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Anona gives a CrewAI crew memory that persists across runs and is shared by every agent in it, with no vector database to run and no LLM key to supply.

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Setup

Give one or more agents Anona-backed memory tools. Each agent can then search past memories and save new ones as part of its own reasoning, the same way it would use any other tool.
as_tools() returns two tools, named Anona: Search memory and Anona: Save memory. Give them to every agent that should share the crew’s memory. Like any CrewAI tool, these are agent-discretionary: nothing calls them automatically. The agent’s LLM decides whether and when to reach for a tool based on its backstory/goal and the task at hand, the same way it decides whether to use a web-search or file-read tool. Say so explicitly, as in the backstory above, or the model may never call Anona: Search memory even though it’s sitting right there in its tool list. The Anona: prefix is deliberate: if the same agent also has CrewAI’s own memory enabled (Crew(memory=True) or an agent-level memory=True), CrewAI auto-attaches its own memory tools too, named Search memory and Save to memory. CrewAI dedups tools by name and silently keeps whichever set was attached last, so an unprefixed name here could lose to, or quietly shadow, CrewAI’s own tool with no error and nothing in any log. The prefix means Anona’s tools and CrewAI’s can coexist on the same agent without either one going missing.

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Why tools, not Crew(memory=...)

CrewAI’s built-in memory (Crew(memory=True) or Memory(storage=...)) does its own embedding and LLM-driven analysis of everything it stores and recalls, and hands a pluggable storage backend only a raw vector on search, never the query text. That can’t be bridged to Anona’s managed, text-based retrieval without running a local embedding model, which is exactly the vector database this integration exists to remove. CrewAI’s memory tools sidestep all of that: an agent calls them with plain text and gets plain text back, and Anona does the actual search and ranking server-side. It also means adopting Anona adds no second LLM requirement on top of whatever model your agents already use. CrewAI’s built-in memory needs its own LLM and embedder for content analysis regardless of storage backend; Anona-backed tools don’t.

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Scoping

Pass user_id, agent_id or session_id to the bridge to isolate memory inside a space, so one crew deployment can serve many end customers without their memories mixing.

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AnonaStorage directly

AnonaStorage(bridge=bridge) also exposes plain save(value) / search(query) / reset() methods (what as_tools() is built from), if you want to call Anona from your own code rather than through the generated tools. reset() is a deliberate no-op that logs a warning: Anona has no all-or-nothing delete endpoint, and a partial wipe (list-then-delete, with no transaction) is worse than none. Clear a space from the Anona dashboard instead.

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Failure behaviour

If Anona is unreachable, the search tool reports no memories found and the crew keeps running. The save tool still returns "Saved to memory.", even when the write failed. The bridge behind every adapter never raises, so there’s no failure for the tool to report. Read "Saved to memory." as “handed off,” not as confirmation the memory is durably stored.