(1) using a graph-based tool directly via createAgent (LangGraph) vs (2) spawning a CompiledSubAgent via DeepAgents' createSubAgentMiddleware for multi-tool search workflows — which approach is better for accuracy and speed?

OSS Product Help LangChain
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Hello everyone — I’d like to start a technical discussion and get practical experience reports.

Context / goal

I’m building an agentic workflow that performs multi-step searches across several specialized search tools. The flow must:

  • orchestrate multiple search tools,

  • validate and (if needed) retry or re-query,

  • maximize result accuracy, and

  • minimize latency.

I see two main approaches and want to understand trade-offs, implementation details, and which one practitioners recommend for production:

Approach A — Graph-as-agent (LangGraph compiled to a runnable)

  • Build the search/validation/retry pipeline as a LangGraph graph (nodes: model, tool calls, validators, loops).

  • Use LangGraph to produce a compiled graph (a runnable agent) that calls tools directly and runs the full orchestration inside the graph engine. LangGraph is explicitly designed to model agent workflows as graphs.

Approach B — Subagent via DeepAgents (createSubAgentMiddleware / CompiledSubAgent)

  • Use DeepAgents as the outer orchestration layer (deep agent), and register a custom LangGraph-based subagent (a CompiledSubAgent or equivalent) via DeepAgents’ subagent/subagent-middleware APIs.

  • The deep agent spawns the subagent for the search subtask, isolating context and leveraging DeepAgents’ middleware (filesystem, planning, subagent lifecycle, etc.). DeepAgents explicitly has SubAgentMiddleware and first-class subagent support.

What I’m specifically asking the community

  1. From a codebase / architecture and maintainability standpoint, what are the real pros and cons of A vs B?

  2. From a runtime performance / latency perspective (especially for multi-tool, multi-retry search flows), which approach tends to be faster and why?

  3. From an accuracy/reliability perspective (tool result validation, deterministic retries, rate-limiting and error handling), which approach gives better control?

  4. Any experience with the CompiledSubAgent API (availability, gotchas, versioning issues)?

  5. Concrete code-level examples or patterns (pseudocode or snippets) that demonstrate how you implemented the search → validate → retry loop in either approach would be extremely helpful.

  6. Recommendations: for a production system where (a) search accuracy is critical and (b) end-to-end latency must remain low under concurrent load, which approach would you recommend and why?

Notes / things I already considered

  • Graph-as-agent (LangGraph) feels like a natural match when the whole flow is a single, well-defined pipeline — you get a single compiled runnable and minimal orchestration overhead.

  • DeepAgents offers middleware (planning, filesystem, subagent lifecycle) and context quarantine via subagents, which can simplify complex systems and isolate failures, but may add orchestration overhead or extra IPC/hand-off work between the parent agent and subagent.

  • There are hints in repositories and community threads about CompiledSubAgent being added/removed or moved across releases — I want to avoid relying on unstable API surfaces.

What I’d appreciate from responders

  • Real-world trade-offs and benchmarks (latency numbers, throughput, memory/CPU usage if available).

  • Specific pitfalls (e.g., context duplication, token overhead, error propagation, debugging complexity).

Thanks in advance — I’ll compile answers and try any suggested patterns.