netflux 0.2.2

Framework for authoring custom agentic applications, treating agents as first-class functions.

netflux is a minimalist Python framework for authoring custom agentic applications. Its core idea is simple but powerful: treat agents exactly like functions — with inputs, outputs, composition (by calling other functions), and side‑effects on stateful structures.

Our goal is a framework that is:

• semantically flexible enough to express workflows or dynamic, open‑ended problem solvers, or any hybrid of the two.
• Agents are first‑class functions. They take typed arguments, return results, can call other functions, and leave a trace of their work.
• Task Decomposition by design: Compose higher-level behavior from cohesive, reusable building blocks—mirroring how we structure libraries and helper functions in traditional programming. This hierarchy is key to building reliable agents with current LLM limitations.
• Exceptions for agents: let agents raise or bubble up exceptions, or attempt to handle and recover, just like traditional code.

---

Quick-Start Demos & Development

To build an agentic app on netflux, just add the pypi dependency to your project. The demos/ are a good guide to rapidly getting started.

To run the demos/, you will need the provider-specific dependencies installed. Once you make a venv, see demos/README.md to run any demo.

python -m venv .venv
source .venv/bin/activate

# Install the library in "editable" mode (`-e`), meaning your source code changes
# are immediately reflected. It also installs the `test` and `all` dependency groups,
# which include `pytest` and all the provider SDKs (Anthropic, Gemini, etc).
pip install -e .[test,all]

# Run all tests.
pytest tests/ -v

The central idea: Function

Everything in netflux is a Function. There are two concrete kinds:

• CodeFunction — Deterministic Python code (your callable) with a declared signature. Think basic utilities, orchestrators, and agent decorators.

  Example utility: TextEditor (func_lib/text_editor.py) provides file viewing and editing commands.

• AgentFunction — An LLM‑backed function with a schema (arguments), a system prompt, and an initial user prompt template. Under the hood it runs an agent loop and can call other Functions in-between thinking. We casually say “Agent” to mean an instance of AgentFunction.

  Example agent: ApplyDiffPatch (func_lib/apply_diff.py) applies unified diff patches (including diffs fenced in markdown) to files. It focuses on applying a patch — tolerating small whitespace/indentation drift and other minor fuzz — while keeping patch creation as a separate concern. This separation lets you review diffs or delegate patch generation to a different agent, avoiding context window dilution for the patch producer (an example of what we mean by "task decomposition").

Because both kinds are just Functions, any Function can call any Function:

• code → code (classic programming),
• code → agent,
• agent → code,
• agent → agent.

Calling an agent is simply invoking a function whose implementation happens to be an LLM reasoning-with-tools loop.

When we say an agent “invokes tools,” that’s physically the LLM issuing tool calls (Anthropic calls it "tools", Gemini calls it "functions"); semantically in netflux, those tools are just Functions made available to the agent. They may map to AgentFunctions or CodeFunctions.

Why treat agents like functions? Because then composition is uniform: code can call code or agents; agents can call code or other agents. Classic programming already covers “code → code”; netflux enables the other three combinations in a principled way that looks consistent.

---

What the framework gives you

• A clear convention for specifying agents, workflows, orchestrators, utilities, etc (your building blocks)
• A minimal execution runtime for running, monitoring, debugging, and tracing agent instances.
• Library of well-tested built-ins (func_lib).

---

Task decomposition, by design

Good programs decompose behavior into cohesive functions. We encourage the same pattern for agents:

• A high‑level AgentFunction breaks work into sub‑tasks and delegates to more specialized sub‑Functions (including other agents).
• This disciplined decomposition matters for agents because focused sub‑tasks with deliberate, limited context are often the bottleneck to reliable LLM execution.
• Circular references are forbidden and recursion is disallowed to prevent runaway scenarios.

There is no notion of “top” vs. “non‑top” functions. Any Function (code or agent) can be invoked as the top‑level entry from your app, or it can be a deeply nested sub‑task inside a broader agent.

Long‑running, workflow‑like agents typically act as orchestrators, breaking problems into sub‑tasks that can change as progress is made.

---

What defines an Agent

Every AgentFunction specifies:

• Invocation schema: typed args, so it can be called like a regular function. Seen by other agents.
• Short description: purpose and usage explanation. Seen by other agents.
• System prompt (usually static).
• First (and only) user turn (templated; substitutions using the input args).
• How to inject specifics—typically string substitution, but any deterministic transform is fine as long as args ⇒ concrete prompt is well‑defined.
• Allowed Functions it may call (task decomposition, sub‑agents, actuators i.e. leaf tools).
• Opt-in to the built‑in RaiseException function so the agent can proactively signal failure by raising an AgentException.

Design note: The agent’s logical reasoning replaces a function’s fixed code body. Otherwise, we treat agents and code functions uniformly—which is the foundation of netflux.

Example: find_bug_agent

find_bug_agent is an instance of AgentFunction (notice that usually it is not even necessary to subtype AgentFunction). It is an agent that inspects one or more source files in a repository, given an error message, searches for likely root causes, and writes a short report under /tmp. It returns the absolute path to the report file it created.

This is an example of task decomposition: the same agent need not be concerned with both RCA'ing the bug and resolving it, in case either or both tasks require substantial effort. This minimizes the context rot and dilution that one sub-task would have on the other, much like how a human organization may delegate these tasks separately.

It uses:

• the built‑in text_editor tool to read/write files,
• the built‑in raise_exception to fail honestly when appropriate.
• a repo_search function (impl not shown for brevity),

find_bug_agent = AgentFunction(
    name="find_bug_agent",
    desc="Inspects an error message (with as many details as possible e.g. stacktrace), "
         "searches workspace for context, and authors an in-depth bug report. "
         "Returns the absolute path to the report written under `/tmp`. "
         "Raises if details are insufficient, has trouble exploring the workspace, or lacks confidence."
    args=[
        FunctionArg("root", str, "Absolute path to the project or a finer-scoped dir subtree."),
        FunctionArg("error_message", str, "Observed error/stack trace or description of failing behavior."),
    ],
    system_prompt=(
        "You are an extremely thorough bug investigator agent (non-conversational). "
        "When invoked with an investigation, run autonomously for an extended period "
        "to explore the project subtree provided, and use extreme critical thinking to "
        # ... (mirror the intent of the `desc`; make sure your prompts are a superset)
    ),
    user_prompt_template=(
        # User prompt is usually composed of the instance-specific details made from `args`.
        "Target project subtree: {root}\n"
        "Observed error: {error_message}\n---\n"
    ),
    # Here we can list any instances of `AgentFunction` or `CodeFunction`.
    # Each callee's schematized entrypoint is automatically exposed to the agent as a tool/function.
    uses=[text_editor, repo_search, raise_exception],
    # Every LLM has types of tasks it is top-ranked for. Set a default or
    # set based on your availability constraints. Can override on each `ctx.invoke()`.
    default_model=Provider.Gemini,
)

---

What exactly is a CodeFunction?

Many frameworks use the word tool for what we call leaf CodeFunctions: file viewers, string replacers, shell runners, etc. These are your lowest‑level building blocks. But higher‑level CodeFunctions are just as important:

• Deterministic orchestrators: fixed logic that fans out work to one or more AgentFunctions (or to other CodeFunctions via direct call, or via the runtime just for observability).
• Decorators/wrappers around agents that enhance behavior. The prime example is Ensemble (func_lib/ensemble.py): it decorates any AgentFunction; when invoked, it launches multiple independent agent runs (optionally across providers) and then forces a follow-up reconciliation of the alternative responses for enhanced results.

Example: fix_bug_workflow (a CodeFunction orchestrator)

This orchestrator does two steps determinstically in order:

1. Invoke find_bug_agent (wrapped in an Ensemble) to produce a report path under /tmp.
2. Invoke bugfixer_agent to generate a unified diff.

But the second agent does another step one or more times:

3. Agent→agent: have bugfixer_agent write the diff to a file and invoke the built‑in apply_diff_patch agent itself to apply it. If apply_diff_patch raises, bugfixer_agent sees the exception, can revise the diff, and retry.

First we define another agent needed, the bugfixer_agent, and then we complete the workflow.

bugfixer_agent = AgentFunction(
    name="bugfixer_agent",
    desc=(
        "Given a bug report and project (sub-)dir, emit a minimal unified diff (`git`-like) that fixes the issue, "
        "save it to a file, and apply it by calling the built-in `apply_diff_patch` agent."
    ),
    args=[
        FunctionArg("root", str, "Absolute path to the project or a finer-scoped dir subtree."),
        FunctionArg("bug_report", str, "Absolute path to the bug report."),
    ],
    system_prompt=(
        # Author a prompt that clearly instructs the agent to **plan** a fix
        # and consider alternatives, before proceeding to implement the fix.
        # This usually leads to much better results on difficult problems.
        # Emphasize: no workarounds, minimal changes but keeping cohesive architecture, etc.
        # Then instruct to use the `apply_diff_patch` as sub-agent to apply the diff.
        # This lets this agent focus on the implementation instead of getting bogged down
        # by file editor calls and without needing whitespace perfection (sub-agent handles well).
        # If `apply_diff_patch` fails, this agent can re-try.
    ),
    user_prompt_template=(
        "Target project subtree: {root}\n"
        "Bug report path: {bug_report}\n---\n"
    ),
    uses=[text_editor, apply_diff_patch, raise_exception],
    default_model=Provider.Anthropic,
)

# Wrap `find_bug_agent` with an Ensemble-of-Answers for extra reliability.
find_bug_ensemble = Ensemble(
    agent=find_bug_agent,
    instances={Provider.Anthropic: 1, Provider.Gemini: 3},
    allow_fail={Provider.Anthropic: 0, Provider.Gemini: 1},
)

def _fix_bug_workflow(ctx: RunContext, *, root: str, error_message: str) -> str:
    # 1) Find the bug (ensembled)
    report_node = ctx.invoke(find_bug_ensemble, {
        "root": root,
        "error_message": error_message,
    })
    report_path = report_node.result()  # path to report returned by the agent

    # 2) Fix the bug: generate a unified diff, write it to a file, and apply it by invoking `apply_diff_patch`.
    fix_node = ctx.invoke(bugfixer_agent, {
        "root": root,
        "bug_report": report_path,
    })
    summary = fix_node.result()  # e.g. "Target successfully patched N hunks." (and/or patch path)

    return f"{summary}\nReport: {report_path}"

# `CodeFunction`s are often just defined as instances, but sometimes it is convenient to define
# them as subclasses of `CodeFunction` (see `func_lib/text_editor.py` example).

fix_bug_workflow = CodeFunction(
    name="fix_bug_workflow",
    desc="Find the bug (ensembled), generate a minimal diff, and apply it.",
    args=[
        FunctionArg("root", str, "Absolute path to the project or a finer-scoped dir subtree."),
        FunctionArg("error_message", str, "Observed error (stack trace or message)."),
    ],
    callable=_fix_bug_workflow,
    # For `CodeFunction`s, the `uses` should still be populated because it
    # helps enforce function hierarchy to prevent risk of agent causing
    # infinite cycles or recursion runaway.
    uses=[find_bug_ensemble, bugfixer_agent, apply_diff_patch],
)

---

runtime: definition and execution

We model each Function invocation as a task executed by a Node.

A Node represents the state and history of a call both while it is executing and after it completes.

We often use the terms "function invocation", "task", and Node interchangeably.

• CodeNode runs a CodeFunction (a single Python call).

• AgentNode runs an AgentFunction (the provider‑specific LLM loop), tracking:

  • the ordered sequence of child Function calls it makes,
  • a full transcript of the LLM session (user/model messages, tool calls and results, and thinking blocks when available) for traceability,
  • token usage accumulated throughout the loop.

A tree of Nodes represents a top‑level task and all of its sub‑tasks. This tree persists after completion (until you delete it) so you can debug and trace what happened.

At any point, if you snapshot the call hierarchy, it looks like a traditional call stack—except a frame may be an agent instead of a piece of deterministic code. Deeper frames tend to be more specialized, and at the bottom you’ll typically find leaf CodeFunctions (e.g., file IO, text replacement, running a shell command).

Key perspectives

• The logical reasoning of an agent replaces the fixed code logic of a function, but otherwise we treat them the same.
• At any moment, a snapshot of the invocation tree reads like a traditional call stack—except that stack frames can be agents or code.
• Highly specialized agents often use only leaf tools (e.g., read/write files) or no tools at all (analysis‑only). Such agents appear deeper in the call stack.

---

Observability: NodeView and snapshots

External consumers (e.g., your UI) do not read Node objects directly—those mutate as tasks run. Instead, you consume NodeView, an immutable, consistent, atomic snapshot of a node and its entire subtree at a single global version.

A minimal watch loop facility is provided for event-driven UI and looks like this:

from netflux.core import NodeState

prev = 0
while True:
    view = node.watch(as_of_seq=prev)   # blocks until there is a newer snapshot
    prev = view.update_seqnum
    
    # read view.state / view.outputs / view.exception / view.children safely
    print(f"[{view.update_seqnum}] node={view.id} state={view.state.name}")
    
    if view.state in TerminalNodeStates:
        break

This ensures your UI only sees consistent views of the task tree.

---

SessionBag & Objects

In OOP, methods are functions that mutate an object’s state. netflux supports similar patterns with a SessionBag, a scoped object store with the lifetime of a task (and handy access to parent/root scopes). There are two important use cases in mind:

• Functions can stash and retrieve objects to act like methods scoped to their parent or root ancestor. A good example of this is an agent that uses bash to perform its task, where it needs to persist a BashSession across command invocations (in this case, the BashSession is kept in the agent-scope SessionBag and the children invocations retrieve it).
• Functions may use the SessionBag objects to pass input/output across sub‑tasks without serializing through text.

---

Concurrency (fan‑out)

A task and its sub‑tasks form a tree where each node’s children are ordered by invocation (child edges record the call sequence). Like Futures, you can launch multiple children in parallel and defer collecting each node.result() until you’re ready to block. For AgentFunctions, this also works when the underlying model supports parallel tool calling.

---

Top‑level tasks vs. sub‑tasks

A top‑level task is any Function call initiated by your app, which is external to and consuming the framework (e.g., a web handler or CLI tool). It can be a coarse orchestrator or a fine‑grained utility; there’s no special status—any Function can be called from the top or from deep inside a tree.

Long‑running, workflow‑like agents usually act as orchestrators, dynamically redefining sub‑tasks as progress is made. Highly specialized agents tend to live deeper in the stack and may use only leaf CodeFunctions (or even no tools if purely analytical). However, nothing stops your from invoking specialized agents directly to create top-level tasks — the framework is agnostic to this.

---

Exceptions

Another core idea is that Exceptions flow fluidly through Functions as in regular programming:

• A CodeFunction can raise for ordinary reasons (bad args, invariants, business logic). They may be raised by the framework during argument validation. Or they may be raised by the function during biz logic execution. They will be presented to agents in function call results. Most LLM providers support some way to indicate error, and we put the Exception stringification in the response (providers/ extensions must do this correctly).
• An AgentFunction can decide to fail by calling the built‑in raise_exception(), which raises an AgentException. Example reasons include missing context, unavailable sub‑functions, irrecoverable or repeated child errors, or determining the task is unsolvable. This reduces hallucinations by encouraging honest failure.
• Downstream code can catch and handle exceptions, or let them bubble to the caller. This is true for both CodeFunctions via normal try/catch, and AgentFunctions which can be instructed how to handle various problems or not. The smarter models get, the more they can handle exceptions autonomously, provided the messages are sufficiently descriptive.

See the detailed Exception Model below for guidance on when agents should raise, bubble, or retry.

---

Cooperative Cancellation

Cooperative Cancellation uses cancellation token chaining, similar to that seen in languages like C# (CancellationTokens) and Go (Contexts). For now we simply use mp.Event for these. Since tasks can be long-running, especially when they are agents, it's imperative to be able to timely interrupt entire trees or sub-trees to save resources when agents are not going in the desired direction or progress is not meeting time deadlines, and also just for responsive user experience.

By "cooperative", we refer to the pattern of cancellation chaining that requires framework consumers to properly adhere to the pattern in order to get the benefit. This means:

• New providers/ extensions (AgentNode subtypes) should check for cancellation at opportune times (before invoking children; before initial remote model invocation or before following up with function results). AgentNodes should post_cancel() in their agent loops and then simply exit the loop (return), or alternatively they can raise cancellationException. See providers/anthropic.py for an example.
• CodeFunction callables should similarly be responsive to self.is_cancel_requested() and simply raise cancellationException as opportune times.
• Always check for cancellation before invoking children tasks.
• Always collect running children (e.g. block on each child node.result()) before responding to a cancellation request.
• If an agent loop or callable is able to determine a success/exception outcome at or near the same time it would respond to cancellation, it should always prioritize concluding with success/exception instead of reacting to the cancellation request. This is because the work was done anyway, so you want the transcripts to show whatever was actually done at the time of cancellation.

---

Providers

Providers are subtypes of AgentNode that bridge the framework’s pattern to each model’s SDK (Anthropic, Gemini, etc.). It is the driver that runs the agent loop and manages function calls, forwarding them through the framework. Adding a new provider means implementing a new AgentNode subtype. See more details below on writing a new providers/ extension.

Token accounting

Each agent instance tracks token usage over its lifetime (updated on every request/response), including input tokens (e.g., cache hits/writes vs. regular), and output tokens (e.g., reasoning vs. final text where available). See the section below for the full accounting fields and how to access them.

---

A tiny end‑to‑end example

Below we reuse the earlier definitions:

• find_bug_agent (AgentFunction)
• bugfixer_agent (AgentFunction)
• find_bug_ensemble (CodeFunction as decorator/wrapper around AgentFunction)
• fix_bug_workflow (CodeFunction as simple static workflow orchestrator)
• built‑ins: text_editor, apply_diff_patch, raise_exception

…and wire them up in a minimal end‑to‑end run. We also show a simple watcher using NodeView to print progress.

# --- Imports from netflux ---
from netflux.core import NodeState
from netflux.providers import Provider
from netflux.runtime import Runtime

# Built-ins
from netflux.func_lib.text_editor import text_editor           # CodeFunction (leaf tool)
from netflux.func_lib.apply_diff import apply_diff_patch       # AgentFunction (built-in)
from netflux.func_lib.raise_exception import raise_exception   # CodeFunction (to raise AgentException)
from netflux.func_lib.ensemble import Ensemble                 # CodeFunction decorator

# Introduced earlier in the sections above (already defined):
#   - repo_search (CodeFunction)
#   - find_bug_agent (AgentFunction)
#   - bugfixer_agent (AgentFunction)
#   - find_bug_ensemble = Ensemble(agent=find_bug_agent, ...)
#   - fix_bug_workflow (CodeFunction orchestrator)

# Demo auth factories (reads api keys for Anthropic & Gemini from file).
# Consumer must always specify the factory functions to create the LLM SDK clients
# since this configures endpoint, authorization mechanism, etc.
from netflux.demos.client_factory import CLIENT_FACTORIES

# Register everything we intend to use.
runtime = Runtime(
    specs=[
        # Our app's custom building blocks:
        repo_search, find_bug_agent, bugfixer_agent, find_bug_ensemble, fix_bug_workflow,
        # Built-ins we depend on:
        text_editor, apply_diff_patch, raise_exception
    ],
    client_factories=CLIENT_FACTORIES,
)

# Invoke the top-level `CodeFunction` task.
# We also could directly invoke any of the agents if we wanted.
root = runtime.get_ctx().invoke(
    fix_bug_workflow,
    {"root": "/repos/my_repo/sub/problem_library", "error_message": "..."}
)

# Optional: simple watcher to show progress (consistent snapshots via NodeView).
prev = 0
while True:
    view = root.watch(as_of_seq=prev)
    prev = view.update_seqnum

    print(f"[{view.update_seqnum}] node={view.id} fn={view.fn.name} state={view.state.name}")
    for child in view.children:
        print(f"  └─ child id={child.id} fn={child.fn.name} state={child.state.name}")

    if view.state in TerminalNodeStates:
        break

# Finally, print the result or surface the exception.
try:
    print(root.result())  # blocks until done; returns output or raises the exception.
except Exception as e:
    print(f"Workflow failed: {e}")

This refined example shows:

• agent → code (find_bug_agent and fix_bug_agent use text_editor, repo_search, and raise_exception)
• code → agent (the fix_bug_workflow orchestrator calls both agents and the built-in apply_diff_patch)
• agent → agent (fix_bug_agent invoking apply_diff_patch one or more times)
• decorator CodeFunction (Ensemble) wrapping an agent to improve reliability
• watcher loop using NodeView to print consistent progress updates

---

Tips & Tricks

Context Engineering

• The framework tries to make it easy to do effective context engineering. Usually higher-level agents will have the role of orchestration or workflow. Lower-level agents will solve more concrete patterns of problem. As LLMs become more sophisticated, a single agent can take on a broader set of responsibilities (more Functions as its disposal, and longer-running). Partition sub-tasks as fine-grained as needed but don't over-partition unnecessarily as this can degrade your evals.
• user prompt: (1) all the agent-specific context of the generic problem background (even if common to all instances this is still not system prompt), (2) the specific problem instance the agent is being invoked to do now.
• may help to be slightly repetitive of system prompt elements in user prompt to get better adherence of critical instructions.
• system prompts kept focused, relevant, small and stable: agent’s role declaration, generic task explanation, non‑negotiable rules/guardrails, output contract, meticulosity, verbosity/brevity, tool‑use policy (steer how often and when to use certain tools, beyond tool schema).
  • "you focus on performance optimization of the algorithm already select; do not propose new algorithms, just optimize impl using the one chosen."
  • "you must use tools to test performance and confirm speedups. You cannot just be speculative -- your results need to be backed up by numbers and you can admit lack of improvement."
• agents may use files for input(s)/output(s). Input filepaths would be given as args, and output filepaths may be returned (Write File tool used prior to returning).
  • Allows the same intermediate output to be re-used by multiple Functions without needing to repeat tokens.

Misc

• structured outputs are discouraged since LLMs are sophisticated enough to parse unstructured outputs from their sub-tasks. However, sometimes strict structured outputs are critical, and this can be enforced by defining CodeFunctions where the arguments are the schema and the Callable performs serialization and/or verification, depending on the reason for the structured output, and returns empty or provides a filepath with the serialized data, etc. You can leverage the framework's Exceptions Model to propagate an Exception if verification fails.
• When authoring agents, place the common prompt before the specifics of the task instance. This is because LLMs are known to pay greater attention to the beginning and end of the context window. Particularly, when giving background information, whatever most heavily will influence the specific actions the agent will take should be placed closer to the end of the prompt.
• human_in_loop()
  • becomes blocking for human input. Human can interject and this content will present forward guidance in the "function" output.
  • implement the hook to human UI using whatever mechanism particular to your application.
  • various reasons why model may choose to invoke: a. sign-off at key points b. lacking confidence and need guidance on the task c. on the verge of raise_exception() and seeking opinion of what to try before doing so.

---

Entities & Architecture

Function

Function is the central abstraction whereby code or agents are both abstracted as merely being kinds of function calls. Specification / metadata describing the agent or code.

• Concrete subtypes must override abstract property uses() -> List[Function], specifying any Functions that can be invoke()d by the Function.
• AgentFunction: can be invoked by any Function.
  • user subtypes to define their own agents (could use abstract properties that user must override).
  • subtype must specify: input vars, system prompt, templated user prompt (var substitution). Each var may be given as strings or filepaths (upon instantiation of the agent, files would have to be loaded and then substitution done by the runner infra instead of asking the agent to do it).
  • subtype specifies uses: List[Function] — the Functions that the agent may invoke.
• CodeFunction: can also be invoked by any Function.
  • some framework built-in subtypes (Ensemble, ThinkMoreDecorator).
  • mostly user subtypes to define any plain python functions that do some deterministic logic, intended to be invoked most often by AgentFunctions or as the top-level request, to coordinate sub-agents doing sub-tasks.
  • may also invoke another CodeFunction within their code although this will be less common.
  • points to a python function Callable. First arg is a RunContext which is used to invoke the framework to run a Function.
  • spec gives the arguments (names, types, description) without the RunContext. Framework will later check that the Callable matches the spec + the RunContext arg present. For now only allow basic primitive types (string, int, float, bool). Use python primitives to indicate types.
  • in user python code (inside the Callable), user can invoke other Function by doing this:
    • invoke another CodeFunction via:
      • Just call the callable directly (regular python code calling a function); framework does not see this happening and it's perfectly allowed.
        • in the case of Ensemble, user could theoretically use: <Ensemble instance>.callable after they have one.
        • Pass the same RunContext through.
        • No need to include the invoked function in the uses() property.
      • Use the framework runner infra to invoke, via RunContext.invoke(). Possible invocation of a CodeFunction must be declared in uses.
    • invoke an AgentFunction:
      • use the framework runner infra to invoke, via RunContext.invoke(). Possible invocation of an AgentFunction must be declared in uses.

Runtime

Runtime is the top-level runner responsible for execution of trees and managing their state.

• Framework-provided object encapsulating all runner infra
• Created with a collection of user-defined Functions (hierarchy) that may be invoked directly or indirectly; Author defines Functions fully before creating a Runtime.
  • runtime = Runtime(specs: List[Function], client_factories: Mapping[Provider, Callable[[], Any]])
    • client_factories are factory functions that return an instance of the client type expected by each provider.
      • Used in AgentNode provider-specific subtypes.
      • Support pluggable configuration and authentication mechanisms unique to the consumer's app.
  • runtime.get_ctx() -> RunContext: return a special RunContext that is outside the scope of any Task (Function invocation).
• During registration, the runtime automatically performs a BFS over each Function's uses graph to discover and register all transitively referenced Functions. Consumers may seed with a partial set; transitives are added automatically. Duplicate names that point to different Function instances are rejected.
• Responsible for creating trees of Nodes that execute Functions.
  • RunContext.invoke() posts Function invocations to the Runtime.
  • Runtime creates child Node for the invocation, updates the relationship in the Node caller, and updates its own Node-indexing data structures.
  • Runtime drives the child Node to start when resources are available (i.e. agent concurrency control is managed by the runtime).
    • CodeNode will always be started immediately.
• Provides consumer interface for querying trees of Nodes.
  • e.g. visualization
  • list_toplevel_views() -> List[NodeView]: get consistent snapshot of all root tasks
  • get_view(node_id: int) -> NodeView: get latest snapshot for any node without blocking
  • watch(node: Node | int, as_of_seq: int = 0, *, timeout: Optional[float] = None) -> Optional[NodeView]: block until newer snapshot available; if timeout elapses, returns None (read more about NodeViews below).
  • To prevent race conditions, consumers should use Runtime to query state via NodeViews and do top-level invocations.

RunContext

RunContext is a common framework interface used by both framework consumers and framework internal impl to invoke Functions.

• serves at least as the interface for:
  1. top-level task invocation; called by an app that is consuming the framework and a collection of Functions (the app or someone else may define these); access via Runtime.get_ctx().
  2. python code for user-defined or framework-builtin CodeFunctions that invoke other Functions.
  3. when some framework component needs to handle agents doing tool calls, that component delegates invocation to the RunContext.
  • e.g. they all use: ctx.invoke(fn: Function, args: Dict[str, Any], provider: Optional[Provider] = None) -> Node
• every Function invocation has a RunContext given to it, providing the interface, but also tracking the particular Function using it.
  • when a Function invokes another Function (including when framework handles AgentFunction invoking any Function via tool call), the RunContext knows its associated invoking Node (identity of the caller) and causes creation of the invoked Node.
    • this information is used to construct the directed edges relationships of the Node tree. A single top-level task invocation is the parent Node of a tree.
• Each top-level ctx.invoke() (by consuming app) initiates one tree of Nodes where the parent Node of the tree is the top-level Task.
  • Each top-level Task is an independent tree with Nodes disjoint from those originating from other top-level tasks.
  • Each top-level Task may originate from the invocation of any Function that was registered with the Runtime constructor, thus being coarse-grained tasks or fine-grained tasks at the top level.
    • General idea: Fine-grained top-level Tasks would appear as shallow trees, that may be comparable to the deepest subtrees of a coarse-grained top-level Task that decomposes into the former -- the latter being a broader-scope task that needs to solve the former's scope of problem perhaps as a mere sub-sub-Task.
• Fields:
  • node: Optional[Node]: a reference to the particular Node identifying this specific Function invocation. None for top-level contexts.
  • runtime: Runtime: a reference to the shared Runtime.
  • object_bags: Dict[SessionScope, SessionBag]: references to session bags accessible at different scopes.
  • cancel_event: Optional[Event]: cooperative cancellation token inherited from the caller unless explicitly overridden by the caller.
• Methods:
  • invoke(fn: Function, args: Dict[str, Any], provider: Optional[Provider] = None, cancel_event: Optional[Event] = None) -> Node: invoke a Function, optionally overriding the cancellation scope, and return the created Node.
  • post_status_update(state: NodeState): update the current node's status.
  • post_success(outputs: Any): mark the current node as successful with given outputs.
  • post_exception(exception: Exception): mark the current node as failed with given exception.
  • post_cancel(): mark the current node as terminally canceled.
  • cancel_requested() -> bool: helper to check whether the associated cancellation token has been triggered. This does not mean that the Node is already canceled and in canceled state -- it means there is active signaled intention to cancel.
• Narrow Scope: RunContext is just a mechanism to pass on Function invocation directives to the Runtime to act on them.

Node

Node is an core abstract object that represents the invocation of a Function (which we also call a "Task").

• AgentNode: represents and manages the state and running of an AgentFunction invocation.
  • AnthropicAgentNode
    • particular implementation when the AgentFunction is invoked with Anthropic LLM (e.g. Opus 4.1).
  • GeminiAgentNode
    • particular implementation when the AgentFunction is invoked with Gemini LLM (e.g. Gemini Pro 2.5).
  • Tracks history of LLM session thus far (which it also uses in tool cycle when doing follow-up request)
    • Subtypes AnthropicAgentNode and GeminiAgentNode store and use the SDK-specific types in their internal impl.
  • node.get_transcript() -> List[TranscriptPart]
    • Subtypes must implement; they must convert the SDK-specific types in the transcription they are tracking to the framework-common TranscriptParts. They never convert types in the reverse direction.
    • For external observers (UIs, tools), prefer NodeView.transcript: tuple[TranscriptPart, ...] which is an immutable snapshot captured at publish time. node.get_transcript() returns a copy of the live list and should not be used concurrently from outside the node’s thread.
  • Child Function invocations are tracked in node.children: List[Node] property.
    • Always ordered to reflect the sequence in which Functions were invoked.
    • For consumers outside the framework, use NodeView.children: tuple[NodeView, ...] instead to access child information safely.
  • Has states (Waiting, Running, Success, Error, Canceled) but also sub-state including tool use (Function invocation) that it is waiting on.
  • AgentNode is completed once it returns final assistant text or the model decides to RaiseException (if it has been given as an option).
  • TokenUsage cumulative accounting must be reportable by every AgentNode and kept up to date throughout the agent loop (updated on every request/response iteration).
    • Subtype implementations must use the provider SDK's token usage meta to track the accumulation.
• CodeNode: represents and manages the state and running of a CodeFunction invocation.
  • Simpler than AgentFunction because it is just a function call (unlike LLM session complexity). Authors invoke Functions directly from within the Callable.
  • CodeNode is completed once it either returns or raises.
• Fields / Properties:
  • id: int: monotonically increasing unique identifier when Node is to be used as key in any lookup. This is one and the same as "task id".
  • fn: Function: which Function the Node is an instance of.
  • inputs: Dict[str, Any]: What the inputs were for the invocation.
  • outputs: Optional[Any]: What the output(s) were from the run (if finished). Usually just an unstructured string.
  • exception: Optional[Exception]: the exception, if there was an exception.
  • state: NodeState: (Waiting, Running, Success, Error, Canceled) enum
  • children: List[Node]: ordered list of child Function invocations made by this Node.
  • Note: External consumers should access this information through NodeView instead of Node directly to avoid race conditions.
  • For agents, NodeView.usage is a deep-copied snapshot and NodeView.transcript is an immutable tuple (empty tuple for CodeNode).

TranscriptPart

TranscriptPart represents each of the parts that are common to the model-specific SDKs in concept. The subtypes:

• UserTextPart
• ModelTextPart
• ToolUsePart
• ToolResultPart
• ThinkingBlockPart
  • including both redacted and non-redacted
  • includes signature field for thinking block signatures
• On every follow-up call, replay the full history in original order.

Ensemble

• Is a CodeFunction that decorates any AgentFunction to do parallel independent invocations followed by reconciliation.
  • First phase: each AgentFunction call proceeds as normal, with args forwarded for normal user prompt substitution.
  • Second phase: same system prompt and substituted user prompt; append to the user prompt each of the completions along with a reconciliation instruction.
• Given any AgentFunction, mostly users will construct one from built-in factory facility (ctor directly):
  • Ensemble(agent: AgentFunction, instances: Dict[Provider, int], name: Optional[str] = None, reconcile_by: Optional[Provider] = None)
    • instances: how many parallel invocations of AgentFunction to do with each model.
  • User uses this when defining their Functions.
• Automatically has a valid inner Callable like any CodeFunction that does the ensembling phases.

SessionBag

• Collection of arbitrary objects that may be read, mutated, and persisted by Functions.
• Each Node created introduces a SessionBag with its lifetime. The Node and its children can access the bag.
  • Thus, the Node can also access its parent's SessionBag, if it has a parent.
• Each Node can also access the SessionBag of the root Node.
• SessionScope: enum of lifetime scopes, each of which would refer to a different SessionBag that a Node can access:
  • TopLevel: Lifetime envelopes all Nodes in a top-level tree. This would give the root Node's bag.
  • Parent: Lifetime of the Node's parent Node. This would give the parent's bag.
  • Self: Lifetime of the Node itself. This gives the Node access to its own bag.
    • The main application of this is for a Function to receive results from its children and to act as a scratchpad.
• Mechanism to do object-oriented programming
  • Function operates on an object and thus can behave like a method.
  • Function can accept arguments that refer to objects; pass data between Nodes by in-memory strong types instead of requiring ser/des or free-form text.
• Mechanism for Function to own its own objects and invoke Functions that read/create/mutate them.
  • Example: an AgentFunction needs its own persistent Bash session (e.g. process tree, env vars, vars, cwd)
    • To be used at random points over its lifetime
    • Example: launch executables asynchronously (in terminal background; running locally on client); retrieve results later after doing other steps.
• RunContext of a Node carries the references to the three scopes of SessionBags.
  • For a root Node, the TopLevel bag is the same as the Self bag. Trying to access the non-existent Parent bag raises NoParentSessionError.
  • For children of the root, the Parent bag is the same as the TopLevel bag.
  • Any deeper Nodes will find the bags of the three SessionScopes to be different.
  • RunContext.get_or_put(scope: SessionScope, namespace: str, key: str, factory: Callable[[], Any]) -> Any
    • To simplify, this is the only mechanism to be used by Functions for access. Concurrency-safe in case of parallel Function invocations. Simplify by invoking factory under the lock since not high-frequency.
    • Function implementations should cooperate to use descript namespaces and keys, composed of static string constants and instance numbers if multiplicity is possible.
  • Runtime is responsible for creating SessionBag with each Node and propagating references to new descendants.
• Framework will currently rely on ref counting, garbage collection, and self-disposing object behavior (author responsibility).
  • Runtime destruction, or explicit user request to delete a finished tree, will induce disposal of all SessionBag-referenced objects and their resources.
  • This keeps objects alive long past their usable scope (potential resource leak), but is very worth the debuggability for finished subtrees. We can make this more configurable in the future (e.g. mandatory finalizers and dispose on Node completion).

Exception Model

• Any Function can raise or bubble up an Exception at any point while running.
  • For CodeFunction, this is just for the vanilla reasons:
    • raise TException(..) in the Callable, e.g. due to contract breakage, bad args, business logic, assertion failure, etc.
    • Bubble-up: its Callable invokes a regular function that in turn raises and the Callable is unable to handle it or recover.
  • For AgentFunction, this is the agent making a proactive intelligent decision that it wants to raise an Exception.
    • All the reasons in classical programming, but also:
      • Agent is unable to do as directed because:
        • lacks context or key knowledge
        • lacks the sub-Functions it needs (leaf tools or sub-agents) due to author error
        • sub-agent (invoked AgentFunction) is not behaving as expected on a sub-task
        • an invoked child Function has raised, and it's unclear how to handle or it's recurring, and there are no alternatives or the alternatives have already been tried.
    • An agent may be given guidance on:
      • when and which Exceptions from children Functions to recover from, versus when to bubble them up.
      • when to decide the given task is unsolvable and give up by raising.
    • Encourage the agent to declare failure to reduce the rate of hallucination.

Framework support mechanisms:

• Framework built-in RaiseException (is-a Function subtype) intended to be provided to agents in their AgentFunction.uses definition, by voluntary opt-in from the agent author.
  • The spec instructs usage directives like:
    • Bubbling up an Exception it can't solve? Include an inner exception type and inner msg inside of the msg arg.
    • No alternatives worked? Very briefly describe what was tried.
    • Missing information or context, don't know how to solve, etc? Describe this very briefly for the caller in case a follow-up attempt could address this.
  • Implementation of the RaiseException callable is a one-liner: raise the AgentException. (Assume CodeFunction never invokes it).
• Differentiation of agent vs. service/infra faults:
  • AgentException: used when an agent decides to invoke raise_exception(msg) by its own volition, for any reason.
    • includes: faulting agent's name and instance id (Node.id).
  • ModelProviderException: used when an AgentNode implementation (providers/) fails for any reason.
    • Always unrelated to the agent's task and never caused by an agent.
    • includes: provider class name, name of agent being processed when provider faulted, instance id (Node.id), inner exception object.
    • Examples:
      • provider AgentNode malimplementation (not following protocol; not using SDK correctly)
      • connection socket broken or can't open
      • authentication / configuration
      • provider is overloaded, client is being rate-limited, any kind of load shedding or quota issue
      • core framework bug (developer regression) e.g. during ctx.invoke(..).
      • other faults by the remote provider service
• Accessing Node.result() will either return the Function output (usually a string) if it was successful, or will raise the Exception from that invocation if there is one (similar to Futures in many languages), regardless of the invoked Function's subtype.
• Provider-specific AgentNode subtypes shall implement this contract:
  • When collecting Function invocation results from ctx.invoke(..).result(), expect the possibility of an Exception being raised and always catch it.
    • Pass on a string representation of the Exception's type and message (with details but never too verbose and never with stacktrace) back to the LLM in the regular follow-up tool cycle, and flag the fault if the provider's SDK has an explicit field for that. Some LLMs are fine-tuned to pay attention to the error flag but most will understand the Exception string properly anyway especially if the detail is present.
    • Includes ValueError for built-in argument type checking (LLM can respond by re-trying).
  • Implement backoff-retry around SDK Exceptions that are known to be transient only.
  • Intercept RaiseException calls and use ctx.post_exception(e) where e is an instance of AgentException. Then exit the run loop.
  • Allow any other unexpected Exception to bubble past run(). The supertype AgentNode will wrap it in a ModelProviderException with context.
  • A batch of parallel tool calls may result in 0, 1, or more of them succeeding or excepting and this is normal.
  • When a model issues a batch of tool calls and one of them is RaiseException (unusual), honor the model's intent and propagate AgentException to end the agent loop after the whole batch is attempted.
• CodeFunction authors guidance:
  • Be aware that Node.result() from invoked functions may raise.
  • Ensure raisable Exceptions from the Callable have descript type names and sufficient detail. If bubbling, sometimes this requires try-catch interception just to augment details (e.g. is the error pertaining to an input or output) and then re-raising.
  • Consider AgentException: may be difficult to handle statically; consider: retry, change provider. If repeatable, wrap attempts, augment context, and bubble up.
  • Consider ModelProviderException: Avoid backoff-retry to prevent multi-layer retry. Log in case of bug. Augment context and re-raise.
• AgentFunction authors guidance:
  • Add the built-in RaiseException to the AgentFunction.uses property to enlist in AgentExceptions.
  • Provide additional guidance on when to raise, when to bubble up, (or how hard to retry alternatives first) in the system and user prompt. Iterate through trial and error. This will be very specific to the agent's purpose and scope.
  • Strongly consider instructing the model to invoke human_in_loop() before considering raise_exception().

NodeView

NodeView is an immutable, consistent snapshot of a Node and, through reference, its subtree. Use it for observation; do not read live Node fields from UIs or other threads.

• Fields

  • children: tuple[NodeView, ...] — immutable ordered children
  • usage: Optional[TokenUsage] — deep-copied snapshot for agents
  • transcript: tuple[TranscriptPart, ...] — immutable transcript snapshot for agents (empty for CodeNode)
  • update_seqnum: int — global sequence when this view was produced
  • Plus core fields: id, fn, inputs, state, outputs, exception, started_at, ended_at

• What triggers a new NodeView

  • Node creation and linking into the tree
  • Status changes (post_status_update), success/exception/cancel
  • Transcript appends (agents call post_transcript_update() after each append)

• Consistency model (origin-only live rebuild)

  • On each change, the origin node’s NodeView is rebuilt from the live Node under a global lock. Each ancestor gets a fresh NodeView by reusing its previous snapshot fields and recomputing only children from current child NodeViews. No live ancestor fields are read.
  • Implication: an ancestor’s usage/transcript reflect the last time that ancestor itself published; child changes do not refresh them. Only children changes propagate up.

• Immutability guarantees

  • children and transcript are tuples in the snapshot. Provider code appends immutable TranscriptParts; ToolUsePart.args are stored as immutable mappings; TokenUsage is deep-copied at snapshot time.

• Watching for updates

  • Use node.watch(as_of_seq=prev_seq) to block until a newer snapshot is available, then set prev_seq = view.update_seqnum for the next iteration.
  • runtime.list_toplevel_views() returns a consistent snapshot of all root NodeViews; runtime.get_view(node_id) fetches the latest view without blocking.

Deferred Features

This is a bucket list of nice-to-haves.

• Concurrency control
  • Limit the number of AgentNodes in the agent loop concurrently, keyed by Provider.
• Replayability, Pausability, Interruptibility, cancellation
  • Pre-requisites:
    • Restart-ability of tree from the state where any Node was just created.
    • Serializability of Node tree state.
    • cancellation, Pause NodeState.
• NodeState.WaitingOnFunction
• Async and Futures
  • RunContext.invoke() -> return Future and generally use async chaining.
• Streaming SDK usage in model provider AgentNode run loops.
  • For observability, debuggability.
  • Compress results into the SDK's full native type blocks after each block is done streaming.
• Fully migrate to Event-Driven Architecture.
  • Single event loop per Runtime; remove the per-Node thread.
• Smarter caching based on past Function instance statistics.
• Cycle & Recursion Prohibition
  • Enforce at Runtime construction.
  • Ensure each Function has legal references to other Functions.
  • Reject if not a DAG.
  • Reject if function type annotations do not match the spec in CodeFunction.

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