Deploy deep agents to LangSmith
Mantenedores costumam responder em até 1 dia
Avaliação
- Dificuldade
- 5/5
- Tempo estimado
- Mais de uma semana
- Facilidade para iniciantes
- 42/100
- Tipo de issue
- Funcionalidade
- Clareza
- Razoavelmente clara
- Status de atividade
- Pouca atividade
- Stack de tecnologia
- fastapi, next.js, python, typescript
- Domínio
- backend, cloud, devops, frontend, observability
Direção de pesquisa
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.
Escrita pelo modelo de indexação a partir do texto da issue.
Descrição
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→ exportsgraphobject - Config:
apps/agent/langgraph.jsonalready exists with graphsample_agentpointing to./main.py:graph - Dependencies: managed via
uv+pyproject.toml - Hosting: currently self-hosted via
uvicornon 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— theenvpath currently points to../../.env(relative to repo root), which may need adjustment for cloud deployment - Add
LANGCHAIN_API_KEY,LANGCHAIN_TRACING_V2=true, andLANGCHAIN_PROJECTto environment config - Ensure
OPENAI_API_KEYis set as a deployment secret in LangSmith - Verify
deepagentsand 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
- Linguagem predominante
- TypeScript
- Estrelas
- 3.9k
- Forks
- 414
- Merge médio
- 4h
- PRs com merge (30d)
- 6
Preparar o ambiente
- Sem Dockerfile nem arquivo Docker Compose
- Sem modelo de pull request
- Ler o guia de contribuição
Primeiros passos
- Leia a issue inteira e depois o guia de contribuição do projeto.
- Comente na issue dizendo que vai assumir — evita que duas pessoas façam o mesmo trabalho.
- Faça um fork do repositório e trabalhe em uma branch.
- Abra um pull request que referencie o número da issue.
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