---
id: coding-agent-finish-oct26
slug: coding-agent-finish-reason-oct-2026
title: "Tool-call finish rate by coding agent, October 2026"
topic: agentic
period: Oct 2026
updated: 2026-11-11
license: CC BY 4.0
canonical: https://www.requesty.ai/data/coding-agent-finish-reason-oct-2026
---

# Tool-call finish rate by coding agent, October 2026

> How agentic is each coding agent? In October 2026 Roo Code ended 95% of calls with a tool call, the highest share. Cline was lowest at 45%.

*Topic: Agentic workloads. Period: Oct 2026. Last updated 2026-11-11.*

## Why it matters

A high tool_calls share means the agent runs long loops of edits and commands. A higher stop share means more single-turn answers.

## Questions this answers

- Which coding agent makes the most tool calls?
- How often do coding agent calls end in a tool call?
- Which agents favor single-turn completions?

## Key findings

1. Roo Code: 95% tool_calls.
2. Zoo Code: 95% tool_calls.
3. GitHub Copilot: 89% tool_calls.
4. Cursor: 89% tool_calls.
5. Pi: 87% tool_calls.

## Data

| Agent | tool_calls rate (percent) |
| --- | --- |
| Roo Code | 95.35% |
| Zoo Code | 95.08% |
| GitHub Copilot | 89.26% |
| Cursor | 88.80% |
| Pi | 86.84% |
| Zed | 85.61% |
| OpenCode | 85.08% |
| Kilo Code | 81.11% |
| Hermes Agent | 76.00% |
| Qwen Code | 73.14% |
| Claude Code | 60.96% |
| Cline | 45.11% |

## Caveats

- Share of all calls, including failed calls with no finish_reason.

## Cite as

**APA.** Requesty (2026). Tool-call finish rate by coding agent, October 2026. Requesty Data. https://www.requesty.ai/data/coding-agent-finish-reason-oct-2026

```bibtex
@misc{requesty_coding_agent_finish_reason_oct_2026,
  author       = {{Requesty}},
  title        = {Tool-call finish rate by coding agent, October 2026},
  year         = {2026},
  howpublished = {\url{https://www.requesty.ai/data/coding-agent-finish-reason-oct-2026}},
  note         = {Requesty Data}
}
```

---

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