance scores
+from Engram's hybrid vector + knowledge-graph recall.
+
+### Integration details
+
+| Retriever | Self-host | Cloud offering | Package |
+| :--- | :---: | :---: | :---: |
+| `EngramRetriever` | ✅ | ✅ | `langchain-engram` |
+
+## Setup
+
+### Installation
+
+```bash
+pip install langchain-engram
+```
+
+### Start the server
+
+```bash
+git clone https://github.com/Harshitk-cp/engram.git
+cd engram
+ENGRAM_SETUP_TOKEN=changeme docker compose up -d
+
+# Get your API key (run once)
+curl -X POST http://localhost:8080/v1/setup \
+  -H "X-Setup-Token: changeme" \
+  -H "Content-Type: application/json" \
+  -d '{"org_name": "My Org"}'
+# → {"api_key": "mk_..."}
+```
+
+### Environment variables
+
+```bash
+export ENGRAM_BASE_URL=http://localhost:8080
+export ENGRAM_API_KEY=mk_...
+```
+
+## Instantiation
+
+```python
+from langchain_engram import EngramRetriever
+
+retriever = EngramRetriever(
+    agent_id="your-agent-uuid",
+    # api_key and base_url read from env if not passed
+    top_k=5,
+    min_confidence=0.6,  # only surface confident memories
+)
+```
+
+## Usage
+
+```python
+docs = retriever.invoke("display preferences")
+for doc in docs:
+    print(doc.page_content)
+    print(f"  confidence: {doc.metadata['confidence']:.0%}")
+    print(f"  tier:       {doc.metadata['tier']}")         # hot / warm / cold
+    print(f"  type:       {doc.metadata['memory_type']}")  # fact / preference / decision
+    print(f"  score:      {doc.metadata.get('score', 'n/a')}")
+```
+
+### In a RetrievalQA chain
+
+```python
+from langchain.chains import RetrievalQA
+from langchain_openai import ChatOpenAI
+
+qa = RetrievalQA.from_chain_type(llm=ChatOpenAI(), retriever=retriever)
+answer = qa.invoke({"query": "What are the user's display preferences?"})
+```
+
+### Document metadata fields
+
+| Field | Type | Description |
+| :--- | :--- | :--- |
+| `memory_id` | `str` | UUID of the memory |
+| `agent_id` | `str` | Owning agent UUID |
+| `memory_type` | `str` | `fact` / `preference` / `decision` / `constraint` |
+| `confidence` | `float` | Calibrated confidence score (0–1) |
+| `tier` | `str` | `hot` / `warm` / `cold` |
+| `source` | `str` | Origin of the memory |
+| `score` | `float` | Combined recall score |
+| `vector_score` | `float` | Vector similarity component |
+| `graph_score` | `float` | Graph traversal component |
+| `created_at` | `str` | ISO 8601 timestamp |
+
+## Configuration
+
+| Parameter | Default | Description |
+| :--- | :--- | :--- |
+| `agent_id` | required | Engram agent UUID |
+| `api_key` | `ENGRAM_API_KEY` env | `mk_` or `rk_` prefixed API key |
+| `base_url` | `ENGRAM_BASE_URL` env | Engram server URL |
+| `top_k` | `10` | Max memories to return |
+| `min_confidence` | `None` | Confidence floor (0–1) |
+| `memory_type` | `None` | Filter by type: `fact`, `preference`, `decision`, `constraint` |
+| `graph_weight` | `None` | Graph/vector blend (0–1); server default 0.4/0.6 |
+
+## Related links
+
+- [Engram documentation](https://engram.ai)
+- [Python SDK (engram.to)](https://pypi.org/project/engram.to/)
+- [GitHub](https://github.com/Harshitk-cp/langchain-engram)
+- [Provider page](/oss/integrations/providers/engram)

src/oss/python/integrations/retrievers/index.mdx

@@ -50,6 +50,7 @@ The below retrievers will search over an external index (e.g., constructed from
 <Card title="Dappier" icon="link" href="/oss/integrations/retrievers/dappier" arrow="true" cta="View guide" />
 <Card title="Elasticsearch" icon="link" href="/oss/integrations/retrievers/elasticsearch_retriever" arrow="true" cta="View guide" />
 <Card title="Egnyte" icon="link" href="/oss/integrations/retrievers/egnyte" arrow="true" cta="View guide" />
+<Card title="Engram" icon="link" href="/oss/integrations/retrievers/engram" arrow="true" cta="View guide" />
 <Card title="Galaxia" icon="link" href="/oss/integrations/retrievers/galaxia-retriever" arrow="true" cta="View guide" />
 <Card title="Google Drive" icon="link" href="/oss/integrations/retrievers/google_drive" arrow="true" cta="View guide" />
 <Card title="Google Vertex AI Search" icon="link" href="/oss/integrations/retrievers/google_vertex_ai_search" arrow="true" cta="View guide" />