Context & Memory Management
What It Is
A set of mechanisms that keep information flowing correctly between nodes and between turns in multi-turn conversations. It solves two problems: the Agent forgetting earlier context (needs memory) and context growing ever larger (needs compression).
Using Context
- Agent node: with the short-term memory switch turned on, it can use the context of past conversation turns via short-term memory
- LLM node: usable after selecting variables as context in the context menu; it can also read context from short-term memory
Generating Context
- Once an Agent node uses context, it automatically compresses tool return content into the context — data fetched by tools is not lost in the next turn
- After an LLM node selects context content, it supports context output; the output context can be referenced by downstream nodes
Saving Context
- When a Chatflow produces output, it supports selecting context variables to save to short-term memory
- Each conversation turn saves its own context information
Context Compression
- The Agent node enables context compression via a switch
- When the context exceeds a certain size, it is compressed automatically: after compression, the current turn's context contains the context information of all previous turns, while the raw context of historical turns is automatically cleared — information retained, size under control
Caching
- Enabling caching shows a confirmation dialog informing you of the risk (the cache may return stale results)
- Cache configuration is disabled in Agentic Chat mode
Recommendations
- Set the memory window to the smallest sufficient value: an oversized window wastes tokens and lets history derail the model; see Prompt Writing Tips
- Context compression itself consumes usage (borne by the same party as the conversation run); factor it into cost estimates for long-conversation scenarios, see Workspace & Plans
- If an LLM node has context configured but the corresponding variable is not inserted in the prompt, you get the error "Please fill in the context variable in the prompt"; see Variables & Data Flow
Related Reading
- LLM Node Documentation — memory feature and memory window configuration
- Agent Node Documentation