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636dac6efb
- Add configurable embedding providers (OpenAI, Ollama) - Fix type hints in base_tool and structured_tool - Add proper json property implementations - Update documentation for memory configuration - Add environment variables for embedding configuration - Fix type errors in task and crew output classes Co-Authored-By: Joe Moura <joao@crewai.com>
1.5 KiB
1.5 KiB
Memory in CrewAI
CrewAI provides a robust memory system that allows agents to retain and recall information from previous interactions.
Configuring Embedding Providers
CrewAI supports multiple embedding providers for memory functionality:
- OpenAI (default) - Requires
OPENAI_API_KEY - Ollama - Requires
CREWAI_OLLAMA_URL(defaults to "http://localhost:11434/api/embeddings")
Environment Variables
Configure the embedding provider using these environment variables:
CREWAI_EMBEDDING_PROVIDER: Provider name (default: "openai")CREWAI_EMBEDDING_MODEL: Model name (default: "text-embedding-3-small")CREWAI_OLLAMA_URL: URL for Ollama API (when using Ollama provider)
Example Configuration
# Using OpenAI (default)
os.environ["OPENAI_API_KEY"] = "your-api-key"
# Using Ollama
os.environ["CREWAI_EMBEDDING_PROVIDER"] = "ollama"
os.environ["CREWAI_EMBEDDING_MODEL"] = "llama2" # or any other model supported by your Ollama instance
os.environ["CREWAI_OLLAMA_URL"] = "http://localhost:11434/api/embeddings" # optional, this is the default
Memory Usage
When an agent has memory enabled, it can access and store information from previous interactions:
agent = Agent(
role="Researcher",
goal="Research AI topics",
backstory="You're an AI researcher",
memory=True # Enable memory for this agent
)
The memory system uses embeddings to store and retrieve relevant information, allowing agents to maintain context across multiple interactions and tasks.