agentkit-gf 0.1.2

Lightweight agents on top of Pydantic AI with true soft tools and conversation history

GF AgentKit

Lightweight agents on top of Pydantic AI with:

• True soft tools (reasoning-first; tools are optional, not mandatory).
• A single “gateway” tool that delegates to a separate executor (to reduce tool gravity).
• Built-in conversation history (persistent transcript managed by the agent).
• A few ready-to-use tools (file I/O, safe process execution, agent delegation).

⚠️ This is a work-in-progress. The API may evolve.

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Why?

Many agent frameworks push models to use tools aggressively (“hard tools”), which can be great for workflow graphs but not for reasoning-first tasks. GF AgentKit makes tool use opt-in:

• SoftToolAgent: the model decides when to ask for a tool using a schema-guided envelope (OPEN_RESPONSE / TOOL_CALL / TOOL_RESULT). The host runs tools and feeds results back as context.
• DelegatingToolsAgent: the model sees one gateway tool (delegate_ops). That tool delegates to an internal executor which owns all the real tools. This reduces tool gravity and lets you keep a clean reasoning loop.

Both agents keep a persistent transcript so follow-ups naturally refer back to earlier turns.

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Installation

pip install agentkit-gf

Requires: Python 3.11+.

You’ll also need an OpenAI key to use gpt-5-nano:

# macOS/Linux
export OPENAI_API_KEY=sk-...

# Windows PowerShell
$env:OPENAI_API_KEY = "sk-..."

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At a Glance

agentkit_gf/
  ├─ _base_agent.py               # shared transcript + constructor glue
  ├─ soft_tool_agent.py           # true soft tools (Envelope schema)
  ├─ delegating_tools_agent.py    # single gateway tool; delegates to executor
  └─ tools/
       ├─ fs.py                   # FileTools: read/write/stat/hash/list (sandboxable)
       ├─ os.py                   # ProcessTools: run_process/run_shell (policy controlled)
       ├─ agent.py                # create_agent_delegation_tool(...) factory
       └─ builtin_tools_matrix.py # BuiltinTool enums + provider validation

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Agents

1) SoftToolAgent (true soft tool)

• The model returns a single Envelope JSON object each hop:

  • OPEN_RESPONSE { text, confidence? }
  • TOOL_CALL { tool, args_json, reason }
  • TOOL_RESULT { tool, args_json, result_json, success, note? } (normally emitted by the host)

• You provide a registry of Python callables (tool_name -> callable(**kwargs)).

• The agent executes tools in host code and feeds a TOOL_RESULT back to the model.

• Maintains an internal transcript across turns.

Minimal example (read a file):

from agentkit_gf.soft_tool_agent import SoftToolAgent
from agentkit_gf.tools.fs import FileTools

file_tools = FileTools(root_dir=".")   # restrict if you like
registry = {"read_text": file_tools.read_text}

agent = SoftToolAgent(model="openai:gpt-5-nano")

prompt = (
    "Read ./notes.txt and tell me the first line.\n"
    "If you need the file, return a TOOL_CALL Envelope for tool 'read_text' with args_json "
    '{"path": "./notes.txt", "max_bytes": 10000}. Then, after TOOL_RESULT, respond with OPEN_RESPONSE.'
)

result = agent.run_soft_sync(prompt, registry, max_steps=5)
print(result.final_text)

Transcript helpers:

print(agent.export_history_text())
agent.reset_history()

2) DelegatingToolsAgent (single gateway tool)

• Presents one tool (delegate_ops) to the model.
• Internally spins up a private executor agent that owns all real tools (including optional provider built-ins like WebSearch).
• You pass in objects or callables; public methods are automatically exposed as tools (optionally prefixed).

Example:

from agentkit_gf.delegating_tools_agent import DelegatingToolsAgent
from agentkit_gf.tools.fs import FileTools
from agentkit_gf.tools.os import ProcessTools
from agentkit_gf.tools.builtin_tools_matrix import BuiltinTool

agent = DelegatingToolsAgent(
    model="openai:gpt-5-nano",
    builtin_enums=[BuiltinTool.WEB_SEARCH],  # optional provider built-ins
    tool_sources=[
        FileTools(root_dir="."),
        ProcessTools(root_cwd=".", allowed_basenames=["python", "bash", "ls"])
    ],
    class_prefix="fs",  # public tool names become "fs_read_text", etc.
    system_prompt=(
        "Answer-first. Use delegate_ops only if a specific missing fact requires it."
    ),
    ops_system_prompt="Execute exactly one tool and return only its result.",
)

reply = agent.run_sync(
    "Read ./notes.txt (use delegate_ops/tool 'fs_read_text' if needed) and summarize the first line."
).output

print(reply)

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Included Tools

FileTools (agentkit_gf.tools.fs)

• read_text(path, max_bytes=200_000, encoding="utf-8")
• read_bytes_base64(path, max_bytes=200_000)
• write_text(path, content, overwrite=False, encoding="utf-8")
• stat(path) / list_dir(path, include_hidden=False, max_entries=1000)
• hash_file(path, algorithm=HashAlgorithm.SHA256)

All enforce fail-fast validation and can be sandboxed with root_dir.

ProcessTools (agentkit_gf.tools.os)

• run_process(argv: Sequence[str], timeout_sec=10, cwd=None) (no shell; recommended)
• run_shell(command: str, timeout_sec=10, cwd=None) (flexible; riskier)

Policy controls:

• root_cwd (path sandbox)
• allowed_basenames (allowlist executables)
• max_output_bytes (clip stdout/stderr)

Agent Delegation Tool (agentkit_gf.tools.agent)

• create_agent_delegation_tool(agent_factory: Callable[[str], Agent]) -> Callable[..., dict]
• Produces a registry callable: delegate_agent(agent_name: str, prompt: str) -> {"output": ...}
• Handy if your soft tool needs to spin up or discover another agent on demand.

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Soft vs. Hard Tools

• Soft tools (this library):

  • The model asks to call a tool via a schema; the host decides and executes.
  • Great for reasoning-first flows where the model should prefer answering from context.

• Hard tools:

  • Registered with the provider; models are often biased to call them.
  • Better for rigid flows or “do X with Y, then Z” pipelines.

You can mix: use SoftToolAgent for reasoning, and DelegatingToolsAgent when you need a single, auditable gateway to real tools (including provider built-ins).

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Extending with Your Own Tools

You can pass objects or callables:

class MyDataOps:
    def summarize_csv(self, path: str, top_n: int = 5) -> dict:
        # ... return JSON-serializable result ...
        return {"summary": "...", "top_n": top_n}

agent = DelegatingToolsAgent(
    model="openai:gpt-5-nano",
    builtin_enums=[],
    tool_sources=[MyDataOps()],
    class_prefix="data",  # exposes "data_summarize_csv"
)

For SoftToolAgent, add to the registry:

registry = {"summarize_csv": MyDataOps().summarize_csv}

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API Reference (quick)

SoftToolAgent

SoftToolAgent(
    model: str,
    system_prompt: str | None = None,
    allow_llm_tool_result: bool = False,
)

run_soft_sync(
    prompt: str,
    tool_registry: Mapping[str, Callable[..., Any]],
    max_steps: int = 4,
) -> SoftRunResult

# history helpers
export_history_text() -> str
export_history_blocks() -> Sequence[str]
reset_history() -> None

Envelope schema: the model returns exactly one JSON object with message.kind in:

• OPEN_RESPONSE { text, confidence? }
• TOOL_CALL { tool, args_json, reason } (args_json must be an object string)
• TOOL_RESULT { tool, args_json, result_json, success, note? }

DelegatingToolsAgent

DelegatingToolsAgent(
    model: str,
    builtin_enums: Sequence[BuiltinTool],
    tool_sources: Sequence[Callable | object | AbstractToolset] = (),
    class_prefix: str | None = None,
    system_prompt: str | None = None,
    ops_system_prompt: str | None = None,
)

# single-step run (history-aware)
run_sync(prompt: str) -> RunResult

This agent exposes only one public tool to the model: delegate_ops(tool, args_json, why) (the executor runs exactly one real tool; results are recorded to the transcript).

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License

See LICENSE - MIT

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