Cogni vs Knownbase vs Bourdon (Agent Memory Architecture for Agency Delivery)
The architecture choice matters more than the vendor shortlist: graph-based recall (Cogni) compounds cross-client reasoning, archive-based memory (Knownbase) compounds project audit trails, and federated recognition (Bourdon) compounds speed across a multi-tool stack. Pick the one whose memory shape matches how your agency actually reuses knowledge, because a memory layer that stores the wrong shape of context just makes re-prompting faster. Whichever you choose, test the export path before you commit a retainer's worth of institutional knowledge to it.
By InnovaAI ResearchPublished
Which should an agency choose?
Cogni vs Knownbase vs Bourdon (Agent Memory Architecture for Agency Delivery)
Cogni
Best for: Agencies whose value comes from cross-client pattern recall, where a fact learned on one retainer should surface on an adjacent account.- Entity-graph retrieval with spreading activation reaches connected facts a vector store misses
- Deterministic retrieval path runs no LLM or GPU, so recall cost stays flat as client volume grows
- Model-agnostic, so the same memory serves whichever model a client engagement standardizes on
- Graph quality depends on disciplined entity naming, and sloppy client taxonomy degrades multi-hop recall
- Reasoning-capable recall is harder to explain to a client than a plain searchable note archive
- Smaller install base means fewer peers to compare retrieval behavior against
Knownbase
Best for: Agencies running long build engagements where the deliverable is code and the audit trail is the product.- Stores architectural decisions, debugging discoveries, and constraints as project-scoped notes
- Connects Claude Code, Codex, Cursor, and ChatGPT through MCP, so switching tools does not reset context
- Tag and project organization maps cleanly to how delivery teams already file client work
- Archive-style memory surfaces what you filed, not what you forgot to file
- Retrieval is search-driven, so recall latency grows with note volume
- Value concentrates in engineering-heavy retainers and thins out on pure strategy accounts
Bourdon
Best for: Multi-agent shops where several tools touch the same client account and duplicated context is the daily tax.- Recognition-first federation pushes recall latency toward zero instead of returning a ranked list
- One federated memory shared across Claude, Codex, Cursor, Copilot, and Devin, across accounts and machines
- A fact learned by one agent is recognized by the others without a manual sync step
- Federation across accounts and machines raises a real question about which client data sits in shared memory
- Recognition-first design offers less control over how a recalled fact is weighted or ranked
- Cross-tool breadth is the pitch, so depth in any single tool's native memory features is thinner
The architecture choice matters more than the vendor shortlist: graph-based recall (Cogni) compounds cross-client reasoning, archive-based memory (Knownbase) compounds project audit trails, and federated recognition (Bourdon) compounds speed across a multi-tool stack. Pick the one whose memory shape matches how your agency actually reuses knowledge, because a memory layer that stores the wrong shape of context just makes re-prompting faster. Whichever you choose, test the export path before you commit a retainer's worth of institutional knowledge to it.