Quick Start
Fastmem — Memory Layer for AI Apps
Give your AI app long-term memory. Fastmem extracts facts from conversations, stores them as semantic vectors, and retrieves relevant context on demand.
pip install fastmemfrom fastmem import FastmemClient
memory = FastmemClient(
api_key="your-api-key"
)# Add memories - facts are automatically extracted
result = memory.add(
user_id="user_123",
text="I'm allergic to peanuts and I work at Google"
)
print(f"Added {result.added_count} memories")# Search for relevant memories
results = memory.search(
user_id="user_123",
query="What food allergies does this user have?"
)
for mem in results:
print(f"- {mem.text} (score: {mem.score:.2f})")Ready to try it?
Test memory extraction and search in the playground
Select or enter a user to see their memories
| Prefix | Memories | Users |
|---|
| User ID | Memories |
|---|
Loading users...
Select a user to view memories
| Context | Memories | Users | Latest Activity |
|---|
| # | Memory | Category | Similarity | Returned |
|---|
Evolution Timeline
Track how user preferences and facts change over time
Select a user and click "Load Insights" to see patterns
API Reference
Complete reference for the Fastmem REST API
/api/add
Add memories from text. Automatically extracts facts using AI.
Request Body
{
"user_id": "string", // Required: Unique user identifier
"text": "string", // Required: Text to extract memories from
"context": "string" // Optional: Context bucket (default: "default")
}import requests
response = requests.post(
"https://api.fastmem.ai/api/add",
headers={"X-API-Key": "your-api-key"},
json={
"user_id": "user_123",
"text": "I'm allergic to peanuts",
"context": "health"
}
)
print(response.json())/api/search
Search for relevant memories using semantic similarity.
Query Parameters
user_id (required) - User identifierquery (required) - Search querytop_k (optional) - Max results (default: 5)context (optional) - Filter by contextresponse = requests.get(
"https://api.fastmem.ai/api/search",
headers={"X-API-Key": "your-api-key"},
params={
"user_id": "user_123",
"query": "What allergies?",
"top_k": 5
}
)
for result in response.json()["results"]:
print(f"{result['text']} - {result['score']:.2f}")/api/memories
List all memories for a user with optional filtering.
Query Parameters
user_id (required) - User identifiercontext (optional) - Filter by contextcategory (optional) - Filter by categoryView all endpoints in the interactive API documentation
Open Swagger UINo logs yet
Make some API calls to see logs here
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