Multi-step stateful task management

For complex LLM agent workflows involving loops, tool calls, conditional branching, retries, multi-agent coordination, and systematic evaluation, I'd start with LangGraph for orchestration and LangSmith or Arize Phoenix for evaluation and tracing.

The key distinction is that you need two capabilities: a framework that controls how the agent executes, and an evaluation system that measures whether the entire execution succeeded—not just whether the final answer looks good. LangGraph explicitly supports customizable agent workflows, while evaluation platforms can inspect execution traces, tool calls, and intermediate decisions.www.langchain.com+2

My top recommendations

1. LangGraph

(Best for complex orchestration)

Build stateful workflows as graphs, with cycles, conditional edges, checkpoints, human approval, and single- or multi-agent execution. It's a strong fit when your agent must repeatedly call tools, inspect results, recover from errors, and decide what to do next.www.langchain.com+2

Best paired with: LangSmith or Phoenix.

Official site and documentation

2. Pydantic AI + Pydantic Evals

(Best for Python-first testing)

A code-first approach to building agents and evaluating both final answers and execution trajectories, including which tools were called and their arguments. Useful if you want evaluation cases and scoring logic defined in Python and integrated into your test workflow.github.com+1

Agent framework ·Evaluation docs

3. Arize Phoenix

(Best open-source observability)

Trace agent execution, inspect tool calls and intermediate steps, run evaluations, annotate failures, and compare experiments. It's particularly attractive when you want an open-source, self-hostable evaluation and observability layer that isn't tied to one agent framework.arize.com+1

Official site

4. LangSmith

Best if you want an integrated development and evaluation platform for LangGraph-based agents. It supports offline datasets, online evaluations, human review, trajectory-level analysis, and regression checks in CI/CD.www.langchain.com+2

Official evaluation documentation

How I'd choose

Braintrust supports scoring individual agent steps and complete runs, while Langfuse provides hierarchical traces, datasets, experiments, and custom evaluations.www.braintrust.dev+1

What your evaluation framework should measure

For complex, iterative workflows, I'd build an evaluation suite around these six dimensions:

One important design choice: evaluate the execution trace as well as the final output. A workflow can produce the correct answer by luck after taking an expensive or unsafe path. Conversely, multiple different tool-call sequences may be valid ways to solve the same task. Your evaluators should distinguish acceptable alternative trajectories from actual failures.

My suggested starting architecture

Agent orchestration

LangGraph: state, loops, branches, tools, retries

Execution traces

Inputs, outputs, tool arguments, errors, timing, tokens

Evaluation harness

Deterministic assertions + task-specific scorers + LLM judges

Regression and optimization

Datasets, repeated runs, score comparisons, CI release gates

Start with a fixed set of representative tasks, record every run, score both task outcomes and execution traces, and turn failures into regression cases. Add online evaluation once the workflow is running in production.

One question to narrow this down

Are you primarily looking for a framework to implement the agent workflow, or a benchmarking/evaluation harness to test complex workflows you already have?

1. Main goal

Build and orchestrate agents

Evaluate and benchmark existing agents

Both: a complete development and evaluation stack

2. Preferred language

Python

TypeScript / JavaScript

Language-agnostic

3. Deployment preference

Open-source / self-hosted

Managed cloud is fine

Either

[Find the best-fit stack]

For the system you described—complex, multi-step, stateful agents that call many tools/APIs—I’d put LangGraph at the top of the shortlist, with Microsoft Agent Framework and OpenAI Agents SDK as strong alternatives depending on your stack.

My practical comparison

Why I'd choose LangGraph

The important distinction is that you don't really want an "agent framework" that simply lets an LLM call tools. You want an orchestration/runtime layer.

For example, your workflow might look like:

User request
     │
     ▼
┌─────────────┐
│ Planner     │
└──────┬──────┘
       │
       ├──────► Search APIs
       │
       ├──────► Database
       │
       ├──────► Specialist Agent
       │              │
       │              ▼
       │         External API
       │
       ▼
┌─────────────┐
│ Validator   │
└──────┬──────┘
       │
   failure?
    /     \
  yes      no
   │        │
 retry      ▼
   │     Human approval
   │        │
   └────┐   ▼
        └► Execute
             │
             ▼
          Persist
             │
             ▼
           Result

For something like this, explicit state + graph/workflow semantics + checkpointing + resumability become much more important than simply having good tool-calling abstractions.

One important 2026 development

Microsoft Agent Framework has become particularly interesting for this exact use case. Its workflow system explicitly supports graph-based execution, shared/run-scoped state, checkpoints, resuming, human-in-the-loop, fan-out/fan-in, and multiple multi-agent orchestration patterns.learn.microsoft.com

It can also run workflows on Durable Task infrastructure so execution can survive failures and resume across distributed workers.learn.microsoft.com

So if you're building an enterprise Azure/.NET/Python system, I'd seriously consider it alongside LangGraph.

Where OpenAI Agents SDK fits

The OpenAI Agents SDK is attractive if you're primarily using OpenAI models and want a comparatively lightweight Python/TypeScript abstraction. It provides tools, MCP, sessions, handoffs, agents-as-tools, guardrails, human-in-the-loop mechanisms, and tracing.openai.github.io

Its orchestration model deliberately lets you mix LLM-driven decisions with code-driven orchestration, which is a useful architecture for complex agents.openai.github.io

Its persistent sessions handle conversational state across runs, including resuming interrupted runs in the JS SDK.openai.github.io

I'd choose it when you want something like:

manager
   │
   ├── research_agent
   ├── coding_agent
   ├── database_tools
   ├── web_tools
   └── api_tools

without wanting to build a large explicit workflow engine.

My recommendation by architecture

Choose LangGraph if:

• workflows can run for minutes/hours/days
• you need checkpoints and recovery
• state is complicated
• there are conditional branches/loops
• multiple agents cooperate
• humans may intervene
• you want significant control over orchestration

Choose Microsoft Agent Framework if:

• you're heavily invested in Azure/.NET
• durable distributed execution is central
• you want explicit graph workflows
• enterprise hosting/operations matter

Choose OpenAI Agents SDK if:

• you're primarily using OpenAI
• Python/TypeScript is your preferred environment
• you want to get productive quickly
• most orchestration can remain relatively lightweight
• you want built-in tracing, sessions, MCP, handoffs, and guardrails

One architectural principle I'd strongly recommend regardless of framework: don't let the LLM control everything. Let the model decide where reasoning is useful, while deterministic code/workflows control things like retries, transactions, permissions, API calls with side effects, state transitions, and approval gates.

If you're building something genuinely complex, I'd start with LangGraph and evaluate Microsoft Agent Framework against it, rather than starting with a purely conversational agent framework.