Graphiti vs TencentDB Agent Memory

Graphiti

Apache-2.0, with FalkorDB, Neo4j or Amazon Neptune as the store. Every add runs LLM extraction, so ingestion costs tokens and time.

TencentDB Agent Memory

A self-hosted team memory stack with layered recall, per-asset visibility and ACLs, documented HTTP APIs and frequent releases, 2.0.0 to 2.0.3 since August. The default deployment publishes its ports on all host interfaces with the core's bearer gate off, data-plane deletes trust a service-ID header, and the proxy's rate-limit and session admin routes have no authentication.

Graphiti

1. Set OPENAI_API_KEY (or another provider's key) and MODEL_NAME before starting the MCP server
2. Use search_memory_facts for facts and search_nodes for entities instead of reading whole episodes
3. Never call clear_graph to fix one fact. Use delete_episode or delete_entity_edge
4. Pass group_ids on every search and add so one user's graph doesn't leak into another's
5. Call get_status to check the database connection before a long ingest

TencentDB Agent Memory

1. Set MEMORY_CORE_GATEWAY_API_KEY, KNOWLEDGE_SERVICE_KEY and a non-default admin key before exposing ports 8420, 8424, 8096 or 8125 beyond the host
2. In headless runs send x-team-id, x-agent-id and x-task-id headers to the proxy. Without all three it falls back to an interactive picker and skips memory
3. Expect every proxied turn to be stored. The proxy records upstream messages and model replies to Memory Core, and injected memory lands in your system prompt
4. Check data.valid on /v3/meta/auth/verify and data.isError on /v3/tools/call. Both can fail inside a success envelope
5. Retry /v3/session/add only on 500, 503 or a network error. The API reference says it isn't idempotent and writes a new object each call