A multi-agent LLM-powered system designed to conduct in-depth, structured research on user-defined topics and automatically generate comprehensive, well-organized reports. The system orchestrates specialized agents that plan research scope, gather information from public web sources, synthesize findings, critique intermediate outputs, and format final deliverables in professional Markdown or PDF format. The core innovation lies in its graph-based, stateful workflow that enables iterative refinement, self-correction, and transparent reasoning traces – addressing common shortcomings of single-pass LLM research tools such as shallow coverage, hallucinations, and lack of source attribution.
The Multi-Agent Research & Report Generator represents a shift from monolithic LLM query-response patterns toward orchestrated, agentic workflows capable of performing complex, multi-step cognitive tasks. By decomposing research into distinct phases handled by purpose-built agents, the system achieves higher factual accuracy, deeper analytical coverage, and structured output suitable for strategic decision-making. Built entirely on open-source components and runnable locally or via lightweight cloud-free deployment, the project serves as both a demonstrable prototype of contemporary agentic AI architecture and a foundation for extensible enterprise knowledge synthesis tools.
The system delivers measurable time compression for knowledge-intensive tasks that currently require hours or days of manual research. It provides consistent report structure, traceable sources, and built-in quality assurance loops while maintaining full data privacy through local execution options. The primary value lies in enabling small teams and individual professionals to produce research outputs comparable to those of dedicated analysts, thereby leveling access to high-quality market, competitive, technical, and strategic intelligence.
All data processing occurs client-side or on user-controlled infrastructure. No user queries or generated content are transmitted to third-party logging services by default. Web search interactions are limited to public APIs with no authentication. Future extensions will incorporate configurable content filters and output redaction capabilities to support domains with heightened compliance requirements. The architecture explicitly separates research retrieval from proprietary knowledge ingestion paths to facilitate regulated deployments.
As a user, I want to:
Establish an agentic research workflow that mirrors human expert research processes: scope definition, information gathering, synthesis, critical review, and polished delivery. The system must remain transparent, controllable, and extensible.
Core capabilities: topic scoping, web research, multi-perspective synthesis, quality assurance, structured reporting. Success metrics: report completeness, source attribution accuracy, user time saved, subjective quality rating.
Information flows through a persistent state object containing: original query, generated outline, search results, draft sections, critique feedback, final report, and metadata (sources, timestamps). All state transitions are logged for traceability.
MVP with five core agents, basic web search integration, Markdown report output, and local execution capability.
Program Increment structure:
Open-source development with clear contribution guidelines. Version pinning for reproducible behavior. Extensive documentation of agent prompts and decision logic to enable community extensions.
Query intake → Research planning → Information retrieval → Synthesis → Critical review → Report formatting → Delivery
Full trace preserved in state object and displayed optionally in UI. Each agent appends its rationale, confidence level, and references used. Enables post-hoc analysis and debugging.
Critic Agent scores draft sections across dimensions (accuracy, completeness, clarity, objectivity). Scores below threshold trigger targeted re-research or re-synthesis. Conflicts between agents resolved via predefined priority (Critic overrides Synthesizer, Planner overrides scope drift).
LangGraph as central orchestrator managing state transitions. Agents implemented as nodes with typed inputs/outputs. Tools (search, future database queries) exposed via LangChain tool-calling interface. All components loosely coupled for independent testing and replacement.
Hybrid retrieval strategy: web search for current events + optional user-provided document corpus ingested into local vector store. Query rewriting and hierarchical retrieval planned for improved precision.
Structured logging of agent invocations, token usage, latency, and decision scores. Exportable traces for analysis and iterative prompt improvement.
No external telemetry by default. All search queries anonymized through public APIs. Local model inference ensures no data exfiltration. Future: optional content filtering and PII redaction.
Transparent prompt and tool usage logging. Configurable guardrails for sensitive topics. Clear disclaimers regarding factual accuracy and source reliability.
Container health checks, graceful degradation on search failures, retry logic with exponential backoff, rate limiting on tool calls to prevent abuse.
Expected outcomes include:
This design establishes a robust, extensible framework for autonomous research capabilities while maintaining transparency, privacy, and alignment with responsible AI development principles.