[DOC] Deep walkthrough: Agentic memory with Chroma + LangChain agents

[tazarov]

Why

Current content is broad but not deep enough for users building production agent systems. We need a step-by-step, runnable example focused on long-term memory behavior.

Proposal

Create a full walkthrough that builds an agent with Chroma-backed memory using LangChain agents.

Scope

• Build an agent that writes and retrieves memory from Chroma.
• Separate memory types (episodic facts, user preferences, task context).
• Add write policies (what to store), read policies (when to retrieve), and memory compaction strategy.
• Show recency/importance weighting in retrieval.
• Include a realistic task scenario and sample conversations.

Deliverables

• Runnable example (script or notebook) in the cookbook.
• Step-by-step walkthrough with checkpoints and expected outputs.
• Troubleshooting section for common failure modes.
• Minimal tests for memory read/write behavior.

Acceptance Criteria

• A user can run the example end-to-end with provided commands.
• The walkthrough demonstrates memory improving multi-turn task performance.
• The guide includes measurable checks (at least one retrieval-quality or behavior check).
• The example documents what to change for production use.