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hylarucoder merged 1 commit into from
May 26, 2026
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docs: verify EverCore quickstart path #232

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3 changes: 2 additions & 1 deletion .gitignore
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Original file line number Diff line number Diff line change
Expand Up @@ -131,6 +131,7 @@ dmypy.json
# Project specific
outputs/
.pytest_cache/
methods/EverCore/docker-compose.override.yaml

evaluation/static_memory_evaluation/logs/
evaluation/static_memory_evaluation/retrieval/
Expand Down Expand Up @@ -222,4 +223,4 @@ evaluation/locomo_evaluation/results_ref/demo/results/
.review_progress.json

# Use-cases: exclude lock files to keep repo lean
use-cases/**/package-lock.json
use-cases/**/package-lock.json
46 changes: 32 additions & 14 deletions README.md
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Expand Up @@ -386,6 +386,8 @@ The fastest way to run a memory system locally is to start with EverCore:
```bash
cd methods/EverCore

# Requires Python 3.12 and Docker.

# Start Docker services
docker compose up -d

Expand Down Expand Up @@ -414,29 +416,45 @@ Server runs at `http://localhost:1995` · [Full Setup Guide](methods/EverCore/do
Store and retrieve memories with simple Python code:

```python
import os
import requests

API_BASE = "http://localhost:1995/api/v1"
API_BASE = os.getenv("EVERCORE_API_BASE", "http://localhost:1995/api/v1")

# 1. Store a conversation memory
requests.post(f"{API_BASE}/memories", json={
"message_id": "msg_001",
"create_time": "2025-02-01T10:00:00+00:00",
"sender": "user_001",
"content": "I love playing soccer on weekends"
})
add_payload = {
"user_id": "user_001",
"session_id": "quickstart_session",
"messages": [
{
"message_id": "msg_001",
"sender_id": "user_001",
"sender_name": "User",
"role": "user",
"timestamp": 1738404000000,
"content": "I love playing soccer on weekends",
}
],
}
add_result = requests.post(f"{API_BASE}/memories", json=add_payload)
add_result.raise_for_status()
add_result = add_result.json()
print(add_result["data"]["status"])

# 2. Search for relevant memories
response = requests.get(f"{API_BASE}/memories/search", json={
search_payload = {
"query": "What sports does the user like?",
"user_id": "user_001",
"method": "hybrid",
"memory_types": ["episodic_memory"],
"retrieve_method": "hybrid"
})
"top_k": 5,
"filters": {"user_id": "user_001"},
}
search_result = requests.post(f"{API_BASE}/memories/search", json=search_payload)
search_result.raise_for_status()
search_result = search_result.json()

result = response.json().get("result", {})
for memory_group in result.get("memories", []):
print(f"Memory: {memory_group}")
for episode in search_result["data"]["episodes"]:
print(episode["episode"])
```

[More Examples](methods/EverCore/docs/usage/USAGE_EXAMPLES.md) · [API Reference](https://docs.evermind.ai/api-reference/introduction) · [Interactive Demos](methods/EverCore/docs/usage/DEMOS.md)
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