stackql-agentflow

not (yet another) python glue framework

A Rust-native multi-agent orchestration framework - pipelines in YAML,
agents powered by Claude, state machines, parallel dispatch, and built-in observability.

A lightweight, opinionated multi-agent orchestration framework written in Rust. Pipelines are defined in YAML. Agents are powered by Claude. State machines are explicit and type-safe. Observability is built in.

Built by StackQL Studios as the engine behind internal AI automation tooling. Designed to be embeddable as a library in any Rust application that needs structured, auditable, multi-agent workflows.

Goals

Correctness over convenience. State transitions are explicit Rust types. An invalid transition is a compile-time or runtime error, not a silent bug buried in a callback chain.

Pipelines as config, behaviour as code. The shape of a pipeline - which agents run, in what order, with what retry policy, dispatching to what downstream agents - lives in a YAML file. The things that make your pipeline unique (custom tools, external integrations) live in Rust code that you own.

Built-in observability. Every pipeline run emits structured events to a broadcast hub. A local web UI with a live agent graph starts automatically. Log sinks (file, console, webhook) are first-class citizens, not an afterthought.

No spaghetti. The Rust ecosystem does not need another Python wrapper around LLM APIs. stackql-agentflow is a Rust-native library with no dependency on LangChain, LangGraph, or any other orchestration framework.

Parallel by default where it makes sense. Fan-out dispatch (one upstream result spawning N parallel downstream agents) and broadcast dispatch (one result sent to N different agents in parallel) are built into the dispatcher using tokio::task::JoinSet.

Auditable. Every agent invocation, state transition, tool call, QA feedback loop, and retry is recorded as a structured PipelineEvent. Events are serialisable to JSONL for long-term audit retention.

What it is not

A Python library
A general-purpose LLM API client
A replacement for task queues or workflow engines like Temporal for long-running jobs
Production-ready v1 software - this is an active early-stage project

Architecture overview

Core modules

graph TD
    subgraph stackql-agentflow
        CONFIG["config\nPipeline, Agent, Tool, State\ndeserialized from YAML"]
        STATE["state\nStateMachine, transitions\nvalidated at runtime"]
        AGENTS["agents\nAgent trait, ClaudeAgentRunner\nAgentContext, AgentOutput"]
        ORCH["orchestrator\nDispatcher fan-out/broadcast\nAggregator all-pass gate"]
        CLAUDE["claude\nClaudeClient HTTP\nMessages"]
        TOOLS["tools\nTool trait\nbuilt-in: FilesystemTool"]
        RETRY["retry\nRetryGovernor\nper-agent attempt tracking"]
        OBS["observability\nEventHub broadcast\nLogSink trait + built-ins\naxum web server + UI"]
    end

    CONFIG --> STATE
    CONFIG --> AGENTS
    CONFIG --> ORCH
    AGENTS --> CLAUDE
    AGENTS --> TOOLS
    ORCH --> AGENTS
    ORCH --> RETRY
    ORCH --> STATE
    AGENTS --> OBS
    ORCH --> OBS
    STATE --> OBS

Pipeline lifecycle

stateDiagram-v2
    [*] --> Initialised : from_yaml

    Initialised --> Running : first agent starts
    Running --> WorkDispatched : fan out work items

    WorkDispatched --> InProgress : first item starts
    InProgress --> QAInProgress : all items generated

    QAInProgress --> QAPassed : all items pass all gates
    QAInProgress --> QAInProgress : feedback loop retry

    QAPassed --> Aggregating : aggregator runs
    Aggregating --> Complete : all gates passed

    QAInProgress --> Failed : max retries exceeded
    Aggregating --> Failed : any item aborted

    Complete --> [*]
    Failed --> [*]

Dispatch strategies

The dispatcher supports three strategies declared in the pipeline YAML.

graph LR
    subgraph parallel_fan_out
        direction TB
        OA[orchestrator_agent]
        W1[worker_agent\nitem_1]
        W2[worker_agent\nitem_2]
        W3[worker_agent\nitem_3]
        OA -->|item 1| W1
        OA -->|item 2| W2
        OA -->|item 3| W3
    end

graph LR
    subgraph parallel_broadcast
        direction TB
        WA[worker_agent]
        QA1[qa_agent_a]
        QA2[qa_agent_b]
        QA3[qa_agent_c]
        WA --> QA1
        WA --> QA2
        WA --> QA3
    end

graph LR
    subgraph sequential
        direction LR
        W[producer_agent] --> R[consumer_agent]
    end

QA feedback loop

QA agents return structured QAIssue objects. The retry governor tracks attempts per agent per work item. If issues are blocking and attempts remain, the dispatcher injects feedback into the next AgentContext and re-dispatches the upstream agent.

sequenceDiagram
    participant D as Dispatcher
    participant W as worker_agent
    participant QA as qa_agent
    participant G as RetryGovernor

    D->>W: run(ctx, attempt=1)
    W-->>D: AgentOutput { passed: true }
    D->>QA: run(ctx, attempt=1)
    QA-->>D: AgentOutput { passed: false, issues: [...] }
    D->>G: record_attempt()
    G-->>D: can_retry = true
    D->>W: run(ctx with feedback, attempt=2)
    W-->>D: AgentOutput { passed: true }
    D->>QA: run(ctx, attempt=2)
    QA-->>D: AgentOutput { passed: true }
    D->>G: record_attempt()
    Note over D,G: item complete

Observability pipeline

graph LR
    subgraph Runtime
        AGENTS[Agents]
        ORCH[Orchestrator]
        STATE[State Machine]
    end

    subgraph EventHub
        BC[broadcast::channel]
    end

    subgraph Sinks
        CON[ConsoleSink\nstdout JSON]
        FILE[FileSink\nJSONL audit log]
        HOOK[WebhookSink\nHTTP POST]
        CUSTOM[YourSink\nimpl LogSink]
    end

    subgraph UI
        WS[WebSocket /ws]
        GRAPH[live agent graph\ncytoscape.js]
        LOG[event log\nitem filter]
    end

    AGENTS -->|emit| BC
    ORCH -->|emit| BC
    STATE -->|emit| BC
    BC --> CON
    BC --> FILE
    BC --> HOOK
    BC --> CUSTOM
    BC --> WS
    WS --> GRAPH
    WS --> LOG

The YAML DSL

A complete pipeline is defined in a single YAML file. Here is the minimal shape:

name: my-pipeline
version: "0.1.0"

defaults:
  model: claude-sonnet-4-6
  max_tokens: 1024
  retry:
    max_attempts: 3
    backoff_ms: 1000

tools:
  - id: filesystem
    type: builtin

  - id: my_tool
    type: plugin
    plugin: "my_crate::tools::MyTool"
    config:
      api_key_env: MY_API_KEY

state_machine:
  initial: initialised
  terminal:
    - complete
    - failed
  states:
    - id: initialised
    - id: running
    - id: complete
    - id: failed

agents:
  - id: producer_agent
    prompt: prompts/producer_agent.md
    tools:
      - filesystem
    transitions:
      on_start: running
      on_complete: running
    dispatch:
      strategy: sequential
      target: reviewer_agent

  - id: reviewer_agent
    prompt: prompts/reviewer_agent.md
    tools: []
    retry:
      max_attempts: 2
      on_fail:
        action: feedback_and_retry
        target: producer_agent
        feedback_path: "$.issues"
    transitions:
      on_pass: complete
      on_fail: running
      on_abort: failed

aggregation:
  strategy: all_pass
  gates:
    - reviewer_agent
  on_complete:
    transition: complete
  on_any_abort:
    transition: failed

Dispatch strategies: sequential, parallel_fan_out, parallel_broadcast

Retry actions: feedback_and_retry (re-dispatch upstream with issues injected), abort

Aggregation strategies: all_pass, any_pass, threshold (with min_pass)

Tool types: builtin (provided by the framework), plugin (implemented by you)

Hello world

The hello-world demo runs a two-agent pipeline: a writer and a reviewer. The writer produces a short explanation of any topic you give it. The reviewer checks quality and either passes it or sends structured feedback back for a rewrite. It exercises the full framework: agent dispatch, the feedback/retry loop, tool calls, state transitions, and the observability UI - with no external dependencies beyond an Anthropic API key.

Prerequisites

Rust 1.75+
An Anthropic API key

Run the demo

git clone https://github.com/stackql-labs/stackql-agentflow
cd stackql-agentflow

cp .env.example .env
# edit .env and set ANTHROPIC_API_KEY

cargo run -p hello-world
# or pass a topic:
cargo run -p hello-world -- "how Rust's ownership model prevents memory bugs"

The observability server starts automatically on port 4000.

observability UI  ->  http://localhost:4000
audit log         ->  hello-world-run.jsonl
ctrl+c to exit

What you will see

The graph initialises with two nodes (writer_agent, reviewer_agent) in a pending state. As the pipeline runs:

writer_agent turns blue (running), produces its output, turns green (passed)
reviewer_agent picks it up - on the first attempt it finds issues, turns red, and the feedback edge pulses orange back to writer_agent
writer_agent reruns with the reviewer's feedback injected into its context
reviewer_agent reviews the revised output and passes - both nodes green
Every state transition, tool call, and retry streams into the event log panel

The full event sequence is also written to hello-world-run.jsonl as JSONL.

Extending the framework

Plugin tools

Implement the Tool trait and register it on the pipeline before calling run().

use agentflow::{tools::traits::Tool, AgentFlowError};
use async_trait::async_trait;
use serde_json::Value;

pub struct MyApiTool {
    api_key: String,
}

#[async_trait]
impl Tool for MyApiTool {
    fn id(&self) -> &str { "my_api_tool" }

    async fn execute(&self, input: Value) -> Result<Value, AgentFlowError> {
        // call your API, return structured JSON
        Ok(serde_json::json!({ "result": "..." }))
    }
}

// register before run:
pipeline.register_tool("my_api_tool", MyApiTool { api_key: "...".into() });

Declare the tool in your pipeline YAML:

tools:
  - id: my_api_tool
    type: plugin

Log sinks

Implement LogSink to forward events to any external system.

use agentflow::{LogSink, observability::event::PipelineEvent, AgentFlowError};
use async_trait::async_trait;

pub struct MyRemoteSink {
    endpoint: String,
}

#[async_trait]
impl LogSink for MyRemoteSink {
    async fn emit(&self, event: &PipelineEvent) -> Result<(), AgentFlowError> {
        // PipelineEvent is fully serde-serialisable - forward wherever you need
        Ok(())
    }
}

// register before run:
pipeline.register_sink(Box::new(MyRemoteSink { endpoint: "...".into() }));

Built-in sinks available out of the box:

Sink	Description
`ConsoleSink`	Prints events as JSON to stdout
`FileSink`	Appends events as JSONL to a file
`WebhookSink`	POSTs each event to an HTTP endpoint

Project structure

stackql-agentflow/
- src/
  - lib.rs                   public API surface
  - agents/
    - traits.rs              Agent trait, AgentContext, AgentOutput, QAIssue
    - runner.rs              ClaudeAgentRunner - default YAML-driven agent impl
    - context.rs             AgentContext builder helpers
  - claude/
    - client.rs              ClaudeClient - reqwest HTTP to Anthropic API
    - message.rs             Message, Role types
  - config/
    - pipeline.rs            PipelineConfig, Pipeline runtime struct
    - agent.rs               AgentConfig, DispatchConfig, RetryConfig
    - tool.rs                ToolConfig
    - state.rs               StateMachineConfig
  - error/
    - mod.rs                 AgentFlowError enum
  - observability/
    - event.rs               PipelineEvent, EventPayload enum
    - hub.rs                 EventHub - broadcast channel + sink fan-out
    - sink.rs                LogSink trait, ConsoleSink, FileSink, WebhookSink
    - server.rs              axum server - UI, /ws WebSocket, /api/config
  - orchestrator/
    - dispatcher.rs          fan-out, broadcast, sequential + retry logic
    - aggregator.rs          all-pass gate evaluation
  - retry/
    - governor.rs            RetryGovernor - attempt tracking + backoff
  - state/
    - machine.rs             StateMachine runtime
    - transitions.rs         transition helpers
  - tools/
    - traits.rs              Tool trait
    - filesystem.rs          built-in FilesystemTool
- static/
  - index.html               self-contained observability UI
- hello-world/               minimal demo crate

Roadmap

Pipeline::run() - wire dispatcher, state machine, and aggregator into the full execution loop
hello-world - replace simulation with a real Claude-powered run
Streaming responses - stream Claude output token-by-token to the UI
Persistence - optional SQLite backend for run history and replay
Multi-run dashboard - track multiple pipeline runs in the UI simultaneously
Crates.io publish - once the API stabilises

Name		Name	Last commit message	Last commit date
Latest commit History 15 Commits
.github/workflows		.github/workflows
examples/hello-world		examples/hello-world
images		images
src		src
static		static
.gitignore		.gitignore
Cargo.lock		Cargo.lock
Cargo.toml		Cargo.toml
LICENSE		LICENSE
README.md		README.md
env.example		env.example

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stackql-agentflow

Table of Contents

Goals

What it is not

Architecture overview

Core modules

Pipeline lifecycle

Dispatch strategies

QA feedback loop

Observability pipeline

The YAML DSL

Hello world

Prerequisites

Run the demo

What you will see

Extending the framework

Plugin tools

Log sinks

Project structure

Roadmap

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stackql-agentflow

Table of Contents

Goals

What it is not

Architecture overview

Core modules

Pipeline lifecycle

Dispatch strategies

QA feedback loop

Observability pipeline

The YAML DSL

Hello world

Prerequisites

Run the demo

What you will see

Extending the framework

Plugin tools

Log sinks

Project structure

Roadmap

About

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