OpenAI Assistants vs AutoGen vs CrewAI: Tool Use and Memory Benchmarks

AI agents are no longer evaluated only on reasoning quality. In real enterprise workflows, the true benchmark is whether an agent can use the right tool with the right parameters while remembering the right context.

Recent research shows that most real-world agent failures happen not during text generation, but during tool execution and memory grounding.

A 2026 agent benchmark found that over 90% of tool-based tasks require active memory to pass the correct arguments, not just recall facts. Memory is directly tied to tool accuracy — not a separate capability.

In this blog, we evaluate OpenAI Assistants (Agents SDK), Microsoft AutoGen, and CrewAI on:

• Tool selection reliability
• Tool safety and validation
• Short-term and long-term memory
• Multi-agent memory coordination
• Production observability

Why Tool Use and Memory Is Important

Modern agents must:

• Call APIs
• Query databases
• Trigger workflows
• Use past context to fill parameters

If memory is weak, tool calls fail. If tool validation is weak, security risks increase.

Research on agent memory security shows that memory poisoning can manipulate future tool decisions if past records are not validated, creating a self-reinforcing error loop.

Frameworks combining memory management, guardrails, and structured tool calling perform better in production.

OpenAI Assistants (Agents SDK): Deterministic Tool Use with Managed Memory

OpenAI’s Agents SDK is designed for production reliability.

It provides:

• Structured function tools with automatic JSON schema validation
• Tool guardrails that block execution before and after calls
• Built-in session memory tracking conversation state
• Long-term structured state memory
• Native tracing for full auditability

Tracing logs every tool call, guardrail check, and handoff, enabling debugging and compliance auditing.

The tight integration between structured memory and tool grounding leads to more deterministic tool execution.

AutoGen

AutoGen excels in multi-agent reasoning and collaboration.

Agents communicate, critique each other, and dynamically decide which tool to use. This is powerful for research and planning workflows.

However, tool use is usually:

• Prompt-driven rather than schema-validated
• Dependent on conversational reasoning
• Validated through custom code

This increases flexibility but also increases hallucination risk if prompts are not tightly controlled.

Memory is not native. Developers integrate vector databases, logs, and custom memory routers. When implemented properly, shared memory becomes powerful — but engineering complexity increases significantly.

CrewAI

CrewAI focuses on role-based teams such as:

• Researcher
• Planner
• Executor

Tool use is mapped to roles and executed sequentially, making it suitable for content pipelines and linear workflows.

However:

• Tool selection is less dynamic
• Validation is prompt-based
• Memory is typically external

CrewAI is faster to prototype but weaker in memory-grounded tool reasoning.

Tool Safety and Misuse

A multi-agent security study found:

• AutoGen agents refused malicious tool requests ~52% of the time
• CrewAI agents refused ~31% of the time
• More than half of attack prompts successfully triggered tool execution

Structured validation and guardrails significantly reduce misuse risk.

Memory as a Tool Accuracy Multiplier

Memory directly affects whether an agent can:

• Fill API parameters correctly
• Retrieve the correct document
• Continue multi-step workflows

Agents often fail when required to use stored context for tool selection — even if they recall information in text form.

OpenAI’s structured state memory improves grounding because:

• Only validated outputs are stored
• Memory is injected as structured data
• Tool calls are grounded in persistent state

AutoGen can replicate this with custom pipelines. CrewAI treats memory more as retrieval than structured planning.

Multi-Agent Memory Coordination

AutoGen remains strongest for dynamic multi-agent collaboration:

• Debate and critique loops
• Shared intermediate reasoning
• Flexible planning chains

OpenAI Assistants support handoffs, but shared memory typically uses structured retrieval.

CrewAI shares outputs sequentially between roles, which is simpler but less adaptive.

Observability

Production systems require traceability.

OpenAI’s tracing records:

• Tool calls
• Guardrail checks
• Inputs and outputs
• Agent handoffs

AutoGen and CrewAI generally require external logging stacks to achieve similar visibility.

In regulated industries, built-in observability becomes a decisive factor.

Security Implications of Memory

Memory introduces a new attack surface.

Malicious memory entries can influence future decisions and reinforce incorrect tool usage.

Hierarchical memory architectures that isolate tool outputs from core reasoning reduce attack success significantly.

Memory architecture is now a core benchmark metric.

Conclusion

OpenAI Assistants lead in:

• Deterministic tool execution
• Built-in memory management
• Guardrails and validation
• Native tracing

AutoGen leads in:

• Multi-agent collaboration
• Dynamic planning
• Custom research-grade architectures

CrewAI leads in:

• Fast role-based pipelines
• Simpler orchestration

The key benchmark insight: tool accuracy is now fundamentally a memory problem. Agents that cannot ground tool calls in validated, structured memory will fail in production workflows, regardless of reasoning quality.