AI Agent Memory Budget Calculator

In multi-agent systems, each agent needs its own memory budget: working memory (in-context tokens), long-term storage (vector DB), and shared state (message bus or DB). Without careful budgeting, costs explode as agents scale. A system with 10 agents each running 1000 calls/day at 2000 tokens each = 20M tokens/day, which at GPT-4o pricing is ~$50/day.

Agent-local memory: each agent has its own context and history — simpler, no coordination overhead, but no cross-agent knowledge sharing. Shared memory: a central vector database or key-value store that all agents can read/write — enables collaboration but adds latency (DB query per call) and requires conflict resolution. Most production systems use both: local working memory + shared long-term store.

Strategies: (1) Sliding window — keep only last N messages in context. (2) Summarization — compress old conversations into a summary every K turns. (3) Selective retrieval — only retrieve relevant context via RAG instead of keeping everything. (4) Memory tiers — hot (in-context), warm (recent cache), cold (vector DB). Most frameworks like LangChain, CrewAI, and AutoGen implement sliding window by default.

For local/single-agent: ChromaDB (free, embedded) or LanceDB. For production multi-agent: Pinecone (managed, fast) or Qdrant (self-hostable). For high-scale: Weaviate or Milvus. Key metrics: query latency (Pinecone <10ms), consistency guarantees, and cost per query. Most managed DBs charge ~$0.10 per 1M queries + storage.

It depends on token usage per agent. At $2.50/1M input tokens (GPT-4o): 1 agent doing 1000 calls/day at 500 tokens each = ~$1.25/day ($37/month). 10 agents = $370/month. 100 agents = $3,700/month. To scale cheaply: use faster/cheaper models for simple agents (Haiku $0.80/1M), reserve expensive models for complex reasoning agents.