AgentFlow

A Multi-Agent Framework for AI-Powered Enterprise API Orchestration

AgentFlow is a Python framework where autonomous AI agents dynamically orchestrate, compose, and self-heal API workflows across enterprise integration platforms — with first-class MuleSoft Anypoint support.

The Problem

Modern enterprises run hundreds of APIs across MuleSoft, AWS API Gateway, Azure APIM, and custom services. Composing these APIs into reliable workflows requires:

• Static orchestration that breaks when APIs change
• Manual error handling per integration point
• No intelligent routing based on latency, cost, or capability
• Zero natural-language accessibility for non-technical stakeholders

The Solution

AgentFlow introduces autonomous AI agents that understand API capabilities semantically and can:

1. Parse natural-language intents into executable API workflows
2. Dynamically discover and compose APIs at runtime
3. Route intelligently based on latency, cost, rate limits, and capability matching
4. Self-heal with circuit breakers, adaptive retries, and fallback chains
5. Collaborate via a multi-agent protocol for complex cross-platform orchestrations

Architecture

┌─────────────────────────────────────────────────┐
│                  Intent Layer                     │
│   Natural Language → Structured API Plan          │
├─────────────────────────────────────────────────┤
│              Agent Orchestrator                   │
│   ┌──────────┐ ┌──────────┐ ┌──────────────┐    │
│   │ Planner  │ │ Executor │ │  Validator    │    │
│   │  Agent   │ │  Agent   │ │    Agent      │    │
│   └──────────┘ └──────────┘ └──────────────┘    │
├─────────────────────────────────────────────────┤
│            Dynamic Router                        │
│   Latency │ Cost │ Rate Limit │ Capability       │
├─────────────────────────────────────────────────┤
│           Resilience Layer                       │
│   Circuit Breaker │ Retry │ Fallback │ Bulkhead  │
├─────────────────────────────────────────────────┤
│              Connector Layer                     │
│   MuleSoft │ REST │ GraphQL │ gRPC │ Custom      │
└─────────────────────────────────────────────────┘

Quick Start

from agentflow import AgentOrchestrator, MuleSoftConnector

# Initialize with MuleSoft Anypoint
orchestrator = AgentOrchestrator(
    connectors=[
        MuleSoftConnector(
            anypoint_url="https://anypoint.mulesoft.com",
            org_id="your-org-id",
            environment="production"
        )
    ]
)

# Natural language orchestration
result = await orchestrator.execute(
    "Fetch customer 12345 from CRM, enrich with credit score, "
    "and create a loan application if score > 700"
)

# (Legacy v1.0 typed API still works; v1.1+ users should prefer the
# HybridIntentParser path shown above.)
from agentflow.agents import PlannerAgent, ExecutorAgent

plan = await PlannerAgent().create_plan(
    intent="Sync inventory across all warehouses",
    available_apis=orchestrator.discover_apis()
)
result = await ExecutorAgent().execute_plan(plan)

v1.1 — Hybrid LLM + rule-based intent parsing

from agentflow import (
    AgentOrchestrator, HybridIntentParser, RESTConnector,
)

# Default: rule-based when offline; swap in an LLM provider for richer parsing.
orchestrator = AgentOrchestrator(
    intent_parser=HybridIntentParser(),
    connectors=[RESTConnector(base_url="https://api.example.com")],
)
result = await orchestrator.execute(
    "Fetch customer 42 from CRM and create an order if KYC is valid"
)

To plug in a real LLM (zero AgentFlow dependency on vendor SDKs):

from agentflow.nlp import CallableLLMProvider, HybridIntentParser, LLMIntentParser

async def call_openai(req):
    # ... your async OpenAI call returning a JSON string ...
    return json_string

provider = CallableLLMProvider(call_openai, name="openai", model="gpt-4o-mini")
parser   = HybridIntentParser(llm_parser=LLMIntentParser(provider=provider))

Key Features

Multi-Agent Collaboration

Each orchestration is handled by specialized agents (Planner, Executor, Validator) that communicate through a shared context and can negotiate execution strategies.

MuleSoft-Native

First-class integration with MuleSoft Anypoint Platform: auto-discovery of APIs from Exchange, RAML/OAS parsing, CloudHub deployment awareness, and runtime policy compliance.

Intelligent Routing

The Dynamic Router scores candidate APIs on latency (P95), cost-per-call, current rate-limit headroom, and semantic capability match — then selects the optimal endpoint in real time.

Self-Healing Resilience

Adaptive circuit breakers learn from failure patterns. Retry policies adjust backoff based on error classification. Fallback chains provide graceful degradation.

Installation

pip install agentflow-orchestrator-orchestrator

Documentation

See the docs/ directory for detailed guides:

• Architecture Deep Dive
• MuleSoft Integration Guide
• Writing Custom Agents
• Routing Strategies

Who's Using AgentFlow?

Are you using AgentFlow at your company or in a project? We'd love to hear from you!

👉 Open an Adoption Story issue — takes 2 minutes and helps the project grow.

Company / Project	Industry	Use Case
Your company here	Your industry	Share your story →

Community

Channel	Purpose
💬 Discussions — Show & Tell	Share what you built
❓ Discussions — Q&A	Ask questions
🔌 Integration Requests	Request a new connector
✨ Feature Requests	Suggest improvements
🐛 Bug Reports	Report issues

If AgentFlow saves you time or solves a real problem, a ⭐ on this repo goes a long way — it helps more engineers find the framework.

Star History

Contributing

Pull requests are welcome — see CONTRIBUTING.md for the dev workflow, and check the good first issue and help wanted labels for places to start. By participating you agree to the Code of Conduct.

License

Apache License 2.0 — see LICENSE for details.

Author

Venkata Pavan Kumar Gummadi

• Research focus: AI-driven API orchestration and enterprise integration intelligence
• GitHub
• LinkedIn
• IEEE Member