Beyond Single-Turn Retrieval
Most memory systems are designed for single-turn retrieval: an agent asks a question, gets context, and moves on. But real-world agent workflows are multi-step. A planning agent defines tasks, a research agent gathers information, a coding agent implements, and a review agent validates. Each step builds on the previous one, and context must flow seamlessly across agent boundaries without ballooning token usage.
Hipp0's Super Brain is the orchestration layer that manages this multi-step flow. It maintains a persistent session that accumulates context across workflow steps, compresses intermediate results at checkpoints, and prefetches decisions that downstream agents are likely to need.
The 4-Phase Pipeline
- Phase 1: Session initialization. Super Brain creates a session context that includes the workflow definition, participating agents, and their roles. It pre-loads relevant decisions from the graph based on the workflow's scope and tags.
- Phase 2: Context accumulation. As each agent completes its step, Super Brain captures the output as a checkpoint. Decisions made during the step are recorded in the graph with full provenance (which agent, which step, what inputs). The accumulated context is available to all subsequent agents.
- Phase 3: Checkpoint compression. At configurable intervals, Super Brain compresses the accumulated context using H0C format. This ensures that downstream agents receive a dense, relevant context package rather than the raw output of every prior step. Compression achieves 10-12x token reduction while preserving all critical decision information.
- Phase 4: Prefetch anticipation. Based on the workflow definition and historical patterns, Super Brain prefetches decisions that the next agent is likely to need. This reduces retrieval latency and ensures agents start each step with maximum relevant context.
Session Management
Each Super Brain session is identified by a unique session ID and can span minutes or days. Sessions support branching (parallel agent workflows), merging (combining results from parallel tracks), and rollback (reverting to a prior checkpoint if a step produces poor results). Session state is persisted in PostgreSQL, so workflows survive process restarts and can be resumed at any checkpoint.
Why This Matters
Without Super Brain, multi-agent workflows either pass raw context (token explosion) or lose context between steps (amnesia). Super Brain solves both problems by acting as the persistent memory backbone for the entire workflow. Early adopters report 3-5x reduction in total token usage for multi-step workflows and significantly more coherent outputs from downstream agents.