mirror of
https://github.com/crewAIInc/crewAI.git
synced 2026-01-10 16:48:30 +00:00
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>
46 lines
1.5 KiB
Markdown
46 lines
1.5 KiB
Markdown
# 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
|
|
|
|
```python
|
|
# 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:
|
|
|
|
```python
|
|
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.
|