Data Management

How data flows through AmritaCore: messages, memory, backends, and the DI contexts that carry state between workflow nodes.

Messages

Type	Role
Message	One conversation message (role, content, tool_calls, reasoning_content)
ToolResult	A tool's output, paired with its tool_call_id
SendMessageWrap	The working context: train + memory + user_query + end_messages
UniResponse	Normalized LLM response (content, tool_calls, reasoning_content, usage)

SendMessageWrap is what strategies mutate — ctx.message.append(...) adds to end_messages, which unwrap() includes in the next request.

DI Contexts

Workflow nodes receive state via type-matched injection — each node declares parameters like loop: AgentLoopState and the interpreter injects the matching instance. Key contexts (all owned by ChatObject):

Context	Carries
SessionMetadata	session/stream ids, timestamps
MemoryContext	runtime memory
AbilityState	config, preset, backend slots
GeneralInput	user input, train, template
WorkingState	the SendMessageWrap
RespState	response + usage
AgentLoopState	strategy, call count, run_state
StrategyPayload	the strategy factory
DatabackendOptions	backend fetch/commit skip flags

In AmritaSense terms this is the standard dependency-injection mechanism — see sense.amritabot.com for the general rules.

Two Deep-Dives

Page	Covers
Data Backend	The AbilityBackend / MemoryBackend interfaces and how to write your own
Memory Model	MemoryModel, the load/commit lifecycle, and the legacy StateContext accessor

Next

Extensions & Integration — adapters, tools, MCP and tokenizers.