How to reset Agents programatically

General
5

@El_Ansole,

Yeah, you’re spot on. I figured crew.reset_memories(command_type="knowledge") would clear out all knowledge memories, but it turns out it only handles knowledge_sources attached directly to the Crew. It completely skips over any Agent-level knowledge_sources. To be honest, this feels like a bit of an oversight in the design, especially since it’s pretty common to want to run standalone Agents without a full Crew, like we discussed over in this other thread.

So, check it out – below I’ve put together a repro of your use case and a sort of poor man’s version of reset that loops through all the Agents in the crew to wipe their knowledge. You can mess with the string_source on each run, and you’ll see the Agent’s knowledge change just like it’s supposed to. Hope this does the trick for you!

BTW, if you think it’d be a solid addition to get this built right into Crew.reset_memories(), you should definitely create an issue for it and make sure to link back to this discussion.

import os
from pathlib import Path
from crewai.utilities import Logger
from crewai.utilities.paths import db_storage_path
from crewai import LLM, Agent, Crew, Process, Task
from crewai.knowledge.source.string_knowledge_source import StringKnowledgeSource

_logger = Logger(verbose=True)

#
# Where's my knowledge?
#

base_knowledge_dir = Path(db_storage_path()) / "knowledge"

_logger.log(
    "info",
    f"Knowledge source location is '{str(base_knowledge_dir)}'",
    color="yellow"
)

#
# Remember everything
#

os.environ["GEMINI_API_KEY"] = "<YOUR_API_KEY>"

string_source = StringKnowledgeSource(
    content=(
        "User name is Max. He is 160 years old and lives in "
        "the middle of the Amazon rainforest. His best friend is "
        "an alligator named 'Croco'."
    )
)

google_embedder = {
    "provider": "google",
    "config": {
        "model": "models/text-embedding-004",
        "api_key": os.environ["GEMINI_API_KEY"],
    },
}

gemini_llm = LLM(
    model="gemini/gemini-2.5-flash-preview-04-17",
    temperature=0.7
)

user_knowledge_agent = Agent(
    role="User Knowledge Expert",
    goal=(
        "Answer questions about users accurately based on available knowledge."
    ),
    backstory=(
        "You are an AI assistant with deep expertise in understanding user profiles "
        "and their specific details. You always stick to the facts provided."
    ),
    llm=gemini_llm,
    knowledge_sources=[string_source],
    embedder=google_embedder,
    allow_delegation=False,
    verbose=True
)

user_knowledge_task = Task(
    description=(
        "Using your knowledge about users, answer the following question:\n\n"
        "'{question}'\n\n"
        "If you don't have the information, state that clearly."
    ),
    expected_output=(
        "A concise and accurate answer to the question based on the user's knowledge base."
    ),
    agent=user_knowledge_agent,
)

user_knowledge_crew = Crew(
    agents=[user_knowledge_agent],
    tasks=[user_knowledge_task],
    process=Process.sequential,
    verbose=True
)

response = user_knowledge_crew.kickoff(
    inputs={"question": "What do you know about Max?"}
)

print(f"[🤖 Response]: {response.raw}")

#
# Forget everything
#
# crew.reset_memories(command_type="knowledge") doesn't actually hit the agent-level memories!
#

_logger.log(
    "info",
    "Resetting all agents' `knowledge` memory...",
    color="yellow"
)

for agent_instance in user_knowledge_crew.agents:
    memory_system = getattr(agent_instance, "knowledge", None)
    if memory_system is not None:
        try:
            memory_system.reset()
        except Exception as e:
            raise RuntimeError(
                f"Failed to reset `knowledge` memory for Agent {agent_instance.role}. "
                "Error: {e}"
            ) from e
        finally:
            setattr(agent_instance, "knowledge", None)