Deep Agent

Guide 6 introduced the agent loop — decide, act, observe, repeat. That loop is general: as long as you give it a set of typed tools, the model can reason about anything. This guide picks a specific useful shape for those tools: a workspace on disk. The result is a deep agent — an agent that treats a directory as its environment, reading and editing files and running Python inside a sandboxed, copy-on-write copy of it.

In other words: where FunctionCallingAgent calls a few narrow helpers (a calculator, a clock), a deep agent has a small but complete toolbox for working on code — the same shape of tools that drive AI coding assistants like Claude Code, Devin, Aider.

When Do You Want One?

The agent loop has the same four phases everywhere — think, decide, act, observe — but the act phase looks very different depending on what tools you give it:

A RAG agent acts by searching documents.
An SQL agent acts by writing SELECT queries.
A deep agent acts by changing the filesystem — reading source files, patching them, running them, looking at the output.

That makes deep agents a fit for tasks where the answer cannot be expressed in a single output: it has to exist somewhere in a workspace by the time the loop ends. Bug fixes, refactors, scaffolds, data-wrangling pipelines, exploratory code reviews — anything where "the result is some new state in the workspace" is the right ending.

The Six Tools

synalinks.DeepAgent wraps a FunctionCallingAgent and pre-wires six tools, all bound to a single working directory you supply:

Tool	Purpose
`list_files(pattern)`	List files matching a glob (e.g. `*/.py`).
`search_files(pattern, glob)`	Grep file contents by regex across files matching a glob. Returns `(path, line_number, line)` matches.
`read_file(path, offset, limit)`	Line-paginated file read. Output carries 1-based `start_line` / `end_line`, so the LM can cite line numbers later without re-reading.
`write_file(path, content)`	Create or overwrite a file (in the sandbox filesystem).
`edit_file(path, old, new)`	Replace an exact substring; rejects 0 or 2+ occurrences so the LM has to add surrounding context to disambiguate (pass `replace_all=True` to override).
`run_bash(command)`	Run a shell command against the sandbox filesystem — pipes, redirects, globs, `&&`, loops, and `python3` (a real interpreter with the full standard library, third-party packages, and network). Write a script with `write_file`, then run it with `python3 script.py`.

Two design choices are worth highlighting:

Line-paginated reads, line-numbered output. read_file does not return character-offset slices. It returns lines — with 1-based start_line / end_line markers. The model thinks in terms of lines, edits are usually targeted at a line, so the read tool's output should match. The line numbers also mean a model can read_file once and then edit_file referencing the exact text it just saw, without re-reading to confirm. Page through long files with offset / limit.
list_files globs, search_files greps. list_files answers "what files are there?" (a glob over paths); search_files answers "where does this pattern appear?" — a regex over file contents, restricted to a glob, returning (path, line_number, line) matches. Two narrow tools instead of one overloaded one keeps the LM's choice shallow: it picks by intent, not by guessing what an empty argument means.

The Security Model

Here is the single idea that makes the deep agent safe to point at a real directory: the tools operate on a sandboxed, copy-on-write copy of the workdir, never the workdir itself.

A copy-on-write filesystem, seeded from your workdir

DeepAgent mounts the workdir in a MirageSandbox. The agent sees your project as it is — read_file, list_files, search_files resolve against the mounted copy — but every mutation (write_file, edit_file, and anything run_bash writes) lands in the sandbox's copy-on-write filesystem, never on the real disk. The host workspace is never modified. (Paths are rooted at the workdir, so the agent stays inside the directory you mounted.)

That means there is no capability to gate: the agent cannot damage the host through its tools, because nothing it does is written back to disk. All six tools are therefore always available.

Code runs in an isolated subprocess, not on the host

run_bash does not shell out to your machine. It runs in Mirage — an isolated, confined CPython subprocess with the workdir mounted as the copy-on-write filesystem. The python3 it exposes is a real interpreter (full standard library, third-party packages, even network), so the agent can write and run ordinary scripts — but its writes stay in the sandbox filesystem, and confinement (seccomp, read-only host binds, an egress allowlist) bounds what it can reach. timeout (default 30s) bounds each execution.

The containment is structural — a sandboxed filesystem plus a confined subprocess — rather than a set of allow_write / allow_bash gates you could forget to turn on.

Inspecting and persisting what the agent did

Because changes live in the sandbox filesystem, you read them back through the sandbox rather than off disk. The DeepAgent instance exposes it as agent.sandbox:

agent.sandbox.changes() → {"written": [...], "deleted": [...]}, a summary of the final sandbox state.
agent.sandbox.diff() → per-path detail of every change ({"path", "kind", "size"}) against the seeded workdir.
await agent.sandbox.read_file(path) → {"content": ...}, the effective content for a path (sandbox value if written, else the seeded file).

If you want any of it on disk, persist it yourself from those reads.

Building the Agent

The constructor signature mirrors FunctionCallingAgent exactly — every parameter on that class is accepted with identical semantics. The additions are workspace-specific:

Param	Required	Default	Notes
`workdir`	yes	—	Must exist and be a directory. Mounted read-through in the sandbox; the agent's writes/edits stay in the overlay and never touch it. (Omit it for an empty in-memory workspace.)
`timeout`	no	`30.0`	Per-execution budget in seconds for `run_bash`.
`tools`	no	`None`	Extra `Tool` instances or async functions to append to the built-ins. Same name-collision and no-leading-underscore rules as `FunctionCallingAgent`.

import synalinks

lm = synalinks.LanguageModel(model="ollama/qwen3:8b")

inputs = synalinks.Input(data_model=synalinks.ChatMessages)
outputs = await synalinks.DeepAgent(
    workdir="/tmp/my_project",
    language_model=lm,
)(inputs)
agent = synalinks.Program(inputs=inputs, outputs=outputs)

You don't need a "read-only mode" to point this at a precious directory: the copy-on-write overlay already guarantees the real workdir is never modified, so even when the agent writes, edits, and runs code, your files on disk stay exactly as they were. To review what it would have changed, read agent.sandbox.changes() after the run.

A Worked Example

A small end-to-end task: scaffold a Python file, ask the agent to extend it, and verify the agent's change actually runs. Keep a reference to the DeepAgent instance — its sandbox is where the overlay changes live, so that is where you read the result (the file on disk is never touched).

import asyncio, os, tempfile
import synalinks

workdir = tempfile.mkdtemp(prefix="deep_demo_")
with open(os.path.join(workdir, "calculator.py"), "w") as f:
    f.write("def add(a, b):
    return a + b
")

lm = synalinks.LanguageModel(model="ollama/qwen3:8b")
inputs = synalinks.Input(data_model=synalinks.ChatMessages)
deep = synalinks.DeepAgent(
    workdir=workdir,
    language_model=lm,
    max_iterations=10,
)
outputs = await deep(inputs)
agent = synalinks.Program(inputs=inputs, outputs=outputs)

task = synalinks.ChatMessages(messages=[synalinks.ChatMessage(
    role="user",
    content=(
        "Open calculator.py, add a `multiply(a, b)` function and an "
        '`if __name__ == "__main__":` block that prints `multiply(6, 7)`, '
        "then use `run_bash` to run `python3 calculator.py` to confirm "
        "it prints 42."
    ),
)])
result = await agent(task)

# Changes live in the sandbox filesystem, not on disk — inspect via the sandbox:
print(deep.sandbox.changes())                       # {'written': ['/calculator.py'], ...}
print((await deep.sandbox.read_file("calculator.py"))["content"])

What the agent will typically do:

read_file("calculator.py") — see the current source, with line markers.
`edit_file("calculator.py", "def add(a, b): return a + b ", "def add(a, b): return a + b

def multiply(a, b): return a * b ")— or write the whole file withwrite_file. The model picks. 3.run_bash("python3 calculator.py")— run the file in the sandbox to verify it prints 42. (Run a script *file*: apython3 -c` snippet can't import the overlay's modules — only file execution sees them.) 4. Stop calling tools; produce the final assistant message.

If the verify step fails (syntax error, wrong number printed), the agent reads the result, edits again, re-runs. The iteration cap is the only thing that stops it from looping forever; in practice a correct change usually lands in 2-3 tool calls.

Compared to Other Agents

DeepAgent is one of several specialized agents that all wrap a FunctionCallingAgent with a workload-specific tool set:

Agent	Bound to	Tools
`FunctionCallingAgent`	nothing	whatever you pass in
`SQLAgent`	a `KnowledgeBase`	schema discovery, table sample, read-only SQL
`VectorRAGAgent`	a `KnowledgeBase`	schema discovery, semantic/keyword search, get-by-id
`DeepAgent`	a workdir	list, search, read, write, edit, run Python

All four accept the same FunctionCallingAgent parameters plus their domain-specific extras. The same tools= slot is available on all of them for layering extra tools on top of the built-ins.

When to pick which:

SQLAgent: data lives in a structured DB; the answer is a computation over rows.
VectorRAGAgent: data lives as documents; the answer is a synthesis grounded in retrieval.
DeepAgent: the answer is a change to a workspace, or a diagnosis that requires running code.
FunctionCallingAgent: you have a bespoke tool set that doesn't match any of the above.

API References

`populate_workspace(workdir)`

Seed the workdir with a tiny Python project for the agent to extend.

Source code in guides/22_deep_agent.py

def populate_workspace(workdir: str) -> None:
    """Seed the workdir with a tiny Python project for the agent to extend."""
    os.makedirs(workdir, exist_ok=True)
    with open(os.path.join(workdir, "calculator.py"), "w") as f:
        f.write('def add(a, b):\n    """Return a + b."""\n    return a + b\n')

Source

- [Tool](https://synalinks.github.io/synalinks/Synalinks%20API/Modules%20API/Core%20Modules/Tool%20module/)
- [ChatMessages (Base DataModels)](https://synalinks.github.io/synalinks/Synalinks%20API/Data%20Models%20API/The%20Base%20DataModels/)
"""

import asyncio
import os
import shutil
import tempfile

from dotenv import load_dotenv

import synalinks


def populate_workspace(workdir: str) -> None:
    """Seed the workdir with a tiny Python project for the agent to extend."""
    os.makedirs(workdir, exist_ok=True)
    with open(os.path.join(workdir, "calculator.py"), "w") as f:
        f.write('def add(a, b):\n    """Return a + b."""\n    return a + b\n')


async def main():
    load_dotenv()
    synalinks.clear_session()
    synalinks.enable_logging()

    workdir = tempfile.mkdtemp(prefix="deep_agent_guide_")
    try:
        populate_workspace(workdir)

        # The LM that drives the agent. Any function-calling-capable model
        # works; pick a small local model for fast iteration during dev.
        language_model = synalinks.LanguageModel(model="ollama/qwen3:8b")

        # Build the agent. The workdir is the only thing DeepAgent adds on
        # top of FunctionCallingAgent's signature; everything else is
        # forwarded with identical semantics.
        # Keep a reference to the DeepAgent: its `sandbox` holds the
        # copy-on-write overlay where the agent's edits land (the workdir
        # on disk is never modified), so that is where we read results.
        inputs = synalinks.Input(data_model=synalinks.ChatMessages)
        deep = synalinks.DeepAgent(
            workdir=workdir,
            language_model=language_model,
            max_iterations=10,  # coding tasks tend to need a few rounds
        )
        outputs = await deep(inputs)

        agent = synalinks.Program(
            inputs=inputs,
            outputs=outputs,
            name="deep_agent",
            description="A coding agent with sandboxed file and Python tools.",
        )
        agent.summary()

        # The task: read the file, add a function, verify by running the file.
        # (Run a script *file* with `python3 calculator.py` — that executes in
        # the sandbox filesystem. A `python3 -c` snippet cannot import the
        # overlay's modules, so the self-check lives inside the file itself.)
        task = synalinks.ChatMessages(
            messages=[
                synalinks.ChatMessage(
                    role="user",
                    content=(
                        "Open calculator.py and add a `multiply(a, b)` function "
                        "in the same style as `add`. Then add an "
                        '`if __name__ == "__main__":` block that prints '
                        "`multiply(6, 7)`, and use `run_bash` to run "
                        "`python3 calculator.py` to confirm it prints 42."
                    ),
                )
            ]
        )
        result = await agent(task)

        # The trajectory captures every tool call and its result; the final
        # assistant message is the model's natural-language answer.
        final = [m for m in result.get("messages", []) if m.get("role") == "assistant"]
        if final:
            print("Agent:", final[-1].get("content"))

        # The agent's edits live in the sandbox filesystem, not on disk.
        # Inspect them through the sandbox: `changes()` summarizes what was
        # written, and `read_file(...)` returns the effective file content.
        print("\nSandbox changes:", deep.sandbox.changes())
        overlay = await deep.sandbox.read_file("calculator.py")
        if overlay:
            print("\n--- calculator.py (sandbox) ---")
            print(overlay["content"])
    finally:
        shutil.rmtree(workdir, ignore_errors=True)


if __name__ == "__main__":
    asyncio.run(main())

Run log

This guide calls synalinks.enable_logging(), so a full run traces every module call. The log below is the unedited output of running the guide above with local models.

Full run log — guides/22_deep_agent.log

(DEBUG) [Synalinks]
Call ID: 599dee13-fe6a-43b3-893e-d37d31719e1c
Parent call ID: None
Module: DeepAgent
Module Name: deep_agent
Module Description: A coding agent whose tools are a sandboxed copy of a workdir.
Data Model JSON Schema:
[
  {
    "$defs": {
      "ChatMessage": {
        "additionalProperties": false,
        "description": "A chat message",
        "properties": {
          "role": {
            "$ref": "#/$defs/ChatRole",
            "description": "The chat message role"
          },
          "reasoning_content": {
            "anyOf": [
              {
                "type": "string"
              },
              {
                "type": "null"
              }
            ],
            "default": null,
            "description": "The reasoning/thinking content of the message. Keyed to match the litellm/DeepSeek `reasoning_content` chat-completion field (a provider extension, not part of the base OpenAI spec), so the message API stays a subset of the litellm-extended chat-completion message.",
            "title": "Reasoning Content"
          },
          "thinking_blocks": {
            "anyOf": [
              {
                "items": {
                  "additionalProperties": true,
                  "type": "object"
                },
                "type": "array"
              },
              {
                "type": "null"
              }
            ],
            "default": null,
            "description": "Opaque provider-native thinking blocks (e.g. Anthropic's signed `thinking_blocks`; a litellm extension, not part of the base OpenAI spec). Carried through verbatim on assistant-message re-injection so multi-turn tool-use round-trips preserve signatures. None for providers that emit reasoning only as text.",
            "title": "Thinking Blocks"
          },
          "content": {
            "anyOf": [
              {
                "type": "string"
              },
              {
                "items": {
                  "additionalProperties": true,
                  "type": "object"
                },
                "type": "array"
              },
              {
                "additionalProperties": true,
                "type": "object"
              },
              {
                "type": "null"
              }
            ],
            "default": null,
            "description": "The content of the message",
            "title": "Content"
          },
          "tool_call_id": {
            "anyOf": [
              {
                "type": "string"
              },
              {
                "type": "null"
              }
            ],
            "default": null,
            "description": "The id of the tool call if role is `tool`",
            "title": "Tool Call Id"
          },
          "tool_calls": {
            "anyOf": [
              {
                "items": {
                  "$ref": "#/$defs/ToolCall"
                },
                "type": "array"
              },
              {
                "type": "null"
              }
            ],
            "default": null,
            "description": "The tool calls of the agent",
            "title": "Tool Calls"
          }
        },
        "required": [
          "role"
        ],
        "title": "ChatMessage",
        "type": "object"
      },
      "ChatRole": {
        "description": "The chat message roles",
        "enum": [
          "system",
          "developer",
          "user",
          "assistant",
          "tool",
          "function"
        ],
        "title": "ChatRole",
        "type": "string"
      },
      "ToolCall": {
        "additionalProperties": false,
        "description": "A tool call, shaped like an OpenAI Chat Completions tool call.\n\nMirrors the wire envelope (`{id, type, function: {name, arguments}}`)\nexcept that `arguments` stays a parsed dict rather than a JSON-encoded\nstring, so modules and agents can read it directly. The string encoding\nis applied only at the wire edge (see `backend.pydantic.chat_completions`).",
        "properties": {
          "id": {
            "description": "The id of the tool call",
            "title": "Id",
            "type": "string"
          },
          "type": {
            "const": "function",
            "default": "function",
            "description": "The tool call type (always `function` today)",
            "title": "Type",
            "type": "string"
          },
          "function": {
            "$ref": "#/$defs/ToolCallFunction",
            "description": "The function invocation (name + arguments)"
          }
        },
        "required": [
          "id",
          "function"
        ],
        "title": "ToolCall",
        "type": "object"
      },
      "ToolCallFunction": {
        "additionalProperties": false,
        "description": "The `function` payload of a tool call (name + parsed arguments).",
        "properties": {
          "name": {
            "description": "The name of the function called",
            "title": "Name",
            "type": "string"
          },
          "arguments": {
            "additionalProperties": true,
            "description": "The arguments of the tool call",
            "title": "Arguments",
            "type": "object"
          }
        },
        "required": [
          "name",
          "arguments"
        ],
        "title": "ToolCallFunction",
        "type": "object"
      }
    },
    "additionalProperties": false,
    "description": "A list of chat messages",
    "properties": {
      "messages": {
        "default": [],
        "description": "The list of chat messages",
        "items": {
          "$ref": "#/$defs/ChatMessage"
        },
        "title": "Messages",
        "type": "array"
      }
    },
    "title": "ChatMessages",
    "type": "object"
  }
]

(DEBUG) [Synalinks]
Call ID: 447dfc4c-18ad-406e-bfd7-fc70ddfeb34e
Parent call ID: 599dee13-fe6a-43b3-893e-d37d31719e1c
Module: Generator
Module Name: tool_calls_generator_deep_agent
Module Description: Use a `LanguageModel` to generate a data model from an arbitrary input data model.
Data Model JSON Schema:
[
  {
    "$defs": {
      "ChatMessage": {
        "additionalProperties": false,
        "description": "A chat message",
        "properties": {
          "role": {
            "$ref": "#/$defs/ChatRole",
            "description": "The chat message role"
          },
          "reasoning_content": {
            "anyOf": [
              {
                "type": "string"
              },
              {
                "type": "null"
              }
            ],
            "default": null,
            "description": "The reasoning/thinking content of the message. Keyed to match the litellm/DeepSeek `reasoning_content` chat-completion field (a provider extension, not part of the base OpenAI spec), so the message API stays a subset of the litellm-extended chat-completion message.",
            "title": "Reasoning Content"
          },
          "thinking_blocks": {
            "anyOf": [
              {
                "items": {
                  "additionalProperties": true,
                  "type": "object"
                },
                "type": "array"
              },
              {
                "type": "null"
              }
            ],
            "default": null,
            "description": "Opaque provider-native thinking blocks (e.g. Anthropic's signed `thinking_blocks`; a litellm extension, not part of the base OpenAI spec). Carried through verbatim on assistant-message re-injection so multi-turn tool-use round-trips preserve signatures. None for providers that emit reasoning only as text.",
            "title": "Thinking Blocks"
          },
          "content": {
            "anyOf": [
              {
                "type": "string"
              },
              {
                "items": {
                  "additionalProperties": true,
                  "type": "object"
                },
                "type": "array"
              },
              {
                "additionalProperties": true,
                "type": "object"
              },
              {
                "type": "null"
              }
            ],
            "default": null,
            "description": "The content of the message",
            "title": "Content"
          },
          "tool_call_id": {
            "anyOf": [
              {
                "type": "string"
              },
              {
                "type": "null"
              }
            ],
            "default": null,
            "description": "The id of the tool call if role is `tool`",
            "title": "Tool Call Id"
          },
          "tool_calls": {
            "anyOf": [
              {
                "items": {
                  "$ref": "#/$defs/ToolCall"
                },
                "type": "array"
              },
              {
                "type": "null"
              }
            ],
            "default": null,
            "description": "The tool calls of the agent",
            "title": "Tool Calls"
          }
        },
        "required": [
          "role"
        ],
        "title": "ChatMessage",
        "type": "object"
      },
      "ChatRole": {
        "description": "The chat message roles",
        "enum": [
          "system",
          "developer",
          "user",
          "assistant",
          "tool",
          "function"
        ],
        "title": "ChatRole",
        "type": "string"
      },
      "ToolCall": {
        "additionalProperties": false,
        "description": "A tool call, shaped like an OpenAI Chat Completions tool call.\n\nMirrors the wire envelope (`{id, type, function: {name, arguments}}`)\nexcept that `arguments` stays a parsed dict rather than a JSON-encoded\nstring, so modules and agents can read it directly. The string encoding\nis applied only at the wire edge (see `backend.pydantic.chat_completions`).",
        "properties": {
          "id": {
            "description": "The id of the tool call",
            "title": "Id",
            "type": "string"
          },
          "type": {
            "const": "function",
            "default": "function",
            "description": "The tool call type (always `function` today)",
            "title": "Type",
            "type": "string"
          },
          "function": {
            "$ref": "#/$defs/ToolCallFunction",
            "description": "The function invocation (name + arguments)"
          }
        },
        "required": [
          "id",
          "function"
        ],
        "title": "ToolCall",
        "type": "object"
      },
      "ToolCallFunction": {
        "additionalProperties": false,
        "description": "The `function` payload of a tool call (name + parsed arguments).",
        "properties": {
          "name": {
            "description": "The name of the function called",
            "title": "Name",
            "type": "string"
          },
          "arguments": {
            "additionalProperties": true,
            "description": "The arguments of the tool call",
            "title": "Arguments",
            "type": "object"
          }
        },
        "required": [
          "name",
          "arguments"
        ],
        "title": "ToolCallFunction",
        "type": "object"
      }
    },
    "additionalProperties": false,
    "description": "A list of chat messages",
    "properties": {
      "messages": {
        "default": [],
        "description": "The list of chat messages",
        "items": {
          "$ref": "#/$defs/ChatMessage"
        },
        "title": "Messages",
        "type": "array"
      }
    },
    "title": "ChatMessages",
    "type": "object"
  }
]

(DEBUG) [Synalinks]
Call ID: 447dfc4c-18ad-406e-bfd7-fc70ddfeb34e
Parent call ID: 599dee13-fe6a-43b3-893e-d37d31719e1c
Module: Generator
Module Name: tool_calls_generator_deep_agent
Module Description: Use a `LanguageModel` to generate a data model from an arbitrary input data model.
Data Model JSON Schema:
[
  {
    "$defs": {
      "ChatRole": {
        "description": "The chat message roles",
        "enum": [
          "system",
          "developer",
          "user",
          "assistant",
          "tool",
          "function"
        ],
        "title": "ChatRole",
        "type": "string"
      },
      "ToolCall": {
        "additionalProperties": false,
        "description": "A tool call, shaped like an OpenAI Chat Completions tool call.\n\nMirrors the wire envelope (`{id, type, function: {name, arguments}}`)\nexcept that `arguments` stays a parsed dict rather than a JSON-encoded\nstring, so modules and agents can read it directly. The string encoding\nis applied only at the wire edge (see `backend.pydantic.chat_completions`).",
        "properties": {
          "id": {
            "description": "The id of the tool call",
            "title": "Id",
            "type": "string"
          },
          "type": {
            "const": "function",
            "default": "function",
            "description": "The tool call type (always `function` today)",
            "title": "Type",
            "type": "string"
          },
          "function": {
            "$ref": "#/$defs/ToolCallFunction",
            "description": "The function invocation (name + arguments)"
          }
        },
        "required": [
          "id",
          "function"
        ],
        "title": "ToolCall",
        "type": "object"
      },
      "ToolCallFunction": {
        "additionalProperties": false,
        "description": "The `function` payload of a tool call (name + parsed arguments).",
        "properties": {
          "name": {
            "description": "The name of the function called",
            "title": "Name",
            "type": "string"
          },
          "arguments": {
            "additionalProperties": true,
            "description": "The arguments of the tool call",
            "title": "Arguments",
            "type": "object"
          }
        },
        "required": [
          "name",
          "arguments"
        ],
        "title": "ToolCallFunction",
        "type": "object"
      }
    },
    "additionalProperties": false,
    "description": "A chat message",
    "properties": {
      "role": {
        "$ref": "#/$defs/ChatRole",
        "description": "The chat message role"
      },
      "reasoning_content": {
        "anyOf": [
          {
            "type": "string"
          },
          {
            "type": "null"
          }
        ],
        "default": null,
        "description": "The reasoning/thinking content of the message. Keyed to match the litellm/DeepSeek `reasoning_content` chat-completion field (a provider extension, not part of the base OpenAI spec), so the message API stays a subset of the litellm-extended chat-completion message.",
        "title": "Reasoning Content"
      },
      "thinking_blocks": {
        "anyOf": [
          {
            "items": {
              "additionalProperties": true,
              "type": "object"
            },
            "type": "array"
          },
          {
            "type": "null"
          }
        ],
        "default": null,
        "description": "Opaque provider-native thinking blocks (e.g. Anthropic's signed `thinking_blocks`; a litellm extension, not part of the base OpenAI spec). Carried through verbatim on assistant-message re-injection so multi-turn tool-use round-trips preserve signatures. None for providers that emit reasoning only as text.",
        "title": "Thinking Blocks"
      },
      "content": {
        "anyOf": [
          {
            "type": "string"
          },
          {
            "items": {
              "additionalProperties": true,
              "type": "object"
            },
            "type": "array"
          },
          {
            "additionalProperties": true,
            "type": "object"
          },
          {
            "type": "null"
          }
        ],
        "default": null,
        "description": "The content of the message",
        "title": "Content"
      },
      "tool_call_id": {
        "anyOf": [
          {
            "type": "string"
          },
          {
            "type": "null"
          }
        ],
        "default": null,
        "description": "The id of the tool call if role is `tool`",
        "title": "Tool Call Id"
      },
      "tool_calls": {
        "anyOf": [
          {
            "items": {
              "$ref": "#/$defs/ToolCall"
            },
            "type": "array"
          },
          {
            "type": "null"
          }
        ],
        "default": null,
        "description": "The tool calls of the agent",
        "title": "Tool Calls"
      }
    },
    "required": [
      "role"
    ],
    "title": "ChatMessage",
    "type": "object"
  }
]

(DEBUG) [Synalinks]
Call ID: fcbea37d-2c14-49ba-8086-e14d0d9b5b04
Parent call ID: 447dfc4c-18ad-406e-bfd7-fc70ddfeb34e
Module: Generator
Module Name: final_generator_deep_agent
Module Description: Use a `LanguageModel` to generate a data model from an arbitrary input data model.
Data Model JSON Schema:
[
  {
    "$defs": {
      "ChatMessage": {
        "additionalProperties": false,
        "description": "A chat message",
        "properties": {
          "role": {
            "$ref": "#/$defs/ChatRole",
            "description": "The chat message role"
          },
          "reasoning_content": {
            "anyOf": [
              {
                "type": "string"
              },
              {
                "type": "null"
              }
            ],
            "default": null,
            "description": "The reasoning/thinking content of the message. Keyed to match the litellm/DeepSeek `reasoning_content` chat-completion field (a provider extension, not part of the base OpenAI spec), so the message API stays a subset of the litellm-extended chat-completion message.",
            "title": "Reasoning Content"
          },
          "thinking_blocks": {
            "anyOf": [
              {
                "items": {
                  "additionalProperties": true,
                  "type": "object"
                },
                "type": "array"
              },
              {
                "type": "null"
              }
            ],
            "default": null,
            "description": "Opaque provider-native thinking blocks (e.g. Anthropic's signed `thinking_blocks`; a litellm extension, not part of the base OpenAI spec). Carried through verbatim on assistant-message re-injection so multi-turn tool-use round-trips preserve signatures. None for providers that emit reasoning only as text.",
            "title": "Thinking Blocks"
          },
          "content": {
            "anyOf": [
              {
                "type": "string"
              },
              {
                "items": {
                  "additionalProperties": true,
                  "type": "object"
                },
                "type": "array"
              },
              {
                "additionalProperties": true,
                "type": "object"
              },
              {
                "type": "null"
              }
            ],
            "default": null,
            "description": "The content of the message",
            "title": "Content"
          },
          "tool_call_id": {
            "anyOf": [
              {
                "type": "string"
              },
              {
                "type": "null"
              }
            ],
            "default": null,
            "description": "The id of the tool call if role is `tool`",
            "title": "Tool Call Id"
          },
          "tool_calls": {
            "anyOf": [
              {
                "items": {
                  "$ref": "#/$defs/ToolCall"
                },
                "type": "array"
              },
              {
                "type": "null"
              }
            ],
            "default": null,
            "description": "The tool calls of the agent",
            "title": "Tool Calls"
          }
        },
        "required": [
          "role"
        ],
        "title": "ChatMessage",
        "type": "object"
      },
      "ChatRole": {
        "description": "The chat message roles",
        "enum": [
          "system",
          "developer",
          "user",
          "assistant",
          "tool",
          "function"
        ],
        "title": "ChatRole",
        "type": "string"
      },
      "ToolCall": {
        "additionalProperties": false,
        "description": "A tool call, shaped like an OpenAI Chat Completions tool call.\n\nMirrors the wire envelope (`{id, type, function: {name, arguments}}`)\nexcept that `arguments` stays a parsed dict rather than a JSON-encoded\nstring, so modules and agents can read it directly. The string encoding\nis applied only at the wire edge (see `backend.pydantic.chat_completions`).",
        "properties": {
          "id": {
            "description": "The id of the tool call",
            "title": "Id",
            "type": "string"
          },
          "type": {
            "const": "function",
            "default": "function",
            "description": "The tool call type (always `function` today)",
            "title": "Type",
            "type": "string"
          },
          "function": {
            "$ref": "#/$defs/ToolCallFunction",
            "description": "The function invocation (name + arguments)"
          }
        },
        "required": [
          "id",
          "function"
        ],
        "title": "ToolCall",
        "type": "object"
      },
      "ToolCallFunction": {
        "additionalProperties": false,
        "description": "The `function` payload of a tool call (name + parsed arguments).",
        "properties": {
          "name": {
            "description": "The name of the function called",
            "title": "Name",
            "type": "string"
          },
          "arguments": {
            "additionalProperties": true,
            "description": "The arguments of the tool call",
            "title": "Arguments",
            "type": "object"
          }
        },
        "required": [
          "name",
          "arguments"
        ],
        "title": "ToolCallFunction",
        "type": "object"
      }
    },
    "additionalProperties": false,
    "description": "A list of chat messages",
    "properties": {
      "messages": {
        "default": [],
        "description": "The list of chat messages",
        "items": {
          "$ref": "#/$defs/ChatMessage"
        },
        "title": "Messages",
        "type": "array"
      }
    },
    "title": "ChatMessages",
    "type": "object"
  }
]

(DEBUG) [Synalinks]
Call ID: fcbea37d-2c14-49ba-8086-e14d0d9b5b04
Parent call ID: 447dfc4c-18ad-406e-bfd7-fc70ddfeb34e
Module: Generator
Module Name: final_generator_deep_agent
Module Description: Use a `LanguageModel` to generate a data model from an arbitrary input data model.
Data Model JSON Schema:
[
  {
    "$defs": {
      "ChatRole": {
        "description": "The chat message roles",
        "enum": [
          "system",
          "developer",
          "user",
          "assistant",
          "tool",
          "function"
        ],
        "title": "ChatRole",
        "type": "string"
      },
      "ToolCall": {
        "additionalProperties": false,
        "description": "A tool call, shaped like an OpenAI Chat Completions tool call.\n\nMirrors the wire envelope (`{id, type, function: {name, arguments}}`)\nexcept that `arguments` stays a parsed dict rather than a JSON-encoded\nstring, so modules and agents can read it directly. The string encoding\nis applied only at the wire edge (see `backend.pydantic.chat_completions`).",
        "properties": {
          "id": {
            "description": "The id of the tool call",
            "title": "Id",
            "type": "string"
          },
          "type": {
            "const": "function",
            "default": "function",
            "description": "The tool call type (always `function` today)",
            "title": "Type",
            "type": "string"
          },
          "function": {
            "$ref": "#/$defs/ToolCallFunction",
            "description": "The function invocation (name + arguments)"
          }
        },
        "required": [
          "id",
          "function"
        ],
        "title": "ToolCall",
        "type": "object"
      },
      "ToolCallFunction": {
        "additionalProperties": false,
        "description": "The `function` payload of a tool call (name + parsed arguments).",
        "properties": {
          "name": {
            "description": "The name of the function called",
            "title": "Name",
            "type": "string"
          },
          "arguments": {
            "additionalProperties": true,
            "description": "The arguments of the tool call",
            "title": "Arguments",
            "type": "object"
          }
        },
        "required": [
          "name",
          "arguments"
        ],
        "title": "ToolCallFunction",
        "type": "object"
      }
    },
    "additionalProperties": false,
    "description": "A chat message",
    "properties": {
      "role": {
        "$ref": "#/$defs/ChatRole",
        "description": "The chat message role"
      },
      "reasoning_content": {
        "anyOf": [
          {
            "type": "string"
          },
          {
            "type": "null"
          }
        ],
        "default": null,
        "description": "The reasoning/thinking content of the message. Keyed to match the litellm/DeepSeek `reasoning_content` chat-completion field (a provider extension, not part of the base OpenAI spec), so the message API stays a subset of the litellm-extended chat-completion message.",
        "title": "Reasoning Content"
      },
      "thinking_blocks": {
        "anyOf": [
          {
            "items": {
              "additionalProperties": true,
              "type": "object"
            },
            "type": "array"
          },
          {
            "type": "null"
          }
        ],
        "default": null,
        "description": "Opaque provider-native thinking blocks (e.g. Anthropic's signed `thinking_blocks`; a litellm extension, not part of the base OpenAI spec). Carried through verbatim on assistant-message re-injection so multi-turn tool-use round-trips preserve signatures. None for providers that emit reasoning only as text.",
        "title": "Thinking Blocks"
      },
      "content": {
        "anyOf": [
          {
            "type": "string"
          },
          {
            "items": {
              "additionalProperties": true,
              "type": "object"
            },
            "type": "array"
          },
          {
            "additionalProperties": true,
            "type": "object"
          },
          {
            "type": "null"
          }
        ],
        "default": null,
        "description": "The content of the message",
        "title": "Content"
      },
      "tool_call_id": {
        "anyOf": [
          {
            "type": "string"
          },
          {
            "type": "null"
          }
        ],
        "default": null,
        "description": "The id of the tool call if role is `tool`",
        "title": "Tool Call Id"
      },
      "tool_calls": {
        "anyOf": [
          {
            "items": {
              "$ref": "#/$defs/ToolCall"
            },
            "type": "array"
          },
          {
            "type": "null"
          }
        ],
        "default": null,
        "description": "The tool calls of the agent",
        "title": "Tool Calls"
      }
    },
    "required": [
      "role"
    ],
    "title": "ChatMessage",
    "type": "object"
  }
]

(DEBUG) [Synalinks]
Call ID: 8f9ac2a3-45e5-416c-a0fe-0bd708e30257
Parent call ID: fcbea37d-2c14-49ba-8086-e14d0d9b5b04
Module: Generator
Module Name: tool_calls_generator_deep_agent
Module Description: Use a `LanguageModel` to generate a data model from an arbitrary input data model.
Data Model JSON Schema:
[
  {
    "$defs": {
      "ChatMessage": {
        "additionalProperties": false,
        "description": "A chat message",
        "properties": {
          "role": {
            "$ref": "#/$defs/ChatRole",
            "description": "The chat message role"
          },
          "reasoning_content": {
            "anyOf": [
              {
                "type": "string"
              },
              {
                "type": "null"
              }
            ],
            "default": null,
            "description": "The reasoning/thinking content of the message. Keyed to match the litellm/DeepSeek `reasoning_content` chat-completion field (a provider extension, not part of the base OpenAI spec), so the message API stays a subset of the litellm-extended chat-completion message.",
            "title": "Reasoning Content"
          },
          "thinking_blocks": {
            "anyOf": [
              {
                "items": {
                  "additionalProperties": true,
                  "type": "object"
                },
                "type": "array"
              },
              {
                "type": "null"
              }
            ],
            "default": null,
            "description": "Opaque provider-native thinking blocks (e.g. Anthropic's signed `thinking_blocks`; a litellm extension, not part of the base OpenAI spec). Carried through verbatim on assistant-message re-injection so multi-turn tool-use round-trips preserve signatures. None for providers that emit reasoning only as text.",
            "title": "Thinking Blocks"
          },
          "content": {
            "anyOf": [
              {
                "type": "string"
              },
              {
                "items": {
                  "additionalProperties": true,
                  "type": "object"
                },
                "type": "array"
              },
              {
                "additionalProperties": true,
                "type": "object"
              },
              {
                "type": "null"
              }
            ],
            "default": null,
            "description": "The content of the message",
            "title": "Content"
          },
          "tool_call_id": {
            "anyOf": [
              {
                "type": "string"
              },
              {
                "type": "null"
              }
            ],
            "default": null,
            "description": "The id of the tool call if role is `tool`",
            "title": "Tool Call Id"
          },
          "tool_calls": {
            "anyOf": [
              {
                "items": {
                  "$ref": "#/$defs/ToolCall"
                },
                "type": "array"
              },
              {
                "type": "null"
              }
            ],
            "default": null,
            "description": "The tool calls of the agent",
            "title": "Tool Calls"
          }
        },
        "required": [
          "role"
        ],
        "title": "ChatMessage",
        "type": "object"
      },
      "ChatRole": {
        "description": "The chat message roles",
        "enum": [
          "system",
          "developer",
          "user",
          "assistant",
          "tool",
          "function"
        ],
        "title": "ChatRole",
        "type": "string"
      },
      "ToolCall": {
        "additionalProperties": false,
        "description": "A tool call, shaped like an OpenAI Chat Completions tool call.\n\nMirrors the wire envelope (`{id, type, function: {name, arguments}}`)\nexcept that `arguments` stays a parsed dict rather than a JSON-encoded\nstring, so modules and agents can read it directly. The string encoding\nis applied only at the wire edge (see `backend.pydantic.chat_completions`).",
        "properties": {
          "id": {
            "description": "The id of the tool call",
            "title": "Id",
            "type": "string"
          },
          "type": {
            "const": "function",
            "default": "function",
            "description": "The tool call type (always `function` today)",
            "title": "Type",
            "type": "string"
          },
          "function": {
            "$ref": "#/$defs/ToolCallFunction",
            "description": "The function invocation (name + arguments)"
          }
        },
        "required": [
          "id",
          "function"
        ],
        "title": "ToolCall",
        "type": "object"
      },
      "ToolCallFunction": {
        "additionalProperties": false,
        "description": "The `function` payload of a tool call (name + parsed arguments).",
        "properties": {
          "name": {
            "description": "The name of the function called",
            "title": "Name",
            "type": "string"
          },
          "arguments": {
            "additionalProperties": true,
            "description": "The arguments of the tool call",
            "title": "Arguments",
            "type": "object"
          }
        },
        "required": [
          "name",
          "arguments"
        ],
        "title": "ToolCallFunction",
        "type": "object"
      }
    },
    "additionalProperties": false,
    "description": "A list of chat messages",
    "properties": {
      "messages": {
        "default": [],
        "description": "The list of chat messages",
        "items": {
          "$ref": "#/$defs/ChatMessage"
        },
        "title": "Messages",
        "type": "array"
      }
    },
    "title": "ChatMessages",
    "type": "object"
  }
]

(DEBUG) [Synalinks]
Call ID: 8f9ac2a3-45e5-416c-a0fe-0bd708e30257
Parent call ID: fcbea37d-2c14-49ba-8086-e14d0d9b5b04
Module: Generator
Module Name: tool_calls_generator_deep_agent
Module Description: Use a `LanguageModel` to generate a data model from an arbitrary input data model.
Data Model JSON Schema:
[
  {
    "$defs": {
      "ChatRole": {
        "description": "The chat message roles",
        "enum": [
          "system",
          "developer",
          "user",
          "assistant",
          "tool",
          "function"
        ],
        "title": "ChatRole",
        "type": "string"
      },
      "ToolCall": {
        "additionalProperties": false,
        "description": "A tool call, shaped like an OpenAI Chat Completions tool call.\n\nMirrors the wire envelope (`{id, type, function: {name, arguments}}`)\nexcept that `arguments` stays a parsed dict rather than a JSON-encoded\nstring, so modules and agents can read it directly. The string encoding\nis applied only at the wire edge (see `backend.pydantic.chat_completions`).",
        "properties": {
          "id": {
            "description": "The id of the tool call",
            "title": "Id",
            "type": "string"
          },
          "type": {
            "const": "function",
            "default": "function",
            "description": "The tool call type (always `function` today)",
            "title": "Type",
            "type": "string"
          },
          "function": {
            "$ref": "#/$defs/ToolCallFunction",
            "description": "The function invocation (name + arguments)"
          }
        },
        "required": [
          "id",
          "function"
        ],
        "title": "ToolCall",
        "type": "object"
      },
      "ToolCallFunction": {
        "additionalProperties": false,
        "description": "The `function` payload of a tool call (name + parsed arguments).",
        "properties": {
          "name": {
            "description": "The name of the function called",
            "title": "Name",
            "type": "string"
          },
          "arguments": {
            "additionalProperties": true,
            "description": "The arguments of the tool call",
            "title": "Arguments",
            "type": "object"
          }
        },
        "required": [
          "name",
          "arguments"
        ],
        "title": "ToolCallFunction",
        "type": "object"
      }
    },
    "additionalProperties": false,
    "description": "A chat message",
    "properties": {
      "role": {
        "$ref": "#/$defs/ChatRole",
        "description": "The chat message role"
      },
      "reasoning_content": {
        "anyOf": [
          {
            "type": "string"
          },
          {
            "type": "null"
          }
        ],
        "default": null,
        "description": "The reasoning/thinking content of the message. Keyed to match the litellm/DeepSeek `reasoning_content` chat-completion field (a provider extension, not part of the base OpenAI spec), so the message API stays a subset of the litellm-extended chat-completion message.",
        "title": "Reasoning Content"
      },
      "thinking_blocks": {
        "anyOf": [
          {
            "items": {
              "additionalProperties": true,
              "type": "object"
            },
            "type": "array"
          },
          {
            "type": "null"
          }
        ],
        "default": null,
        "description": "Opaque provider-native thinking blocks (e.g. Anthropic's signed `thinking_blocks`; a litellm extension, not part of the base OpenAI spec). Carried through verbatim on assistant-message re-injection so multi-turn tool-use round-trips preserve signatures. None for providers that emit reasoning only as text.",
        "title": "Thinking Blocks"
      },
      "content": {
        "anyOf": [
          {
            "type": "string"
          },
          {
            "items": {
              "additionalProperties": true,
              "type": "object"
            },
            "type": "array"
          },
          {
            "additionalProperties": true,
            "type": "object"
          },
          {
            "type": "null"
          }
        ],
        "default": null,
        "description": "The content of the message",
        "title": "Content"
      },
      "tool_call_id": {
        "anyOf": [
          {
            "type": "string"
          },
          {
            "type": "null"
          }
        ],
        "default": null,
        "description": "The id of the tool call if role is `tool`",
        "title": "Tool Call Id"
      },
      "tool_calls": {
        "anyOf": [
          {
            "items": {
              "$ref": "#/$defs/ToolCall"
            },
            "type": "array"
          },
          {
            "type": "null"
          }
        ],
        "default": null,
        "description": "The tool calls of the agent",
        "title": "Tool Calls"
      }
    },
    "required": [
      "role"
    ],
    "title": "ChatMessage",
    "type": "object"
  }
]

(DEBUG) [Synalinks]
Call ID: e7424184-87b3-445e-8e47-114421df889b
Parent call ID: 8f9ac2a3-45e5-416c-a0fe-0bd708e30257
Module: Generator
Module Name: final_generator_deep_agent
Module Description: Use a `LanguageModel` to generate a data model from an arbitrary input data model.
Data Model JSON Schema:
[
  {
    "$defs": {
      "ChatMessage": {
        "additionalProperties": false,
        "description": "A chat message",
        "properties": {
          "role": {
            "$ref": "#/$defs/ChatRole",
            "description": "The chat message role"
          },
          "reasoning_content": {
            "anyOf": [
              {
                "type": "string"
              },
              {
                "type": "null"
              }
            ],
            "default": null,
            "description": "The reasoning/thinking content of the message. Keyed to match the litellm/DeepSeek `reasoning_content` chat-completion field (a provider extension, not part of the base OpenAI spec), so the message API stays a subset of the litellm-extended chat-completion message.",
            "title": "Reasoning Content"
          },
          "thinking_blocks": {
            "anyOf": [
              {
                "items": {
                  "additionalProperties": true,
                  "type": "object"
                },
                "type": "array"
              },
              {
                "type": "null"
              }
            ],
            "default": null,
            "description": "Opaque provider-native thinking blocks (e.g. Anthropic's signed `thinking_blocks`; a litellm extension, not part of the base OpenAI spec). Carried through verbatim on assistant-message re-injection so multi-turn tool-use round-trips preserve signatures. None for providers that emit reasoning only as text.",
            "title": "Thinking Blocks"
          },
          "content": {
            "anyOf": [
              {
                "type": "string"
              },
              {
                "items": {
                  "additionalProperties": true,
                  "type": "object"
                },
                "type": "array"
              },
              {
                "additionalProperties": true,
                "type": "object"
              },
              {
                "type": "null"
              }
            ],
            "default": null,
            "description": "The content of the message",
            "title": "Content"
          },
          "tool_call_id": {
            "anyOf": [
              {
                "type": "string"
              },
              {
                "type": "null"
              }
            ],
            "default": null,
            "description": "The id of the tool call if role is `tool`",
            "title": "Tool Call Id"
          },
          "tool_calls": {
            "anyOf": [
              {
                "items": {
                  "$ref": "#/$defs/ToolCall"
                },
                "type": "array"
              },
              {
                "type": "null"
              }
            ],
            "default": null,
            "description": "The tool calls of the agent",
            "title": "Tool Calls"
          }
        },
        "required": [
          "role"
        ],
        "title": "ChatMessage",
        "type": "object"
      },
      "ChatRole": {
        "description": "The chat message roles",
        "enum": [
          "system",
          "developer",
          "user",
          "assistant",
          "tool",
          "function"
        ],
        "title": "ChatRole",
        "type": "string"
      },
      "ToolCall": {
        "additionalProperties": false,
        "description": "A tool call, shaped like an OpenAI Chat Completions tool call.\n\nMirrors the wire envelope (`{id, type, function: {name, arguments}}`)\nexcept that `arguments` stays a parsed dict rather than a JSON-encoded\nstring, so modules and agents can read it directly. The string encoding\nis applied only at the wire edge (see `backend.pydantic.chat_completions`).",
        "properties": {
          "id": {
            "description": "The id of the tool call",
            "title": "Id",
            "type": "string"
          },
          "type": {
            "const": "function",
            "default": "function",
            "description": "The tool call type (always `function` today)",
            "title": "Type",
            "type": "string"
          },
          "function": {
            "$ref": "#/$defs/ToolCallFunction",
            "description": "The function invocation (name + arguments)"
          }
        },
        "required": [
          "id",
          "function"
        ],
        "title": "ToolCall",
        "type": "object"
      },
      "ToolCallFunction": {
        "additionalProperties": false,
        "description": "The `function` payload of a tool call (name + parsed arguments).",
        "properties": {
          "name": {
            "description": "The name of the function called",
            "title": "Name",
            "type": "string"
          },
          "arguments": {
            "additionalProperties": true,
            "description": "The arguments of the tool call",
            "title": "Arguments",
            "type": "object"
          }
        },
        "required": [
          "name",
          "arguments"
        ],
        "title": "ToolCallFunction",
        "type": "object"
      }
    },
    "additionalProperties": false,
    "description": "A list of chat messages",
    "properties": {
      "messages": {
        "default": [],
        "description": "The list of chat messages",
        "items": {
          "$ref": "#/$defs/ChatMessage"
        },
        "title": "Messages",
        "type": "array"
      }
    },
    "title": "ChatMessages",
    "type": "object"
  }
]

(DEBUG) [Synalinks]
Call ID: e7424184-87b3-445e-8e47-114421df889b
Parent call ID: 8f9ac2a3-45e5-416c-a0fe-0bd708e30257
Module: Generator
Module Name: final_generator_deep_agent
Module Description: Use a `LanguageModel` to generate a data model from an arbitrary input data model.
Data Model JSON Schema:
[
  {
    "$defs": {
      "ChatRole": {
        "description": "The chat message roles",
        "enum": [
          "system",
          "developer",
          "user",
          "assistant",
          "tool",
          "function"
        ],
        "title": "ChatRole",
        "type": "string"
      },
      "ToolCall": {
        "additionalProperties": false,
        "description": "A tool call, shaped like an OpenAI Chat Completions tool call.\n\nMirrors the wire envelope (`{id, type, function: {name, arguments}}`)\nexcept that `arguments` stays a parsed dict rather than a JSON-encoded\nstring, so modules and agents can read it directly. The string encoding\nis applied only at the wire edge (see `backend.pydantic.chat_completions`).",
        "properties": {
          "id": {
            "description": "The id of the tool call",
            "title": "Id",
            "type": "string"
          },
          "type": {
            "const": "function",
            "default": "function",
            "description": "The tool call type (always `function` today)",
            "title": "Type",
            "type": "string"
          },
          "function": {
            "$ref": "#/$defs/ToolCallFunction",
            "description": "The function invocation (name + arguments)"
          }
        },
        "required": [
          "id",
          "function"
        ],
        "title": "ToolCall",
        "type": "object"
      },
      "ToolCallFunction": {
        "additionalProperties": false,
        "description": "The `function` payload of a tool call (name + parsed arguments).",
        "properties": {
          "name": {
            "description": "The name of the function called",
            "title": "Name",
            "type": "string"
          },
          "arguments": {
            "additionalProperties": true,
            "description": "The arguments of the tool call",
            "title": "Arguments",
            "type": "object"
          }
        },
        "required": [
          "name",
          "arguments"
        ],
        "title": "ToolCallFunction",
        "type": "object"
      }
    },
    "additionalProperties": false,
    "description": "A chat message",
    "properties": {
      "role": {
        "$ref": "#/$defs/ChatRole",
        "description": "The chat message role"
      },
      "reasoning_content": {
        "anyOf": [
          {
            "type": "string"
          },
          {
            "type": "null"
          }
        ],
        "default": null,
        "description": "The reasoning/thinking content of the message. Keyed to match the litellm/DeepSeek `reasoning_content` chat-completion field (a provider extension, not part of the base OpenAI spec), so the message API stays a subset of the litellm-extended chat-completion message.",
        "title": "Reasoning Content"
      },
      "thinking_blocks": {
        "anyOf": [
          {
            "items": {
              "additionalProperties": true,
              "type": "object"
            },
            "type": "array"
          },
          {
            "type": "null"
          }
        ],
        "default": null,
        "description": "Opaque provider-native thinking blocks (e.g. Anthropic's signed `thinking_blocks`; a litellm extension, not part of the base OpenAI spec). Carried through verbatim on assistant-message re-injection so multi-turn tool-use round-trips preserve signatures. None for providers that emit reasoning only as text.",
        "title": "Thinking Blocks"
      },
      "content": {
        "anyOf": [
          {
            "type": "string"
          },
          {
            "items": {
              "additionalProperties": true,
              "type": "object"
            },
            "type": "array"
          },
          {
            "additionalProperties": true,
            "type": "object"
          },
          {
            "type": "null"
          }
        ],
        "default": null,
        "description": "The content of the message",
        "title": "Content"
      },
      "tool_call_id": {
        "anyOf": [
          {
            "type": "string"
          },
          {
            "type": "null"
          }
        ],
        "default": null,
        "description": "The id of the tool call if role is `tool`",
        "title": "Tool Call Id"
      },
      "tool_calls": {
        "anyOf": [
          {
            "items": {
              "$ref": "#/$defs/ToolCall"
            },
            "type": "array"
          },
          {
            "type": "null"
          }
        ],
        "default": null,
        "description": "The tool calls of the agent",
        "title": "Tool Calls"
      }
    },
    "required": [
      "role"
    ],
    "title": "ChatMessage",
    "type": "object"
  }
]

(DEBUG) [Synalinks]
Call ID: 599dee13-fe6a-43b3-893e-d37d31719e1c
Parent call ID: None
Module: DeepAgent
Module Name: deep_agent
Module Description: A coding agent whose tools are a sandboxed copy of a workdir.
Data Model JSON Schema:
[
  {
    "$defs": {
      "ChatMessage": {
        "additionalProperties": false,
        "description": "A chat message",
        "properties": {
          "role": {
            "$ref": "#/$defs/ChatRole",
            "description": "The chat message role"
          },
          "reasoning_content": {
            "anyOf": [
              {
                "type": "string"
              },
              {
                "type": "null"
              }
            ],
            "default": null,
            "description": "The reasoning/thinking content of the message. Keyed to match the litellm/DeepSeek `reasoning_content` chat-completion field (a provider extension, not part of the base OpenAI spec), so the message API stays a subset of the litellm-extended chat-completion message.",
            "title": "Reasoning Content"
          },
          "thinking_blocks": {
            "anyOf": [
              {
                "items": {
                  "additionalProperties": true,
                  "type": "object"
                },
                "type": "array"
              },
              {
                "type": "null"
              }
            ],
            "default": null,
            "description": "Opaque provider-native thinking blocks (e.g. Anthropic's signed `thinking_blocks`; a litellm extension, not part of the base OpenAI spec). Carried through verbatim on assistant-message re-injection so multi-turn tool-use round-trips preserve signatures. None for providers that emit reasoning only as text.",
            "title": "Thinking Blocks"
          },
          "content": {
            "anyOf": [
              {
                "type": "string"
              },
              {
                "items": {
                  "additionalProperties": true,
                  "type": "object"
                },
                "type": "array"
              },
              {
                "additionalProperties": true,
                "type": "object"
              },
              {
                "type": "null"
              }
            ],
            "default": null,
            "description": "The content of the message",
            "title": "Content"
          },
          "tool_call_id": {
            "anyOf": [
              {
                "type": "string"
              },
              {
                "type": "null"
              }
            ],
            "default": null,
            "description": "The id of the tool call if role is `tool`",
            "title": "Tool Call Id"
          },
          "tool_calls": {
            "anyOf": [
              {
                "items": {
                  "$ref": "#/$defs/ToolCall"
                },
                "type": "array"
              },
              {
                "type": "null"
              }
            ],
            "default": null,
            "description": "The tool calls of the agent",
            "title": "Tool Calls"
          }
        },
        "required": [
          "role"
        ],
        "title": "ChatMessage",
        "type": "object"
      },
      "ChatRole": {
        "description": "The chat message roles",
        "enum": [
          "system",
          "developer",
          "user",
          "assistant",
          "tool",
          "function"
        ],
        "title": "ChatRole",
        "type": "string"
      },
      "ToolCall": {
        "additionalProperties": false,
        "description": "A tool call, shaped like an OpenAI Chat Completions tool call.\n\nMirrors the wire envelope (`{id, type, function: {name, arguments}}`)\nexcept that `arguments` stays a parsed dict rather than a JSON-encoded\nstring, so modules and agents can read it directly. The string encoding\nis applied only at the wire edge (see `backend.pydantic.chat_completions`).",
        "properties": {
          "id": {
            "description": "The id of the tool call",
            "title": "Id",
            "type": "string"
          },
          "type": {
            "const": "function",
            "default": "function",
            "description": "The tool call type (always `function` today)",
            "title": "Type",
            "type": "string"
          },
          "function": {
            "$ref": "#/$defs/ToolCallFunction",
            "description": "The function invocation (name + arguments)"
          }
        },
        "required": [
          "id",
          "function"
        ],
        "title": "ToolCall",
        "type": "object"
      },
      "ToolCallFunction": {
        "additionalProperties": false,
        "description": "The `function` payload of a tool call (name + parsed arguments).",
        "properties": {
          "name": {
            "description": "The name of the function called",
            "title": "Name",
            "type": "string"
          },
          "arguments": {
            "additionalProperties": true,
            "description": "The arguments of the tool call",
            "title": "Arguments",
            "type": "object"
          }
        },
        "required": [
          "name",
          "arguments"
        ],
        "title": "ToolCallFunction",
        "type": "object"
      }
    },
    "additionalProperties": false,
    "description": "A list of chat messages",
    "properties": {
      "messages": {
        "default": [],
        "description": "The list of chat messages",
        "items": {
          "$ref": "#/$defs/ChatMessage"
        },
        "title": "Messages",
        "type": "array"
      }
    },
    "title": "ChatMessages",
    "type": "object"
  }
]

Program: deep_agent
description: 'A coding agent with sandboxed file and Python tools.'
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━┓
┃ Module (type)               ┃ Output Schema                    ┃  Vars # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━┩
│ input_module (InputModule)  │ ChatRole: Literal['system',      │       0 │
│                             │ 'developer', 'user',             │         │
│                             │ 'assistant', 'tool', 'function'] │         │
│                             │                                  │         │
│                             │ ToolCallFunction:                │         │
│                             │   name: str                      │         │
│                             │   arguments: dict                │         │
│                             │                                  │         │
│                             │ ToolCall:                        │         │
│                             │   id: str                        │         │
│                             │   type: Literal['function'] =    │         │
│                             │ 'function'                       │         │
│                             │   function: ToolCallFunction     │         │
│                             │                                  │         │
│                             │ ChatMessage:                     │         │
│                             │   role: ChatRole                 │         │
│                             │   reasoning_content: str | None  │         │
│                             │ = None                           │         │
│                             │   thinking_blocks: list | None = │         │
│                             │ None                             │         │
│                             │   content: str | list | dict |   │         │
│                             │ None = None                      │         │
│                             │   tool_call_id: str | None =     │         │
│                             │ None                             │         │
│                             │   tool_calls: list[ToolCall] |   │         │
│                             │ None = None                      │         │
│                             │                                  │         │
│                             │ ChatMessages:                    │         │
│                             │   messages: list[ChatMessage] =  │         │
│                             │ []                               │         │
├─────────────────────────────┼──────────────────────────────────┼─────────┤
│ deep_agent (DeepAgent)      │ ChatRole: Literal['system',      │       2 │
│                             │ 'developer', 'user',             │         │
│                             │ 'assistant', 'tool', 'function'] │         │
│                             │                                  │         │
│                             │ ToolCallFunction:                │         │
│                             │   name: str                      │         │
│                             │   arguments: dict                │         │
│                             │                                  │         │
│                             │ ToolCall:                        │         │
│                             │   id: str                        │         │
│                             │   type: Literal['function'] =    │         │
│                             │ 'function'                       │         │
│                             │   function: ToolCallFunction     │         │
│                             │                                  │         │
│                             │ ChatMessage:                     │         │
│                             │   role: ChatRole                 │         │
│                             │   reasoning_content: str | None  │         │
│                             │ = None                           │         │
│                             │   thinking_blocks: list | None = │         │
│                             │ None                             │         │
│                             │   content: str | list | dict |   │         │
│                             │ None = None                      │         │
│                             │   tool_call_id: str | None =     │         │
│                             │ None                             │         │
│                             │   tool_calls: list[ToolCall] |   │         │
│                             │ None = None                      │         │
│                             │                                  │         │
│                             │ ChatMessages:                    │         │
│                             │   messages: list[ChatMessage] =  │         │
│                             │ []                               │         │
└─────────────────────────────┴──────────────────────────────────┴─────────┘
[Synalinks]
Call ID: 19ef9e6d-d878-4cbd-b256-987dc5b5776f
Parent call ID: None
Module: Functional
Module Name: deep_agent
Module Description: A coding agent with sandboxed file and Python tools.
Data Model JSON:
[
  {
    "messages": [
      {
        "role": "user",
        "content": "Open calculator.py and add a `multiply(a, b)` function in the same style as `add`. Then add an `if __name__ == \"__main__\":` block that prints `multiply(6, 7)`, and use `run_bash` to run `python3 calculator.py` to confirm it prints 42."
      }
    ]
  }
]

[Synalinks]
Call ID: b69e23c0-3ad8-4eab-9613-408518369dca
Parent call ID: 19ef9e6d-d878-4cbd-b256-987dc5b5776f
Module: DeepAgent
Module Name: deep_agent
Module Description: A coding agent whose tools are a sandboxed copy of a workdir.
Data Model JSON:
[
  {
    "messages": [
      {
        "role": "user",
        "content": "Open calculator.py and add a `multiply(a, b)` function in the same style as `add`. Then add an `if __name__ == \"__main__\":` block that prints `multiply(6, 7)`, and use `run_bash` to run `python3 calculator.py` to confirm it prints 42."
      }
    ]
  }
]

[Synalinks]
Call ID: 4e06ff61-7eed-4498-a58a-638751105aaf
Parent call ID: b69e23c0-3ad8-4eab-9613-408518369dca
Module: Generator
Module Name: tool_calls_generator_deep_agent
Module Description: Use a `LanguageModel` to generate a data model from an arbitrary input data model.
Data Model JSON:
[
  {
    "messages": [
      {
        "role": "user",
        "content": "Open calculator.py and add a `multiply(a, b)` function in the same style as `add`. Then add an `if __name__ == \"__main__\":` block that prints `multiply(6, 7)`, and use `run_bash` to run `python3 calculator.py` to confirm it prints 42."
      }
    ]
  }
]

[Synalinks]
Call ID: 681bbbc1-e8be-445a-9caf-ed87ef30b64b
Parent call ID: 4e06ff61-7eed-4498-a58a-638751105aaf
Module: LanguageModel
Module Name: language_model
Module Description: A language model API wrapper.
Data Model JSON:
[
  {
    "messages": [
      {
        "role": "system",
        "content": "<instructions>\nYou are a software engineering assistant working inside a sandboxed,\ncopy-on-write filesystem.\n\nWorkdir: /tmp/deep_agent_guide_2xzctol8\nAvailable tools: read_file, list_files, search_files, write_file,\nedit_file, run_bash\n\nPlan:\n1. Use `list_files` to discover files (glob, e.g. `**/*.py`).\n2. Use `search_files` to grep file contents by regex across a glob.\n3. Use `read_file` to read a file; it returns the requested lines with\n   1-based `start_line` / `end_line`. Page through long files with\n   `offset` / `limit` (raise `offset` to read further in).\n4. Use `edit_file` for surgical changes (preferred over `write_file`).\n5. Use `run_bash` to run shell commands against the filesystem — pipes,\n   redirects, globs, `&&`, loops, and `python3` (e.g. `python3 script.py`\n   after `write_file`-ing it). `python3` is a real interpreter with the\n   full standard library, third-party packages and network.\n6. Once you have the answer, stop calling tools and respond.\n\nNotes:\n- The filesystem is host-safe: edits and new files land in the sandbox's\n  mounted filesystem and never modify the real workspace on disk.\n- If the conversation includes an `InputsSummary`, you only see field\n  previews and sizes — the full, untruncated input values are saved as the\n  JSON file named in its `inputs_file` field. Read it with the `read_file`\n  tool (or `run_bash` `cat`) rather than retyping values from the preview.\n</instructions>\n"
      },
      {
        "role": "user",
        "content": "Open calculator.py and add a `multiply(a, b)` function in the same style as `add`. Then add an `if __name__ == \"__main__\":` block that prints `multiply(6, 7)`, and use `run_bash` to run `python3 calculator.py` to confirm it prints 42."
      }
    ]
  }
]

[Synalinks]
Call ID: 681bbbc1-e8be-445a-9caf-ed87ef30b64b
Parent call ID: 4e06ff61-7eed-4498-a58a-638751105aaf
Module: LanguageModel
Module Name: language_model
Module Description: A language model API wrapper.
Data Model JSON:
[
  {
    "role": "assistant",
    "content": "",
    "reasoning_content": "Okay, let's tackle this step by step. First, the user wants me to open calculator.py and add a multiply function similar to the add function. Then, add an if __name__ block to print multiply(6,7) and run the script to check if it outputs 42.\n\nFirst, I need to check if calculator.py exists. The user mentioned it, so I'll use list_files with a glob pattern like '**/*.py' to find it. Once I have the path, I can read the file to see its current content. Let me start by listing the files in the workdir to locate calculator.py.\n\nAssuming the file is found, I'll read it to understand the existing structure. The add function is probably defined with def add(a, b): and returns a + b. The multiply function should follow the same style, so I'll add def multiply(a, b): return a * b.\n\nNext, I need to add the if __name__ == \"__main__\": block. This block should call multiply(6,7) and print the result. So, after defining multiply, I'll insert that block.\n\nOnce the code is modified, I'll use run_bash to execute python3 calculator.py and verify the output is 42. If there are any errors during this process, I'll need to adjust the code accordingly. Let me proceed step by step, ensuring each part is correctly implemented.\n",
    "tool_calls": [
      {
        "id": "call_7z9nn0nx",
        "type": "function",
        "function": {
          "name": "list_files",
          "arguments": {
            "pattern": "**/*.py"
          }
        }
      }
    ]
  }
]

[Synalinks]
Call ID: 4e06ff61-7eed-4498-a58a-638751105aaf
Parent call ID: b69e23c0-3ad8-4eab-9613-408518369dca
Module: Generator
Module Name: tool_calls_generator_deep_agent
Module Description: Use a `LanguageModel` to generate a data model from an arbitrary input data model.
Data Model JSON:
[
  {
    "role": "assistant",
    "content": "",
    "reasoning_content": "Okay, let's tackle this step by step. First, the user wants me to open calculator.py and add a multiply function similar to the add function. Then, add an if __name__ block to print multiply(6,7) and run the script to check if it outputs 42.\n\nFirst, I need to check if calculator.py exists. The user mentioned it, so I'll use list_files with a glob pattern like '**/*.py' to find it. Once I have the path, I can read the file to see its current content. Let me start by listing the files in the workdir to locate calculator.py.\n\nAssuming the file is found, I'll read it to understand the existing structure. The add function is probably defined with def add(a, b): and returns a + b. The multiply function should follow the same style, so I'll add def multiply(a, b): return a * b.\n\nNext, I need to add the if __name__ == \"__main__\": block. This block should call multiply(6,7) and print the result. So, after defining multiply, I'll insert that block.\n\nOnce the code is modified, I'll use run_bash to execute python3 calculator.py and verify the output is 42. If there are any errors during this process, I'll need to adjust the code accordingly. Let me proceed step by step, ensuring each part is correctly implemented.\n",
    "tool_calls": [
      {
        "id": "call_7z9nn0nx",
        "type": "function",
        "function": {
          "name": "list_files",
          "arguments": {
            "pattern": "**/*.py"
          }
        }
      }
    ]
  }
]

[Synalinks]
Call ID: 7e57fef6-a221-4c4c-b7cb-a8650e98339d
Parent call ID: 681bbbc1-e8be-445a-9caf-ed87ef30b64b
Module: Tool
Module Name: list_files
Module Description: List files in the mounted virtual filesystem matching a glob.
Keyword Arguments:
{
  "pattern": "**/*.py"
}

[Synalinks]
Call ID: 7e57fef6-a221-4c4c-b7cb-a8650e98339d
Parent call ID: 681bbbc1-e8be-445a-9caf-ed87ef30b64b
Module: Tool
Module Name: list_files
Module Description: List files in the mounted virtual filesystem matching a glob.
Data Model JSON:
[
  {
    "files": [
      "/calculator.py"
    ],
    "total": 1,
    "offset": 1,
    "truncated": false
  }
]

[Synalinks]
Call ID: 44711f88-b34d-4942-a8b3-4f7153a9061a
Parent call ID: 7e57fef6-a221-4c4c-b7cb-a8650e98339d
Module: Generator
Module Name: tool_calls_generator_deep_agent
Module Description: Use a `LanguageModel` to generate a data model from an arbitrary input data model.
Data Model JSON:
[
  {
    "messages": [
      {
        "role": "user",
        "content": "Open calculator.py and add a `multiply(a, b)` function in the same style as `add`. Then add an `if __name__ == \"__main__\":` block that prints `multiply(6, 7)`, and use `run_bash` to run `python3 calculator.py` to confirm it prints 42."
      },
      {
        "role": "assistant",
        "content": "",
        "reasoning_content": "Okay, let's tackle this step by step. First, the user wants me to open calculator.py and add a multiply function similar to the add function. Then, add an if __name__ block to print multiply(6,7) and run the script to check if it outputs 42.\n\nFirst, I need to check if calculator.py exists. The user mentioned it, so I'll use list_files with a glob pattern like '**/*.py' to find it. Once I have the path, I can read the file to see its current content. Let me start by listing the files in the workdir to locate calculator.py.\n\nAssuming the file is found, I'll read it to understand the existing structure. The add function is probably defined with def add(a, b): and returns a + b. The multiply function should follow the same style, so I'll add def multiply(a, b): return a * b.\n\nNext, I need to add the if __name__ == \"__main__\": block. This block should call multiply(6,7) and print the result. So, after defining multiply, I'll insert that block.\n\nOnce the code is modified, I'll use run_bash to execute python3 calculator.py and verify the output is 42. If there are any errors during this process, I'll need to adjust the code accordingly. Let me proceed step by step, ensuring each part is correctly implemented.\n",
        "tool_calls": [
          {
            "id": "call_7z9nn0nx",
            "type": "function",
            "function": {
              "name": "list_files",
              "arguments": {
                "pattern": "**/*.py"
              }
            }
          }
        ]
      },
      {
        "role": "tool",
        "content": {
          "files": [
            "/calculator.py"
          ],
          "total": 1,
          "offset": 1,
          "truncated": false
        },
        "tool_call_id": "call_7z9nn0nx"
      }
    ]
  }
]

[Synalinks]
Call ID: 097cf5bc-8ee0-4540-add5-82f36a237097
Parent call ID: 44711f88-b34d-4942-a8b3-4f7153a9061a
Module: LanguageModel
Module Name: language_model
Module Description: A language model API wrapper.
Data Model JSON:
[
  {
    "messages": [
      {
        "role": "system",
        "content": "<instructions>\nYou are a software engineering assistant working inside a sandboxed,\ncopy-on-write filesystem.\n\nWorkdir: /tmp/deep_agent_guide_2xzctol8\nAvailable tools: read_file, list_files, search_files, write_file,\nedit_file, run_bash\n\nPlan:\n1. Use `list_files` to discover files (glob, e.g. `**/*.py`).\n2. Use `search_files` to grep file contents by regex across a glob.\n3. Use `read_file` to read a file; it returns the requested lines with\n   1-based `start_line` / `end_line`. Page through long files with\n   `offset` / `limit` (raise `offset` to read further in).\n4. Use `edit_file` for surgical changes (preferred over `write_file`).\n5. Use `run_bash` to run shell commands against the filesystem — pipes,\n   redirects, globs, `&&`, loops, and `python3` (e.g. `python3 script.py`\n   after `write_file`-ing it). `python3` is a real interpreter with the\n   full standard library, third-party packages and network.\n6. Once you have the answer, stop calling tools and respond.\n\nNotes:\n- The filesystem is host-safe: edits and new files land in the sandbox's\n  mounted filesystem and never modify the real workspace on disk.\n- If the conversation includes an `InputsSummary`, you only see field\n  previews and sizes — the full, untruncated input values are saved as the\n  JSON file named in its `inputs_file` field. Read it with the `read_file`\n  tool (or `run_bash` `cat`) rather than retyping values from the preview.\n</instructions>\n"
      },
      {
        "role": "user",
        "content": "Open calculator.py and add a `multiply(a, b)` function in the same style as `add`. Then add an `if __name__ == \"__main__\":` block that prints `multiply(6, 7)`, and use `run_bash` to run `python3 calculator.py` to confirm it prints 42."
      },
      {
        "role": "assistant",
        "reasoning_content": "Okay, let's tackle this step by step. First, the user wants me to open calculator.py and add a multiply function similar to the add function. Then, add an if __name__ block to print multiply(6,7) and run the script to check if it outputs 42.\n\nFirst, I need to check if calculator.py exists. The user mentioned it, so I'll use list_files with a glob pattern like '**/*.py' to find it. Once I have the path, I can read the file to see its current content. Let me start by listing the files in the workdir to locate calculator.py.\n\nAssuming the file is found, I'll read it to understand the existing structure. The add function is probably defined with def add(a, b): and returns a + b. The multiply function should follow the same style, so I'll add def multiply(a, b): return a * b.\n\nNext, I need to add the if __name__ == \"__main__\": block. This block should call multiply(6,7) and print the result. So, after defining multiply, I'll insert that block.\n\nOnce the code is modified, I'll use run_bash to execute python3 calculator.py and verify the output is 42. If there are any errors during this process, I'll need to adjust the code accordingly. Let me proceed step by step, ensuring each part is correctly implemented.\n",
        "content": "",
        "tool_calls": [
          {
            "id": "call_7z9nn0nx",
            "type": "function",
            "function": {
              "name": "list_files",
              "arguments": {
                "pattern": "**/*.py"
              }
            }
          }
        ]
      },
      {
        "role": "tool",
        "content": {
          "files": [
            "/calculator.py"
          ],
          "total": 1,
          "offset": 1,
          "truncated": false
        },
        "tool_call_id": "call_7z9nn0nx"
      }
    ]
  }
]

/home/yoan/SynalinksWorkspace/synalinks/synalinks/src/modules/language_models/language_model.py:938: UserWarning: Error occured while trying to call <LanguageModel model=ollama_chat/qwen3:8b api_base=http://localhost:11434>: Empty response from the language model: no content or tool_calls returned.
Received response=
  warnings.warn(
[Synalinks]Retrying synalinks.src.modules.language_models.language_model.LanguageModel._call_with_retry.<locals>._do_call in 1 seconds as it raised ValueError: Empty response from the language model: no content or tool_calls returned..
[Synalinks]Retrying synalinks.src.modules.language_models.language_model.LanguageModel._call_with_retry.<locals>._do_call in 2 seconds as it raised ValueError: Empty response from the language model: no content or tool_calls returned..
[Synalinks]
Call ID: 097cf5bc-8ee0-4540-add5-82f36a237097
Parent call ID: 44711f88-b34d-4942-a8b3-4f7153a9061a
Module: LanguageModel
Module Name: language_model
Module Description: A language model API wrapper.
Data Model JSON:
[
  {
    "role": "assistant",
    "content": "",
    "reasoning_content": "Okay, I need to open calculator.py and add the multiply function. Let me first read the existing content to understand the structure. I'll use read_file with the path /calculator.py. Let's see what's inside.\n",
    "tool_calls": [
      {
        "id": "call_dkwcirtc",
        "type": "function",
        "function": {
          "name": "read_file",
          "arguments": {
            "path": "/calculator.py"
          }
        }
      }
    ]
  }
]

[Synalinks]
Call ID: 44711f88-b34d-4942-a8b3-4f7153a9061a
Parent call ID: 7e57fef6-a221-4c4c-b7cb-a8650e98339d
Module: Generator
Module Name: tool_calls_generator_deep_agent
Module Description: Use a `LanguageModel` to generate a data model from an arbitrary input data model.
Data Model JSON:
[
  {
    "role": "assistant",
    "content": "",
    "reasoning_content": "Okay, I need to open calculator.py and add the multiply function. Let me first read the existing content to understand the structure. I'll use read_file with the path /calculator.py. Let's see what's inside.\n",
    "tool_calls": [
      {
        "id": "call_dkwcirtc",
        "type": "function",
        "function": {
          "name": "read_file",
          "arguments": {
            "path": "/calculator.py"
          }
        }
      }
    ]
  }
]

[Synalinks]
Call ID: 51dc7fda-d6a1-4e34-8f4a-0860e09960c2
Parent call ID: 097cf5bc-8ee0-4540-add5-82f36a237097
Module: Tool
Module Name: read_file
Module Description: Read a text file from the mounted virtual filesystem, by line range.
Keyword Arguments:
{
  "path": "/calculator.py"
}

[Synalinks]
Call ID: 51dc7fda-d6a1-4e34-8f4a-0860e09960c2
Parent call ID: 097cf5bc-8ee0-4540-add5-82f36a237097
Module: Tool
Module Name: read_file
Module Description: Read a text file from the mounted virtual filesystem, by line range.
Data Model JSON:
[
  {
    "content": "def add(a, b):\n    \"\"\"Return a + b.\"\"\"\n    return a + b\n",
    "start_line": 1,
    "end_line": 3,
    "total_lines": 3,
    "truncated": false
  }
]

[Synalinks]
Call ID: 1c3b9f10-b42b-44f5-90a8-9da4bbb03a9f
Parent call ID: 51dc7fda-d6a1-4e34-8f4a-0860e09960c2
Module: Generator
Module Name: tool_calls_generator_deep_agent
Module Description: Use a `LanguageModel` to generate a data model from an arbitrary input data model.
Data Model JSON:
[
  {
    "messages": [
      {
        "role": "user",
        "content": "Open calculator.py and add a `multiply(a, b)` function in the same style as `add`. Then add an `if __name__ == \"__main__\":` block that prints `multiply(6, 7)`, and use `run_bash` to run `python3 calculator.py` to confirm it prints 42."
      },
      {
        "role": "assistant",
        "content": "",
        "reasoning_content": "Okay, let's tackle this step by step. First, the user wants me to open calculator.py and add a multiply function similar to the add function. Then, add an if __name__ block to print multiply(6,7) and run the script to check if it outputs 42.\n\nFirst, I need to check if calculator.py exists. The user mentioned it, so I'll use list_files with a glob pattern like '**/*.py' to find it. Once I have the path, I can read the file to see its current content. Let me start by listing the files in the workdir to locate calculator.py.\n\nAssuming the file is found, I'll read it to understand the existing structure. The add function is probably defined with def add(a, b): and returns a + b. The multiply function should follow the same style, so I'll add def multiply(a, b): return a * b.\n\nNext, I need to add the if __name__ == \"__main__\": block. This block should call multiply(6,7) and print the result. So, after defining multiply, I'll insert that block.\n\nOnce the code is modified, I'll use run_bash to execute python3 calculator.py and verify the output is 42. If there are any errors during this process, I'll need to adjust the code accordingly. Let me proceed step by step, ensuring each part is correctly implemented.\n",
        "tool_calls": [
          {
            "id": "call_7z9nn0nx",
            "type": "function",
            "function": {
              "name": "list_files",
              "arguments": {
                "pattern": "**/*.py"
              }
            }
          }
        ]
      },
      {
        "role": "tool",
        "content": {
          "files": [
            "/calculator.py"
          ],
          "total": 1,
          "offset": 1,
          "truncated": false
        },
        "tool_call_id": "call_7z9nn0nx"
      },
      {
        "role": "assistant",
        "content": "",
        "reasoning_content": "Okay, I need to open calculator.py and add the multiply function. Let me first read the existing content to understand the structure. I'll use read_file with the path /calculator.py. Let's see what's inside.\n",
        "tool_calls": [
          {
            "id": "call_dkwcirtc",
            "type": "function",
            "function": {
              "name": "read_file",
              "arguments": {
                "path": "/calculator.py"
              }
            }
          }
        ]
      },
      {
        "role": "tool",
        "content": {
          "content": "def add(a, b):\n    \"\"\"Return a + b.\"\"\"\n    return a + b\n",
          "start_line": 1,
          "end_line": 3,
          "total_lines": 3,
          "truncated": false
        },
        "tool_call_id": "call_dkwcirtc"
      }
    ]
  }
]

[Synalinks]
Call ID: 90486972-61e5-4730-9532-2dc33b1cfe49
Parent call ID: 1c3b9f10-b42b-44f5-90a8-9da4bbb03a9f
Module: LanguageModel
Module Name: language_model
Module Description: A language model API wrapper.
Data Model JSON:
[
  {
    "messages": [
      {
        "role": "system",
        "content": "<instructions>\nYou are a software engineering assistant working inside a sandboxed,\ncopy-on-write filesystem.\n\nWorkdir: /tmp/deep_agent_guide_2xzctol8\nAvailable tools: read_file, list_files, search_files, write_file,\nedit_file, run_bash\n\nPlan:\n1. Use `list_files` to discover files (glob, e.g. `**/*.py`).\n2. Use `search_files` to grep file contents by regex across a glob.\n3. Use `read_file` to read a file; it returns the requested lines with\n   1-based `start_line` / `end_line`. Page through long files with\n   `offset` / `limit` (raise `offset` to read further in).\n4. Use `edit_file` for surgical changes (preferred over `write_file`).\n5. Use `run_bash` to run shell commands against the filesystem — pipes,\n   redirects, globs, `&&`, loops, and `python3` (e.g. `python3 script.py`\n   after `write_file`-ing it). `python3` is a real interpreter with the\n   full standard library, third-party packages and network.\n6. Once you have the answer, stop calling tools and respond.\n\nNotes:\n- The filesystem is host-safe: edits and new files land in the sandbox's\n  mounted filesystem and never modify the real workspace on disk.\n- If the conversation includes an `InputsSummary`, you only see field\n  previews and sizes — the full, untruncated input values are saved as the\n  JSON file named in its `inputs_file` field. Read it with the `read_file`\n  tool (or `run_bash` `cat`) rather than retyping values from the preview.\n</instructions>\n"
      },
      {
        "role": "user",
        "content": "Open calculator.py and add a `multiply(a, b)` function in the same style as `add`. Then add an `if __name__ == \"__main__\":` block that prints `multiply(6, 7)`, and use `run_bash` to run `python3 calculator.py` to confirm it prints 42."
      },
      {
        "role": "assistant",
        "reasoning_content": "Okay, let's tackle this step by step. First, the user wants me to open calculator.py and add a multiply function similar to the add function. Then, add an if __name__ block to print multiply(6,7) and run the script to check if it outputs 42.\n\nFirst, I need to check if calculator.py exists. The user mentioned it, so I'll use list_files with a glob pattern like '**/*.py' to find it. Once I have the path, I can read the file to see its current content. Let me start by listing the files in the workdir to locate calculator.py.\n\nAssuming the file is found, I'll read it to understand the existing structure. The add function is probably defined with def add(a, b): and returns a + b. The multiply function should follow the same style, so I'll add def multiply(a, b): return a * b.\n\nNext, I need to add the if __name__ == \"__main__\": block. This block should call multiply(6,7) and print the result. So, after defining multiply, I'll insert that block.\n\nOnce the code is modified, I'll use run_bash to execute python3 calculator.py and verify the output is 42. If there are any errors during this process, I'll need to adjust the code accordingly. Let me proceed step by step, ensuring each part is correctly implemented.\n",
        "content": "",
        "tool_calls": [
          {
            "id": "call_7z9nn0nx",
            "type": "function",
            "function": {
              "name": "list_files",
              "arguments": {
                "pattern": "**/*.py"
              }
            }
          }
        ]
      },
      {
        "role": "tool",
        "content": {
          "files": [
            "/calculator.py"
          ],
          "total": 1,
          "offset": 1,
          "truncated": false
        },
        "tool_call_id": "call_7z9nn0nx"
      },
      {
        "role": "assistant",
        "reasoning_content": "Okay, I need to open calculator.py and add the multiply function. Let me first read the existing content to understand the structure. I'll use read_file with the path /calculator.py. Let's see what's inside.\n",
        "content": "",
        "tool_calls": [
          {
            "id": "call_dkwcirtc",
            "type": "function",
            "function": {
              "name": "read_file",
              "arguments": {
                "path": "/calculator.py"
              }
            }
          }
        ]
      },
      {
        "role": "tool",
        "content": {
          "content": "def add(a, b):\n    \"\"\"Return a + b.\"\"\"\n    return a + b\n",
          "start_line": 1,
          "end_line": 3,
          "total_lines": 3,
          "truncated": false
        },
        "tool_call_id": "call_dkwcirtc"
      }
    ]
  }
]

[Synalinks]
Call ID: 90486972-61e5-4730-9532-2dc33b1cfe49
Parent call ID: 1c3b9f10-b42b-44f5-90a8-9da4bbb03a9f
Module: LanguageModel
Module Name: language_model
Module Description: A language model API wrapper.
Data Model JSON:
[
  {
    "role": "assistant",
    "content": "",
    "reasoning_content": "Okay, the current content of calculator.py is just the add function. The user wants to add a multiply function in the same style. So I'll need to write the multiply function next. Let me think about how to structure that.\n\nThe multiply function should take two parameters, a and b, and return their product. So the code would be:\n\ndef multiply(a, b):\n    \"\"\"Return a * b.\"\"\"\n    return a * b\n\nI need to add this after the add function. Then, add the if __name__ block that calls multiply(6,7). Let me check if there's any existing code beyond the add function. The read_file response shows only three lines, so the file is currently short. I'll append the new code.\n\nWait, maybe the file has more lines that aren't shown? The total_lines is 3, so the file is small. I'll proceed to edit the file by adding the multiply function and the main block. Let me use the edit_file function to make these changes. Let me construct the new content.\n\nOriginal content:\ndef add(a, b):\n    \"\"\"Return a + b.\"\"\"\n    return a + b\n\nNew content will be:\ndef add(a, b):\n    \"\"\"Return a + b.\"\"\"\n    return a + b\n\ndef multiply(a, b):\n    \"\"\"Return a * b.\"\"\"\n    return a * b\n\nif __name__ == \"__main__\":\n    print(multiply(6, 7))\n\nSo I'll replace the existing content with this new content. Wait, but edit_file requires specifying the old text and new text. Alternatively, since the file is short, maybe it's easier to write the entire content again. Let me use write_file instead. Let me check the functions available. The user mentioned that edit_file is preferred over write_file for surgical changes, but in this case, since the file is small, writing the entire content might be simpler.\n\nSo I'll use write_file with the path /calculator.py and the new content. Let me make sure to include all the existing code plus the new parts. Let me structure the new content properly. Then, after writing, I'll run the script using run_bash to confirm it outputs 42.\n",
    "tool_calls": [
      {
        "id": "call_b88h9iwd",
        "type": "function",
        "function": {
          "name": "write_file",
          "arguments": {
            "content": "def add(a, b):\n    \"\"\"Return a + b.\"\"\"\n    return a + b\n\ndef multiply(a, b):\n    \"\"\"Return a * b.\"\"\"\n    return a * b\n\nif __name__ == \"__main__\":\n    print(multiply(6, 7))",
            "path": "/calculator.py"
          }
        }
      }
    ]
  }
]

[Synalinks]
Call ID: 1c3b9f10-b42b-44f5-90a8-9da4bbb03a9f
Parent call ID: 51dc7fda-d6a1-4e34-8f4a-0860e09960c2
Module: Generator
Module Name: tool_calls_generator_deep_agent
Module Description: Use a `LanguageModel` to generate a data model from an arbitrary input data model.
Data Model JSON:
[
  {
    "role": "assistant",
    "content": "",
    "reasoning_content": "Okay, the current content of calculator.py is just the add function. The user wants to add a multiply function in the same style. So I'll need to write the multiply function next. Let me think about how to structure that.\n\nThe multiply function should take two parameters, a and b, and return their product. So the code would be:\n\ndef multiply(a, b):\n    \"\"\"Return a * b.\"\"\"\n    return a * b\n\nI need to add this after the add function. Then, add the if __name__ block that calls multiply(6,7). Let me check if there's any existing code beyond the add function. The read_file response shows only three lines, so the file is currently short. I'll append the new code.\n\nWait, maybe the file has more lines that aren't shown? The total_lines is 3, so the file is small. I'll proceed to edit the file by adding the multiply function and the main block. Let me use the edit_file function to make these changes. Let me construct the new content.\n\nOriginal content:\ndef add(a, b):\n    \"\"\"Return a + b.\"\"\"\n    return a + b\n\nNew content will be:\ndef add(a, b):\n    \"\"\"Return a + b.\"\"\"\n    return a + b\n\ndef multiply(a, b):\n    \"\"\"Return a * b.\"\"\"\n    return a * b\n\nif __name__ == \"__main__\":\n    print(multiply(6, 7))\n\nSo I'll replace the existing content with this new content. Wait, but edit_file requires specifying the old text and new text. Alternatively, since the file is short, maybe it's easier to write the entire content again. Let me use write_file instead. Let me check the functions available. The user mentioned that edit_file is preferred over write_file for surgical changes, but in this case, since the file is small, writing the entire content might be simpler.\n\nSo I'll use write_file with the path /calculator.py and the new content. Let me make sure to include all the existing code plus the new parts. Let me structure the new content properly. Then, after writing, I'll run the script using run_bash to confirm it outputs 42.\n",
    "tool_calls": [
      {
        "id": "call_b88h9iwd",
        "type": "function",
        "function": {
          "name": "write_file",
          "arguments": {
            "content": "def add(a, b):\n    \"\"\"Return a + b.\"\"\"\n    return a + b\n\ndef multiply(a, b):\n    \"\"\"Return a * b.\"\"\"\n    return a * b\n\nif __name__ == \"__main__\":\n    print(multiply(6, 7))",
            "path": "/calculator.py"
          }
        }
      }
    ]
  }
]

[Synalinks]
Call ID: aa5a6566-ac6a-4086-a3ed-3f1ef218e622
Parent call ID: 90486972-61e5-4730-9532-2dc33b1cfe49
Module: Tool
Module Name: write_file
Module Description: Write a text file in the mounted virtual filesystem.
Keyword Arguments:
{
  "content": "def add(a, b):\n    \"\"\"Return a + b.\"\"\"\n    return a + b\n\ndef multiply(a, b):\n    \"\"\"Return a * b.\"\"\"\n    return a * b\n\nif __name__ == \"__main__\":\n    print(multiply(6, 7))",
  "path": "/calculator.py"
}

[Synalinks]
Call ID: aa5a6566-ac6a-4086-a3ed-3f1ef218e622
Parent call ID: 90486972-61e5-4730-9532-2dc33b1cfe49
Module: Tool
Module Name: write_file
Module Description: Write a text file in the mounted virtual filesystem.
Data Model JSON:
[
  {
    "written": "/calculator.py",
    "bytes": 171
  }
]

[Synalinks]
Call ID: 8d8cff5a-0ed6-40a8-813f-866aa641302e
Parent call ID: aa5a6566-ac6a-4086-a3ed-3f1ef218e622
Module: Generator
Module Name: tool_calls_generator_deep_agent
Module Description: Use a `LanguageModel` to generate a data model from an arbitrary input data model.
Data Model JSON:
[
  {
    "messages": [
      {
        "role": "user",
        "content": "Open calculator.py and add a `multiply(a, b)` function in the same style as `add`. Then add an `if __name__ == \"__main__\":` block that prints `multiply(6, 7)`, and use `run_bash` to run `python3 calculator.py` to confirm it prints 42."
      },
      {
        "role": "assistant",
        "content": "",
        "reasoning_content": "Okay, let's tackle this step by step. First, the user wants me to open calculator.py and add a multiply function similar to the add function. Then, add an if __name__ block to print multiply(6,7) and run the script to check if it outputs 42.\n\nFirst, I need to check if calculator.py exists. The user mentioned it, so I'll use list_files with a glob pattern like '**/*.py' to find it. Once I have the path, I can read the file to see its current content. Let me start by listing the files in the workdir to locate calculator.py.\n\nAssuming the file is found, I'll read it to understand the existing structure. The add function is probably defined with def add(a, b): and returns a + b. The multiply function should follow the same style, so I'll add def multiply(a, b): return a * b.\n\nNext, I need to add the if __name__ == \"__main__\": block. This block should call multiply(6,7) and print the result. So, after defining multiply, I'll insert that block.\n\nOnce the code is modified, I'll use run_bash to execute python3 calculator.py and verify the output is 42. If there are any errors during this process, I'll need to adjust the code accordingly. Let me proceed step by step, ensuring each part is correctly implemented.\n",
        "tool_calls": [
          {
            "id": "call_7z9nn0nx",
            "type": "function",
            "function": {
              "name": "list_files",
              "arguments": {
                "pattern": "**/*.py"
              }
            }
          }
        ]
      },
      {
        "role": "tool",
        "content": {
          "files": [
            "/calculator.py"
          ],
          "total": 1,
          "offset": 1,
          "truncated": false
        },
        "tool_call_id": "call_7z9nn0nx"
      },
      {
        "role": "assistant",
        "content": "",
        "reasoning_content": "Okay, I need to open calculator.py and add the multiply function. Let me first read the existing content to understand the structure. I'll use read_file with the path /calculator.py. Let's see what's inside.\n",
        "tool_calls": [
          {
            "id": "call_dkwcirtc",
            "type": "function",
            "function": {
              "name": "read_file",
              "arguments": {
                "path": "/calculator.py"
              }
            }
          }
        ]
      },
      {
        "role": "tool",
        "content": {
          "content": "def add(a, b):\n    \"\"\"Return a + b.\"\"\"\n    return a + b\n",
          "start_line": 1,
          "end_line": 3,
          "total_lines": 3,
          "truncated": false
        },
        "tool_call_id": "call_dkwcirtc"
      },
      {
        "role": "assistant",
        "content": "",
        "reasoning_content": "Okay, the current content of calculator.py is just the add function. The user wants to add a multiply function in the same style. So I'll need to write the multiply function next. Let me think about how to structure that.\n\nThe multiply function should take two parameters, a and b, and return their product. So the code would be:\n\ndef multiply(a, b):\n    \"\"\"Return a * b.\"\"\"\n    return a * b\n\nI need to add this after the add function. Then, add the if __name__ block that calls multiply(6,7). Let me check if there's any existing code beyond the add function. The read_file response shows only three lines, so the file is currently short. I'll append the new code.\n\nWait, maybe the file has more lines that aren't shown? The total_lines is 3, so the file is small. I'll proceed to edit the file by adding the multiply function and the main block. Let me use the edit_file function to make these changes. Let me construct the new content.\n\nOriginal content:\ndef add(a, b):\n    \"\"\"Return a + b.\"\"\"\n    return a + b\n\nNew content will be:\ndef add(a, b):\n    \"\"\"Return a + b.\"\"\"\n    return a + b\n\ndef multiply(a, b):\n    \"\"\"Return a * b.\"\"\"\n    return a * b\n\nif __name__ == \"__main__\":\n    print(multiply(6, 7))\n\nSo I'll replace the existing content with this new content. Wait, but edit_file requires specifying the old text and new text. Alternatively, since the file is short, maybe it's easier to write the entire content again. Let me use write_file instead. Let me check the functions available. The user mentioned that edit_file is preferred over write_file for surgical changes, but in this case, since the file is small, writing the entire content might be simpler.\n\nSo I'll use write_file with the path /calculator.py and the new content. Let me make sure to include all the existing code plus the new parts. Let me structure the new content properly. Then, after writing, I'll run the script using run_bash to confirm it outputs 42.\n",
        "tool_calls": [
          {
            "id": "call_b88h9iwd",
            "type": "function",
            "function": {
              "name": "write_file",
              "arguments": {
                "content": "def add(a, b):\n    \"\"\"Return a + b.\"\"\"\n    return a + b\n\ndef multiply(a, b):\n    \"\"\"Return a * b.\"\"\"\n    return a * b\n\nif __name__ == \"__main__\":\n    print(multiply(6, 7))",
                "path": "/calculator.py"
              }
            }
          }
        ]
      },
      {
        "role": "tool",
        "content": {
          "written": "/calculator.py",
          "bytes": 171
        },
        "tool_call_id": "call_b88h9iwd"
      }
    ]
  }
]

[Synalinks]
Call ID: 0fe281f5-795c-495e-a301-e11501893db4
Parent call ID: 8d8cff5a-0ed6-40a8-813f-866aa641302e
Module: LanguageModel
Module Name: language_model
Module Description: A language model API wrapper.
Data Model JSON:
[
  {
    "messages": [
      {
        "role": "system",
        "content": "<instructions>\nYou are a software engineering assistant working inside a sandboxed,\ncopy-on-write filesystem.\n\nWorkdir: /tmp/deep_agent_guide_2xzctol8\nAvailable tools: read_file, list_files, search_files, write_file,\nedit_file, run_bash\n\nPlan:\n1. Use `list_files` to discover files (glob, e.g. `**/*.py`).\n2. Use `search_files` to grep file contents by regex across a glob.\n3. Use `read_file` to read a file; it returns the requested lines with\n   1-based `start_line` / `end_line`. Page through long files with\n   `offset` / `limit` (raise `offset` to read further in).\n4. Use `edit_file` for surgical changes (preferred over `write_file`).\n5. Use `run_bash` to run shell commands against the filesystem — pipes,\n   redirects, globs, `&&`, loops, and `python3` (e.g. `python3 script.py`\n   after `write_file`-ing it). `python3` is a real interpreter with the\n   full standard library, third-party packages and network.\n6. Once you have the answer, stop calling tools and respond.\n\nNotes:\n- The filesystem is host-safe: edits and new files land in the sandbox's\n  mounted filesystem and never modify the real workspace on disk.\n- If the conversation includes an `InputsSummary`, you only see field\n  previews and sizes — the full, untruncated input values are saved as the\n  JSON file named in its `inputs_file` field. Read it with the `read_file`\n  tool (or `run_bash` `cat`) rather than retyping values from the preview.\n</instructions>\n"
      },
      {
        "role": "user",
        "content": "Open calculator.py and add a `multiply(a, b)` function in the same style as `add`. Then add an `if __name__ == \"__main__\":` block that prints `multiply(6, 7)`, and use `run_bash` to run `python3 calculator.py` to confirm it prints 42."
      },
      {
        "role": "assistant",
        "reasoning_content": "Okay, let's tackle this step by step. First, the user wants me to open calculator.py and add a multiply function similar to the add function. Then, add an if __name__ block to print multiply(6,7) and run the script to check if it outputs 42.\n\nFirst, I need to check if calculator.py exists. The user mentioned it, so I'll use list_files with a glob pattern like '**/*.py' to find it. Once I have the path, I can read the file to see its current content. Let me start by listing the files in the workdir to locate calculator.py.\n\nAssuming the file is found, I'll read it to understand the existing structure. The add function is probably defined with def add(a, b): and returns a + b. The multiply function should follow the same style, so I'll add def multiply(a, b): return a * b.\n\nNext, I need to add the if __name__ == \"__main__\": block. This block should call multiply(6,7) and print the result. So, after defining multiply, I'll insert that block.\n\nOnce the code is modified, I'll use run_bash to execute python3 calculator.py and verify the output is 42. If there are any errors during this process, I'll need to adjust the code accordingly. Let me proceed step by step, ensuring each part is correctly implemented.\n",
        "content": "",
        "tool_calls": [
          {
            "id": "call_7z9nn0nx",
            "type": "function",
            "function": {
              "name": "list_files",
              "arguments": {
                "pattern": "**/*.py"
              }
            }
          }
        ]
      },
      {
        "role": "tool",
        "content": {
          "files": [
            "/calculator.py"
          ],
          "total": 1,
          "offset": 1,
          "truncated": false
        },
        "tool_call_id": "call_7z9nn0nx"
      },
      {
        "role": "assistant",
        "reasoning_content": "Okay, I need to open calculator.py and add the multiply function. Let me first read the existing content to understand the structure. I'll use read_file with the path /calculator.py. Let's see what's inside.\n",
        "content": "",
        "tool_calls": [
          {
            "id": "call_dkwcirtc",
            "type": "function",
            "function": {
              "name": "read_file",
              "arguments": {
                "path": "/calculator.py"
              }
            }
          }
        ]
      },
      {
        "role": "tool",
        "content": {
          "content": "def add(a, b):\n    \"\"\"Return a + b.\"\"\"\n    return a + b\n",
          "start_line": 1,
          "end_line": 3,
          "total_lines": 3,
          "truncated": false
        },
        "tool_call_id": "call_dkwcirtc"
      },
      {
        "role": "assistant",
        "reasoning_content": "Okay, the current content of calculator.py is just the add function. The user wants to add a multiply function in the same style. So I'll need to write the multiply function next. Let me think about how to structure that.\n\nThe multiply function should take two parameters, a and b, and return their product. So the code would be:\n\ndef multiply(a, b):\n    \"\"\"Return a * b.\"\"\"\n    return a * b\n\nI need to add this after the add function. Then, add the if __name__ block that calls multiply(6,7). Let me check if there's any existing code beyond the add function. The read_file response shows only three lines, so the file is currently short. I'll append the new code.\n\nWait, maybe the file has more lines that aren't shown? The total_lines is 3, so the file is small. I'll proceed to edit the file by adding the multiply function and the main block. Let me use the edit_file function to make these changes. Let me construct the new content.\n\nOriginal content:\ndef add(a, b):\n    \"\"\"Return a + b.\"\"\"\n    return a + b\n\nNew content will be:\ndef add(a, b):\n    \"\"\"Return a + b.\"\"\"\n    return a + b\n\ndef multiply(a, b):\n    \"\"\"Return a * b.\"\"\"\n    return a * b\n\nif __name__ == \"__main__\":\n    print(multiply(6, 7))\n\nSo I'll replace the existing content with this new content. Wait, but edit_file requires specifying the old text and new text. Alternatively, since the file is short, maybe it's easier to write the entire content again. Let me use write_file instead. Let me check the functions available. The user mentioned that edit_file is preferred over write_file for surgical changes, but in this case, since the file is small, writing the entire content might be simpler.\n\nSo I'll use write_file with the path /calculator.py and the new content. Let me make sure to include all the existing code plus the new parts. Let me structure the new content properly. Then, after writing, I'll run the script using run_bash to confirm it outputs 42.\n",
        "content": "",
        "tool_calls": [
          {
            "id": "call_b88h9iwd",
            "type": "function",
            "function": {
              "name": "write_file",
              "arguments": {
                "content": "def add(a, b):\n    \"\"\"Return a + b.\"\"\"\n    return a + b\n\ndef multiply(a, b):\n    \"\"\"Return a * b.\"\"\"\n    return a * b\n\nif __name__ == \"__main__\":\n    print(multiply(6, 7))",
                "path": "/calculator.py"
              }
            }
          }
        ]
      },
      {
        "role": "tool",
        "content": {
          "written": "/calculator.py",
          "bytes": 171
        },
        "tool_call_id": "call_b88h9iwd"
      }
    ]
  }
]

[Synalinks]
Call ID: 0fe281f5-795c-495e-a301-e11501893db4
Parent call ID: 8d8cff5a-0ed6-40a8-813f-866aa641302e
Module: LanguageModel
Module Name: language_model
Module Description: A language model API wrapper.
Data Model JSON:
[
  {
    "role": "assistant",
    "content": "",
    "reasoning_content": "Okay, I've updated calculator.py with the multiply function and the main block. Now I need to run the script to check if it outputs 42. Let me use run_bash to execute python3 calculator.py and capture the output. If it works, the output should be 42. Let's do that.\n",
    "tool_calls": [
      {
        "id": "call_c5gaqi66",
        "type": "function",
        "function": {
          "name": "run_bash",
          "arguments": {
            "command": "python3 /calculator.py"
          }
        }
      }
    ]
  }
]

[Synalinks]
Call ID: 8d8cff5a-0ed6-40a8-813f-866aa641302e
Parent call ID: aa5a6566-ac6a-4086-a3ed-3f1ef218e622
Module: Generator
Module Name: tool_calls_generator_deep_agent
Module Description: Use a `LanguageModel` to generate a data model from an arbitrary input data model.
Data Model JSON:
[
  {
    "role": "assistant",
    "content": "",
    "reasoning_content": "Okay, I've updated calculator.py with the multiply function and the main block. Now I need to run the script to check if it outputs 42. Let me use run_bash to execute python3 calculator.py and capture the output. If it works, the output should be 42. Let's do that.\n",
    "tool_calls": [
      {
        "id": "call_c5gaqi66",
        "type": "function",
        "function": {
          "name": "run_bash",
          "arguments": {
            "command": "python3 /calculator.py"
          }
        }
      }
    ]
  }
]

[Synalinks]
Call ID: 26f57ea4-bf37-421f-ab6e-2b9d747b8436
Parent call ID: 0fe281f5-795c-495e-a301-e11501893db4
Module: Tool
Module Name: run_bash
Module Description: Run a shell command in the sandbox's isolated Mirage shell.
Keyword Arguments:
{
  "command": "python3 /calculator.py"
}

[Synalinks]
Call ID: 26f57ea4-bf37-421f-ab6e-2b9d747b8436
Parent call ID: 0fe281f5-795c-495e-a301-e11501893db4
Module: Tool
Module Name: run_bash
Module Description: Run a shell command in the sandbox's isolated Mirage shell.
Data Model JSON:
[
  {
    "ok": true,
    "stdout": "42\n",
    "stderr": "",
    "exit_code": 0
  }
]

[Synalinks]
Call ID: fe25fa97-c262-4c9f-9c16-6efd187f8f3d
Parent call ID: 26f57ea4-bf37-421f-ab6e-2b9d747b8436
Module: Generator
Module Name: tool_calls_generator_deep_agent
Module Description: Use a `LanguageModel` to generate a data model from an arbitrary input data model.
Data Model JSON:
[
  {
    "messages": [
      {
        "role": "user",
        "content": "Open calculator.py and add a `multiply(a, b)` function in the same style as `add`. Then add an `if __name__ == \"__main__\":` block that prints `multiply(6, 7)`, and use `run_bash` to run `python3 calculator.py` to confirm it prints 42."
      },
      {
        "role": "assistant",
        "content": "",
        "reasoning_content": "Okay, let's tackle this step by step. First, the user wants me to open calculator.py and add a multiply function similar to the add function. Then, add an if __name__ block to print multiply(6,7) and run the script to check if it outputs 42.\n\nFirst, I need to check if calculator.py exists. The user mentioned it, so I'll use list_files with a glob pattern like '**/*.py' to find it. Once I have the path, I can read the file to see its current content. Let me start by listing the files in the workdir to locate calculator.py.\n\nAssuming the file is found, I'll read it to understand the existing structure. The add function is probably defined with def add(a, b): and returns a + b. The multiply function should follow the same style, so I'll add def multiply(a, b): return a * b.\n\nNext, I need to add the if __name__ == \"__main__\": block. This block should call multiply(6,7) and print the result. So, after defining multiply, I'll insert that block.\n\nOnce the code is modified, I'll use run_bash to execute python3 calculator.py and verify the output is 42. If there are any errors during this process, I'll need to adjust the code accordingly. Let me proceed step by step, ensuring each part is correctly implemented.\n",
        "tool_calls": [
          {
            "id": "call_7z9nn0nx",
            "type": "function",
            "function": {
              "name": "list_files",
              "arguments": {
                "pattern": "**/*.py"
              }
            }
          }
        ]
      },
      {
        "role": "tool",
        "content": {
          "files": [
            "/calculator.py"
          ],
          "total": 1,
          "offset": 1,
          "truncated": false
        },
        "tool_call_id": "call_7z9nn0nx"
      },
      {
        "role": "assistant",
        "content": "",
        "reasoning_content": "Okay, I need to open calculator.py and add the multiply function. Let me first read the existing content to understand the structure. I'll use read_file with the path /calculator.py. Let's see what's inside.\n",
        "tool_calls": [
          {
            "id": "call_dkwcirtc",
            "type": "function",
            "function": {
              "name": "read_file",
              "arguments": {
                "path": "/calculator.py"
              }
            }
          }
        ]
      },
      {
        "role": "tool",
        "content": {
          "content": "def add(a, b):\n    \"\"\"Return a + b.\"\"\"\n    return a + b\n",
          "start_line": 1,
          "end_line": 3,
          "total_lines": 3,
          "truncated": false
        },
        "tool_call_id": "call_dkwcirtc"
      },
      {
        "role": "assistant",
        "content": "",
        "reasoning_content": "Okay, the current content of calculator.py is just the add function. The user wants to add a multiply function in the same style. So I'll need to write the multiply function next. Let me think about how to structure that.\n\nThe multiply function should take two parameters, a and b, and return their product. So the code would be:\n\ndef multiply(a, b):\n    \"\"\"Return a * b.\"\"\"\n    return a * b\n\nI need to add this after the add function. Then, add the if __name__ block that calls multiply(6,7). Let me check if there's any existing code beyond the add function. The read_file response shows only three lines, so the file is currently short. I'll append the new code.\n\nWait, maybe the file has more lines that aren't shown? The total_lines is 3, so the file is small. I'll proceed to edit the file by adding the multiply function and the main block. Let me use the edit_file function to make these changes. Let me construct the new content.\n\nOriginal content:\ndef add(a, b):\n    \"\"\"Return a + b.\"\"\"\n    return a + b\n\nNew content will be:\ndef add(a, b):\n    \"\"\"Return a + b.\"\"\"\n    return a + b\n\ndef multiply(a, b):\n    \"\"\"Return a * b.\"\"\"\n    return a * b\n\nif __name__ == \"__main__\":\n    print(multiply(6, 7))\n\nSo I'll replace the existing content with this new content. Wait, but edit_file requires specifying the old text and new text. Alternatively, since the file is short, maybe it's easier to write the entire content again. Let me use write_file instead. Let me check the functions available. The user mentioned that edit_file is preferred over write_file for surgical changes, but in this case, since the file is small, writing the entire content might be simpler.\n\nSo I'll use write_file with the path /calculator.py and the new content. Let me make sure to include all the existing code plus the new parts. Let me structure the new content properly. Then, after writing, I'll run the script using run_bash to confirm it outputs 42.\n",
        "tool_calls": [
          {
            "id": "call_b88h9iwd",
            "type": "function",
            "function": {
              "name": "write_file",
              "arguments": {
                "content": "def add(a, b):\n    \"\"\"Return a + b.\"\"\"\n    return a + b\n\ndef multiply(a, b):\n    \"\"\"Return a * b.\"\"\"\n    return a * b\n\nif __name__ == \"__main__\":\n    print(multiply(6, 7))",
                "path": "/calculator.py"
              }
            }
          }
        ]
      },
      {
        "role": "tool",
        "content": {
          "written": "/calculator.py",
          "bytes": 171
        },
        "tool_call_id": "call_b88h9iwd"
      },
      {
        "role": "assistant",
        "content": "",
        "reasoning_content": "Okay, I've updated calculator.py with the multiply function and the main block. Now I need to run the script to check if it outputs 42. Let me use run_bash to execute python3 calculator.py and capture the output. If it works, the output should be 42. Let's do that.\n",
        "tool_calls": [
          {
            "id": "call_c5gaqi66",
            "type": "function",
            "function": {
              "name": "run_bash",
              "arguments": {
                "command": "python3 /calculator.py"
              }
            }
          }
        ]
      },
      {
        "role": "tool",
        "content": {
          "ok": true,
          "stdout": "42\n",
          "stderr": "",
          "exit_code": 0
        },
        "tool_call_id": "call_c5gaqi66"
      }
    ]
  }
]

[Synalinks]
Call ID: 481cfb59-d00b-4a57-a902-43995f2e3bca
Parent call ID: fe25fa97-c262-4c9f-9c16-6efd187f8f3d
Module: LanguageModel
Module Name: language_model
Module Description: A language model API wrapper.
Data Model JSON:
[
  {
    "messages": [
      {
        "role": "system",
        "content": "<instructions>\nYou are a software engineering assistant working inside a sandboxed,\ncopy-on-write filesystem.\n\nWorkdir: /tmp/deep_agent_guide_2xzctol8\nAvailable tools: read_file, list_files, search_files, write_file,\nedit_file, run_bash\n\nPlan:\n1. Use `list_files` to discover files (glob, e.g. `**/*.py`).\n2. Use `search_files` to grep file contents by regex across a glob.\n3. Use `read_file` to read a file; it returns the requested lines with\n   1-based `start_line` / `end_line`. Page through long files with\n   `offset` / `limit` (raise `offset` to read further in).\n4. Use `edit_file` for surgical changes (preferred over `write_file`).\n5. Use `run_bash` to run shell commands against the filesystem — pipes,\n   redirects, globs, `&&`, loops, and `python3` (e.g. `python3 script.py`\n   after `write_file`-ing it). `python3` is a real interpreter with the\n   full standard library, third-party packages and network.\n6. Once you have the answer, stop calling tools and respond.\n\nNotes:\n- The filesystem is host-safe: edits and new files land in the sandbox's\n  mounted filesystem and never modify the real workspace on disk.\n- If the conversation includes an `InputsSummary`, you only see field\n  previews and sizes — the full, untruncated input values are saved as the\n  JSON file named in its `inputs_file` field. Read it with the `read_file`\n  tool (or `run_bash` `cat`) rather than retyping values from the preview.\n</instructions>\n"
      },
      {
        "role": "user",
        "content": "Open calculator.py and add a `multiply(a, b)` function in the same style as `add`. Then add an `if __name__ == \"__main__\":` block that prints `multiply(6, 7)`, and use `run_bash` to run `python3 calculator.py` to confirm it prints 42."
      },
      {
        "role": "assistant",
        "reasoning_content": "Okay, let's tackle this step by step. First, the user wants me to open calculator.py and add a multiply function similar to the add function. Then, add an if __name__ block to print multiply(6,7) and run the script to check if it outputs 42.\n\nFirst, I need to check if calculator.py exists. The user mentioned it, so I'll use list_files with a glob pattern like '**/*.py' to find it. Once I have the path, I can read the file to see its current content. Let me start by listing the files in the workdir to locate calculator.py.\n\nAssuming the file is found, I'll read it to understand the existing structure. The add function is probably defined with def add(a, b): and returns a + b. The multiply function should follow the same style, so I'll add def multiply(a, b): return a * b.\n\nNext, I need to add the if __name__ == \"__main__\": block. This block should call multiply(6,7) and print the result. So, after defining multiply, I'll insert that block.\n\nOnce the code is modified, I'll use run_bash to execute python3 calculator.py and verify the output is 42. If there are any errors during this process, I'll need to adjust the code accordingly. Let me proceed step by step, ensuring each part is correctly implemented.\n",
        "content": "",
        "tool_calls": [
          {
            "id": "call_7z9nn0nx",
            "type": "function",
            "function": {
              "name": "list_files",
              "arguments": {
                "pattern": "**/*.py"
              }
            }
          }
        ]
      },
      {
        "role": "tool",
        "content": {
          "files": [
            "/calculator.py"
          ],
          "total": 1,
          "offset": 1,
          "truncated": false
        },
        "tool_call_id": "call_7z9nn0nx"
      },
      {
        "role": "assistant",
        "reasoning_content": "Okay, I need to open calculator.py and add the multiply function. Let me first read the existing content to understand the structure. I'll use read_file with the path /calculator.py. Let's see what's inside.\n",
        "content": "",
        "tool_calls": [
          {
            "id": "call_dkwcirtc",
            "type": "function",
            "function": {
              "name": "read_file",
              "arguments": {
                "path": "/calculator.py"
              }
            }
          }
        ]
      },
      {
        "role": "tool",
        "content": {
          "content": "def add(a, b):\n    \"\"\"Return a + b.\"\"\"\n    return a + b\n",
          "start_line": 1,
          "end_line": 3,
          "total_lines": 3,
          "truncated": false
        },
        "tool_call_id": "call_dkwcirtc"
      },
      {
        "role": "assistant",
        "reasoning_content": "Okay, the current content of calculator.py is just the add function. The user wants to add a multiply function in the same style. So I'll need to write the multiply function next. Let me think about how to structure that.\n\nThe multiply function should take two parameters, a and b, and return their product. So the code would be:\n\ndef multiply(a, b):\n    \"\"\"Return a * b.\"\"\"\n    return a * b\n\nI need to add this after the add function. Then, add the if __name__ block that calls multiply(6,7). Let me check if there's any existing code beyond the add function. The read_file response shows only three lines, so the file is currently short. I'll append the new code.\n\nWait, maybe the file has more lines that aren't shown? The total_lines is 3, so the file is small. I'll proceed to edit the file by adding the multiply function and the main block. Let me use the edit_file function to make these changes. Let me construct the new content.\n\nOriginal content:\ndef add(a, b):\n    \"\"\"Return a + b.\"\"\"\n    return a + b\n\nNew content will be:\ndef add(a, b):\n    \"\"\"Return a + b.\"\"\"\n    return a + b\n\ndef multiply(a, b):\n    \"\"\"Return a * b.\"\"\"\n    return a * b\n\nif __name__ == \"__main__\":\n    print(multiply(6, 7))\n\nSo I'll replace the existing content with this new content. Wait, but edit_file requires specifying the old text and new text. Alternatively, since the file is short, maybe it's easier to write the entire content again. Let me use write_file instead. Let me check the functions available. The user mentioned that edit_file is preferred over write_file for surgical changes, but in this case, since the file is small, writing the entire content might be simpler.\n\nSo I'll use write_file with the path /calculator.py and the new content. Let me make sure to include all the existing code plus the new parts. Let me structure the new content properly. Then, after writing, I'll run the script using run_bash to confirm it outputs 42.\n",
        "content": "",
        "tool_calls": [
          {
            "id": "call_b88h9iwd",
            "type": "function",
            "function": {
              "name": "write_file",
              "arguments": {
                "content": "def add(a, b):\n    \"\"\"Return a + b.\"\"\"\n    return a + b\n\ndef multiply(a, b):\n    \"\"\"Return a * b.\"\"\"\n    return a * b\n\nif __name__ == \"__main__\":\n    print(multiply(6, 7))",
                "path": "/calculator.py"
              }
            }
          }
        ]
      },
      {
        "role": "tool",
        "content": {
          "written": "/calculator.py",
          "bytes": 171
        },
        "tool_call_id": "call_b88h9iwd"
      },
      {
        "role": "assistant",
        "reasoning_content": "Okay, I've updated calculator.py with the multiply function and the main block. Now I need to run the script to check if it outputs 42. Let me use run_bash to execute python3 calculator.py and capture the output. If it works, the output should be 42. Let's do that.\n",
        "content": "",
        "tool_calls": [
          {
            "id": "call_c5gaqi66",
            "type": "function",
            "function": {
              "name": "run_bash",
              "arguments": {
                "command": "python3 /calculator.py"
              }
            }
          }
        ]
      },
      {
        "role": "tool",
        "content": {
          "ok": true,
          "stdout": "42\n",
          "stderr": "",
          "exit_code": 0
        },
        "tool_call_id": "call_c5gaqi66"
      }
    ]
  }
]

[Synalinks]
Call ID: 481cfb59-d00b-4a57-a902-43995f2e3bca
Parent call ID: fe25fa97-c262-4c9f-9c16-6efd187f8f3d
Module: LanguageModel
Module Name: language_model
Module Description: A language model API wrapper.
Data Model JSON:
[
  {
    "role": "assistant",
    "content": "The `multiply(6, 7)` function correctly returns `42`. The script executed successfully, and the output is as expected.\n\nFinal result:\n```\n42\n```",
    "reasoning_content": "Okay, the user wanted to add a multiply function to calculator.py and test it with 6*7. I followed the steps: listed files to find calculator.py, read its content, added the multiply function and the main block using write_file, then ran the script. The output was 42, which is correct. So everything works. I should confirm the result and let the user know it's done.\n"
  }
]

[Synalinks]
Call ID: fe25fa97-c262-4c9f-9c16-6efd187f8f3d
Parent call ID: 26f57ea4-bf37-421f-ab6e-2b9d747b8436
Module: Generator
Module Name: tool_calls_generator_deep_agent
Module Description: Use a `LanguageModel` to generate a data model from an arbitrary input data model.
Data Model JSON:
[
  {
    "role": "assistant",
    "content": "The `multiply(6, 7)` function correctly returns `42`. The script executed successfully, and the output is as expected.\n\nFinal result:\n```\n42\n```",
    "reasoning_content": "Okay, the user wanted to add a multiply function to calculator.py and test it with 6*7. I followed the steps: listed files to find calculator.py, read its content, added the multiply function and the main block using write_file, then ran the script. The output was 42, which is correct. So everything works. I should confirm the result and let the user know it's done.\n"
  }
]

[Synalinks]
Call ID: 1d486404-7b24-4894-8f5b-8db757cd2df8
Parent call ID: 481cfb59-d00b-4a57-a902-43995f2e3bca
Module: Generator
Module Name: final_generator_deep_agent
Module Description: Use a `LanguageModel` to generate a data model from an arbitrary input data model.
Data Model JSON:
[
  {
    "messages": [
      {
        "role": "user",
        "content": "Open calculator.py and add a `multiply(a, b)` function in the same style as `add`. Then add an `if __name__ == \"__main__\":` block that prints `multiply(6, 7)`, and use `run_bash` to run `python3 calculator.py` to confirm it prints 42."
      },
      {
        "role": "assistant",
        "content": "",
        "reasoning_content": "Okay, let's tackle this step by step. First, the user wants me to open calculator.py and add a multiply function similar to the add function. Then, add an if __name__ block to print multiply(6,7) and run the script to check if it outputs 42.\n\nFirst, I need to check if calculator.py exists. The user mentioned it, so I'll use list_files with a glob pattern like '**/*.py' to find it. Once I have the path, I can read the file to see its current content. Let me start by listing the files in the workdir to locate calculator.py.\n\nAssuming the file is found, I'll read it to understand the existing structure. The add function is probably defined with def add(a, b): and returns a + b. The multiply function should follow the same style, so I'll add def multiply(a, b): return a * b.\n\nNext, I need to add the if __name__ == \"__main__\": block. This block should call multiply(6,7) and print the result. So, after defining multiply, I'll insert that block.\n\nOnce the code is modified, I'll use run_bash to execute python3 calculator.py and verify the output is 42. If there are any errors during this process, I'll need to adjust the code accordingly. Let me proceed step by step, ensuring each part is correctly implemented.\n",
        "tool_calls": [
          {
            "id": "call_7z9nn0nx",
            "type": "function",
            "function": {
              "name": "list_files",
              "arguments": {
                "pattern": "**/*.py"
              }
            }
          }
        ]
      },
      {
        "role": "tool",
        "content": {
          "files": [
            "/calculator.py"
          ],
          "total": 1,
          "offset": 1,
          "truncated": false
        },
        "tool_call_id": "call_7z9nn0nx"
      },
      {
        "role": "assistant",
        "content": "",
        "reasoning_content": "Okay, I need to open calculator.py and add the multiply function. Let me first read the existing content to understand the structure. I'll use read_file with the path /calculator.py. Let's see what's inside.\n",
        "tool_calls": [
          {
            "id": "call_dkwcirtc",
            "type": "function",
            "function": {
              "name": "read_file",
              "arguments": {
                "path": "/calculator.py"
              }
            }
          }
        ]
      },
      {
        "role": "tool",
        "content": {
          "content": "def add(a, b):\n    \"\"\"Return a + b.\"\"\"\n    return a + b\n",
          "start_line": 1,
          "end_line": 3,
          "total_lines": 3,
          "truncated": false
        },
        "tool_call_id": "call_dkwcirtc"
      },
      {
        "role": "assistant",
        "content": "",
        "reasoning_content": "Okay, the current content of calculator.py is just the add function. The user wants to add a multiply function in the same style. So I'll need to write the multiply function next. Let me think about how to structure that.\n\nThe multiply function should take two parameters, a and b, and return their product. So the code would be:\n\ndef multiply(a, b):\n    \"\"\"Return a * b.\"\"\"\n    return a * b\n\nI need to add this after the add function. Then, add the if __name__ block that calls multiply(6,7). Let me check if there's any existing code beyond the add function. The read_file response shows only three lines, so the file is currently short. I'll append the new code.\n\nWait, maybe the file has more lines that aren't shown? The total_lines is 3, so the file is small. I'll proceed to edit the file by adding the multiply function and the main block. Let me use the edit_file function to make these changes. Let me construct the new content.\n\nOriginal content:\ndef add(a, b):\n    \"\"\"Return a + b.\"\"\"\n    return a + b\n\nNew content will be:\ndef add(a, b):\n    \"\"\"Return a + b.\"\"\"\n    return a + b\n\ndef multiply(a, b):\n    \"\"\"Return a * b.\"\"\"\n    return a * b\n\nif __name__ == \"__main__\":\n    print(multiply(6, 7))\n\nSo I'll replace the existing content with this new content. Wait, but edit_file requires specifying the old text and new text. Alternatively, since the file is short, maybe it's easier to write the entire content again. Let me use write_file instead. Let me check the functions available. The user mentioned that edit_file is preferred over write_file for surgical changes, but in this case, since the file is small, writing the entire content might be simpler.\n\nSo I'll use write_file with the path /calculator.py and the new content. Let me make sure to include all the existing code plus the new parts. Let me structure the new content properly. Then, after writing, I'll run the script using run_bash to confirm it outputs 42.\n",
        "tool_calls": [
          {
            "id": "call_b88h9iwd",
            "type": "function",
            "function": {
              "name": "write_file",
              "arguments": {
                "content": "def add(a, b):\n    \"\"\"Return a + b.\"\"\"\n    return a + b\n\ndef multiply(a, b):\n    \"\"\"Return a * b.\"\"\"\n    return a * b\n\nif __name__ == \"__main__\":\n    print(multiply(6, 7))",
                "path": "/calculator.py"
              }
            }
          }
        ]
      },
      {
        "role": "tool",
        "content": {
          "written": "/calculator.py",
          "bytes": 171
        },
        "tool_call_id": "call_b88h9iwd"
      },
      {
        "role": "assistant",
        "content": "",
        "reasoning_content": "Okay, I've updated calculator.py with the multiply function and the main block. Now I need to run the script to check if it outputs 42. Let me use run_bash to execute python3 calculator.py and capture the output. If it works, the output should be 42. Let's do that.\n",
        "tool_calls": [
          {
            "id": "call_c5gaqi66",
            "type": "function",
            "function": {
              "name": "run_bash",
              "arguments": {
                "command": "python3 /calculator.py"
              }
            }
          }
        ]
      },
      {
        "role": "tool",
        "content": {
          "ok": true,
          "stdout": "42\n",
          "stderr": "",
          "exit_code": 0
        },
        "tool_call_id": "call_c5gaqi66"
      }
    ]
  }
]

[Synalinks]
Call ID: 48a513fa-3d82-4b2d-a527-08bd70f42523
Parent call ID: 1d486404-7b24-4894-8f5b-8db757cd2df8
Module: LanguageModel
Module Name: language_model
Module Description: A language model API wrapper.
Data Model JSON:
[
  {
    "messages": [
      {
        "role": "system",
        "content": "<instructions>\nYou are a software engineering assistant working inside a sandboxed,\ncopy-on-write filesystem.\n\nWorkdir: /tmp/deep_agent_guide_2xzctol8\nAvailable tools: read_file, list_files, search_files, write_file,\nedit_file, run_bash\n\nPlan:\n1. Use `list_files` to discover files (glob, e.g. `**/*.py`).\n2. Use `search_files` to grep file contents by regex across a glob.\n3. Use `read_file` to read a file; it returns the requested lines with\n   1-based `start_line` / `end_line`. Page through long files with\n   `offset` / `limit` (raise `offset` to read further in).\n4. Use `edit_file` for surgical changes (preferred over `write_file`).\n5. Use `run_bash` to run shell commands against the filesystem — pipes,\n   redirects, globs, `&&`, loops, and `python3` (e.g. `python3 script.py`\n   after `write_file`-ing it). `python3` is a real interpreter with the\n   full standard library, third-party packages and network.\n6. Once you have the answer, stop calling tools and respond.\n\nNotes:\n- The filesystem is host-safe: edits and new files land in the sandbox's\n  mounted filesystem and never modify the real workspace on disk.\n- If the conversation includes an `InputsSummary`, you only see field\n  previews and sizes — the full, untruncated input values are saved as the\n  JSON file named in its `inputs_file` field. Read it with the `read_file`\n  tool (or `run_bash` `cat`) rather than retyping values from the preview.\n</instructions>\n"
      },
      {
        "role": "user",
        "content": "Open calculator.py and add a `multiply(a, b)` function in the same style as `add`. Then add an `if __name__ == \"__main__\":` block that prints `multiply(6, 7)`, and use `run_bash` to run `python3 calculator.py` to confirm it prints 42."
      },
      {
        "role": "assistant",
        "reasoning_content": "Okay, let's tackle this step by step. First, the user wants me to open calculator.py and add a multiply function similar to the add function. Then, add an if __name__ block to print multiply(6,7) and run the script to check if it outputs 42.\n\nFirst, I need to check if calculator.py exists. The user mentioned it, so I'll use list_files with a glob pattern like '**/*.py' to find it. Once I have the path, I can read the file to see its current content. Let me start by listing the files in the workdir to locate calculator.py.\n\nAssuming the file is found, I'll read it to understand the existing structure. The add function is probably defined with def add(a, b): and returns a + b. The multiply function should follow the same style, so I'll add def multiply(a, b): return a * b.\n\nNext, I need to add the if __name__ == \"__main__\": block. This block should call multiply(6,7) and print the result. So, after defining multiply, I'll insert that block.\n\nOnce the code is modified, I'll use run_bash to execute python3 calculator.py and verify the output is 42. If there are any errors during this process, I'll need to adjust the code accordingly. Let me proceed step by step, ensuring each part is correctly implemented.\n",
        "content": "",
        "tool_calls": [
          {
            "id": "call_7z9nn0nx",
            "type": "function",
            "function": {
              "name": "list_files",
              "arguments": {
                "pattern": "**/*.py"
              }
            }
          }
        ]
      },
      {
        "role": "tool",
        "content": {
          "files": [
            "/calculator.py"
          ],
          "total": 1,
          "offset": 1,
          "truncated": false
        },
        "tool_call_id": "call_7z9nn0nx"
      },
      {
        "role": "assistant",
        "reasoning_content": "Okay, I need to open calculator.py and add the multiply function. Let me first read the existing content to understand the structure. I'll use read_file with the path /calculator.py. Let's see what's inside.\n",
        "content": "",
        "tool_calls": [
          {
            "id": "call_dkwcirtc",
            "type": "function",
            "function": {
              "name": "read_file",
              "arguments": {
                "path": "/calculator.py"
              }
            }
          }
        ]
      },
      {
        "role": "tool",
        "content": {
          "content": "def add(a, b):\n    \"\"\"Return a + b.\"\"\"\n    return a + b\n",
          "start_line": 1,
          "end_line": 3,
          "total_lines": 3,
          "truncated": false
        },
        "tool_call_id": "call_dkwcirtc"
      },
      {
        "role": "assistant",
        "reasoning_content": "Okay, the current content of calculator.py is just the add function. The user wants to add a multiply function in the same style. So I'll need to write the multiply function next. Let me think about how to structure that.\n\nThe multiply function should take two parameters, a and b, and return their product. So the code would be:\n\ndef multiply(a, b):\n    \"\"\"Return a * b.\"\"\"\n    return a * b\n\nI need to add this after the add function. Then, add the if __name__ block that calls multiply(6,7). Let me check if there's any existing code beyond the add function. The read_file response shows only three lines, so the file is currently short. I'll append the new code.\n\nWait, maybe the file has more lines that aren't shown? The total_lines is 3, so the file is small. I'll proceed to edit the file by adding the multiply function and the main block. Let me use the edit_file function to make these changes. Let me construct the new content.\n\nOriginal content:\ndef add(a, b):\n    \"\"\"Return a + b.\"\"\"\n    return a + b\n\nNew content will be:\ndef add(a, b):\n    \"\"\"Return a + b.\"\"\"\n    return a + b\n\ndef multiply(a, b):\n    \"\"\"Return a * b.\"\"\"\n    return a * b\n\nif __name__ == \"__main__\":\n    print(multiply(6, 7))\n\nSo I'll replace the existing content with this new content. Wait, but edit_file requires specifying the old text and new text. Alternatively, since the file is short, maybe it's easier to write the entire content again. Let me use write_file instead. Let me check the functions available. The user mentioned that edit_file is preferred over write_file for surgical changes, but in this case, since the file is small, writing the entire content might be simpler.\n\nSo I'll use write_file with the path /calculator.py and the new content. Let me make sure to include all the existing code plus the new parts. Let me structure the new content properly. Then, after writing, I'll run the script using run_bash to confirm it outputs 42.\n",
        "content": "",
        "tool_calls": [
          {
            "id": "call_b88h9iwd",
            "type": "function",
            "function": {
              "name": "write_file",
              "arguments": {
                "content": "def add(a, b):\n    \"\"\"Return a + b.\"\"\"\n    return a + b\n\ndef multiply(a, b):\n    \"\"\"Return a * b.\"\"\"\n    return a * b\n\nif __name__ == \"__main__\":\n    print(multiply(6, 7))",
                "path": "/calculator.py"
              }
            }
          }
        ]
      },
      {
        "role": "tool",
        "content": {
          "written": "/calculator.py",
          "bytes": 171
        },
        "tool_call_id": "call_b88h9iwd"
      },
      {
        "role": "assistant",
        "reasoning_content": "Okay, I've updated calculator.py with the multiply function and the main block. Now I need to run the script to check if it outputs 42. Let me use run_bash to execute python3 calculator.py and capture the output. If it works, the output should be 42. Let's do that.\n",
        "content": "",
        "tool_calls": [
          {
            "id": "call_c5gaqi66",
            "type": "function",
            "function": {
              "name": "run_bash",
              "arguments": {
                "command": "python3 /calculator.py"
              }
            }
          }
        ]
      },
      {
        "role": "tool",
        "content": {
          "ok": true,
          "stdout": "42\n",
          "stderr": "",
          "exit_code": 0
        },
        "tool_call_id": "call_c5gaqi66"
      }
    ]
  }
]

[Synalinks]
Call ID: 48a513fa-3d82-4b2d-a527-08bd70f42523
Parent call ID: 1d486404-7b24-4894-8f5b-8db757cd2df8
Module: LanguageModel
Module Name: language_model
Module Description: A language model API wrapper.
Data Model JSON:
[
  {
    "role": "assistant",
    "content": "The `multiply(6, 7)` function returns `42` as expected. ✅\n\nFinal code in `calculator.py`:\n```python\ndef add(a, b):\n    \"\"\"Return a + b.\"\"\"\n    return a + b\n\ndef multiply(a, b):\n    \"\"\"Return a * b.\"\"\"\n    return a * b\n\nif __name__ == \"__main__\":\n    print(multiply(6, 7))\n```\n\nOutput from `python3 calculator.py`:\n```\n42\n```",
    "reasoning_content": "Okay, the user wanted to add a multiply function to calculator.py and test it with 6*7. I followed the steps: listed files to find calculator.py, read the existing add function, then wrote the multiply function and the main block. After running the script, the output was 42 as expected. Everything worked smoothly. No errors encountered, so the task is complete.\n"
  }
]

[Synalinks]
Call ID: 1d486404-7b24-4894-8f5b-8db757cd2df8
Parent call ID: 481cfb59-d00b-4a57-a902-43995f2e3bca
Module: Generator
Module Name: final_generator_deep_agent
Module Description: Use a `LanguageModel` to generate a data model from an arbitrary input data model.
Data Model JSON:
[
  {
    "role": "assistant",
    "content": "The `multiply(6, 7)` function returns `42` as expected. ✅\n\nFinal code in `calculator.py`:\n```python\ndef add(a, b):\n    \"\"\"Return a + b.\"\"\"\n    return a + b\n\ndef multiply(a, b):\n    \"\"\"Return a * b.\"\"\"\n    return a * b\n\nif __name__ == \"__main__\":\n    print(multiply(6, 7))\n```\n\nOutput from `python3 calculator.py`:\n```\n42\n```",
    "reasoning_content": "Okay, the user wanted to add a multiply function to calculator.py and test it with 6*7. I followed the steps: listed files to find calculator.py, read the existing add function, then wrote the multiply function and the main block. After running the script, the output was 42 as expected. Everything worked smoothly. No errors encountered, so the task is complete.\n"
  }
]

[Synalinks]
Call ID: b69e23c0-3ad8-4eab-9613-408518369dca
Parent call ID: 19ef9e6d-d878-4cbd-b256-987dc5b5776f
Module: DeepAgent
Module Name: deep_agent
Module Description: A coding agent whose tools are a sandboxed copy of a workdir.
Data Model JSON:
[
  {
    "messages": [
      {
        "role": "user",
        "content": "Open calculator.py and add a `multiply(a, b)` function in the same style as `add`. Then add an `if __name__ == \"__main__\":` block that prints `multiply(6, 7)`, and use `run_bash` to run `python3 calculator.py` to confirm it prints 42."
      },
      {
        "role": "assistant",
        "reasoning_content": "Okay, let's tackle this step by step. First, the user wants me to open calculator.py and add a multiply function similar to the add function. Then, add an if __name__ block to print multiply(6,7) and run the script to check if it outputs 42.\n\nFirst, I need to check if calculator.py exists. The user mentioned it, so I'll use list_files with a glob pattern like '**/*.py' to find it. Once I have the path, I can read the file to see its current content. Let me start by listing the files in the workdir to locate calculator.py.\n\nAssuming the file is found, I'll read it to understand the existing structure. The add function is probably defined with def add(a, b): and returns a + b. The multiply function should follow the same style, so I'll add def multiply(a, b): return a * b.\n\nNext, I need to add the if __name__ == \"__main__\": block. This block should call multiply(6,7) and print the result. So, after defining multiply, I'll insert that block.\n\nOnce the code is modified, I'll use run_bash to execute python3 calculator.py and verify the output is 42. If there are any errors during this process, I'll need to adjust the code accordingly. Let me proceed step by step, ensuring each part is correctly implemented.\n",
        "content": "",
        "tool_calls": [
          {
            "id": "call_7z9nn0nx",
            "type": "function",
            "function": {
              "name": "list_files",
              "arguments": {
                "pattern": "**/*.py"
              }
            }
          }
        ]
      },
      {
        "role": "tool",
        "content": {
          "files": [
            "/calculator.py"
          ],
          "total": 1,
          "offset": 1,
          "truncated": false
        },
        "tool_call_id": "call_7z9nn0nx"
      },
      {
        "role": "assistant",
        "reasoning_content": "Okay, I need to open calculator.py and add the multiply function. Let me first read the existing content to understand the structure. I'll use read_file with the path /calculator.py. Let's see what's inside.\n",
        "content": "",
        "tool_calls": [
          {
            "id": "call_dkwcirtc",
            "type": "function",
            "function": {
              "name": "read_file",
              "arguments": {
                "path": "/calculator.py"
              }
            }
          }
        ]
      },
      {
        "role": "tool",
        "content": {
          "content": "def add(a, b):\n    \"\"\"Return a + b.\"\"\"\n    return a + b\n",
          "start_line": 1,
          "end_line": 3,
          "total_lines": 3,
          "truncated": false
        },
        "tool_call_id": "call_dkwcirtc"
      },
      {
        "role": "assistant",
        "reasoning_content": "Okay, the current content of calculator.py is just the add function. The user wants to add a multiply function in the same style. So I'll need to write the multiply function next. Let me think about how to structure that.\n\nThe multiply function should take two parameters, a and b, and return their product. So the code would be:\n\ndef multiply(a, b):\n    \"\"\"Return a * b.\"\"\"\n    return a * b\n\nI need to add this after the add function. Then, add the if __name__ block that calls multiply(6,7). Let me check if there's any existing code beyond the add function. The read_file response shows only three lines, so the file is currently short. I'll append the new code.\n\nWait, maybe the file has more lines that aren't shown? The total_lines is 3, so the file is small. I'll proceed to edit the file by adding the multiply function and the main block. Let me use the edit_file function to make these changes. Let me construct the new content.\n\nOriginal content:\ndef add(a, b):\n    \"\"\"Return a + b.\"\"\"\n    return a + b\n\nNew content will be:\ndef add(a, b):\n    \"\"\"Return a + b.\"\"\"\n    return a + b\n\ndef multiply(a, b):\n    \"\"\"Return a * b.\"\"\"\n    return a * b\n\nif __name__ == \"__main__\":\n    print(multiply(6, 7))\n\nSo I'll replace the existing content with this new content. Wait, but edit_file requires specifying the old text and new text. Alternatively, since the file is short, maybe it's easier to write the entire content again. Let me use write_file instead. Let me check the functions available. The user mentioned that edit_file is preferred over write_file for surgical changes, but in this case, since the file is small, writing the entire content might be simpler.\n\nSo I'll use write_file with the path /calculator.py and the new content. Let me make sure to include all the existing code plus the new parts. Let me structure the new content properly. Then, after writing, I'll run the script using run_bash to confirm it outputs 42.\n",
        "content": "",
        "tool_calls": [
          {
            "id": "call_b88h9iwd",
            "type": "function",
            "function": {
              "name": "write_file",
              "arguments": {
                "content": "def add(a, b):\n    \"\"\"Return a + b.\"\"\"\n    return a + b\n\ndef multiply(a, b):\n    \"\"\"Return a * b.\"\"\"\n    return a * b\n\nif __name__ == \"__main__\":\n    print(multiply(6, 7))",
                "path": "/calculator.py"
              }
            }
          }
        ]
      },
      {
        "role": "tool",
        "content": {
          "written": "/calculator.py",
          "bytes": 171
        },
        "tool_call_id": "call_b88h9iwd"
      },
      {
        "role": "assistant",
        "reasoning_content": "Okay, I've updated calculator.py with the multiply function and the main block. Now I need to run the script to check if it outputs 42. Let me use run_bash to execute python3 calculator.py and capture the output. If it works, the output should be 42. Let's do that.\n",
        "content": "",
        "tool_calls": [
          {
            "id": "call_c5gaqi66",
            "type": "function",
            "function": {
              "name": "run_bash",
              "arguments": {
                "command": "python3 /calculator.py"
              }
            }
          }
        ]
      },
      {
        "role": "tool",
        "content": {
          "ok": true,
          "stdout": "42\n",
          "stderr": "",
          "exit_code": 0
        },
        "tool_call_id": "call_c5gaqi66"
      },
      {
        "role": "assistant",
        "reasoning_content": "Okay, the user wanted to add a multiply function to calculator.py and test it with 6*7. I followed the steps: listed files to find calculator.py, read the existing add function, then wrote the multiply function and the main block. After running the script, the output was 42 as expected. Everything worked smoothly. No errors encountered, so the task is complete.\n",
        "content": "The `multiply(6, 7)` function returns `42` as expected. ✅\n\nFinal code in `calculator.py`:\n```python\ndef add(a, b):\n    \"\"\"Return a + b.\"\"\"\n    return a + b\n\ndef multiply(a, b):\n    \"\"\"Return a * b.\"\"\"\n    return a * b\n\nif __name__ == \"__main__\":\n    print(multiply(6, 7))\n```\n\nOutput from `python3 calculator.py`:\n```\n42\n```"
      }
    ]
  }
]

[Synalinks]
Call ID: 19ef9e6d-d878-4cbd-b256-987dc5b5776f
Parent call ID: None
Module: Functional
Module Name: deep_agent
Module Description: A coding agent with sandboxed file and Python tools.
Data Model JSON:
[
  {
    "messages": [
      {
        "role": "user",
        "content": "Open calculator.py and add a `multiply(a, b)` function in the same style as `add`. Then add an `if __name__ == \"__main__\":` block that prints `multiply(6, 7)`, and use `run_bash` to run `python3 calculator.py` to confirm it prints 42."
      },
      {
        "role": "assistant",
        "reasoning_content": "Okay, let's tackle this step by step. First, the user wants me to open calculator.py and add a multiply function similar to the add function. Then, add an if __name__ block to print multiply(6,7) and run the script to check if it outputs 42.\n\nFirst, I need to check if calculator.py exists. The user mentioned it, so I'll use list_files with a glob pattern like '**/*.py' to find it. Once I have the path, I can read the file to see its current content. Let me start by listing the files in the workdir to locate calculator.py.\n\nAssuming the file is found, I'll read it to understand the existing structure. The add function is probably defined with def add(a, b): and returns a + b. The multiply function should follow the same style, so I'll add def multiply(a, b): return a * b.\n\nNext, I need to add the if __name__ == \"__main__\": block. This block should call multiply(6,7) and print the result. So, after defining multiply, I'll insert that block.\n\nOnce the code is modified, I'll use run_bash to execute python3 calculator.py and verify the output is 42. If there are any errors during this process, I'll need to adjust the code accordingly. Let me proceed step by step, ensuring each part is correctly implemented.\n",
        "content": "",
        "tool_calls": [
          {
            "id": "call_7z9nn0nx",
            "type": "function",
            "function": {
              "name": "list_files",
              "arguments": {
                "pattern": "**/*.py"
              }
            }
          }
        ]
      },
      {
        "role": "tool",
        "content": {
          "files": [
            "/calculator.py"
          ],
          "total": 1,
          "offset": 1,
          "truncated": false
        },
        "tool_call_id": "call_7z9nn0nx"
      },
      {
        "role": "assistant",
        "reasoning_content": "Okay, I need to open calculator.py and add the multiply function. Let me first read the existing content to understand the structure. I'll use read_file with the path /calculator.py. Let's see what's inside.\n",
        "content": "",
        "tool_calls": [
          {
            "id": "call_dkwcirtc",
            "type": "function",
            "function": {
              "name": "read_file",
              "arguments": {
                "path": "/calculator.py"
              }
            }
          }
        ]
      },
      {
        "role": "tool",
        "content": {
          "content": "def add(a, b):\n    \"\"\"Return a + b.\"\"\"\n    return a + b\n",
          "start_line": 1,
          "end_line": 3,
          "total_lines": 3,
          "truncated": false
        },
        "tool_call_id": "call_dkwcirtc"
      },
      {
        "role": "assistant",
        "reasoning_content": "Okay, the current content of calculator.py is just the add function. The user wants to add a multiply function in the same style. So I'll need to write the multiply function next. Let me think about how to structure that.\n\nThe multiply function should take two parameters, a and b, and return their product. So the code would be:\n\ndef multiply(a, b):\n    \"\"\"Return a * b.\"\"\"\n    return a * b\n\nI need to add this after the add function. Then, add the if __name__ block that calls multiply(6,7). Let me check if there's any existing code beyond the add function. The read_file response shows only three lines, so the file is currently short. I'll append the new code.\n\nWait, maybe the file has more lines that aren't shown? The total_lines is 3, so the file is small. I'll proceed to edit the file by adding the multiply function and the main block. Let me use the edit_file function to make these changes. Let me construct the new content.\n\nOriginal content:\ndef add(a, b):\n    \"\"\"Return a + b.\"\"\"\n    return a + b\n\nNew content will be:\ndef add(a, b):\n    \"\"\"Return a + b.\"\"\"\n    return a + b\n\ndef multiply(a, b):\n    \"\"\"Return a * b.\"\"\"\n    return a * b\n\nif __name__ == \"__main__\":\n    print(multiply(6, 7))\n\nSo I'll replace the existing content with this new content. Wait, but edit_file requires specifying the old text and new text. Alternatively, since the file is short, maybe it's easier to write the entire content again. Let me use write_file instead. Let me check the functions available. The user mentioned that edit_file is preferred over write_file for surgical changes, but in this case, since the file is small, writing the entire content might be simpler.\n\nSo I'll use write_file with the path /calculator.py and the new content. Let me make sure to include all the existing code plus the new parts. Let me structure the new content properly. Then, after writing, I'll run the script using run_bash to confirm it outputs 42.\n",
        "content": "",
        "tool_calls": [
          {
            "id": "call_b88h9iwd",
            "type": "function",
            "function": {
              "name": "write_file",
              "arguments": {
                "content": "def add(a, b):\n    \"\"\"Return a + b.\"\"\"\n    return a + b\n\ndef multiply(a, b):\n    \"\"\"Return a * b.\"\"\"\n    return a * b\n\nif __name__ == \"__main__\":\n    print(multiply(6, 7))",
                "path": "/calculator.py"
              }
            }
          }
        ]
      },
      {
        "role": "tool",
        "content": {
          "written": "/calculator.py",
          "bytes": 171
        },
        "tool_call_id": "call_b88h9iwd"
      },
      {
        "role": "assistant",
        "reasoning_content": "Okay, I've updated calculator.py with the multiply function and the main block. Now I need to run the script to check if it outputs 42. Let me use run_bash to execute python3 calculator.py and capture the output. If it works, the output should be 42. Let's do that.\n",
        "content": "",
        "tool_calls": [
          {
            "id": "call_c5gaqi66",
            "type": "function",
            "function": {
              "name": "run_bash",
              "arguments": {
                "command": "python3 /calculator.py"
              }
            }
          }
        ]
      },
      {
        "role": "tool",
        "content": {
          "ok": true,
          "stdout": "42\n",
          "stderr": "",
          "exit_code": 0
        },
        "tool_call_id": "call_c5gaqi66"
      },
      {
        "role": "assistant",
        "reasoning_content": "Okay, the user wanted to add a multiply function to calculator.py and test it with 6*7. I followed the steps: listed files to find calculator.py, read the existing add function, then wrote the multiply function and the main block. After running the script, the output was 42 as expected. Everything worked smoothly. No errors encountered, so the task is complete.\n",
        "content": "The `multiply(6, 7)` function returns `42` as expected. ✅\n\nFinal code in `calculator.py`:\n```python\ndef add(a, b):\n    \"\"\"Return a + b.\"\"\"\n    return a + b\n\ndef multiply(a, b):\n    \"\"\"Return a * b.\"\"\"\n    return a * b\n\nif __name__ == \"__main__\":\n    print(multiply(6, 7))\n```\n\nOutput from `python3 calculator.py`:\n```\n42\n```"
      }
    ]
  }
]

Agent: The `multiply(6, 7)` function returns `42` as expected. ✅

Final code in `calculator.py`:
```python
def add(a, b):
    """Return a + b."""
    return a + b

def multiply(a, b):
    """Return a * b."""
    return a * b

if __name__ == "__main__":
    print(multiply(6, 7))

Output from python3 calculator.py:

Sandbox changes: {'written': ['/calculator.py'], 'deleted': []}

--- calculator.py (sandbox) --- def add(a, b): """Return a + b.""" return a + b

def multiply(a, b): """Return a * b.""" return a * b

if name == "main": print(multiply(6, 7))

```