mentedb-crewai
MenteDB helpers for CrewAI and AutoGen style multi agent setups: a memory class that stores and searches team memories in MenteDB, and a tool class that runs MQL queries.
Installation
pip install mentedb-crewai
Status
MenteDBCrewMemory and MenteDBTool are plain Python classes. They do not
subclass CrewAI's memory or BaseTool types, so CrewAI rejects them when they
are passed directly as Agent(memory=...) or Agent(tools=[...]) (checked
with CrewAI 1.15). Call them from your own code, or wrap them in a CrewAI tool
yourself, as shown below.
Only one MenteDB instance can open a data directory at a time. Give every
MenteDBCrewMemory and MenteDBTool its own data_dir, or share one object
between agents.
Components
MenteDBCrewMemory
Stores memories tagged with a space name (and the optional agent_name) and
searches within that space.
from mentedb_crewai import MenteDBCrewMemory
memory = MenteDBCrewMemory(data_dir="./crew-memory", space="dev-team")
# Store context from agent work
memory.store("The team decided to use PostgreSQL for the main database.")
# Search for relevant memories in this space
results = memory.search("database decision", k=5)
# -> [{"content": ..., "score": ..., "id": ..., "memory_type": ...}]
# Text of memories matching the task (MQL recall, up to 10 memories)
context = memory.get_context("Plan the database migration")
Memory is stored on disk, so a later run that opens the same data_dir can
search what earlier runs stored.
MenteDBTool
Runs an MQL (Mente Query Language) query and returns the assembled text.
from mentedb_crewai import MenteDBTool
memory_tool = MenteDBTool(data_dir="./tool-memory")
print(memory_tool.run("RECALL similar(\"database decision\") LIMIT 5"))
Using it from a CrewAI agent
Wrap the call in a CrewAI tool:
from crewai import Agent
from crewai.tools import tool
from mentedb_crewai import MenteDBCrewMemory
memory = MenteDBCrewMemory(data_dir="./crew-memory", space="research-team")
@tool("Team memory search")
def team_memory(query: str) -> str:
"""Search what the team already knows about a topic."""
return "\n".join(r["content"] for r in memory.search(query, k=5))
analyst = Agent(
role="Analyst",
goal="Analyze data using historical context",
backstory="Knows the team's past decisions.",
tools=[team_memory],
)
Configuration
| Parameter | Default | Description |
|---|---|---|
data_dir |
./mentedb-data |
Path to the MenteDB data directory |
space |
default |
Tag used to group and filter memories (MenteDBCrewMemory) |
agent_name |
None |
Extra tag added to stored memories (MenteDBCrewMemory) |
get_context accepts a token_budget argument, but it is currently ignored.
License
Apache 2.0
1 maintainer
Metadata
1 maintainer
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