LionAGI — expanded note

URL: https://github.com/khive-ai/lionagi

Status: OK

Quick summary

LionAGI is an orchestration framework / “intelligence OS” for building structured, multi-step AI workflows that combine LLMs, tool integrations, and programmatic validation. It emphasizes typed I/O (Pydantic), ReAct-style reasoning + acting, multi-model/provider support, and observability (action logs, branch histories). It’s designed for reproducible, debuggable agentic flows rather than one-off chat usage.

Core concepts (at a glance)

Branches (conversation / workflow contexts) to hold prompt state and history.
Pydantic-typed responses and validators to make outputs structured and machine-consumable.
ReAct-style flows: reason → call tool → observe → continue reasoning.
Tool adapters: user-provided code that LLMs can call (APIs, shell, CI, device-management).
Multi-provider model support: route tasks to different LLM providers or local engines.
Observability: message/action logs, DataFrame-friendly history export, verbose chain-of-thought for debugging.

Typical architectures & components

Model Provider Layer: OpenAI / Anthropic / Perplexity / Ollama / custom.
Orchestration Layer: LionAGI Branches & planners that decide which tools to call and how to sequence steps.
Tool Layer: adapters for external systems (HTTP APIs, CI runners, device managers, test harnesses).
Storage / Retrieval: optional RAG components (embedding stores, vector DB) integrated per-project.
Monitoring / Logging: store action logs for auditing, replay, and debugging.

Example workflows (concrete use cases)

Multi-step analysis: LLM synthesizes evidence, calls document-parse tool, then produces structured summary.
Programmatic test generation: convert user acceptance criteria into typed test steps (Pydantic schema), dispatch runners, collect results.
Autonomous triage: detect failure, fetch logs, summarize, create prioritized issue with suggested fix.
Continuous synthetic monitoring: scheduled runbooks that exercise endpoints/devices and create alerts + runbooks automatically.

Getting started (short)

Install (check repo for latest): pip install lionagi
Create a Branch, wire a model provider, define Pydantic schemas for structured outputs, and add tool adapters for external actions.
Use verbose ReAct mode for development to observe chain-of-thought before switching to production-safe modes.

(Implementation details and exact API calls are in the repo README — check https://github.com/khive-ai/lionagi.)

Best practices

Use Pydantic schemas to constrain model outputs; validate early.
Keep tooling adapters thin and idempotent. Log all inputs/outputs.
Add safety checks / human-in-the-loop gates for destructive actions.
Rate-limit model calls and cache repeated prompts/pieces of context where possible.
Instrument action logs for auditability — make logs exportable to DataFrames or logs storage.

Integrations to consider

CI systems: GitHub Actions, GitLab CI, Jenkins (to dispatch tests or create PRs).
Device management: FleetDM, MDM APIs for fleet-targeted tests or deployment checks.
Observability & metrics: Prometheus, Sentry, ELK — export run metrics and failures.
Issue trackers: GitHub Issues, Jira — auto-create and populate tickets with structured outputs.
Vector DBs & retrieval: Pinecone, Milvus, Weaviate — when combining with RAG.

Benefits / strengths

Strong emphasis on typed, validated outputs (reduces downstream brittleness).
Suited for complex, multi-step flows that require tool calls and branching logic.
Transparent debugging through verbose action logs and ReAct traces.
Multi-model flexibility for best-of-breed routing (e.g., one model for summarization, another for planning).

Limitations / risks

Not a turnkey product for any single application — you build adapters and schemas.
LLM cost and latency at scale — plan caching and rate-limits.
Automated remediation or destructive actions require strict guardrails and testing.
If you need heavy data-centric RAG features, you’ll likely need to integrate a separate indexing layer.

Architecture — orchestration & fleet patterns

This compact architecture shows how LionAGI fits as an orchestration layer for multi-step AI workflows (including QE/fleet use cases). It focuses on components, data flows, scaling considerations, and guardrails.

Goals

Use LionAGI to orchestrate typed, auditable workflows that combine LLM planning with deterministic tool actions.
Support dispatching work to a fleet of runners (CI agents, device agents, FleetDM-managed hosts).
Maintain observability, replayability, and safety for automated or semi-automated remediation.

High-level components

Model Provider Layer
- Providers: OpenAI, Anthropic, Perplexity, Ollama, internal models
- Responsibilities: LLM inference, routing to best model per task
LionAGI Orchestration Layer
- Branches: workflow contexts and histories
- Planners/ReAct controllers: decide actions, call tools, loop until goal
- Validators: Pydantic schemas and custom checks
- Action logs: structured records of tool calls and agent reasoning
Tool & Adapter Layer
- CI/API adapters: GitHub Actions, GitLab, Jenkins
- Device management adapters: FleetDM, MDM API, SSH, OTA services
- Test harnesses: test runners, synthetic monitoring agents, fuzzers
- Ticketing/Issue adapters: GitHub Issues, Jira
Storage & Retrieval
- Artifact store: object storage (S3) for logs, screenshots, traces
- Vector DB / RAG: Pinecone, Milvus, Weaviate for contextual retrieval
- Metadata DB: lightweight relational DB for run metadata, indexing
Observability & Control Plane
- Logging: ELK / Loki / structured logs (JSON), exportable DataFrames
- Metrics & Alerts: Prometheus + Alertmanager, SLO dashboards
- Human-in-the-loop UI: approvals, manual triage, PR review

Data flow (simple sequence)

User or schedule triggers a workflow (goal) in LionAGI.
Branch planner asks an LLM to decompose the goal into typed steps (Pydantic TestPlan).
For each step, planner chooses a target runner using adapters (FleetDM query or CI tag) and dispatches via a tool call.
Runner executes test, uploads artifacts to object store, and posts result to a callback endpoint or polled endpoint.
LionAGI action log records the tool call and response; LLM reasons on results and decides next steps (retry, escalate, file issue).
If issue creation is chosen, an adapter creates a ticket with a structured payload and links to artifacts.
Final structured summary (Pydantic TestResultSummary) is emitted and stored with the run metadata.

Diagram (Graphviz)

digraph lionagi_arch {  
  rankdir=LR;  
  node [shape=box, style=rounded];  
  
  user [label="User / Scheduler"];  
  lionagi [label="LionAGI\n(Branch / Planner / Validators)"];  
  models [label="Model Providers\n(OpenAI/Anthropic/Ollama)"];  
  tools [label="Tool Adapters\n(CI / FleetDM / HTTP)\n"];  
  runners [label="Runners / Devices / CI Workers"];  
  artifacts [label="Artifact Store\n(S3 / MinIO)"];  
  observ [label="Observability\n(Logs, Metrics, Tickets, Vector DB)"];  
  
  user -> lionagi;  
  lionagi -> models [label="LLM calls"];  
  lionagi -> tools [label="tool calls"];  
  tools -> runners [label="dispatch / webhook"];  
  runners -> artifacts [label="upload artifacts"];  
  runners -> tools [label="callback / status"];  
  lionagi -> observ [label="action logs & summaries"];  
  artifacts -> observ;  
  tools -> observ [style=dashed];  
}

(If you prefer Mermaid, I can include a Mermaid diagram as well.)

Deployment & scaling notes

Run LionAGI controller as a service (k8s deployment) with autoscaling based on queue depth of incoming workflows.
Model providers are external; use local model MCPs (Ollama, vLLM) where low-latency/on-prem inference is required.
Runners (test agents) should be managed separately (FleetDM, k8s pods, VM Fleet) and expose a stable API to the tool adapters.
Offload heavy artifact processing (video frames, large logs) to separate workers and reference via object URLs to keep the action logs small.

Security & safety

Gate destructive tools behind allow_changes boolean and require human approval for high-risk workflows.
Sign and verify callbacks from runners; use authentication tokens per adapter.
Redact PII before storing artifacts or sending to third-party LLM providers. Use privacy-preserving embedding if needed.
Rate-limit LLM usage and enforce cost budgets at the model provider layer.

Observability & reproducibility

Store Branch histories and action logs in structured JSON; support exporting to DataFrames for analysis.
Keep mappings between Branch runs and external artifacts/tickets for traceability.
Add retry logic and idempotency keys to tools to avoid duplicate side-effects.

Guardrails & human-in-the-loop

Include explicit review steps (“approval” tool) before PR merges or destructive remediation.
Emit a natural-language runbook for any remediation action the agent proposes, and require a human confirmation token to proceed.

Recommended minimal stack for a PoC

LionAGI controller (k8s or VM)
One model provider (OpenAI or local Ollama) configured via provider adapter
Simple HTTP runner (small test harness) reachable by a Tool adapter
S3-compatible artifact store (MinIO)
Relational DB (Postgres) to index runs and metadata
Observability: ELK or Loki + Grafana for dashboards

Starter recipe — generate typed test-plan → dispatch → summarize

This minimal starter includes Pydantic schemas, a Branch pseudocode flow, a tool adapter snippet, and runner contract. Adapt the code to the LionAGI API version you use.

Pydantic schemas

from pydantic import BaseModel, Field  
from typing import List, Literal  
  
class TestStep(BaseModel):  
    id: str  
    description: str  
    runner_selector: str  # e.g., "fleet:ubuntu-22.04 tags:webserver"  
    command: str  
    timeout_seconds: int = 300  
  
class TestPlan(BaseModel):  
    plan_id: str  
    objective: str  
    steps: List[TestStep]  
  
class StepResult(BaseModel):  
    id: str  
    success: bool  
    stdout: str | None  
    stderr: str | None  
    artifacts: List[str] = []  # S3 URLs  
  
class TestResultSummary(BaseModel):  
    plan_id: str  
    overall_success: bool  
    step_results: List[StepResult]  
    summary: str

LionAGI Branch pseudocode

# Pseudocode — adapt to actual lionagi API  
from lionagi import Branch, ModelProvider  
  
provider = ModelProvider("openai", api_key=...)  
branch = Branch(provider=provider, system_prompt="You are a QA planner. Return TestPlan JSON strictly matching the TestPlan schema.")  
  
# 1) generate TestPlan from objective  
objective = "Verify login flow on v2.1 for ubuntu webservers"  
response = branch.call("Generate a TestPlan for the following objective:\n" + objective, response_schema=TestPlan)  
plan: TestPlan = response.parsed  
  
# 2) dispatch each step via tool call 'dispatch_test'  
for step in plan.steps:  
    dispatch_payload = {"step_id": step.id, "runner_selector": step.runner_selector, "command": step.command}  
    # 'dispatch_test' is a tool adapter that triggers a runner and returns an execution_id  
    tool_result = branch.call_tool("dispatch_test", input=dispatch_payload)  
    # record tool_result in action log  
  
# 3) poll or wait for callbacks from runners — once results arrive, feed back into branch  
for result in collected_results:  
    branch.call("Process step result", input=result)  
  
# 4) ask model to summarize final TestResultSummary  
final = branch.call("Summarize the test run and produce a TestResultSummary JSON", response_schema=TestResultSummary)  
summary: TestResultSummary = final.parsed  
print(summary.json())

Tool adapter: simple HTTP dispatch (Flask example)

# A tiny runner adapter that LionAGI can call via HTTP  
from flask import Flask, request, jsonify  
import requests  
  
app = Flask(__name__)  
  
@app.route('/dispatch_test', methods=['POST'])  
def dispatch_test():  
    payload = request.json  
    # choose a runner (simple round-robin or query FleetDM API)  
    runner_url = choose_runner(payload['runner_selector'])  
    res = requests.post(runner_url + '/run', json={"command": payload['command'], "timeout": payload.get('timeout_seconds', 300)})  
    # assume runner returns execution_id and callback_url  
    return jsonify(res.json())  
  
if __name__ == '__main__':  
    app.run(port=8080)

Runner contract (examples)

/run POST { command, timeout } → { execution_id, status_url }
Runner posts result to /callback with { execution_id, success, stdout, stderr, artifacts }

Notes

Use allow_changes and human approvals for any destructive or write actions.
Add idempotency keys when dispatching to avoid duplicates on retries.
Attach logs/artifacts to S3 and reference them in the TestResultSummary.

Status: OK — updated 2025-11-07

Explorer

LionAGI — orchestration & alternatives