Synthesize multi-source research
Multi-Agent Research
Plan -> execute parallel subagents -> synthesize findings. Deep research at machine speed with parallel pipeline execution.
Key Capabilities
Planning Mode
LLM generates a subtask plan with dependencies. Independent tasks run in parallel, dependent tasks wait for prerequisites.
Parallel Subagents
Each subtask dispatches to a specialized agent module. Supervisor strategy coordinates, parallel strategy maximizes throughput.
Automated Synthesis
After all subtasks complete, a synthesis module combines findings into a structured research report with citations.
Agent-as-Tool
Any flow can be invoked as a tool during research. The LLM decides when to call specialized flows for deeper analysis.
Memory Across Sessions
Long-term memory layers persist findings across research sessions. Build cumulative knowledge.
Time-Travel Debug
Checkpoint every step. Fork from any point to explore alternative research paths without re-running the full pipeline.
How It Works
Define Research Goal
Describe what you want to research. The planner decomposes it into a structured plan of subtasks with dependencies.
Agents Execute
Subagents run in parallel where possible, respecting dependency edges. Each returns structured findings.
Synthesize Results
A synthesis module combines all findings into a coherent report with source attribution and confidence scores.
Iterate & Refine
Fork from any checkpoint, adjust parameters, or add follow-up questions. Memory persists across iterations.
Technical Stack
Ready to build production
Self-host in minutes with Docker, or use the cloud. Either way, you own your data and your models.