Agent-MemoryForge

Agent-MemoryForge is a memory infrastructure product designed for AI agents that need persistent, searchable, and auditable memory across conversations and tasks. It works as a dedicated memory layer alongside existing agent frameworks like LangChain, LangGraph, OpenAI Agents SDK, AutoGen, and CrewAI—without requiring replacement of your current stack. The system provides tenant and workspace isolation, short-term conversation checkpoints, active working memory, semantic facts, graph relationships, and async memory distillation through an authenticated REST API and Python SDK. Operations teams benefit from structured memory management, quota accounting, encrypted workspace secrets, and audit logs that support debugging and compliance requirements.

[{"step":"Enable Agent-MemoryForge integration in your AI agent's configuration. In Azure OpenAI, set the 'memory' parameter to 'enabled' and configure the Neo4j connection string for persistent storage.","tip":"Use the Azure Portal to verify the integration is active. Check the 'Memory' tab in your agent's dashboard to confirm the Neo4j connection is established."},{"step":"Structure your interactions to include contextual information. When starting a new session, reference prior interactions using [PREVIOUS_INTERACTIONS] placeholder. For example: 'Based on our discussion yesterday about the Aurora microservice memory leak, let's continue...'","tip":"Use consistent terminology and identifiers (e.g., microservice names, cluster IDs) across interactions to improve memory retrieval accuracy."},{"step":"Leverage the 'Memory Update' section to explicitly store critical information. After resolving a task, summarize key takeaways and store them for future reference.","tip":"Prioritize storing actionable insights, pending tasks, and unresolved issues. Use tags or labels in Neo4j to categorize memories (e.g., 'bug-fix', 'deployment-plan')."},{"step":"Retrieve historical context when needed. Use the 'Context Summary' section to review prior interactions before addressing a new task. In Neo4j, query the 'AgentMemory' graph for related nodes.","tip":"For complex tasks, break down the problem into sub-tasks and query Neo4j for relevant memories before generating a response. This ensures continuity and reduces redundant work."},{"step":"Monitor memory usage and performance. Use Azure Monitor or Neo4j's query performance tools to track memory consumption and retrieval latency. Adjust the memory storage settings if performance degrades.","tip":"Set up alerts in Azure Monitor for high memory usage in the Neo4j instance. This helps identify potential bottlenecks in memory retrieval."}]