Gemini 3 Parallel Function-Call Regression Breaks n8n LangChain Agents with Postgres Chat Memory
A regression in n8n's LangChain agent integration with Gemini 3 Flash Preview causes multi-turn agent conversations to fail: parallel function calls are persisted to Postgres Chat Memory without a required thought_signature, leading to a 400 error on the subsequent turn. This is an operational risk for workflows that rely on multi-step agent interactions and persisted agent state.
What happened
When using the n8n LangChain agent node with Gemini 3 (flash preview) and Postgres Chat Memory, parallel function calls returned in a single model response were stored without their required thought_signature. The immediate turn that produced the parallel calls succeeds, but the next user turn fails with a 400 from Vertex before any tools run. The missing thought_signature in persisted AI messages prevents the agent from reconstructing a valid state for the next model call.
Why it matters
This is a state-corruption/regression issue rather than purely model instability. Persisted chat memory is the canonical source of truth for multi-turn agents; corrupting that memory with incomplete function-call metadata causes reproducible failures. For production automation-customer support bots, approval workflows, or orchestration pipelines-this can manifest as unexplained session failures, reduced automation reliability, and increased human intervention.
Practical impact for businesses
Enterprises using n8n + LangChain + Vertex must treat this as a systemic availability and integrity risk. Intermittent 400s during turn reconstruction create poor UX and can break SLA-bound processes. The issue also complicates debugging because the failure appears on the subsequent turn, masking the true origin in persisted chat state.
What leaders and engineering teams should do
- Confirm n8n/node versions and revert to the last-known-good release (pre-regression) or apply vendor patches when available.
- Add validation on chat-memory writes: reject or quarantine AI messages that lack expected function-call metadata (thought_signature).
- Instrument and monitor 400/invalid-function-call errors and add end-to-end tests for parallel tool-call scenarios.
- Coordinate with n8n/Vertex/LangChain maintainers for a fix and ensure schema compatibility tests are part of the CI pipeline.
Taking these steps will reduce the blast radius of similar regressions and make multi-turn agent flows more robust in production.
Original Source
n8n Community