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Eduard van Valkenburg
6138487888
* Phase 2: Embedding clients for Ollama, Bedrock, and Azure AI Inference Add embedding client implementations to existing provider packages: - OllamaEmbeddingClient: Text embeddings via Ollama's embed API - BedrockEmbeddingClient: Text embeddings via Amazon Titan on Bedrock - AzureAIInferenceEmbeddingClient: Text and image embeddings via Azure AI Inference, supporting Content | str input with separate model IDs for text (AZURE_AI_INFERENCE_EMBEDDING_MODEL_ID) and image (AZURE_AI_INFERENCE_IMAGE_EMBEDDING_MODEL_ID) endpoints Additional changes: - Rename EmbeddingCoT -> EmbeddingT, EmbeddingOptionsCoT -> EmbeddingOptionsT - Add otel_provider_name passthrough to all embedding clients - Register integration pytest marker in all packages - Add lazy-loading namespace exports for Ollama and Bedrock embeddings - Add image embedding sample using Cohere-embed-v3-english - Add azure-ai-inference dependency to azure-ai package Part of #1188 Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com> * Fix mypy duplicate name and ruff lint issues - Rename second 'vector' variable to 'img_vector' in image embedding loop - Combine nested with statements in tests - Remove unused result assignments in tests Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com> * updates from feedback * Fix CI failures in embedding usage handling - Fix Azure AI embedding mypy issues by normalizing vectors to list[float], safely accumulating optional usage token fields, and filtering None entries before constructing GeneratedEmbeddings - Avoid Bandit false positive by initializing usage details as an empty dict - Update OpenAI embedding tests to assert canonical usage keys (input_token_count/total_token_count) Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com> --------- Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
6138487888
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2026-02-25 17:45:08 +00:00
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Get Started with Microsoft Agent Framework Mem0
Please install this package via pip:
pip install agent-framework-mem0 --pre
Memory Context Provider
The Mem0 context provider enables persistent memory capabilities for your agents, allowing them to remember user preferences and conversation context across different sessions and threads.
Basic Usage Example
See the Mem0 basic example which demonstrates:
- Setting up an agent with Mem0 context provider
- Teaching the agent user preferences
- Retrieving information using remembered context across new threads
- Persistent memory
Telemetry
Mem0's telemetry is disabled by default when using this package. If you want to enable telemetry, set the environment variable before importing:
import os
os.environ["MEM0_TELEMETRY"] = "true"
from agent_framework.mem0 import Mem0ContextProvider