Skip to main content

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

Project description

AgentFlow

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

CI Python 3.10+ License: Apache 2.0 PyPI version Code style: ruff PRs Welcome

AgentFlow is a production-grade 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:

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

Star History Chart

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

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

agentflow_orchestrator-1.3.0.tar.gz (82.9 kB view details)

Uploaded Source

Built Distribution

If you're not sure about the file name format, learn more about wheel file names.

agentflow_orchestrator-1.3.0-py3-none-any.whl (103.2 kB view details)

Uploaded Python 3

File details

Details for the file agentflow_orchestrator-1.3.0.tar.gz.

File metadata

  • Download URL: agentflow_orchestrator-1.3.0.tar.gz
  • Upload date:
  • Size: 82.9 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? Yes
  • Uploaded via: twine/6.1.0 CPython/3.13.12

File hashes

Hashes for agentflow_orchestrator-1.3.0.tar.gz
Algorithm Hash digest
SHA256 20971e85f71ad0f27a1aac7f2bcf856cd33ac978622730f5f4cdee6679f6af7c
MD5 f7f38c9705c0ffaf99a37488cbcdb95f
BLAKE2b-256 866294ef3de3d85b84db3f264eae309143e5f40eca64b6e2cd14d2cc201f6ec2

See more details on using hashes here.

Provenance

The following attestation bundles were made for agentflow_orchestrator-1.3.0.tar.gz:

Publisher: publish.yml on venkatapgummadi/agentflow

Attestations: Values shown here reflect the state when the release was signed and may no longer be current.

File details

Details for the file agentflow_orchestrator-1.3.0-py3-none-any.whl.

File metadata

File hashes

Hashes for agentflow_orchestrator-1.3.0-py3-none-any.whl
Algorithm Hash digest
SHA256 dd2a4ae3b149ff15e6db869be9f097b2f6beb3ebcbd9aef32180c928debfef91
MD5 71dd37951f3590a32b74b549f7c12c0e
BLAKE2b-256 6b295c67e4f0f265468355e69c8c40205e8b4a8500ae27a091193359f4409f57

See more details on using hashes here.

Provenance

The following attestation bundles were made for agentflow_orchestrator-1.3.0-py3-none-any.whl:

Publisher: publish.yml on venkatapgummadi/agentflow

Attestations: Values shown here reflect the state when the release was signed and may no longer be current.

Supported by

AWS Cloud computing and Security Sponsor Datadog Monitoring Depot Continuous Integration Fastly CDN Google Download Analytics Pingdom Monitoring Sentry Error logging StatusPage Status page