---
id: coding-agent-finish-apr26
slug: coding-agent-finish-reason-apr-2026
title: "Tool-call finish rate by coding agent, April 2026"
topic: agentic
period: Apr 2026
updated: 2026-05-16
license: CC BY 4.0
canonical: https://requesty.ai/data/coding-agent-finish-reason-apr-2026
---

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

> How do coding agent API calls end? In April 2026, Roo Code leads with 91% of calls finishing via tool_calls, the primary agentic pattern. Claude Code follows at 73%. Cline (81% stop) and Aider (87% stop) favor single-turn completions. Kilo Code shows 63% tool_calls and 28% stop, a balanced mix of agentic and single-turn patterns.

*Topic: Agentic workloads. Period: Apr 2026. Last updated 2026-05-16.*

## Why it matters

Finish reasons reveal fundamental architectural differences between coding agents. A high tool_calls rate indicates an agentic loop pattern where the model invokes tools (read files, write code, run tests) as part of a multi-step workflow. A high stop rate indicates single-turn generation. The industry-wide shift from stop-dominant to tool-call-dominant patterns over twelve months reflects the broader move toward autonomous coding workflows.

## Questions this answers

- Which coding agents use tool calls the most?
- How do coding agent API calls typically finish?
- What percentage of Claude Code calls end with tool calls?
- Are coding agents becoming more agentic over time?

## Key findings

1. Roo Code: 91% tool_calls. Transitioned from 100% stop-based (early 2025) to almost entirely tool-call-based.
2. Claude Code: 73% tool_calls, 20% stop. Heavy agentic loop with some single-turn reasoning calls.
3. Cline: 81% stop. Primarily single-turn completions rather than multi-step tool use.
4. Kilo Code: 63% tool_calls, 28% stop. A balanced approach between agentic and single-turn patterns.
5. The industry has shifted from stop-dominant to tool-call-dominant patterns over twelve months.

## Data

| Agent | tool_calls rate (percent) |
| --- | --- |
| Roo Code | 91.10% |
| OpenCode | 87.30% |
| Forge | 79.30% |
| Claude Code | 73.20% |
| Zed | 66.70% |
| Kilo Code | 62.50% |
| Cline | 16.00% |
| Aider | 12.40% |

## Caveats

- Finish reasons are reported by the upstream model provider. Interpretation varies slightly across providers.
- "empty/failed" includes both true failures and calls where the model returned an empty response.
- Agents with very low traffic (Cursor, GitHub Copilot, Codex CLI) are excluded.

## Cite as

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

```bibtex
@misc{requesty_coding_agent_finish_reason_apr_2026,
  author       = {{Requesty}},
  title        = {Tool-call finish rate by coding agent, April 2026},
  year         = {2026},
  howpublished = {\url{https://requesty.ai/data/coding-agent-finish-reason-apr-2026}},
  note         = {Requesty Data}
}
```

---

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