CrewAI - Mengram Docs

The mengram-crewai package wires Mengram memory into any CrewAI agent as tools.

Installation

pip install mengram-crewai

Quick start

from mengram_crewai import mengram_tools
from crewai import Agent, Task, Crew

tools = mengram_tools(api_key="om-your-key", user_id="user-123")

agent = Agent(
    role="Support Engineer",
    goal="Help users with technical issues using past context",
    backstory="You remember every prior interaction and learned workflow.",
    tools=tools,
)

task = Task(
    description="Help the user debug their deployment issue",
    expected_output="A resolution grounded in the user's history",
    agent=agent,
)

result = Crew(agents=[agent], tasks=[task]).kickoff()

api_key falls back to MENGRAM_API_KEY and user_id to MENGRAM_USER_ID if omitted.

Available tools

mengram_tools() returns three tools ready to pass to Agent(tools=...):

Tool	Description
`search_memory`	Semantic search over facts, preferences, and past events
`save_memory`	Save a durable fact or decision to long-term memory
`get_procedures`	Retrieve learned workflows — steps, success/failure record, and preconditions to verify

Procedural learning

get_procedures is the distinctive tool. When a workflow has failed before, Mengram returns it with the preconditions that were violated last time, so the agent verifies them up front instead of repeating the mistake.

tools = mengram_tools(api_key="om-...", user_id="user-123")
# The agent calls get_procedures("deploy to Railway") and receives, e.g.:
#   deploy-railway (v3, 11 ok / 1 failed) — verify first: run migrations before push

Options

mengram_tools(
    api_key="om-...",       # or MENGRAM_API_KEY
    user_id="user-123",     # or MENGRAM_USER_ID; multi-user isolation
    limit=5,                # max results per search/procedures call
    base_url="https://...", # self-hosted instances
)