Agentsop Output Format By Model

Output-Format-by-Model — 让格式服务于任务，而不是反过来

> One-liner: Different output formats carry different cognitive load for the model. Code-in-JSON is the canonical proof: the same model writes worse code when wrapped in a JSON tool-call than when emitted as plain text + diff. The reverse failure (asking for prose when you need a typed object) is just as common. Pick format per (task × consumer), not by reflex.

1. 何时激活 (When to activate)

Activate this skill before committing to an output schema in any of these situations:

| Trigger | Signal |

|---|---|

| Designing a coder-agent | "should the model return a apply_patch tool call or plain-text diff?" |

| Adding a tool to an existing agent | "tool input has a code / query / sql / regex field — should I nest it in JSON or leave it as a string?" |

| Building extraction / classification | "should I use dspy.Predict typed fields, Pydantic + response_format=json_schema, or just markdown?" |

| Wiring an evaluator | "the metric needs a number — but the model also has to *reason* to produce it" |

| Migrating a working prompt to "structured outputs" | someone said "let's make it safer with JSON schema" |

| Tool-call harness adds latency / errors | repeated json.JSONDecodeError, escaping bugs, truncated outputs |

Anti-triggers (skip this skill):

• The format is fixed by an external API (e.g. you must return OpenAI function-call JSON — no choice).
• One-shot exploratory prompting where no consumer parses the output yet.
• The task is *itself* about JSON (e.g. "fix this malformed JSON") — see §6.

2. 核心心智模型 (Core mental model)

2.1 Three-layer claim

            ┌──────────────────────────────────────────────────┐
            │ FORMAT FOLLOWS FUNCTION                          │
            │                                                  │
            │  Some formats add cognitive load to the model    │
            │  and measurably degrade quality on the           │
            │  *content* the format is supposed to wrap.       │
            └──────────────────────────────────────────────────┘
                         ▲                          ▲
                         │                          │
         What's being consumed?       Who consumes it?
         (code? prose? entities?      (human reader? parser?
          number? action selection?)   downstream LM? compiler?)
                         │                          │
                         └──────────┬───────────────┘
                                    ▼
                       FORMAT SELECTION
                  (text+diff | markdown | JSON | tool_use
                   | grammar-constrained | typed field)

Aider's code-in-json benchmark is the load-bearing empirical anchor:

• GPT-4 Turbo refactor benchmark with unified diff: 61% pass.
• Same model with JSON-wrapped code: 20% pass.
• That is a 3× swing from format alone, not from model choice or prompt rewording. [aider.chat/2023/12/21/unified-diffs.html, aider.chat/2024/08/14/code-in-json.html]
• The pattern holds across every major model tested in 2024 (Claude Sonnet, DeepSeek Coder, GPT-4o). Even with OpenAI strict-mode JSON validity enforced, the code *inside* the JSON degraded — more SyntaxError / IndentationError. Sonnet kept syntax clean but still scored lower overall. [aider.chat/2024/08/14/code-in-json.html]

Academic generalization, same year:

• "Let Me Speak Freely?" (arXiv 2408.02442) — format-restricted reasoning drops 10–15% on math and complex analysis vs free-form-then-convert. [arxiv.org/abs/2408.02442]
• StructEval (arXiv 2505.20139) — even o1-mini only scores 75.58 average on structural-output generation. [arxiv.org/html/2505.20139v1]

2.2 The two reflexes to unlearn

| Reflex | When it's wrong |

|---|---|

| "Structured output is always safer." | False for code, multi-step reasoning, free-form prose. Strictness ≠ quality of contents. |

| "Markdown is only for humans." | False — markdown is *also* the highest-fidelity wire format for many LM-to-LM hand-offs (Aider uses it; DSPy's default chat adaptor uses field-marked markdown over JSON for many signatures). |

2.3 Mental model: format as a *tax* on the generation surface

Every formatting requirement consumes some of the model's attention budget. The tax is:

• Low for formats the model has seen millions of times in pretraining (markdown, unified diff, Python).
• Medium for JSON containing simple scalar fields (entity extraction, dates, booleans).
• High for JSON containing code, multi-line strings with escapes, or deeply nested reasoning. Each \n becomes \\n; each quote becomes \"; the model has to track this *while also* solving the actual problem.

⇒ Heuristic: the more semantically dense the content, the cheaper the format must be.

3. SOP 工作流 (Decision workflow)

Three questions, in order:

Step 1 — What's being consumed?

| Content type | Default format | Why |

|---|---|---|

| Source code edits | plain text + diff format (SEARCH/REPLACE, unified diff, or str_replace tool with code as a *single string field*) | Aider 20%→61% on GPT-4 Turbo; same direction across all models. [aider.chat/2024/08/14/code-in-json.html] |

| Source code (full file rewrite) | plain text or markdown fenced block | Same reason. JSON-wrap adds escaping tax. |

| Structured data extraction (entities, dates, IDs, classifications) | JSON / Pydantic / typed OutputField | Schema *helps* here — fields are the task. |

| Action selection (which tool to call) | JSON tool_use | Tool name + scalar args. The decision is structured by definition. |

| Action *body* (SQL, regex, code, file contents) | single string field inside tool_use — do not sub-structure | Same code-in-JSON penalty applies to any code-shaped payload. |

| Reasoning / intermediate steps | markdown or Python-style scratchpad | CoT in JSON measurably degrades; see "Let Me Speak Freely?" [arxiv.org/abs/2408.02442] |

| Numeric answer with reasoning | markdown reasoning + final answer in fenced block or final-line convention | Don't force the reasoning into a JSON reasoning field — it shortens and stiffens. |

| Free-form prose (summary, explanation, customer reply) | markdown | Native to instruction-tuned models. |

| Mixed (e.g., extract entities AND rewrite the document) | split into two calls or two passes — see §5 Case B | One format can't serve two contents well. |

Step 2 — Who consumes the output?

| Consumer | Constraint | Implication |

|---|---|---|

| Human in a chat UI | Render-friendly | Markdown wins. JSON is hostile. |

| json.loads / Pydantic parser | Must be valid | JSON with schema, or a known-safe envelope (<result>...</result>) with markdown body. |

| Downstream LM (LM-to-LM pipeline) | Reads what was written | Markdown is *more* robust than JSON when content includes code/math; the next LM ingests it natively. |

| Compiler / interpreter (PoT, code-exec sandbox) | Must be a valid program | Output as code in a fenced block, not as a JSON program field. |

| Tool dispatcher (function calling) | Needs tool name + args | JSON tool_use with scalar args; multi-line bodies go in a single string field. |

| Diff applier (Aider, git apply, str_replace) | Must apply cleanly | Format dictated by the applier: SEARCH/REPLACE for Aider diff, unified diff for git, str_replace for Anthropic editor tool. |

Step 3 — Which model am I targeting?

Format support is model-specific. Aider maintains a per-model edit-format default precisely because of this. [aider.chat/docs/more/edit-formats.html]

| Model family | Best code-edit format | Notes |

|---|---|---|

| GPT-4 Turbo / GPT-4o | udiff or SEARCH/REPLACE diff | 20%→61% with udiff on refactor. [aider.chat/2023/12/21/unified-diffs.html] |

| Claude 3.5/3.7 Sonnet | SEARCH/REPLACE diff | Tendency to write *too much* — instruct minimal blocks. [aider.chat/2024/07/01/sonnet-not-lazy.html] |

| Gemini family | diff-fenced | Path inside the fence. [aider.chat/docs/more/edit-formats.html] |

| GPT-4.1 / OpenAI patch tool | patch protocol | OpenAI-specific, multi-action robust. |

| Weak / local models (Llama-3-8B class, GPT-3.5) | whole file rewrite | Diff parsing failures dominate; whole-file is dumb-but-stable. |

| Reasoning models (o1, o3) as architect | architect mode: reasoner emits prose plan → editor model emits diff | o1-preview alone: 79.7%; o1-preview + Sonnet editor: 82.7%. [aider.chat/2024/09/26/architect.html] |

Combined decision tree:

                    What's being emitted?
                            │
        ┌───────────────────┼───────────────────┐
        │                   │                   │
      code              structured           free-form
      edits              data fields          prose/reasoning
        │                   │                   │
        ▼                   ▼                   ▼
   diff/SEARCH-       JSON / Pydantic /    markdown
   REPLACE/           tool_use scalar
   str_replace,       args
   code as string
        │                   │                   │
        ▼                   ▼                   ▼
   pick per-model   wrap any code-          do NOT
   edit format      shaped payload in        force into
   (Aider table)    a single string field    JSON schema

4. 操作模型 (Task → format pairs)

Ten concrete operations. Format choice is *not* arbitrary — each citation is the empirical anchor.

| # | Task | Recommended format | Rationale / Evidence |

|---|---|---|---|

| 1 | "Edit auth.py to use JWT" (coder-agent core loop) | Plain-text SEARCH/REPLACE diff in markdown fence | Aider's measured 3× on GPT-4 Turbo, generalizes across models. [aider.chat/2024/08/14/code-in-json.html] |

| 2 | "Apply this patch to a file via Claude's text-editor tool" | Anthropic str_replace tool — old_str / new_str as string fields, no JSON sub-structure inside the code | Matches Anthropic's published tool-design guidance: fields should be high-signal scalars, not low-level identifiers. [docs.anthropic.com text-editor-tool] |

| 3 | "Extract invoice fields (vendor, amount, date, line items)" | JSON / Pydantic / DSPy typed OutputField | Schema is the task. Structured-output benchmarks show high accuracy here. [arxiv.org/html/2505.20139v1] |

| 4 | "Classify ticket priority (P0/P1/P2/P3)" | Single-token output or JSON {"priority": "..."} | Trivial; structure helps determinism. |

| 5 | "Answer a math word problem" | Markdown reasoning + final answer in \boxed{} or fenced final line | "Let Me Speak Freely?" — 10–15% degradation when locked into JSON reasoning. [arxiv.org/abs/2408.02442] |

| 6 | "Decide which tool to call next" | JSON tool_use with tool name + scalar args | Action selection is *intrinsically* structured. |

| 7 | "Generate the SQL query for that tool" | Tool args contain {"sql": "SELECT ..."} — SQL as a raw single string, no further JSON sub-structure | Same family as code-in-JSON; SQL is code-shaped. |

| 8 | "Write a customer-support reply" | Markdown | Native to instruction tuning; JSON-wrap costs nothing useful. |

| 9 | "Summarize a paper and return key claims as a list" | Markdown prose + a fenced claims: YAML or JSON block at the end (two-zone output) | Best of both: prose flows freely, downstream parser reads the trailing block. |

| 10 | "Rewrite a long document AND extract entities" | Two passes: pass 1 rewrite (markdown), pass 2 extract (JSON, fed the pass-1 output) | One call cannot serve both contents at peak quality. |

5. 困境决策案例 (Dilemma cases / worked examples)

Case A — "I'm writing an extractor for invoice fields. Plain text or JSON?"

Trigger: New extraction pipeline. Fields = vendor_name, total_amount, invoice_date, line_items[].

Constraints:

• Output is consumed by a downstream Python parser.
• Some line items contain free-text descriptions with newlines and quotes.
• Pydantic validation is desired.

Decision steps:

• The content is structured data, not code. JSON tax is low here — fields are short scalars or short strings.
• Use Pydantic + OpenAI response_format=json_schema or DSPy typed OutputFields. Field names carry the semantic load (DSPy's "Signatures carry semantic load" principle [dspy.ai/learn/programming/signatures]).
• For line_items[].description containing free-text: still inside JSON — descriptions are *prose data*, not code. Escaping cost is real but bounded.
• Watch for: extraction-then-rewrite mission creep. If a future spec says "also rewrite the invoice text in cleaner language" → switch to Case B's two-pass approach.

Outcome: JSON wins. Schema validation catches missing fields cheaply; no measurable degradation expected on this content shape.

Case B — "Same task but the extractor *also* rewrites the invoice text"

Trigger: PM adds requirement — "and produce a cleaned-up version of the invoice document body."

Constraints:

• "Cleaned text" is multi-paragraph prose with embedded numbers, possibly tables.
• Putting cleaned_text: "..." inside the same JSON forces multi-paragraph escaping and competes with the extraction reasoning for attention.

Decision steps:

• Recognize this as the mixed-content trap. One LM call cannot serve both contents at peak.
• Two solutions, choose by latency budget:
• Two-pass (preferred): Call 1 returns {vendor, amount, date, line_items} as JSON. Call 2 receives the original doc + extracted JSON, returns markdown rewrite.
• Two-zone single call: Prompt requests markdown rewrite first, then ===EXTRACTION=== separator, then a fenced JSON block. Parser splits on the separator. Cheaper but more fragile.
• Do not nest the markdown rewrite as a JSON string field. The model will compress it and lose structure.

Outcome: Plain text + JSON split, not unified JSON. Format follows content, even within one logical task.

Case C — "Tool call decision, but the tool's input is SQL"

Trigger: Building a data-analyst agent. One tool is run_query(sql: str).

Constraints:

• The agent must decide *which* tool to call (structured choice).
• The agent must *write SQL* (code-shaped content).
• Provider is OpenAI / Anthropic tool_use.

Decision steps:

• The tool-selection layer is intrinsically structured — use JSON tool_use. The model picks run_query vs read_file vs list_tables.
• The tool body is code. Per Aider's findings, code in JSON degrades. Mitigations:
• Make sql a single top-level string field. Don't sub-structure it ({from: ..., where: ...}). Let the model write idiomatic SQL.
• Keep other args as scalars (e.g. dry_run: bool).
• Expect *some* quality hit vs returning SQL as plain text; budget for retry/repair.
• If quality is unacceptable: switch the architecture. Generate SQL in a *separate* plain-text LM call (markdown fenced `sql block), then a thin downstream agent wraps it into the tool call. This is the Aider "architect" pattern transplanted to data tools. [aider.chat/2024/09/26/architect.html]

Outcome: JSON for the decision; single string for the SQL body; consider a two-step generate-then-dispatch if quality matters.

Case D — "Reviewer says 'use structured output everywhere for safety'"

Trigger: Eng-org-wide push to add response_format=json_schema to every LM call.

Constraints:

• The org has been bitten by json.loads failures.
• Some calls produce code; some produce prose; some produce typed objects.
• The reviewer is conflating "schema-valid" with "quality."

Decision steps:

• Surface the empirical finding: JSON validity ≠ content quality. Cite Aider's 20%→61% (unified diff vs JSON) [aider.chat/2024/08/14/code-in-json.html] and "Let Me Speak Freely?" [arxiv.org/abs/2408.02442].
• Audit each call by content type using §3 Step 1 table.
• For code-emitting calls: keep markdown; add a defensive parser (regex extract from fenced block). The robustness cost is small; the quality gain is large.
• For typed-object calls: yes, add json_schema. This is where structured output earns its keep.
• Document the policy: "Structured output is mandatory for typed-data extraction; forbidden for code edits and multi-step reasoning; optional for everything else."

Outcome: Reject the blanket policy; replace it with a content-type-driven one. This is the inverse of the "JSON everywhere" reflex.

6. 反模式与边界 (Anti-patterns & boundaries)

Anti-patterns

• "JSON everywhere" reflex. Structured ≠ better. Code, multi-step reasoning, long-form prose all degrade inside JSON envelopes — Aider measured 3× on code; arXiv 2408.02442 measured 10–15% on reasoning. Use JSON where the *content itself is structured*.
• "Markdown only when a human reads it" reflex. Markdown is also an excellent LM-to-LM wire format and often the highest-quality format for the producing model. DSPy's default chat adaptor uses field-marked markdown for many signatures even with no human in the loop. [dspy.ai/learn/programming/signatures/]
• Wrapping code in JSON tool calls without budgeting quality loss. If your harness *requires* tool calls, fine — but acknowledge the tax and put the code in a single string field. Don't sub-structure it into {language, lines: [...], imports: [...]}. Every level of nesting compounds the escape tax.
• Forcing reasoning into a reasoning JSON field. Models compress and stiffen when reasoning is inside JSON — they treat it as a label rather than as thinking. Prefer free markdown reasoning followed by a structured tail.
• Using JSON schema as a substitute for prompt engineering. Schema enforces *shape*. It does nothing for *correctness of contents*. Write the prompt as if there were no schema, then add the schema for safety.
• Asking for typed data as freeform prose, then post-hoc parsing with regex. The inverse mistake. If the consumer is a parser and the fields are scalars, use a schema — the model is happy to oblige.
• Mixing code and prose in one JSON document. Use two-zone output (prose followed by fenced block) or two passes.
• Ignoring per-model edit-format defaults. Aider's defaults exist because the same edit format performs differently across models. Don't override without an A/B. [aider.chat/docs/more/edit-formats.html]
• Treating tool-use schemas as documentation-free zones. Anthropic's tool-writing guide emphasizes high-signal field names (name, file_type) over technical identifiers (uuid, mime_type). The model reads your schema as part of its prompt. [anthropic.com/engineering/writing-tools-for-agents]

10. "The task is fixing JSON, so format doesn't matter." Counter-edge case: when the *content itself is JSON* (e.g. "fix this malformed JSON"), output it as a fenced `json block in markdown — not as a JSON tool-call wrapping JSON-as-string. Even here, the rule holds: code-shaped content goes in a plain string surface, not nested escapes. [arxiv.org/html/2510.04717v1 — JSON Whisperer]

Boundaries (when format choice doesn't matter much)

• The model is overwhelmingly capable for the task (GPT-5-class on entity extraction with 4 scalar fields). The format tax is dwarfed by headroom.
• The output is one token / one number / yes-no. Any format works; pick by parser convenience.
• The pipeline is one-shot exploration with no downstream consumer.
• You're forced into a format by an external contract (e.g. publishing to a webhook expecting JSON). Then your job is mitigation (single string fields for code-shaped payloads), not format choice.

7. 跨框架对照 (Ecosystem cross-reference)

How major frameworks implement format selection. Use this to translate the principles into the stack you're already in.

Aider edit formats [aider.chat/docs/more/edit-formats.html]

| Format | Wire shape | When |

|---|---|---|

| whole | Full file in markdown fence | Weak models, fallback |

| diff (SEARCH/REPLACE) | Two fences per edit, byte-exact match | GPT-4o, Sonnet, most strong models — default |

| diff-fenced | Path inside fence | Gemini |

| udiff | GNU unified-diff style | GPT-4 Turbo — the empirical anchor: 20%→61% |

| patch | OpenAI patch protocol | GPT-4.1 |

| editor-diff / editor-whole | Slim prompt for sub-model | Architect mode editor |

No JSON-wrapped edit format exists — it was tested and rejected. [aider.chat/2024/08/14/code-in-json.html]

DSPy adaptors [dspy.ai/learn/programming/signatures/]

DSPy's Signature → Module pipeline ships with multiple adaptors that materialize the same logical I/O contract into different wire formats:

• ChatAdapter (default): field-marked markdown with [[ ## field_name ## ]] headers. Used even for typed outputs because models reliably emit it.
• JSONAdapter: schema-enforced JSON. Used when the consumer must parse strictly (e.g., feeding another typed pipeline).
• TwoStepAdapter: free-form generation, then a second cheaper call reformats into JSON. Direct application of the "Let Me Speak Freely?" finding — generate freely, format separately. [arxiv.org/abs/2408.02442]

DSPy's recommendation: stay on ChatAdapter unless a downstream consumer demands JSON. The framework itself encodes the principle of this skill.

Outlines / Guidance / LMQL (grammar libs)

• Token-level grammar enforcement (regex, CFG, JSON schema). Guarantees *validity*, not *quality of content*.
• Best used for: forced enums, numeric ranges, valid JSON shells around scalar fields.
• Worst used for: forcing code or long prose through a JSON grammar — you'll get valid JSON containing degraded code.
• Pair with this skill: choose format using §3, then use Outlines to *enforce* whichever format you chose.

OpenAI tool_use / function calling

• Tool selection: structured by definition. Use it.
• Tool arguments: keep scalar where possible; wrap code-shaped payloads in a single string field.
• response_format=json_schema for typed extraction: yes. For code generation: avoid; if forced, single string field with code as the type.
• "Strict mode" (strict: true) guarantees schema validity but does not fix content degradation — Aider tested this explicitly. [aider.chat/2024/08/14/code-in-json.html]

Anthropic tool_use / text-editor tool [docs.anthropic.com text-editor-tool]

• Built-in text_editor tool: commands view, str_replace, create, insert, undo_edit. str_replace takes old_str and new_str as single string fields — explicitly avoiding the JSON-nests-code anti-pattern. This is the principle of this skill made into a first-party API.
• Tool-writing guidance: high-signal scalar field names; avoid low-level identifiers in tool schemas. [anthropic.com/engineering/writing-tools-for-agents]

Cross-framework summary

Layer                  | Format mechanism
-----------------------|-----------------------------------
Generation grammar     | Outlines / Guidance — enforce
Per-task adaptor       | DSPy ChatAdapter vs JSONAdapter
Per-model edit format  | Aider's edit-format table
Tool envelope          | OpenAI / Anthropic tool_use
Wire serialization     | markdown vs JSON vs YAML

Every layer can independently make a wrong format choice. This skill operates at the per-task layer: decide first *what format the content wants*, then let each lower layer enforce it.

Quick reference card

┌──────────────────────────────────────────────────────────────────────┐
│                        FORMAT DECISION CARD                          │
├──────────────────────────────────────────────────────────────────────┤
│ Code edits          → text + diff (per-model: udiff/SEARCH/whole)    │
│                       [Aider: 20% → 61% on GPT-4 Turbo]              │
│ Code generation     → markdown fenced block                          │
│ SQL / regex / shell → string field inside tool_use, NOT sub-JSON     │
│ Entity extraction   → JSON / Pydantic / typed OutputField            │
│ Classification      → single token or JSON {label}                   │
│ Action selection    → JSON tool_use                                  │
│ Reasoning + answer  → markdown CoT + fenced final answer             │
│                       [Format-restricted reasoning: -10 to -15%]     │
│ Prose / explanation → markdown                                       │
│ Mixed content       → two passes OR two-zone output                  │
├──────────────────────────────────────────────────────────────────────┤
│ NEVER:                                                               │
│  • Nest code inside JSON sub-structure                               │
│  • Force CoT reasoning into a JSON "reasoning" field                 │
│  • Use schema as substitute for prompt engineering                   │
│  • Assume strict-mode JSON fixes content quality                     │
└──────────────────────────────────────────────────────────────────────┘

引用源 (Citations)

Primary empirical anchors:

• Aider code-in-JSON benchmark: [aider.chat/2024/08/14/code-in-json.html] — "All of the models did worse on the benchmark when asked to return code in a structured JSON response."
• Aider unified diff vs JSON: [aider.chat/2023/12/21/unified-diffs.html] — GPT-4 Turbo refactor 20% → 61% with udiff; "lazy comments" cut to 1/3.
• Aider edit-format docs: [aider.chat/docs/more/edit-formats.html]
• Aider architect mode: [aider.chat/2024/09/26/architect.html] — o1-preview alone 79.7% → o1-preview + Sonnet editor 82.7%.
• Aider Sonnet-not-lazy: [aider.chat/2024/07/01/sonnet-not-lazy.html]

Academic generalization:

• "Let Me Speak Freely?" — arXiv 2408.02442 [arxiv.org/abs/2408.02442] — Format restrictions cause 10–15% drop on reasoning tasks.
• StructEval — arXiv 2505.20139 [arxiv.org/html/2505.20139v1] — Even o1-mini 75.58 avg on structural-output generation.
• JSON Whisperer (JSON-editing) — arXiv 2510.04717 [arxiv.org/html/2510.04717v1]
• To Diff or Not to Diff (2025 edit-format study) — [arxiv.org/html/2604.27296]

Framework / API docs:

• DSPy Signatures + adaptors: [dspy.ai/learn/programming/signatures/]
• DSPy FAQ on structure vs free-form: [dspy.ai/faqs/]
• Anthropic text-editor tool: [docs.anthropic.com/en/docs/agents-and-tools/tool-use/text-editor-tool]
• Anthropic writing-tools-for-agents: [anthropic.com/engineering/writing-tools-for-agents]
• OpenAI structured outputs: [platform.openai.com/docs/guides/structured-outputs]

Companion skills in this collection:

• aider-sop-skill/SKILL.md — full edit-format treatment in coder-agent context.
• dspy-sop-skill/SKILL.md — adaptor selection within compiled programs.