akgentic-tool

Tool infrastructure and domain tools for the Akgentic multi-agent framework (open-source bundle). Define, compose, and expose capabilities to LLM agents through a unified channel system — as tool calls, system prompt injections, structured context state, or programmatic commands.

Table of Contents

• Overview
• Installation
• Quick Start
• Architecture
  • ToolCard
  • ToolFactory
  • BaseToolParam: Configuration, Not Schema
  • Channel System
• Migration: moved import paths
• Observers: How a Tool Acts on the System
• Tool State Events
• Tool Actors
• Deferred Results: Never Block a Tool Actor
• Tool Catalog
  • WorkspaceTool
  • PlanningTool
  • KnowledgeGraphTool
  • VectorStoreTool
  • SearchTool
  • TeamTool
  • MetadataTool
  • NotificationTool
  • SkillTool
  • MCPTool
  • ExecTool
• Error Handling
• Optional Extras
• Development
• License

Overview

akgentic-tool is the capability layer between the Akgentic actor system and the LLM agents running inside it. It provides:

• Abstract contracts — ToolCard and BaseToolParam define the serializable configuration model every tool follows; ToolFactory aggregates multiple cards into agent-ready callables
• Channel system — each capability declares whether it surfaces as a TOOL_CALL (LLM invokes it), a SYSTEM_PROMPT (rendered once into the frozen system block), an LLM_CONTEXT (structured context state appended into the context tail per turn, as a delta), or a COMMAND (programmatic call from another agent or the orchestrator)
• Observer protocols — ToolObserver, ActorToolObserver, and TeamManagementToolObserver give tools access to the actor system, event emission, and team lifecycle hooks.

  Migration note (ADR-018): ToolCallEvent has been removed from akgentic-tool. Tool call observability is now handled by akgentic-llm. Import ToolCallEvent and ToolReturnEvent from akgentic.llm.event instead.

• RetriableError — tools signal recoverable failures; ToolFactory translates them to the framework-specific retry exception without coupling tool logic to pydantic-ai
• Domain tools — eleven production-ready tool implementations covering workspace I/O, task planning, knowledge graph, web search, team management, the team's business context, on-demand skill guidance, vector-store configuration, MCP server integration, sandboxed shell execution, and self-scheduled notifications

ToolCard(s)
    │
    ▼
ToolFactory
    │
    ├── get_tools()           → list[Callable]  ─────▶ LLM ReAct loop
    ├── get_system_prompts()  → list[Callable]  ─────▶ injected into LLM context
    ├── get_context_states()  → list[Callable[[], ContextState | None]] ▶ per-turn context deltas
    ├── get_context_updater() → ContextUpdater  ─────▶ composes one delta block per turn
    ├── get_commands()        → dict[type, Callable] ▶ orchestrator / other agents
    └── get_toolsets()        → list[Any]       ─────▶ pydantic-ai toolset objects

get_toolsets() is typed list[Any] because its elements are runtime pydantic-ai objects: an MCPToolset, or a PrefixedToolset wrapping one when the connection configures a tool_prefix.

Installation

Published on PyPI. Python 3.12 or newer.

uv add akgentic-tool
# or
pip install akgentic-tool

That is the whole install. akgentic-core, pydantic-ai, tavily-python and httpx come with it as ordinary dependencies — no workspace checkout, no submodules.

Installing Extras

The base install gives you the ToolCard / ToolFactory machinery and every tool's text path. Each extra adds one optional surface — see Optional Extras below for what degrades without it:

# Semantic search for planning and knowledge graph (numpy + OpenAI embeddings)
uv add "akgentic-tool[vector_search]"

# Weaviate backend for the vector store (weaviate-client)
uv add "akgentic-tool[weaviate]"

# Binary file reading for workspace_read (PDF, DOCX, XLSX, PPTX via MarkItDown)
uv add "akgentic-tool[docs]"

# Image resizing for workspace_view (Pillow)
uv add "akgentic-tool[vision]"

# Everything
uv add "akgentic-tool[vector_search,weaviate,docs,vision]"

As part of the framework bundle

akgentic-framework is the meta-distribution that pins every akgentic package at versions built and tested together. Install akgentic-tool through it when you want the release-wide pin rather than a single package:

pip install "akgentic-framework[tool]"   # this package + its closure, release-pinned
pip install "akgentic-framework[all]"    # the whole framework

Working on the package itself

To develop akgentic-tool rather than use it, clone the open-source bundle akgentic-framework, which carries every package together as submodules:

git clone git@github.com:b12consulting/akgentic-framework.git
cd akgentic-framework
git submodule update --init
# uncomment the two "SOURCE MODE" blocks in pyproject.toml
uv sync

Source mode resolves akgentic-* to the local checkouts, editable.

Quick Start

Attach tools to an agent configuration:

from akgentic.tool import (
    ToolFactory,
    WorkspaceTool,
)
from akgentic.tool.planning import PlanningTool
from akgentic.tool.search import SearchTool

# Build a factory with multiple tools
factory = ToolFactory(
    tool_cards=[
        WorkspaceTool(),          # full read/write workspace access
        PlanningTool(),           # shared team task board
        SearchTool(),             # Tavily web search + fetch
    ],
    observer=agent,               # ActorToolObserver (provided by BaseAgent)
    retry_exception=ModelRetry,   # pydantic-ai retry — injected by BaseAgent
)

# Get callables ready for pydantic-ai agent registration
tools = factory.get_tools()            # LLM-callable functions
prompts = factory.get_system_prompts() # static prompt injections
states = factory.get_context_states()  # structured context state, delivered as deltas
commands = factory.get_commands()      # programmatic calls from orchestrator

Grant read-only workspace access to a reviewer agent:

WorkspaceTool(read_only=True)

Custom planning configuration with semantic search:

PlanningTool(
    get_planning=GetPlanning(filter_by_agent=False),  # show all tasks, not just own
)

Architecture

The package follows a two-layer design: a core layer of abstract contracts and a domain layer of independent tool implementations. Domain submodules never import each other — cross- tool composition happens at the agent level.

┌──────────────────────────────────────────────────────────────────┐
│  Domain Tools                                                    │
│  workspace │ planning │ knowledge_graph │ search │ team          │
│  vector_store │ mcp │ sandbox │ notification                     │
├──────────────────────────────────────────────────────────────────┤
│  Core Layer: ToolCard, BaseToolParam, ToolFactory, Channels      │
│              RetriableError, Observer protocols                   │
├──────────────────────────────────────────────────────────────────┤
│  Vector infrastructure (optional): VectorIndex, EmbeddingService │
├──────────────────────────────────────────────────────────────────┤
│  akgentic-core (Pykka actors, ActorAddress, Orchestrator)        │
└──────────────────────────────────────────────────────────────────┘

ToolCard

ToolCard is the base class for all tool configurations. It is a Pydantic model — fully serializable, round-trippable through model_dump() / model_validate().

Serialization rules (Golden Rule 1b):

• All fields must use serializable types (primitives, BaseModel subclasses, enums, collections)
• ConfigDict(arbitrary_types_allowed=True) is forbidden on any ToolCard subclass
• Runtime state (actor proxies, filesystem handles) goes in PrivateAttr — excluded from serialization

class MyTool(ToolCard):
    config_value: str = "default"
    _runtime_handle: Handle | None = PrivateAttr(default=None)  # not serialized

    def observer(self, observer: ActorToolObserver) -> "MyTool":
        self._observer = observer
        self._runtime_handle = setup_handle()
        return self

    def get_tools(self) -> list[Callable]:
        handle = self._runtime_handle

        def my_tool(input: str) -> str:
            """Do something with input."""
            try:
                return handle.process(input)
            except ValueError as e:
                raise RetriableError(f"Invalid input: {e}")

        return [my_tool]

Context state: the get_context_states() hook

A stateful card exposes its volatile prompt content as structured context state on the LLM_CONTEXT channel, through get_context_states(). The hook returns zero-arg providers — callables returning a ContextState snapshot, or None when the state is unavailable (collected observer, stopped actor). Providers never raise; the default is []. It sits alongside get_system_prompts(), which stays for static text that belongs in the cached prefix.

ContextState is a small contract:

• render_full() — the whole state, as the model should first see it; "" when there is nothing to say.
• render_delta(previous) — what changed since previous; None when nothing did. The caller guarantees previous is the same concrete type, and a first-seen state is always rendered full — render_delta is never asked to diff against nothing.

Composition lives in this package: ContextUpdater reads every provider, diffs each against its persisted baseline, and returns the block. The agent layer's one remaining job is to append what it gets back (ADR-037 §6-8) — wiring that akgentic-agent's Epic 21 rebuilds against a released akgentic-tool carrying this contract. That is a documented merge-order dependency, not a defect of this package. See Persisted baselines and the ContextUpdater below.

The channel a capability declares is the card author's promise — SYSTEM_PROMPT means rendered once, cached prefix; LLM_CONTEXT means per-turn delta at the tail. Choose carefully: a capability exposed on a channel its card cannot serve is dropped silently — no error, no warning. A GetPlanning left on SYSTEM_PROMPT after the card stopped overriding get_system_prompts() would simply vanish from the prompt.

Two mechanics back the hook:

• Aggregation — ToolFactory.get_context_states() iterates cards in dependency order. A provider's key is its callable __name__ (the same convention get_command_registry() uses), and two providers sharing a name raise ValueError at build time, naming both owning cards — never silent shadowing.
• Persisted-payload migration — a card persisted with an explicit expose: ["system_prompt", ...] before its content moved would resolve to a channel the card no longer serves. normalize_system_prompt_to_llm_context rewrites that persisted exposure to LLM_CONTEXT, attached as a field_validator per param class, on moved params only — never globally, which would silently drag static content out of the cached prefix.

Persisted baselines and the ContextUpdater

The per-provider baselines are persisted, so a restored agent resumes delta delivery instead of re-sending a full snapshot on its first turn. They live in ToolState — context_baselines (dict[str, ContextState], keyed by provider __name__) and context_update_seq (int) — carried by the agent's own state object and written out by the agent's existing state checkpoints. This package ships the slot, the engine and the contract; the state field that carries them is akgentic-agent's half of the same decision, on the merge-order dependency noted above.

They are a cache, never a record. The durable record of what the model was told is the message history; the baselines only spare the engine from repeating it. This package therefore has no save path — no event, no retry, no write-ahead anything — because it needs none.

The engine. ToolFactory.get_context_updater() builds a ContextUpdater from the factory's observer and the same providers get_context_states() yields — so a duplicate provider __name__ fails through this path too, and a factory whose observer is missing or is not an ActorToolObserver raises ValueError at team-creation time rather than going silently inert. Its surface is two methods:

• compose_update(messages) returns at most one **Context update N** block per turn, or None when nothing changed. It never appends — the append stays with the agent layer.
• reset() zeroes both fields. That is the /clear path, and the only legitimate zeroing of the counter: it matches a history whose markers were wiped along with it.

The trust gate. Baselines are honoured only while the last marker they claim to have delivered is still visible in the history. If the persisted counter has fallen behind the markers, the baselines are kept and the next delta simply re-states what the missed blocks said — a repeat, never an omission. If the last delivered marker is no longer visible at all (compaction, trimming, an out-of-band wipe), the baselines are dropped and the next block is a full snapshot. A wrong, stale or missing save therefore degrades to a wider delta or a full snapshot, never to a lost update.

The trap: never cache a ToolState reference. Read it through the observer — observer.state.tool_state — on every call. The agent replaces its state object wholesale on restore, so a ToolState, a carrier, or a strong observer reference stored anywhere outside the agent's state goes silently stale. It is the first thing a review of this area looks for.

Both types are importable from the package root:

from akgentic.tool import ContextUpdater, ToolState

ToolFactory

Aggregates multiple ToolCard instances into flat lists. When retry_exception is set, wraps every tool callable with a converter that catches RetriableError and re-raises it as the framework-specific exception (e.g., pydantic-ai's ModelRetry).

ToolFactory(
    tool_cards=[tool_a, tool_b],
    observer=agent,
    retry_exception=ModelRetry,
)

Beside the five aggregators it also builds one runtime object: get_context_updater() returns a ContextUpdater over this factory's observer and its context-state providers — see Persisted baselines and the ContextUpdater above.

BaseToolParam: Configuration, Not Schema

Every custom field on a BaseToolParam subclass must be read at factory bind time and influence the tool's runtime behavior — as a closure variable, function default, or observer setup value. Fields that merely mirror the LLM-facing function signature are dead code: they look configurable but have no effect.

Rule: if a developer writes MyParam(field=value), that value must influence runtime behavior. LLM-facing parameters belong exclusively on the factory-produced function signature.

# CORRECT — field captured at bind time, controls behavior
class GetPlanning(BaseToolParam):
    filter_by_agent: bool = True  # read by factory, stored in closure

# CORRECT — no custom fields, search params live on the function signature
class SearchGraph(BaseToolParam):
    expose: set[Channels] = {TOOL_CALL, COMMAND}

# WRONG — fields duplicate function signature but are never read
class BadParam(BaseToolParam):
    status: str | None = None  # never consumed by factory

Channel System

The Channels enum (TOOL_CALL, SYSTEM_PROMPT, LLM_CONTEXT, COMMAND) controls how a capability is surfaced. Each BaseToolParam subclass declares its expose set. A single capability can appear on multiple channels simultaneously.

Channel	Consumer	Invocation
TOOL_CALL	LLM agent	Called by the LLM during the ReAct loop
SYSTEM_PROMPT	LLM context	Rendered once into the frozen system block
LLM_CONTEXT	LLM context	Structured context state appended into the context tail per turn, as a delta
COMMAND	Orchestrator / agents	Called programmatically via proxy_call

The two prompt-bearing channels carry different promises, and picking between them is the card author's contract with the runtime:

• SYSTEM_PROMPT — rendered once into the frozen system block, never re-rendered, part of the cached prefix. Static content only.
• LLM_CONTEXT — structured context state appended into the context tail per turn, as a delta. The channel for volatile tool state that must not invalidate the cached prefix. That per-turn delta is composed by the ContextUpdater against the baselines persisted in ToolState on the agent's state.

class GetPlanning(BaseToolParam):
    expose: set[Channels] = {LLM_CONTEXT, COMMAND}    # context state, never a direct LLM call

class GetPlanningTask(BaseToolParam):
    expose: set[Channels] = {TOOL_CALL, COMMAND}      # LLM + programmatic

BaseToolParam.instructions appends runtime guidance to a tool's docstring without modifying source — useful for injecting team-specific constraints at configuration time.

Migration: moved import paths

Two modules were reorganised: akgentic.tool.event was split by audience, and akgentic.tool.vector moved next to the code built on it. Both old paths keep a compatibility façade, which emits a DeprecationWarning on attribute access — not at import time, so code that touches none of the moved symbols is never warned. No removal release is scheduled for either façade. The Internal-tier courtesy entries that no stored payload can name have been withdrawn: those old paths now raise ImportError.

Importing from the akgentic.tool package root needs no migration at all. That surface is unchanged, and reaching a symbol through it emits no warning. The root is also the supported home of the global observers — from akgentic.tool import ToolObserver has always worked and still does.

Still shimmed — resolves, with a DeprecationWarning

Old path	New home	Tier
akgentic.tool.event.ToolStateEvent	akgentic.tool.core.event	Stable
akgentic.tool.event.CommandArg	akgentic.tool.core.event	Stable
akgentic.tool.event.CommandDescriptor	akgentic.tool.core.event	Stable
akgentic.tool.event.CommandsAnnouncedEvent	akgentic.tool.core.event	Stable
akgentic.tool.vector.VectorEntry	akgentic.tool.vector_store.vector	Internal

These stay shimmed because the façades are load-bearing beyond source compatibility, and persisted data is what makes them so. __model__ markers are written for Pydantic models and dataclasses, and resolving one is import_module plus getattr on the path recorded at write time — so any row stored before a move keeps naming the old path for as long as that row exists. VectorEntry is on this list for that reason alone, and it is the only entry of its module that qualifies. The event symbols additionally have a source consumer: a sibling package imports CommandsAnnouncedEvent from the old path.

Withdrawn — raises ImportError, move now

Old path	Import instead
akgentic.tool.event.ToolObserver	akgentic.tool (root) or akgentic.tool.core.observer
akgentic.tool.event.ActorToolObserver	akgentic.tool (root) or akgentic.tool.core.observer
akgentic.tool.event.TeamManagementToolObserver	akgentic.tool (root) or akgentic.tool.team.observer
akgentic.tool.vector.EmbeddingService	akgentic.tool (root) or akgentic.tool.vector_store.vector
akgentic.tool.vector.VectorIndex	akgentic.tool (root) or akgentic.tool.vector_store.vector

The observers are Stable-tier symbols — but the promise attaches to the package root, their supported surface, not to every path they historically resolved from. Their event.py residence was an accident of the pre-split layout. The akgentic.tool.vector module was Internal-tier in its entirety, and everything in it that cannot appear in a stored payload went with it — a plain class is never written to a __model__ marker, so nothing in a database can ask for one back.

What the two tiers mean

The tier is not a measure of how important a symbol is. It says whether its import path is something you may build against, and the two answers carry different promises.

Stable — a supported surface. These are the contracts a custom ToolCard author outside this package writes against: the akgentic.tool package root, the core abstractions (ToolCard, BaseToolParam, ToolFactory, Channels, CommandRegistry), the global observers ToolObserver and ActorToolObserver at the package root, ToolStateEvent, and the command-discovery models. That surface is part of the API: if a symbol on it moves, it is shimmed, and the shim is kept.

Internal — not a surface. These belong to one specific tool: TeamManagementToolObserver is TeamTool's contract, the vector primitives are vector_store's. They move freely with the tool that owns them. A shim entry for one is a courtesy, not a guarantee — it exists because removing a working import for no reason is rude, not because the path was ever promised. Treating it as a promise would freeze this package's internal structure by accident, which is exactly what the split was done to avoid.

An internal symbol may also be removed outright, not merely moved — and then there is no shim and no warning. ToolStatePayload went first: an alias for the knowledge graph's delta type that stopped annotating anything once ToolStateEvent.payload was typed structurally. The courtesy entries above were withdrawn the same way: the observer entries in event.py, and every entry of akgentic.tool.vector except VectorEntry. Importing a withdrawn path raises ImportError rather than warning.

That exception is the general rule rather than a special case, and it is worth stating on its own: a symbol's tier governs its import path, not its wire format. Internal-tier says no caller may build against the path. It says nothing about the rows already on disk that name it, and those rows are not a caller — they cannot be migrated by editing an import, and they outlive the release that moved the class. So a persisted model keeps its old path regardless of tier, and the courtesy that may be withdrawn from it is only the courtesy owed to source.

If one of your imports is in the Withdrawn table, it has already stopped working — move it to the path in its Import instead column.

Observers: How a Tool Acts on the System

A ToolCard is inert. It is fully serializable configuration, and that is a hard rule rather than a default: every field must round-trip through Pydantic, so a card cannot hold an actor proxy, a connection, or an open file. Taken literally, a card has no way to reach the running system at all.

The observer is the inversion that resolves this. At wiring time ToolFactory calls observer() on every card, handing it the agent that owns it. From that moment the observer is the tool's only channel to the runtime — and because it arrives after construction and is never a field, the card stays serializable. Everything a tool does to the system, it does through the observer.

Three levels — ask for the least you need

The observer is a Protocol, and there are three, each extending the one above it:

Protocol	What it adds	What that lets a tool do
ToolObserver	notify_event(event)	Emit a domain event onto the orchestrator's stream. Nothing more.
ActorToolObserver	myAddress, orchestrator, team_id, state, proxy_ask(...)	Reach any actor by address — including a singleton tool actor. Reach the persisted tool-layer slot, state.tool_state, read live on every call.
TeamManagementToolObserver	createActor(...), on_hire(...), on_fire(...)	Create actors, and change the team's membership.

Each capability is gated by the level above it: a tool that only emits events cannot reach an actor, and a tool that reaches actors cannot hire anyone. Declare the narrowest level your tool genuinely uses. The third level is domain-specific rather than general — TeamTool is its only consumer in this package — which is why it lives beside that tool instead of on the core surface.

Narrow in an accessor, not in the signature

One trap catches the obvious reading of "ask for the level you need". Do not narrow the observer() parameter. ToolFactory attaches one observer to every card uniformly, so a card demanding a richer parameter type is not substitutable for its base — a Liskov violation that a type checker will happily let you write and the factory will break at runtime.

Keep the base parameter type, and narrow in your own accessor:

class MyTool(ToolCard):
    # A proxy is not serializable: runtime handles are private attributes, never fields.
    _activity_proxy: TeamActivityActor | None = PrivateAttr(default=None)

    def observer(self, observer: ToolObserver) -> "MyTool":   # base type, always
        super().observer(observer)
        obs = self._team_observer()                           # narrow here instead
        # The observer hands you the orchestrator's *address*; ask it for a proxy first.
        orchestrator = obs.proxy_ask(obs.orchestrator, Orchestrator)
        address = orchestrator.getChildrenOrCreate(...)
        self._activity_proxy = obs.proxy_ask(address, TeamActivityActor)
        return self

    def _team_observer(self) -> TeamManagementToolObserver:
        return cast(TeamManagementToolObserver, self._observer)

TeamTool and PlanningTool both ship exactly this shape.

The observer is held weakly

ToolCard stores the observer through a weak reference. A tool, its closures and its command registry must never keep a stopped agent alive, and a strong reference in any one of them would do it. Closures are the easy mistake, which is why they capture the accessor rather than the agent.

The consequence to plan for: using a tool after its owning agent has stopped raises ToolObserverGone. That is a defined outcome, not a crash — the framework telling you the owner is gone. There are two accessors for exactly this reason: one raises ToolObserverGone, the other returns None. Synchronous in-life code uses the raising form; a closure that may outlive its agent uses the None-returning one and handles the None.

Do not stash the observer in a field of your own to avoid this. It would not be serializable, and it would reintroduce the strong reference the weak one exists to prevent.

Tool State Events

A stateful tool actor's state is the point of it — the plan, the graph, the index. Clients want to follow that state as it changes, and ToolStateEvent is how a tool actor tells them: by broadcasting what changed, not what it now holds.

ToolStateEvent(tool_id="#KnowledgeGraphTool", seq=7, payload=delta)

• tool_id — the name of the emitting tool actor, #-prefixed like the actor itself. A client following several stateful tools in one team routes on it.
• seq — a per-tool monotonic counter starting at 1. Per-tool, not per-team: two tool actors each number their own stream independently. A consumer detects a missed event by watching it.
• payload — the delta itself.

The envelope inherits team_id, timestamp, id, sender and display_type from the framework's Message base without overriding any of them, so it travels on the ordinary event stream.

Why deltas, and why there is no snapshot protocol

A tool actor's state can be large, and it changes in small increments. Republishing all of it on every mutation would be wasteful in the ordinary case and useless in the interesting one — a client that wants to show "three entities were added" cannot recover that from two snapshots without diffing them itself.

So the event carries the increment, and it rides the path the orchestrator already has: notify_event puts it on the orchestrator's event stream, which is recorded in team history. A client that joins late does not ask for a snapshot — there is no snapshot request and no snapshot message — it replays the history it would have replayed anyway and applies the deltas in order. Tool state reconstructs itself out of the normal replay path, which is why the mechanism needs no protocol of its own.

payload is structurally typed

payload is declared as any serializable model, not as a union of the concrete delta types. That is deliberate: a union naming the knowledge graph's delta and its peers would make the package-global envelope depend on every domain that emits one — exactly the dependency the package layout forbids.

The concrete class is not lost. Serialization tags the payload with a __model__ marker naming its class, so a consumer deserializes the real object and discriminates on the object, not on the envelope — an isinstance check, in Python terms. Your own delta type needs no registration and no entry in any union; it needs only to be a serializable model.

One caveat if you ever move a delta class between modules: that marker records the class's module path, so it moves when the class moves. The payload's own fields are unaffected.

The emit-before-return contract

A mutation method emits its event before it returns — and before it raises, if it collected errors along the way. A caller that gets a return value knows the event is already on its way; a caller that gets an exception still gets the events for the work that did succeed.

The knowledge graph is the shipped example. update_graph applies its entity and relation changes, builds a delta from what it actually added, modified and removed, then:

delta = KnowledgeGraphStateEvent(
    entities_added=created_entities,
    entities_modified=modified_entities,
    entities_removed=deleted_entity_ids,
    relations_added=created_relations,
    relations_removed=merged_relations_removed,
)

self.state.notify_state_change()

if self._delta_is_non_empty(delta):
    self._state_event_seq += 1
    self.notify_event(
        ToolStateEvent(tool_id=KG_ACTOR_NAME, seq=self._state_event_seq, payload=delta)
    )

if errors:
    raise RetriableError("Update errors: " + "; ".join(errors))
return "Done"

Two details worth copying:

• An empty delta emits nothing. "Emit before return" is not "emit unconditionally" — a call that changed nothing is not a state change, and broadcasting it would make every consumer filter noise.
• seq advances only when an event is actually emitted, inside the guard. Numbering therefore has no gaps for suppressed empty deltas — which is what makes a gap meaningful to a consumer.

Tool Actors

Most tools are stateless: the card holds configuration, the callable does its work and returns. A few are not. A plan, a knowledge graph and a vector index are shared, mutable state that outlives any single tool call, and the framework gives that state a home — a tool actor, one per team, that every agent carrying the card talks to.

Six ship in this package today: #VectorStore, #PlanningTool, #KnowledgeGraphTool, #SandboxActor, #TeamActivity and #NotificationTool.

One per team, and what that buys

Shared state. Ten agents carrying PlanningTool do not get ten plans. They get ten proxies to one #PlanningTool, so when the researcher marks a task done the writer sees it. Give each agent its own copy and the tool stops meaning anything — agents would be coordinating through state they cannot both see.

Centralised processing. One embedding path, one sandbox, one store, rather than N. The expensive machinery is built once, and configuration that must agree — which model embeds, which sandbox mode is permitted — is decided in one place instead of being replicated per agent and left to drift.

No locks. An actor processes one message at a time, so a tool actor's mutations cannot interleave. Two agents updating the graph in the same instant are serialised by the mailbox, not by anything you write, which is why a tool actor's methods can read-modify-write without a mutex. The same one-thread property is why the next section exists: it also means a slow method blocks everyone queued behind it.

State that persists itself. A tool actor's state reaches the team's event store without the tool arranging it — the actor calls notify_state_change(), and the framework snapshots the state and restores it when the team resumes. Persistence here is a property of being an actor, not something a tool implements.

Binding one: getChildrenOrCreate, never check-then-create

A tool binds its actor through orchestrator_proxy.getChildrenOrCreate(...), which is idempotent: it returns the existing singleton or creates it, in one step. The actor is created as a child of the orchestrator — the team's single orchestrator owning it is what guarantees unicity.

The obvious alternative is a bug. "Ask whether it exists, create it if it does not" is two messages with a window between them — two agents wiring the same tool at startup both look, both find nothing, and both create. That is not a theoretical race. It produced duplicate singletons, which is the exact failure the singleton pattern exists to prevent, arrived at by the code written to prevent it.

The # prefix is a teardown invariant

Every tool actor's name starts with #, and that is not a naming convention you may opt out of. The orchestrator decides what counts as a tool actor by that prefix, and drives a two-phase stop with it: regular members first, tool actors only once no regular member remains — which is what stops a tool actor being torn down while an agent is still calling it. If your tool creates an actor, prefix its name.

What the prefix does and does not buy is covered in Teardown: why the # prefix is not cosmetic, below.

Deferred Results: Never Block a Tool Actor

A tool actor is a team singleton with one thread. If a method that callers reach via proxy_ask performs slow external work — an LLM call, a document conversion, a sandbox run, any network round-trip — that actor is occupied for the whole call and every other team member queuing on it is blocked. The obvious mitigation does not work: a Pykka timeout= on the ask abandons the future without cancelling the work, so the actor stays occupied and its mailbox backs up.

The pattern: a cache actor that never performs slow work, short-lived workers that do, and a bounded caller-side poll.

tool closure                     #CacheActor                   #defer-<key> (worker)
     │                                 │                                   │
     │── get(key) ─ask────────────────▶│  dict lookup, O(1)                │
     │◀──────────────────── None ──────│                                   │
     │── request(key, payload) ─ask ──▶│  not cached, not in-flight        │
     │                                 │──── createActor + tell ──────────▶│
     │                                 │                                   │
     │       … poll_deferred: N × (sleep, get(key)) …                      │  blocking call
     │                                 │◀───── deliver(key, value) tell ───│
     │◀──────────────────── value ─────│                                   │  self.stop()

The cache actor's thread is held only for dict lookups, so N members query it concurrently while one production is in flight. The caller waits on its own thread — which is why the poll budget is bounded and a degraded answer always exists.

import uuid

from akgentic.core.agent_config import BaseConfig
from akgentic.core.agent_state import BaseState
from akgentic.tool.core.deferred import DeferredResultActor, DeferredWorker, poll_deferred

class SummaryCache(DeferredResultActor[BaseConfig, BaseState, uuid.UUID, str]):
    def worker_class(self) -> type[DeferredWorker]:
        return SummarizerWorker

# In the tool closure — `cache` is an ask proxy, and it is the only proxy there is:
summary = cache.get(message_id)              # ask — O(1) dict lookup
if summary is None:
    cache.request(message_id, payload)       # TELL-shaped, called on the ask proxy
    summary = poll_deferred(lambda: cache.get(message_id), attempts=5, delay=0.4)
if summary is None:
    summary = text[:200] + "…"               # degraded answer, always available

The four type parameters are ConfigType, StateType, the hashable cache key K, and the produced value V — the first two because Akgent already declares them. deferred is deliberately not on the akgentic.tool.core façade; import akgentic.tool.core.deferred directly.

Calling request on the ask proxy is not a partial adoption of the mechanism. request adds to the in-flight set, spawns a worker and tells it the payload — all O(1) on the cache actor's thread, so the ask never waits on external work. A tool closure holds an ask proxy and nothing else: ActorToolObserver exposes no tell proxy.

Seven rules — all of them, or none

1. The cache actor never performs the slow call. It spawns, caches, and answers get.
2. One worker per key, short-lived, self-stopping. Never reused, never accumulates state.
3. The worker's actor name MUST start with #. See the teardown note below.
4. De-duplicate through the in-flight set. Three callers, one key ⇒ one external call.
5. Failures are cached negatively. A failed key does not respawn a worker on every poll; retry policy is a TTL on the negative entry, never an uncapped respawn.
6. The cache is capped (LRU). An uncapped cache on a team singleton leaks for the life of the team.
7. Callers poll with a bounded budget and always have a degraded answer. An unbounded ask — with or without a timeout — is forbidden.

Teardown: why the # prefix is not cosmetic

Every actor announces itself to the orchestrator on start, so a spawned worker is a visible team member. The orchestrator stops tool actors only once no non-tool member remains, and it decides what is a tool actor by the # name prefix.

What the prefix does not buy is a faster teardown. A worker is a child of its cache actor, and stop_children(blocking=True) waits for it under either name — so a worker mid-call holds its parent's stop open whatever it is called, total teardown time is the same with or without the prefix, and the stop backstop fires in both cases or neither.

What the prefix buys is sibling release. Named #defer-…, a worker is a tool actor, so phase 2 tears down unrelated tool actors — #PlanningTool, #KnowledgeGraphTool, … — in parallel. Named summarize-abc123 it counts as a regular member, and every one of those siblings serializes behind a worker it has nothing to do with.

Because the parent's stop blocks on its children either way, every worker must bound its own external call with an explicit timeout below the orchestrator's stop backstop — and hand that budget to its I/O client. A Python thread cannot be cancelled, so a timeout that does not reach the client is decoration.

Tool Catalog

Eleven tool cards ship with the package. Each one has its own README next to the code, covering the ToolCard definition, every field and every nested capability parameter, and the full configuration surface — environment, extras, actor wiring and failure modes. The entries below are the index; the detail lives beside the module it documents.

Tool	Module	What it does	Reference
WorkspaceTool	akgentic.tool.workspace	Team-scoped filesystem: read, list, glob, grep, view, write, edit, patch	README
PlanningTool	akgentic.tool.planning	Shared task board backed by the #PlanningTool actor	README
KnowledgeGraphTool	akgentic.tool.knowledge_graph	Entities and relations with hybrid keyword + semantic search	README
VectorStoreTool	akgentic.tool.vector_store	Configuration-only card owning the shared embedding store	README
SearchTool	akgentic.tool.search	Web search, fetch and crawl via Tavily	README
TeamTool	akgentic.tool.team	Hire, fire, roster, role profiles, and who is busy right now	README
MetadataTool	akgentic.tool.metadata	The team's business context, rendered once into every agent's prefix	README
NotificationTool	akgentic.tool.notification	Delayed messages an agent schedules to itself	README
SkillTool	akgentic.tool.skill	A library of skills: the menu in the prefix, the bodies on demand	README
MCPTool	akgentic.tool.mcp	External MCP servers as pydantic-ai toolsets	README
ExecTool	akgentic.tool.sandbox	Sandboxed shell execution in the team workspace	README

WorkspaceTool

Sandboxed read/write access to a shared team filesystem — workspace_read, workspace_list, workspace_glob, workspace_grep, workspace_view on the read side; workspace_write, workspace_edit, workspace_multi_edit, workspace_patch, workspace_delete, workspace_mkdir on the write side. One class covers both modes via a read_only: bool gate. All paths are anchored to <AKGENTIC_WORKSPACES_ROOT>/<workspace_id or team_id> and traversal out of that root is rejected.

from akgentic.tool import WorkspaceTool

WorkspaceTool()                                      # full access (default)
WorkspaceTool(read_only=True)                        # read tools only
WorkspaceTool(workspace_id="shared")                 # shared workspace across teams
WorkspaceTool(read_only=True, workspace_glob=False)  # fine-grained capability control

Binary reads (PDF, DOCX, XLSX, PPTX) need akgentic-tool[docs]; image resizing for workspace_view needs akgentic-tool[vision]. Both degrade rather than fail.

Full reference → src/akgentic/tool/workspace/README.md — every capability parameter, the DocumentReader two-pass extraction, resource seeding, sidecar caching and the edit-matching cascade.

PlanningTool

Shared actor-based task board for multi-agent teams. A singleton PlanActor (named #PlanningTool) is created by the orchestrator as one of its children — get-or-create semantics guarantee unicity — and persists across all agents' tool calls. The plan is exposed as structured context state on LLM_CONTEXT, delivered into the context tail as per-turn deltas and scoped to each agent's own tasks by default.

from akgentic.tool.planning import GetPlanning, PlanningTool

PlanningTool()                                                  # default config
PlanningTool(get_planning=GetPlanning(filter_by_agent=False))   # show all tasks
PlanningTool(vector_store=False)                                # keyword-only search

Semantic search needs akgentic-tool[vector_search] and a VectorStoreTool in the team; without either it degrades to keyword-only.

Full reference → src/akgentic/tool/planning/README.md — task model constraints, the four capabilities and their channels, collection configuration and the depends_on contract.

KnowledgeGraphTool

Persistent actor-based knowledge graph for structured entity and relationship storage with hybrid keyword + semantic search. The graph summary is exposed as structured context state on LLM_CONTEXT — delivered into the context tail as deltas — and scales as O(types + roots) rather than O(entities), so a large graph stays affordable as context.

from akgentic.tool.knowledge_graph import KnowledgeGraphTool

KnowledgeGraphTool()
KnowledgeGraphTool(read_only=True)

Requires akgentic-tool[vector_search] — the dependency is checked at wiring time even when vector_store=False.

Full reference → src/akgentic/tool/knowledge_graph/README.md — the mutation and query models, search modes and expansion flags, scoring, and the state-delta events.

VectorStoreTool

Configuration-only companion card for the VectorStoreActor singleton — it exposes no LLM tools, system prompts, or commands (get_tools() returns []). Its sole runtime job is to ensure the singleton exists when the observer attaches. Consumer cards (PlanningTool, KnowledgeGraphTool) never create the actor themselves: they look it up by name and declare a conditional depends_on: ["VectorStoreTool"], so ToolFactory's topological sort wires this card first.

from akgentic.tool.vector_store import VectorStoreTool

VectorStoreTool()                                  # "#VectorStore", OpenAI embeddings
VectorStoreTool(vector_store_name="#VectorStore-RAG", embedding_provider="azure")

Collections are configured on the consumer card (collection: CollectionConfig), and Weaviate connection settings are deliberately not fields on any card — they are infrastructure, injected by the deployment layer.

Full reference → src/akgentic/tool/vector_store/README.md — CollectionConfig in full, the service protocol, asynchronous embedding, and multi-store setups.

SearchTool

Web search and content fetching via the Tavily API: web_search, web_fetch and web_crawl. Every capability parameter becomes the default value of the corresponding tool argument, so configuration biases the model without removing its judgement.

from akgentic.tool.search import SearchTool, WebCrawl

SearchTool()
SearchTool(web_crawl=WebCrawl(max_depth=2, limit=50))

Requires the TAVILY_API_KEY environment variable. A missing or invalid key never raises — the tool returns a message telling the model it is unavailable.

Full reference → src/akgentic/tool/search/README.md — every Tavily parameter with its accepted range, and how crawl_instructions differs from the inherited instructions.

TeamTool

Exposes team management capabilities (hire/fire agents, roster, role profiles) to the LLM, and answers who is working right now, and on what. The roster and the hireable-role catalog are exposed as structured context state on LLM_CONTEXT, delivered into the context tail as deltas. Used by BaseAgent in akgentic-agent to let orchestrator-level agents extend the team at runtime. Requires a TeamManagementToolObserver.

from akgentic.tool.team import ActivitySummarizer, GetTeamActivity, TeamTool

TeamTool()                                      # hire/fire/roster/profiles + team_activity()
                                                #   (truncation only — no actor, no model call)
TeamTool(get_team_activity=False)               # team management only
TeamTool(get_team_activity=GetTeamActivity(     # + summaries on demand; #TeamActivity is created
    summarizer=ActivitySummarizer(model="openai:gpt-5.2-mini"),
))

team_activity defaults to on because the truncate-only report is derived from telemetry the orchestrator already keeps — it costs nothing. The #TeamActivity cache actor is created only when a summarizer is configured, and the callable's signature follows the configuration: summarize_over is absent from the schema when nothing could produce a summary.

Full reference → src/akgentic/tool/team/README.md — the hire/fire channel split, partial-success reporting, the two activity gates, and how "busy" is derived from telemetry.

MetadataTool

Renders the team's business context — the model the deployment wrote with Orchestrator.set_metadata() — into every agent's system prompt, from one operator-written template. Without it the same facts get copied into every role's backstory, where they duplicate and drift away from the authoritative copy. The card owns no actor and holds no state beyond the block it rendered. Alone among the cards here its capability ships off, there being no template a framework could supply: a RenderMetadata turns it on, and a card left unconfigured contributes nothing rather than failing.

from akgentic.tool.core import COMMAND
from akgentic.tool.metadata import MetadataTool, RenderMetadata

MetadataTool(render_metadata=RenderMetadata(
    header="Team context",
    template="Fiscal year: {fiscal_year}. Engagement: {engagement}.",
))

MetadataTool(render_metadata=RenderMetadata(     # command only — nothing in the prompt
    template="Fiscal year: {fiscal_year}.",
    expose={COMMAND},
))

Placeholders are bare field names of the team's metadata model — no dotted paths, indices, conversions or format specs — and a template that breaks that rule raises ValueError at wiring time, next to the mistake. A name the model does not declare raises there too, but only when the team already holds metadata: set_metadata may legitimately run after the agents start, so otherwise the name check moves to the first render, where it degrades to an empty block and an ERROR in the log rather than raising.

The block is a snapshot. It is rendered once, at the first render that succeeds, and a later set_metadata is not reflected. That is deliberate, not a limitation waiting to be fixed: re-reading per turn would make the system prompt volatile and one write would invalidate every agent's prefix cache. (A degraded render caches nothing, so metadata that arrives just after start-up still produces its block on a later turn.) A deployment whose business context genuinely changes mid-life does not want this card.

expose defaults to {SYSTEM_PROMPT, COMMAND}: the prompt the agents read, and team_metadata() for a human who wants to see exactly what they were given. get_tools() is always empty — the model is never handed a tool to fetch metadata, which would cost a round trip for content that never changes and require the model to know it should ask.

Full reference → src/akgentic/tool/metadata/README.md — the template grammar in full, both validation points, the degradation table, and the recipes for metadata set before and after the team starts.

NotificationTool

Lets an agent schedule a message to itself, delivered after a delay — to defer its own attention, check a long-running result later, or nudge itself if nothing has happened by then. A team singleton (named #NotificationTool) holds the pending entries and delivers them.

from akgentic.tool import NotificationTool

NotificationTool()                            # AgentMessage delivery, 300 s cap
NotificationTool(max_delay_seconds=60)        # tighter cap
NotificationTool(message_class="acme_core.messages.ReminderMessage")

Ownership is scoped per agent: listing can be widened to the whole team with pending_notification(all=True), but cancel authority never widens with it. Entries store an absolute due time, so a delay that expired while the team was stopped simply fires on resume.

Full reference → src/akgentic/tool/notification/README.md — the message_class validation contract, delivery and grace semantics, and the /-command surface.

SkillTool

A library of domain guidance — a refund procedure, an escalation policy, a report playbook — split by size and volatility. The menu (one name — description line per skill) is small and immutable, so it goes into the frozen system prefix. The bodies are large and optional, so they arrive at the tail on demand, as ordinary tool returns. Without the split every agent pays for every playbook on every turn, and the instructions that matter for the turn compete with seven that do not.

from akgentic.tool import SkillTool
from akgentic.tool.core import SYSTEM_PROMPT, TOOL_CALL
from akgentic.tool.skill import SkillEntry, Skills

REFUND = SkillEntry(
    name="refund-policy",
    description="How refunds are approved, and the thresholds that need a second signature.",
    content="Refunds under 100 EUR are approved by the agent handling the case. …",
)
ESCALATION = SkillEntry(
    name="escalation",
    description="When to escalate to a human, and what the handover must contain.",
    content="Escalate whenever the customer asks for a person, or after two failed fixes. …",
)

SkillTool(skills=Skills(skills=[REFUND, ESCALATION]))   # all three channels (default)

SkillTool(skills=Skills(                                # prompt + tool only: no /skills command
    skills=[REFUND, ESCALATION],
    expose={SYSTEM_PROMPT, TOOL_CALL},
))

SkillTool(skills=False)                                 # capability removed entirely

Three channels, each carrying what it is good at. SYSTEM_PROMPT carries the menu — a header line, then one name — description line per skill. TOOL_CALL carries use_skill(name). COMMAND registers skills(), the same menu rendered for a human, from the same renderer, so it is an honest answer to what was this agent actually given?

use_skill returns the body as its tool result, in the same turn — "{name}\n\n{content}", not an acknowledgement and not a next-turn delivery. The model asked because it needs the body for the answer it is composing, so anything arriving on the next turn arrives after the answer it was needed for. An unknown name raises RetriableError listing the available names, so the model corrects itself in-loop.

One loading rule. A body lives in the conversation until a compaction or the sliding window drops it; after that the model re-calls use_skill. The menu is in the frozen prefix and survives that, which is why re-calling always works — a restart is not a special case. That placement is load-bearing rather than merely economical: a menu at the tail would be one compaction away from an agent that no longer knows its skills exist.

The prefix cost is O(skills), not O(content) — only name and description are rendered, so body size is paid solely by the conversations that ask for it. The card holds no state: it keeps no loaded set, so calling use_skill on the same name again is the intended recovery rather than a redundancy to suppress.

Full reference → src/akgentic/tool/skill/README.md — every field of SkillEntry and Skills with what it costs, the menu quoted as rendered, the loading model, and the failure modes worth knowing.

MCPTool

Integrates external Model Context Protocol servers as native pydantic-ai toolsets over three transports: streamable-http (default), sse, and stdio. MCPTool takes exactly one connection, on a required singular connection field.

from akgentic.tool.mcp import MCPTool, MCPHTTPConnectionConfig, MCPStdioConnectionConfig

MCPTool(connection=MCPHTTPConnectionConfig(url="https://mcp.acme.example/api/v1/endpoint"))
MCPTool(connection=MCPStdioConnectionConfig(stdio_command="uvx", stdio_args=["acme-mcp-server"]))

get_tools() is always empty — MCP capabilities reach the agent through get_toolsets(). The transport is always taken from the config, never inferred from the URL, so transport="sse" must be requested explicitly.

Full reference → src/akgentic/tool/mcp/README.md — both connection models field by field, the SSE timeout subtlety, tool prefixing, diagnostics and the OAuth helpers.

ExecTool

Sandboxed shell command execution inside the team workspace. A single SandboxActor is spawned per team and reused across all ExecTool calls; the backend is selected via the mode field, with auto probing the host (bwrap → seatbelt → docker → local).

from akgentic.tool import ExecTool

ExecTool()                        # auto mode (default)
ExecTool(mode="docker")           # persistent container per team
ExecTool(workspace_id="shared")   # share the workspace directory with WorkspaceTool

Commands are checked against the ALLOWED_COMMANDS allowlist — first token only. Disallowed commands and backend failures come back as strings, never as exceptions. Set AKGENTIC_SANDBOX_IMAGE to use a pre-built Docker image instead of the auto-built one.

Full reference → src/akgentic/tool/sandbox/README.md — the four backends compared (isolation, timeouts, rlimits, network), the Docker image lifecycle, and how to register a backend of your own.

Error Handling

RetriableError (defined in akgentic.tool.errors) is the single signal for recoverable failures. Tools raise it with a clear, actionable message. ToolFactory translates it to the framework-specific retry exception (e.g., pydantic-ai ModelRetry) via injection — tool logic stays framework-agnostic.

from akgentic.tool.errors import RetriableError

def my_tool(path: str) -> str:
    """Read a file."""
    try:
        return backend.read(path)
    except FileNotFoundError:
        raise RetriableError(f"File not found: {path}")
    except PermissionError:
        raise RetriableError("Path escapes workspace root — use a relative path")

Rule: no raw Python exception should escape a tool callable. An unhandled exception produces no tool response and stalls the agent's ReAct loop.

Exception	Treatment
FileNotFoundError	Wrap as RetriableError("File not found: {path}")
PermissionError (path escape)	Wrap as RetriableError("Path escapes workspace root ...")
re.error (bad regex)	Wrap as RetriableError("Invalid regex pattern: {error}")
RuntimeError (uninitialised state)	Let propagate — programming error, not an LLM error

Optional Extras

Extra	Packages	Enables
vector_search	openai>=1.0.0, numpy>=1.26.0	Semantic search in PlanningTool and KnowledgeGraphTool
weaviate	weaviate-client>=4.9.0	Weaviate backend for the vector store
docs	markitdown[pdf,docx,xlsx,xls,pptx,outlook]>=0.1	Binary file reading in workspace_read
vision	Pillow>=10.0	Image resizing + sidecar cache in workspace_view

No extra is required at import time. When one is absent the affected feature either falls back or fails with an actionable message: planning falls back to keyword-only search, image resizing is skipped with a one-time warning, workspace binary reads raise ValueError with an install hint, and selecting the Weaviate backend raises ImportError with install instructions.

Development

Prerequisites

• Python 3.12+
• uv package manager

Setup

uv sync --all-extras

Commands

# Run tests
uv run pytest tests/

# Run tests with coverage
uv run pytest tests/ --cov=akgentic.tool --cov-fail-under=80

# Lint
uv run ruff check src/ tests/

# Format
uv run ruff format src/ tests/

# Type check
uv run mypy src/

CI Pipeline

Every pull request runs the full quality gate via GitHub Actions (.github/workflows/ci.yml):

The repository is checked out standalone and akgentic-* dependencies resolve from PyPI, so CI runs the same repo-relative commands listed above:

Step	Command	Gate
Type check	mypy src/ (strict, Python 3.12)	Zero errors
Lint	ruff check src/	Zero errors
Tests	pytest tests/ --cov=akgentic.tool --cov-branch --cov-fail-under=80	All pass, ≥ 80% branch coverage

The CI badge at the top of this README reflects the current state of master. PRs are blocked from merging until all three steps are green.

Project Structure

src/akgentic/tool/
    __init__.py               # Public API
    py.typed                  # PEP 561 typing marker
    core/
    │   __init__.py           # Façade: ToolCard, BaseToolParam, ToolFactory, Channels
    │   channels.py           # Channels enum: TOOL_CALL, SYSTEM_PROMPT, LLM_CONTEXT, COMMAND
    │   context_state.py      # ContextState ABC — diffable prompt state for LLM_CONTEXT
    │   state.py              # ToolState — the persisted per-agent tool-layer slot
    │   params.py             # BaseToolParam, normalize_system_prompt_to_llm_context
    │   card.py               # ToolCard
    │   dependencies.py       # Topological ordering of cards by depends_on
    │   commands.py           # CommandRegistry
    │   context_update.py     # ContextUpdater — one Context update block per turn
    │   factory.py            # ToolFactory
    │   event.py              # ToolStateEvent, CommandArg, CommandDescriptor,
    │   │                     #   CommandsAnnouncedEvent — package-global contracts
    │   observer.py           # ToolObserver, ActorToolObserver — the global observers;
    │                         #   ToolStateCarrier — the structural carrier of ToolState
    │   └── deferred.py       # DeferredResultActor, DeferredWorker, poll_deferred
    │                         #   NOT on the façade — import akgentic.tool.core.deferred
    errors.py                 # RetriableError
    event.py                  # Compatibility façade only — the symbols that lived here
    │                         #   moved to core/, team/ and knowledge_graph/.
    │                         #   See "Migration: moved import paths"
    vector.py                 # Compatibility façade only — moved to
    │                         #   vector_store/vector.py. See the migration table
    vector_store/
    │   README.md           # VectorStoreTool reference — fields, CollectionConfig, backends
    │   vector.py             # VectorEntry, EmbeddingService, VectorIndex
    │   │                     #   [optional: vector_search extra]
    │   protocol.py           # VectorStore Protocol, VectorStoreConfig, data models
    │   inmemory.py           # InMemory backend
    │   weaviate.py           # Weaviate backend [optional: weaviate extra]
    │   actor.py              # VectorStoreActor singleton
    │   embedding_actor.py    # EmbeddingActor (non-blocking embedding); spawned as
    │                         #   "#embed-<collection>-<request_id>" (teardown
    │                         #   invariant — see Deferred Results)
    │   └── tool.py           # VectorStoreTool ToolCard
    planning/
    │   README.md           # PlanningTool reference — capabilities, task models, wiring
    │   planning_actor.py     # Task models, PlanConfig, PlanActor
    │   state.py              # TaskRow, PlanningState — planning context state + deltas
    │   └── planning.py       # PlanningTool ToolCard
    knowledge_graph/
    │   README.md           # KnowledgeGraphTool reference — params, search modes, deltas
    │   models.py             # Entity, Relation, CRUD + query models
    │   event.py              # Re-exports KnowledgeGraphStateEvent, this domain's delta
    │   kg_actor.py           # KnowledgeGraphActor
    │   state.py              # RootRow, KnowledgeGraphSummaryState — summary state + deltas
    │   └── kg_tool.py        # KnowledgeGraphTool ToolCard
    search/
    │   README.md           # SearchTool reference — Tavily parameters and ranges
    │   └── search.py         # SearchTool (Tavily)
    team/
    │   README.md           # TeamTool reference — hire/fire channels, activity gates
    │   team.py               # TeamTool — hire/fire/roster/profiles + get_team_activity
    │   state.py              # TeamMemberRow/TeamRosterState, RoleRow/RoleCatalogState
    │   observer.py           # TeamManagementToolObserver — TeamTool's own contract
    │   └── activity.py       # team_activity models, GetTeamActivity,
    │                         #   ActivitySummarizer, TeamActivityActor, SummarizerWorker
    metadata/
    │   README.md           # MetadataTool reference — template grammar, snapshot contract
    │   __init__.py           # Public exports: MetadataTool, RenderMetadata
    │   └── tool.py           # MetadataTool ToolCard + RenderMetadata; no actor, no state
    notification/
    │   README.md           # NotificationTool reference — message_class contract, delivery
    │   __init__.py           # Public exports: NotificationTool, its capability params,
    │                         #   NotificationActor, models
    │   models.py             # PendingNotification, NotificationConfig,
    │                         #   NotificationState, resolve_message_class
    │   actor.py              # NotificationActor singleton "#NotificationTool" + tick loop
    │   └── tool.py           # NotificationTool ToolCard + RegisterNotification,
    │                         #   PendingNotifications, CancelNotification
    skill/
    │   README.md           # SkillTool reference — the three channels, the loading model
    │   __init__.py           # Public exports: SkillEntry, Skills, SkillTool
    │   └── tool.py           # SkillTool ToolCard + SkillEntry, Skills, MENU_HEADER;
    │                         #   no actor, no state
    mcp/
    │   README.md           # MCPTool reference — transports, timeouts, diagnostics
    │   mcp.py                # MCPTool, connection configs
    │   └── oauth_handler.py  # OAuth 2.0 flow
    workspace/
        README.md           # WorkspaceTool reference — every capability parameter
        workspace.py          # Workspace Protocol, Filesystem, get_workspace()
        edit.py               # EditMatcher (7-strategy), FilePatch, parse_patch
        readers.py            # DocumentReader (Pydantic BaseModel), TEXT_EXTENSIONS
        └── tool.py           # WorkspaceTool ToolCard
    sandbox/
        README.md           # ExecTool reference — backends compared, allowlist, image
        __init__.py           # Public exports: ExecTool, SandboxActor subclasses, models
        actor.py              # SandboxActor (abstract), SandboxConfig, ALLOWED_COMMANDS
        local.py              # LocalSandboxActor (subprocess, resource limits)
        docker.py             # DockerSandboxActor (persistent container per team)
        seatbelt.py           # SeatbeltSandboxActor (macOS Apple Seatbelt)
        bwrap.py              # BwrapSandboxActor (Linux bubblewrap)
        tool.py               # ExecTool ToolCard, SANDBOX_ACTOR_CLASSES registry
        └── sandbox.Dockerfile # Bundled image definition for akgentic-sandbox:latest
tests/                        # Tests organised by domain

License

This project is licensed under the GNU Affero General Public License v3.0 (AGPL-3.0).

Dual licensing & CLA — Akgentic is available under the AGPL-3.0 open-source license. A commercial license is also planned for organizations that require alternative terms. Contact Yuma for more information. External contributions will be accepted once a Contributor License Agreement (CLA) is in place. Until then, please hold off on submitting pull requests.