Top 10+ Agentic Orchestration Frameworks & Tools

updated on Oct 27, 2025

79% of executives are already adopting AI agents, although 19% of firms struggle with coordination.¹ They cannot manage agents across different applications. Agentic orchestration offers the solution.

Explore agentic orchestration is, its patterns, and the top frameworks that enable multi-agent collaboration.

Agentic orchestration benchmark

We built a travel planning assistant with 5 specialized agents and 2 external APIs across four leading agentic frameworks: LangGraph, LangChain, CrewAI, and AutoGen, and benchmarked their performance by measuring latency and token usage at different stages of the workflow to analyze the efficiency of agentic orchestration across different architectures.

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All frameworks successfully completed the task across 100 run each. However, LangGraph finished 2.2x faster than CrewAI, while LangChain and AutoGen showed 8-9x differences in token efficiency. This reflects fundamental architectural decisions in how each framework orchestrates multi-agent workflows from the orchestration layer, how frameworks route messages, manage state, and coordinate agent handoffs.

To understand why, we measured each phase of the agent lifecycle.

Performance by agents

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Parser agent: Agent performs simple text extraction with minimal complexity. All frameworks show similar latency.

Flight finder agent: We can see significant differences in latency and token usage. This agent uses the flight API tool, and we observe a notable “agent-to-tool gap” the time between when the agent starts and when it actually calls the tool. We’ll examine this gap in detail later in our analysis, where we’ll see that 5 seconds of CrewAI’s 9-second latency comes from this gap.

Weather reporter agent: We see the same ranking pattern continue for both latency and token usage as observed in the Flight Finder agent.

LangChain generates significantly more tokens and higher latency compared to other frameworks, except CrewAI, whose overhead primarily stems from the agent-to-tool gap. This stems from LangChain’s memory management approach, which maintains intermediate steps and full conversation history, creating overhead in multi-agent workflows.

LangGraph emerges as the fastest framework with the fewest tokens. Its graph-based architecture passes only necessary state deltas between nodes rather than full conversation histories, resulting in minimal token
usage and reduced latency.

Activity Agent: Most frameworks demonstrate relatively close performance. Without tool calls, all frameworks converge to similar ranges (6-8 sec for latency, 650-744 for tokens), suggesting the
variation is primarily LLM generation time with minimal orchestration overhead. However, the real performance gap emerges in the Travel Planner agent.

Travel planner agent: Agent receives and synthesizes outputs from all four previous agents (parser, flight finder, weather reporter, and activity recommender) in every framework. However, how each framework
handles this context aggregation reveals fundamental architectural differences.

CrewAI passes the complete, unmodified output of each previous task directly into the planner’s context through its context parameter system. The LLM receives the full tokens of prior agent outputs plus the task
description itself. This approach is not a limitation but a core design philosophy: CrewAI prioritizes comprehensive, context-aware synthesis where agents have complete visibility into previous work. The
result is a detailed 5,339-token itinerary that thoroughly integrates all available information.

LangChain, AutoGen, and LangGraph handle context differently. While all three frameworks do pass previous agent outputs to the planner, they implement various optimization strategies that reduce the cumulative context burden. LangChain’s memory management can compress or summarize intermediate outputs and the framework may not preserve the full verbosity of each agent’s response when chaining them together. This results in the 3,187-token output more concise than CrewAI but still substantial.

AutoGen shows similar behavior with 3,316 tokens, suggesting comparable context handling approaches between these two frameworks. LangGraph’s graph-based state management passes only necessary state
deltas between nodes, resulting in the most efficient 2,589-token output through its optimized state transitions.

Agent to tool gap

Agent-to-Tool Gap is the time between when an agent receives its task and when it actually invokes the tool.

CrewAI’s 5-second gap in the Flight Finder represents actual deliberation time, while other frameworks show near-instantaneous tool calls.

CrewAI’s architecture embodies an autonomous agent philosophy. When the Flight Finder agent receives its task, it doesn’t immediately execute the get_flights tool. Instead, it follows a reasoning process:

Understanding the task: The agent analyzes what information it needs to accomplish the goal
Evaluating options: It considers available tools and determines which one is most appropriate
Planning the approach: The agent decides on parameters and execution strategy
Taking action: Finally, it invokes the tool with the determined parameters. This 5-second gap is CrewAI literally “thinking” before acting, a design choice prioritizing decision quality and autonomous reasoning over raw speed. The agent isn’t told “use this specific tool”; it independently determines the best course of action.

CrewAI does not provide an option to disable deliberation and switch to direct tool calling.

In contrast, LangGraph, LangChain, and Autogen frameworks use direct tool execution approaches, achieving sub-millisecond execution gaps. LangChain and LangGraph support ReAct-style agents, which display reasoning in the “thought → action → observation” pattern. However, the “Thought” component in ReAct is purely text-based prompting . For example, the LLM might generate “Thought: I should…”. This introduces some extra token generation, but it does not create a separate deliberation cycle like CrewAI’s 5-second gap. These “thought” steps are generated within the same LLM call, as part of a single generation process.

Agent to agent orchestration overhead

We measured agent-to-agent latency by calculating the average time between one agent’s completion and the next agent’s start across 100 runs, but the differences were minimal at the millisecond level. This reveals that framework architecture matters most for tool execution patterns and context management, not agent handoffs. The performance differences between frameworks stem from tool deliberation and context synthesis, not the time spent switching between agents.

What is agentic orchestration?

Agentic orchestration coordinates autonomous AI agents within a unified system to complete complex tasks and structured tasks across multiple systems and domains. It builds on earlier forms of automation by enabling multi-agent orchestration, where multiple agents collaborate under an orchestration layer that governs communication, task planning, and execution.

Unlike static automation scripts, agentic orchestration leverages generative AI and AI models to adapt to context, minimize the need for human intervention, and enable seamless execution across diverse systems.

Core principles

Autonomy: Agents can act independently within their defined roles, supported by function calling to external systems.
Collaboration: Multiple AI agents communicate to resolve complex problems, distribute multiple tasks, and achieve end-to-end automation.
Alignment: Systems maintain consistent objectives and ensure compliance with organizational and regulatory requirements in highly regulated industries.
Observability: Logs, monitoring tools, and evaluations enable continuous monitoring and continuous optimization.
Human oversight: Human-in-the-loop approaches combine automation with human input in high-risk or ambiguous contexts.

Orchestration patterns

Agentic orchestration can be categorized into several patterns based on how agents are coordinated within a system. These patterns determine the flow of tasks, the communication between agents, and the overall system architecture.

Figure 1: Centralized vs. Decentralized orchestration approaches ²

Centralized orchestration

In this pattern, a single manager or router agent is responsible for assigning tasks, controlling the workflow, and ensuring that objectives are met. The manager acts as a central hub, directing tasks to specialized agents based on predefined rules or a dynamic plan.

Specific patterns within this category include:

Sequential orchestration: A linear pipeline where a manager directs tasks through a fixed, step-by-step sequence of agents. This is ideal for processes with clear dependencies, like data processing pipelines.

Figure 2: Sequential orchestration example³

Magentic orchestration: A more advanced form of centralized control where the manager agent dynamically builds and refines a plan to solve complex, open-ended problems. The manager directs and delegates tasks as needed to a team of specialized agents.
Hierarchical orchestration: A scalable, tiered structure where a manager-subordinate relationship is used to handle complex tasks across multiple departments or teams.

Figure 3: Magentic orchestration example⁴

Decentralized orchestration

This pattern eliminates the single point of control, enabling multiple agents to interact directly and complete a complex task. This approach enhances resilience and offers greater flexibility for collaborative problem-solving.

Specific patterns within this category include:

Group chat orchestration: Agents collaborate through a shared conversation thread, building on each other’s contributions to reach a decision or solve a problem. A chat manager may facilitate the discussion, but agents communicate directly to achieve a consensus.

Figure 4: Group Chat orchestration example⁵

Handoff orchestration: Agents dynamically delegate tasks to one another without the need for a central manager. Each agent can assess the task and decide to either handle it or transfer it to another agent with more appropriate expertise, similar to a referral system.

Figure 5: Agent handoff orchestration example⁶

Federated Orchestration

This pattern is helpful for highly regulated or distributed environments. It enables collaboration across different organizational silos or systems while maintaining data governance and security. It often combines elements of both centralized and decentralized approaches to manage a wider network of agents and systems.

Figure 6: Federated orchestration approach⁷

Tools and frameworks

Several AI agent frameworks provide the infrastructure for agentic workflows and multi-agent orchestration. Some of them include:

Note that these tools are listed based on the number of GitHub stars they received.

LangGraph by LangChain: Provides modular design and graph-based workflows for complex workflows and structured tasks.
MetaGPT by FoundationAgents: Encodes role-based collaboration (e.g., software engineer, QA) to coordinate multiple agents in software development.
AutoGen by Microsoft: Focuses on conversational collaboration between digital agents, often configured as planner–executor–critic loops.
CrewAI: Organizes specialized agents into “crews” with role-specific goals, useful for business processes and routine operations.
Agents SDK by OpenAI: Enables lightweight orchestration and agent handoffs with function calling to external tools.
CAMEL-AI: Provides modular societies of autonomous AI agents with coordinators for large-scale simulations and complex processes.
Agent Development Kit by Google: Supports multi-agent orchestration with integrated evaluation, debugging, and deployment capabilities.
Langroid: Implements an actor-model style for multi-agent orchestration, emphasizing modularity and delegation.
BeeAI: Emphasizes interoperability through the model context protocol and integration of third-party agents for seamless integration.
Azure AI Foundation Agent Service: Enables the operation of agents across development, deployment, and production by abstracting infrastructure complexity.

Compare these frameworks and learn their core capabilities:

Agentic orchestration applications

Agentic orchestration is the critical capability that transforms individual agents into a cohesive, goal-oriented system. The following are real-world applications where multi-agent systems coordinate to deliver business value.

Business processes

Agentic orchestration enables end-to-end automation across multiple departments and systems. It coordinates specialized agents to handle complex, multi-step workflows without manual handoffs.

Human resources: Orchestrates a team of agents to manage the entire employee lifecycle, from onboarding and policy Q&A to workforce management and offboarding.
Customer onboarding:
Customer operations: Orchestrated systems improve service quality by managing customer interactions across channels, with a group of agents handling initial queries, providing information from different databases, and handing off complex issues to a human-in-the-loop for verification.

Explore AI agents for workflow automation

Supply chain

Agentic orchestration enhances supply chain management by coordinating multiple, specialized agents to manage and optimize a complex network of planning, sourcing, logistics, and inventory management.

Predictive maintenance: An orchestration platform coordinates agents to analyze real-time equipment data, predict potential failures, and automatically trigger a maintenance agent to schedule a repair or order new parts.
Inventory management: Agents are orchestrated to track stock levels, automatically reorder supplies when a threshold is met, and communicate with logistics agents to handle real-time disruptions like shipping delays.
Supplier onboarding: A coordinated system of digital agents handles the entire process, from running compliance checks and generating contracts to integrating new suppliers into the company’s existing workflows.

Enterprise systems

Agentic orchestration provides the core logic for AI-driven processes that require seamless collaboration across different enterprise platforms, such as ERP, CRM, and RPA.

Purchase-to-pay: A series of orchestrated agents manages the full procurement cycle, from a purchasing agent placing an order to an accounts payable agent processing the invoice for payment, cutting cycle times and boosting transparency.
Order-to-cash: A multi-agent system speeds up the entire journey from order receipt to payment by coordinating agents that handle order processing, fulfillment, and accounts receivable, improving cash flow and customer satisfaction.
Dispute resolution: An orchestrated workflow automates claim and chargeback tracking by having one agent gather information, another analyze the dispute, and a third communicate the resolution, simplifying the process and making it faster.

Explore how AI agents are used in enterprise systems, such as:

Banking and Financial Services

In this sector, orchestration is utilized for complex, risk-sensitive workflows that necessitate multiple agents collaborating to ensure accuracy and compliance.

Regulatory compliance: A coordinated system of agents enforces compliance by validating customer information against watchlists, flagging discrepancies, and maintaining a transparent audit trail of every action for regulatory review.
Loan and mortgage processing: An orchestrated workflow enables a group of agents to handle the entire loan approval process—from gathering and verifying documents to applying financial models and providing final authorization for review by a human analyst.
Fraud detection and prevention: This is a classic example of orchestration, where one agent monitors transactions, another identifies and flags suspicious activity, and a third freezes the account and generates an incident report for a human security team.

Check out how AI agents and agentic LLMs are utilized in finance:

Energy and utilities

Agentic orchestration allows for the management of highly distributed and complex systems, such as power grids and workforce management, by enabling specialized agents to communicate and act in real-time.

Grid management: A multi-agent system with distinct agents for generation stations, distribution hubs, individual smart meters, and smart grid solutions works together to balance energy supply and demand, optimize distribution, and prevent outages.
Meter-to-cash: An orchestrated meter-to-cash process can automate the entire billing cycle, coordinating agents that handle automated meter reading, bill generation, and payment collection to improve accuracy and efficiency.
Workforce management: An orchestration system optimizes how field technicians are scheduled and deployed by having agents coordinate to track technician availability, assign tasks based on location and skill, and provide real-time updates on job progress.

Telecom

In telecom, orchestration is used to manage and automate large-scale, complex networks and customer-facing operations.

Network operations: A coordinated system of agents monitors different parts of the network to automatically detect faults, diagnose the problem, and trigger a series of actions to resolve it, ensuring network reliability and minimizing downtime.
Customer onboarding: Orchestration speeds up the process by having agents coordinate to handle SIM activation, device setup, and service enablement, providing a seamless customer experience from start to finish.
Billing and revenue management: An orchestrated workflow automates complex billing adjustments, payments, and refunds by having specialized agents manage each step, which boosts accuracy and customer satisfaction.

Multi-Agent Systems: Applications & Comparison of Tools

Benefits

Operational efficiency: Streamlines routine operations, reduces costs, and enhances scalability.
Operational agility: Enables dynamically responding to real-time data and disruptions.
Seamless collaboration: Ensures cooperation between agents, humans, and multiple systems.
Competitive advantages: Supports innovation while allowing AI systems to operate alongside human staff.
Improved satisfaction: Drives superior customer experiences and measurable improvements in service quality.

Challenges

Governance: Requires robust data governance to prevent risks from multiple agents interacting with diverse systems.
Compliance: Systems must ensure compliance in highly regulated industries, especially in finance and healthcare.
Human oversight: Effective deployment requires clear thresholds for human intervention and escalation.
Seamless integration with existing workflows and legacy systems remains a significant barrier. These older systems may be built on outdated architectures that are not compatible with modern AI technologies.

Benchmark methodology

Workflow architecture

Our sequential agent workflow processes travel requests through five stages:

Parser agent: Extracts structured data from natural language input (“I want to travel from Berlin to Rome on October 25, 2025. I will stay for 3 days”) to identify origin, destination, dates, and duration.
Flight finder agent: Calls the Amadeus API to retrieve available flights using extracted IATA codes and departure dates.
Weather reporter agent: Fetches weather forecasts for the destination across the stay duration using WeatherAPI.
Activity recommender agent: Matches activities to weather conditions (museums for rain, outdoor tours for sunshine).
Travel planner agent: Synthesizes all previous outputs into a comprehensive day-by-day itinerary with
flights, weather predictions, and recommended activities.

Controlled variables

To ensure fair comparison, we maintained identical components across all frameworks:
LLM configuration:

Model: Claude Haiku 4.5 via OpenRouter
Temperature: 0.1
No maximum token limits imposed on any agent

Tool functions:

Identical Python implementations of get_flights() and get_weather() across all frameworks
External API calls to Amadeus (flights) and WeatherAPI (weather)

Test Parameters

Sample size: 100 runs per framework
Execution mode: Sequential agent execution (no parallel processing)
Metric aggregation: Average values across all runs

Measured metrics

Pipeline latency: Total end-to-end execution time from input to final itinerary
Agent-to-agent transitions: Framework overhead between sequential agent handoffs
Latency per agent: Individual execution time for each of the five agents
Agent-to-tool gap: Time elapsed from agent initialization to first tool invocation
Token usage: Output tokens generated.

Timing implementation: All timing captured using Python’s time.time() with millisecond precision. For each agent, we recorded start time before execution and end time after completion, calculating latency as the
difference. For tool execution, we measured time immediately before calling the API and immediately after receiving the response. Agent-to-agent transitions captured the gap between when one agent completes and
when the framework starts the next agent, this pure framework overhead excludes LLM and tool execution time.

Token counting: We used a dual-source approach for accuracy:

Framework built-in tracking (when available):

LangChain: cb.total_tokens from callbacks
LangGraph: Token usage from state checkpoints
AutoGen: agent.get_total_usage() from chat results

Tiktoken estimation (fallback for Claude via OpenRouter)
Since Claude doesn’t expose token counts via OpenRouter in all frameworks, we used tiktoken as a consistent approximation across implementations.

Observability infrastructure: All metrics validated through observability tools:

Laminar: Real-time trace collection, latency measurements, and token tracking.
AgentOps: Agent execution tracking, performance monitoring.
These platforms provided ground-truth validation for our manual instrumentation, ensuring measurement
accuracy across different frameworks.

Results aggregated as means across 100 runs.