Python SDK for memos — persistent brain framework for AI agents
Project description
MEMOS Python SDK
Python SDK for MEMOS — persistent brain framework for AI agents.
Installation
pip install memos-ai
Publish Verification
python3 -m venv verify
source verify/bin/activate
pip install memos-ai
python3
from memos import MemosClient
print(MemosClient)
Expected output:
<class 'memos.client.MemosClient'>
Quick Start
from memos import MemosClient
client = MemosClient(
api_key="your_api_key",
agent_id="your_agent_id"
)
# Store a memory
memory = client.store_memory(
content="User prefers Python over JavaScript",
type="semantic",
importance=4
)
# Search memories
results = client.search("language preferences")
for r in results:
print(f"{r.score:.2f} — {r.content}")
# Ask with memory context
response = client.query("What language does this user prefer?")
print(response.answer)
# Trigger dream consolidation
dream = client.trigger_dream()
print(f"Created {dream.new_memories_created} new memories")
Authentication
Get your API key and agent ID from https://memos.io/dashboard
Methods Reference
| Method | Parameters | Returns | Description |
|---|---|---|---|
store_memory |
content (str), type (str="episodic"), importance (int=3), tags (list[str]=None) |
Memory |
Store a new memory for the agent |
list_memories |
None | list[Memory] |
List all memories for the agent |
delete_memory |
memory_id (str) |
bool |
Delete a specific memory by ID |
search |
query (str), search_type (str="keyword"), limit (int=10) |
list[SearchResult] |
Search agent memories |
query |
question (str), include_sources (bool=True), conversation_history (list[dict]=None) |
RAGResponse |
Ask a question using RAG |
trigger_dream |
None | DreamResult |
Trigger a dream consolidation cycle |
list_skills |
None | list[Skill] |
List all available skills in the marketplace |
execute_skill |
skill_id (str), input (str) |
SkillResult |
Execute a skill |
run_pipeline |
steps (list[dict]), input (str) |
dict |
Run a multi-step pipeline |
get_identity |
None | dict |
Get the agent's identity and reputation |
Error Handling
from memos import MemosError, AuthError, RateLimitError
try:
client.store_memory("...")
except AuthError:
print("Check your API key at memos.io/profile")
except RateLimitError:
print("Rate limit hit — slow down requests")
except MemosError as e:
print(f"API error {e.status_code}: {e.message}")
Context Manager
with MemosClient(api_key="...", agent_id="...") as client:
client.store_memory("...")
# HTTP connection closed automatically
Requirements
Python 3.8+
No additional dependencies beyond httpx.
License
MIT
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