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Deploy deep agents to LangSmith

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Seit 06.4.2026.

  • #80 von @jerelvelarde — offen

Bewertung

Schwierigkeit
5/5
Geschätzter Aufwand
Über eine Woche
Anfängerfreundlichkeit
42/100
Issue-Typ
Feature
Klarheit
Größtenteils klar
Aktivitätsstatus
Ruhig
Tech-Stack
fastapi, next.js, python, typescript
Bereich
backend, cloud, devops, frontend, observability

Rechercherichtung

Start by reading apps/agent/main.py, apps/agent/langgraph.json, and pyproject.toml, then review the LangSmith deployment documentation. Trace how the frontend CopilotKit API route reaches the current FastAPI/Uvicorn service. Done means the agent deploys successfully, skills and dependencies work, frontend requests reach the cloud URL, traces appear in LangSmith, and state synchronization is verified.

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Beschreibung

Overview

Deploy the LangGraph deep agent (apps/agent) to LangSmith's managed cloud platform (formerly LangGraph Platform) for production hosting, observability, and tracing.

Background

The agent already uses the deepagents SDK (create_deep_agent()) built on LangGraph, and has a langgraph.json config in place. LangSmith Cloud provides managed deployment with built-in tracing, streaming, and persistence — replacing the current self-hosted FastAPI/Uvicorn setup.

Current State

  • Agent framework: LangGraph 1.0.7 + DeepAgents via create_deep_agent()
  • Entry point: apps/agent/main.py → exports graph object
  • Config: apps/agent/langgraph.json already exists with graph sample_agent pointing to ./main.py:graph
  • Dependencies: managed via uv + pyproject.toml
  • Hosting: currently self-hosted via uvicorn on port 8123
  • Checkpointing: in-memory BoundedMemorySaver (200 threads, no persistence across restarts)

What Needs to Happen

1. LangSmith Account & Deployment Setup
  • Ensure LangSmith Plus plan (or higher) is active — required for cloud deployments
  • Connect the GitHub repo to LangSmith via Deployments → + New Deployment
2. Configuration Updates
  • Review langgraph.json — the env path currently points to ../../.env (relative to repo root), which may need adjustment for cloud deployment
  • Add LANGCHAIN_API_KEY, LANGCHAIN_TRACING_V2=true, and LANGCHAIN_PROJECT to environment config
  • Ensure OPENAI_API_KEY is set as a deployment secret in LangSmith
  • Verify deepagents and all dependencies resolve correctly in the cloud build environment
3. Compatibility Considerations
  • CopilotKitMiddleware: Verify it works within LangGraph Platform's execution model (not running inside FastAPI directly)
  • BoundedMemorySaver: Cloud deployments provide managed persistence (Postgres-backed checkpointer) — evaluate whether to replace the custom in-memory saver
  • Skills directory: Confirm skills/ markdown files are included in the deployment bundle (they're loaded via file path at runtime)
  • ag-ui-langgraph endpoint: Determine if the FastAPI wrapper (add_langgraph_fastapi_endpoint) is still needed or if LangSmith handles this natively
4. Frontend Integration
  • Update the frontend CopilotKit API route to point to the LangSmith deployment URL instead of localhost:8123
  • Add deployment URL as an environment variable for the Next.js app
  • Test bidirectional agent state sync (todos) through the cloud-hosted agent
5. Observability
  • Confirm traces appear in LangSmith for all agent runs
  • Set up a LangSmith project for organizing traces
  • Verify tool call traces (query_data, plan_visualization, manage_todos, etc.) are captured

Risks & Open Questions

  • Cold start latency: LangSmith cloud deployments may have cold starts — measure impact on UX
  • Cost: Plus plan is $39/seat/mo + usage-based billing for agent runs and uptime
  • Skills file loading: The agent loads skills from Path(__file__).parent / "skills" — need to verify this resolves correctly in the containerized cloud environment
  • Rate limiting: Current app has optional IP-based rate limiting in FastAPI middleware — this won't carry over to LangSmith; determine if platform-level rate limiting is sufficient

References

Vorherrschende Sprache
TypeScript
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4 Std.
Gemergte PRs (30 T.)
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Entwicklungsumgebung

Erste Schritte

  1. Lesen Sie das ganze Issue und danach den Beitragsleitfaden des Projekts.
  2. Schreiben Sie ins Issue, dass Sie es übernehmen — das erspart doppelte Arbeit.
  3. Forken Sie das Repository und arbeiten Sie in einem Branch.
  4. Öffnen Sie einen Pull Request, der die Issue-Nummer nennt.

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