mirror of
https://github.com/magnus919/agent-skills.git
synced 2026-09-16 05:56:30 +03:00
Magnus Hedemark
6c193ae2d8
DSPy v1.1.0: validation audit, worked RAG compilation example, expand ref table Haystack v1.1.0: validation audit, file converters/YAML/component types, +2 refs CrewAI v1.1.0: validation audit, unified Memory system, Flows docs, +3 refs AutoGen v1.1.0: validation audit, v0.4 migration guide, AgentTool, streaming, +2 refs All API surfaces validated against official docs.
1.6 KiB
1.6 KiB
CrewAI Memory System
CrewAI v1.15+ uses a unified Memory class that replaces separate short-term, long-term, entity, and external memory types with a single intelligent API.
Enabling Memory
from crewai import Crew
crew = Crew(
agents=[agent1, agent2],
tasks=[task1, task2],
memory=True, # Enables unified memory for all agents
)
How Memory Works
When memory=True is set at the Crew level:
- Memory is shared — all agents in the crew can access context from prior tasks
- Short-term persistence — within a single crew execution, agents remember context across tasks
- Entity tracking — the system tracks entities (people, places, concepts) mentioned across agent conversations
- Long-term patterns — across multiple crew runs, the system learns from successful patterns
Memory Configuration
from crewai import Crew, MemoryConfig
crew = Crew(
agents=[agent1, agent2],
tasks=[task1, task2],
memory=True,
memory_config=MemoryConfig(
embedder="openai", # Embedding provider for memory storage
dimensions=1536, # Embedding dimensions
),
)
Memory Reset
crew.reset_memories() # Clear all stored memory
Practical Patterns
- Within a single crew run: Memory is automatic. Agents reference prior task outputs through
context. - Across crew runs: Memory enables the system to learn from past execution patterns.
- For state-dependent tools: Set
cache=Falseon tools that shouldn't return cached results. - For long-running systems: Periodically call
reset_memories()to prevent memory bloat.