LangGraph + Python Orchestration
We designed a graph-based agent runtime so each product could share tools, memory, and guardrails without copy-pasting prompt chains.
Architecture & workflows
How this product is built, deployed, and operated in production.
Delivery flow
End-to-end delivery flow for this project, from intake through the final handoff.
Challenge
Three apps needed multi-step reasoning, tool use, and retries, but early chains were brittle and hard to debug in production.
How we did it
Modeled each workflow as a LangGraph state machine in Python, with typed nodes for planning, tool calls, validation, and response formatting.
Outcome
Shared orchestration cut duplicate logic across apps and made failed steps retryable with clear traces for support teams.
Tech stack
Step-by-step
- 1
User intent
App sends goal + context to the orchestrator API
- 2
Planner node
LangGraph picks the next tool or reasoning step
- 3
Tool / RAG
Fetch data, call APIs, or query knowledge
- 4
Critic node
Validate output against schema and policy
- 5
Response
Return structured result to the mobile/web client
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