الذاكرة

from crewai import Memory

memory = Memory()

# Store -- the LLM infers scope, categories, and importance
memory.remember("We decided to use PostgreSQL for the user database.")

# Retrieve -- results ranked by composite score (semantic + recency + importance)
matches = memory.recall("What database did we choose?")
for m in matches:
    print(f"[{m.score:.2f}] {m.record.content}")

# Tune scoring for a fast-moving project
memory = Memory(recency_weight=0.5, recency_half_life_days=7)

# Forget
memory.forget(scope="/project/old")

# Explore the self-organized scope tree
print(memory.tree())
print(memory.info("/"))

from crewai import Memory

memory = Memory()

# Build up knowledge
memory.remember("The API rate limit is 1000 requests per minute.")
memory.remember("Our staging environment uses port 8080.")
memory.remember("The team agreed to use feature flags for all new releases.")

# Later, recall what you need
matches = memory.recall("What are our API limits?", limit=5)
for m in matches:
    print(f"[{m.score:.2f}] {m.record.content}")

# Extract atomic facts from a longer text
raw = """Meeting notes: We decided to migrate from MySQL to PostgreSQL
next quarter. The budget is $50k. Sarah will lead the migration."""

facts = memory.extract_memories(raw)
# ["Migration from MySQL to PostgreSQL planned for next quarter",
#  "Database migration budget is $50k",
#  "Sarah will lead the database migration"]

for fact in facts:
    memory.remember(fact)

from crewai import Crew, Agent, Task, Process, Memory

# Option 1: Default memory
crew = Crew(
    agents=[researcher, writer],
    tasks=[research_task, writing_task],
    process=Process.sequential,
    memory=True,
    verbose=True,
)

# Option 2: Custom memory with tuned scoring
memory = Memory(
    recency_weight=0.4,
    semantic_weight=0.4,
    importance_weight=0.2,
    recency_half_life_days=14,
)
crew = Crew(
    agents=[researcher, writer],
    tasks=[research_task, writing_task],
    memory=memory,
)

from crewai import Agent, Memory

memory = Memory()

# Researcher gets a private scope -- only sees /agent/researcher
researcher = Agent(
    role="Researcher",
    goal="Find and analyze information",
    backstory="Expert researcher with attention to detail",
    memory=memory.scope("/agent/researcher"),
)

# Writer uses crew shared memory (no agent-level memory set)
writer = Agent(
    role="Writer",
    goal="Produce clear, well-structured content",
    backstory="Experienced technical writer",
    # memory not set -- uses crew._memory when crew has memory enabled
)

from crewai.flow.flow import Flow, listen, start

class ResearchFlow(Flow):
    @start()
    def gather_data(self):
        findings = "PostgreSQL handles 10k concurrent connections. MySQL caps at 5k."
        self.remember(findings, scope="/research/databases")
        return findings

    @listen(gather_data)
    def write_report(self, findings):
        # Recall past research to provide context
        past = self.recall("database performance benchmarks")
        context = "\n".join(f"- {m.record.content}" for m in past)
        return f"Report:\nNew findings: {findings}\nPrevious context:\n{context}"

/
  /company
    /company/engineering
    /company/product
  /project
    /project/alpha
    /project/beta
  /agent
    /agent/researcher
    /agent/writer

memory = Memory()

# LLM infers scope from content
memory.remember("We chose PostgreSQL for the user database.")
# -> might be placed under /project/decisions or /engineering/database

# You can also specify scope explicitly
memory.remember("Sprint velocity is 42 points", scope="/team/metrics")

print(memory.tree())
# / (15 records)
#   /project (8 records)
#     /project/alpha (5 records)
#     /project/beta (3 records)
#   /agent (7 records)
#     /agent/researcher (4 records)
#     /agent/writer (3 records)

print(memory.info("/project/alpha"))
# ScopeInfo(path='/project/alpha', record_count=5,
#           categories=['architecture', 'database'],
#           oldest_record=datetime(...), newest_record=datetime(...),
#           child_scopes=[])

memory = Memory()

# Create a scope for a specific agent
agent_memory = memory.scope("/agent/researcher")

# Everything is relative to /agent/researcher
agent_memory.remember("Found three relevant papers on LLM memory.")
# -> stored under /agent/researcher

agent_memory.recall("relevant papers")
# -> searches only under /agent/researcher

# Narrow further with subscope
project_memory = agent_memory.subscope("project-alpha")
# -> /agent/researcher/project-alpha

memory = Memory()
# Each project gets its own branch
memory.remember("Using microservices architecture", scope="/project/alpha/architecture")
memory.remember("GraphQL API for client apps", scope="/project/beta/api")

# Recall across all projects
memory.recall("API design decisions")

# Or within a specific project
memory.recall("API design", scope="/project/beta")

memory = Memory()

# Researcher has private findings
researcher_memory = memory.scope("/agent/researcher")

# Writer can read from both its own scope and shared company knowledge
writer_view = memory.slice(
    scopes=["/agent/writer", "/company/knowledge"],
    read_only=True,
)

memory = Memory()

# Each customer gets isolated context
memory.remember("Prefers email communication", scope="/customer/acme-corp")
memory.remember("On enterprise plan, 50 seats", scope="/customer/acme-corp")

# Shared product docs are accessible to all agents
memory.remember("Rate limit is 1000 req/min on enterprise plan", scope="/product/docs")

memory = Memory()

# Agent can recall from its own scope AND company knowledge,
# but cannot write to company knowledge
agent_view = memory.slice(
    scopes=["/agent/researcher", "/company/knowledge"],
    read_only=True,
)

matches = agent_view.recall("company security policies", limit=5)
# Searches both /agent/researcher and /company/knowledge, merges and ranks results

agent_view.remember("new finding")  # Raises PermissionError (read-only)

view = memory.slice(scopes=["/team/alpha", "/team/beta"], read_only=False)

# Must specify scope when writing
view.remember("Cross-team decision", scope="/team/alpha", categories=["decisions"])

composite = semantic_weight * similarity + recency_weight * decay + importance_weight * importance

# Sprint retrospective: favor recent memories, short half-life
memory = Memory(
    recency_weight=0.5,
    semantic_weight=0.3,
    importance_weight=0.2,
    recency_half_life_days=7,
)

# Architecture knowledge base: favor important memories, long half-life
memory = Memory(
    recency_weight=0.1,
    semantic_weight=0.5,
    importance_weight=0.4,
    recency_half_life_days=180,
)

# Only 2 records are stored (the third is a near-duplicate of the first)
memory.remember_many([
    "CrewAI supports complex workflows.",
    "Python is a great language.",
    "CrewAI supports complex workflows.",  # dropped by intra-batch dedup
])

# Returns immediately -- save happens in background
memory.remember_many(["Fact A.", "Fact B.", "Fact C."])

# recall() automatically waits for pending saves before searching
matches = memory.recall("facts")  # sees all 3 records

memory = Memory()
memory.remember_many(["Fact A.", "Fact B."])

# Option 1: Wait for pending saves
memory.drain_writes()

# Option 2: Drain and shut down the background pool
memory.close()

# Tag memories with their origin
memory.remember("User prefers dark mode", source="user:alice")
memory.remember("System config updated", source="admin")
memory.remember("Agent found a bug", source="agent:debugger")

# Recall only memories from a specific source
matches = memory.recall("user preferences", source="user:alice")

# Store a private memory
memory.remember("Alice's API key is sk-...", source="user:alice", private=True)

# This recall sees the private memory (source matches)
matches = memory.recall("API key", source="user:alice")

# This recall does NOT see it (different source)
matches = memory.recall("API key", source="user:bob")

# Admin access: see all private records regardless of source
matches = memory.recall("API key", include_private=True)

# Shallow: pure vector search, no LLM
matches = memory.recall("What did we decide?", limit=10, depth="shallow")

# Deep (default): intelligent retrieval with LLM analysis for long queries
matches = memory.recall(
    "Summarize all architecture decisions from this quarter",
    limit=10,
    depth="deep",
)

memory = Memory(
    confidence_threshold_high=0.9,   # Only synthesize when very confident
    confidence_threshold_low=0.4,    # Explore deeper more aggressively
    exploration_budget=2,            # Allow up to 2 exploration rounds
    query_analysis_threshold=200,    # Skip LLM for queries shorter than this
)

from crewai import Memory

# As a config dict
memory = Memory(embedder={"provider": "openai", "config": {"model_name": "text-embedding-3-small"}})

# As a pre-built callable
from crewai.rag.embeddings.factory import build_embedder
embedder = build_embedder({"provider": "ollama", "config": {"model_name": "mxbai-embed-large"}})
memory = Memory(embedder=embedder)

from crewai import Crew

crew = Crew(
    agents=[...],
    tasks=[...],
    memory=True,
    embedder={"provider": "openai", "config": {"model_name": "text-embedding-3-small"}},
)

from crewai import Memory, LLM

# Default: gpt-4o-mini
memory = Memory()

# Use a different OpenAI model
memory = Memory(llm="gpt-4o")

# Use Anthropic
memory = Memory(llm="anthropic/claude-3-haiku-20240307")

# Use Ollama for fully local/private analysis
memory = Memory(llm="ollama/llama3.2")

# Use Google Gemini
memory = Memory(llm="gemini/gemini-2.0-flash")

# Pass a pre-configured LLM instance with custom settings
llm = LLM(model="gpt-4o", temperature=0)
memory = Memory(llm=llm)

memory = Memory(
    llm="ollama/llama3.2",
    embedder={"provider": "ollama", "config": {"model_name": "mxbai-embed-large"}},
)

memory.tree()                        # Formatted tree of scopes and record counts
memory.tree("/project", max_depth=2) # Subtree view
memory.info("/project")              # ScopeInfo: record_count, categories, oldest/newest
memory.list_scopes("/")              # Immediate child scopes
memory.list_categories()             # Category names and counts
memory.list_records(scope="/project/alpha", limit=20)  # Records in a scope, newest first

from crewai.events import BaseEventListener, MemoryQueryCompletedEvent

class MemoryMonitor(BaseEventListener):
    def setup_listeners(self, crewai_event_bus):
        @crewai_event_bus.on(MemoryQueryCompletedEvent)
        def on_done(source, event):
            if getattr(event, "source_type", None) == "unified_memory":
                print(f"Query '{event.query}' completed in {event.query_time_ms:.0f}ms")

crewai memory                              # Opens the TUI browser
crewai memory --storage-path ./my_memory   # Point to a specific directory

crew.reset_memories(command_type="memory")  # Resets unified memory
# Or on a Memory instance:
memory.reset()                    # All scopes
memory.reset(scope="/project/old")  # Only that subtree