How namangoyal88/multi-agent-team works
This local-first research application turns a question into a planned, evidence-backed research package. A Streamlit user submits and monitors a job through the API; the workflow asks the NVIDIA-backed Director to plan optional stages, retrieves literature and invokes Groq specialists, optionally runs a restricted experiment, then performs mandatory verification, reporting, and evaluation with optional evidence refinement. SQLite-backed workspace and memory retain job data and prior research. The README documents the major stages and integrations; some wiring is inferred from that description because workflow and API excerpts are partial.
Main entry points: workflow.py LangGraph workflow · workspace.py Research workspace · api.py Research API · api_client.py API client
Components 22
User interface
- Research dashboard
frontend/streamlit_app.py - API client
frontend/api_client.py
Job orchestration
- Research API
research_team/api.py - Job management
research_team/jobs.py - Service wiring
research_team/services.py - LangGraph workflow
research_team/workflow.py
Research execution
- Deep Research Director
research_team/agents/deep_director.py - Specialist runner
research_team/agents/runner.py - Literature retrieval
research_team/tools/toolkit.py - Search adapters
research_team/tools/adapters.py - Experiment sandbox
research_team/experiments/sandbox.py
Evidence and storage
- Claim verification
research_team/verification.py - Report and evaluation
research_team/reporting.py - Research workspace
research_team/workspace.py - Research memory
research_team/memory.py - SQLite database
data/research_team.db
Models and tools
- Model router
research_team/llm/router.py - Chat client
research_team/llm/client.py
Other components
- Research user
- NVIDIA model service
- Groq model service
- Research source APIs
Connections 29
- Research user → Research dashboard: submits question
- Research dashboard → API client: requests research
- API client → Research API: calls endpoints
- Research API → Job management: manages jobs
- Job management → LangGraph workflow: runs workflow
- Research API → Service wiring: builds services
- LangGraph workflow → Deep Research Director: requests plan
- LangGraph workflow → Literature retrieval: retrieves evidence
- Literature retrieval → Search adapters: dispatches searches
- Search adapters → Research source APIs: queries sources
- LangGraph workflow → Specialist runner: runs stages
- Specialist runner → Model router: requests model call
- Deep Research Director → NVIDIA model service: plans and delegates
- Deep Research Director → Literature retrieval: uses search tools
- Model router → Chat client: routes calls
- Chat client → Groq model service: sends chat requests
- Chat client → NVIDIA model service: sends chat requests
- Specialist runner → Research workspace: records agent runs
- LangGraph workflow → Experiment sandbox: runs optional experiments
- LangGraph workflow → Claim verification: verifies claims
- LangGraph workflow → Report and evaluation: generates report and evaluation
- LangGraph workflow → Research workspace: stores job artifacts
- LangGraph workflow → Research memory: recalls and updates
- Research workspace → SQLite database: reads and writes
- Research memory → Research workspace: uses persistence
- Claim verification → Research workspace: reads papers
- Report and evaluation → Research workspace: uses research results
- Research API → Research workspace: serves research data
- API client → Research API: fetches run views