Alternatives to AgentHub
Compare AgentHub alternatives for your business or organization using the curated list below. SourceForge ranks the best alternatives to AgentHub in 2026. Compare features, ratings, user reviews, pricing, and more from AgentHub competitors and alternatives in order to make an informed decision for your business.
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Maxim
Maxim
Maxim is an agent simulation, evaluation, and observability platform that empowers modern AI teams to deploy agents with quality, reliability, and speed. Maxim's end-to-end evaluation and data management stack covers every stage of the AI lifecycle, from prompt engineering to pre & post release testing and observability, data-set creation & management, and fine-tuning. Use Maxim to simulate and test your multi-turn workflows on a wide variety of scenarios and across different user personas before taking your application to production. Features: Agent Simulation Agent Evaluation Prompt Playground Logging/Tracing Workflows Custom Evaluators- AI, Programmatic and Statistical Dataset Curation Human-in-the-loop Use Case: Simulate and test AI agents Evals for agentic workflows: pre and post-release Tracing and debugging multi-agent workflows Real-time alerts on performance and quality Creating robust datasets for evals and fine-tuning Human-in-the-loop workflowsStarting Price: $29/seat/month -
2
Agenta
Agenta
Agenta is an open-source LLMOps platform designed to help teams build reliable AI applications with integrated prompt management, evaluation workflows, and system observability. It centralizes all prompts, experiments, traces, and evaluations into one structured hub, eliminating scattered workflows across Slack, spreadsheets, and emails. With Agenta, teams can iterate on prompts collaboratively, compare models side-by-side, and maintain full version history for every change. Its evaluation tools replace guesswork with automated testing, LLM-as-a-judge, human annotation, and intermediate-step analysis. Observability features allow developers to trace failures, annotate logs, convert traces into tests, and monitor performance regressions in real time. Agenta helps AI teams transition from siloed experimentation to a unified, efficient LLMOps workflow for shipping more reliable agents and AI products.Starting Price: Free -
3
Patronus AI
Patronus AI
Patronus AI is an automated AI evaluation, security, and optimization platform for LLM applications and agentic systems. It helps teams confidently deploy AI products at scale by generating test suites, running experiments, logging traces, comparing outputs, monitoring production interactions, and evaluating model performance in real time. It provides industry-leading evaluators for RAG hallucinations, context quality, image relevance, answer correctness, prompt injection, sensitive data leakage, toxicity, bias, and other safety or reliability risks. Patronus Evaluators can score AI outputs on specific dimensions, and teams can also create custom evaluators for use-case-specific criteria. Its platform combines dashboards, APIs, plug-and-play evaluations, logs, traces, side-by-side comparisons, visualizations, analytics, and real-time alerts to help teams detect mistakes, benchmark models, improve prompts, and understand system behavior over time. -
4
Vivgrid
Vivgrid
Vivgrid is a development platform for AI agents that emphasizes observability, debugging, safety, and global deployment infrastructure. It gives you full visibility into agent behavior, logging prompts, memory fetches, tool usage, and reasoning chains, letting developers trace where things break or deviate. You can test, evaluate, and enforce safety policies (like refusal rules or filters), and incorporate human-in-the-loop checks before going live. Vivgrid supports the orchestration of multi-agent systems with stateful memory, routing tasks dynamically across agent workflows. On the deployment side, it operates a globally distributed inference network to ensure low-latency (sub-50 ms) execution and exposes metrics like latency, cost, and usage in real time. It aims to simplify shipping resilient AI systems by combining debugging, evaluation, safety, and deployment into one stack, so you're not stitching together observability, infrastructure, and orchestration.Starting Price: $25 per month -
5
AgentKit
OpenAI
AgentKit is a unified suite of tools designed to streamline the process of building, deploying, and optimizing AI agents. It introduces Agent Builder, a visual canvas that lets developers compose multi-agent workflows via drag-and-drop nodes, set guardrails, preview runs, and version workflows. The Connector Registry centralizes the management of data and tool integrations across workspaces and ensures governance and access control. ChatKit enables frictionless embedding of agentic chat interfaces, customizable to match branding and experience, into web or app environments. To support robust performance and reliability, AgentKit enhances its evaluation infrastructure with datasets, trace grading, automated prompt optimization, and support for third-party models. It also supports reinforcement fine-tuning to push agent capabilities further.Starting Price: Free -
6
potpie
potpie
Potpie is an open source platform that enables developers to create AI agents tailored to their codebases, automating tasks such as debugging, testing, system design, onboarding, code review, and documentation. By transforming your codebase into a comprehensive knowledge graph, Potpie's agents gain deep contextual understanding, allowing them to perform engineering tasks with high precision. It offers over five ready-to-use agents, including those specialized in stack trace analysis and integration test generation. Developers can also build custom agents using simple prompts, facilitating seamless integration into existing workflows. Potpie provides a user-friendly chat interface and supports a VS Code extension for direct integration into development environments. With features like multi-LLM support, developers can integrate various AI models to optimize performance and flexibility.Starting Price: $ 1 per month -
7
OpenAI Agents SDK
OpenAI
The OpenAI Agents SDK enables you to build agentic AI apps in a lightweight, easy-to-use package with very few abstractions. It's a production-ready upgrade of our previous experimentation for agents, Swarm. The Agents SDK has a very small set of primitives, agents, which are LLMs equipped with instructions and tools; handoffs, which allow agents to delegate to other agents for specific tasks; and guardrails, which enable the inputs to agents to be validated. In combination with Python, these primitives are powerful enough to express complex relationships between tools and agents, and allow you to build real-world applications without a steep learning curve. In addition, the SDK comes with built-in tracing that lets you visualize and debug your agentic flows, evaluate them, and even fine-tune models for your application.Starting Price: Free -
8
Future AGI
Future AGI
Future AGI is an open-source, end-to-end AI agent engineering platform that covers the full lifecycle: simulate, evaluate, optimize, monitor, protect, gateway, and guardrail - all from one place. It helps teams ship self-improving AI agents by collapsing fragmented tooling into one platform and one feedback loop: simulate edge cases before launch, evaluate what happens in production, protect users in real time, and turn every trace into signal for the next version. Key capabilities include 70+ built-in evaluation templates covering quality, safety, factuality, RAG retrieval, bias, audio, and image evaluation, OpenTelemetry-native tracing, agent optimization, and real-time guardrails (PII detection, prompt injection blocking). SDKs are available in Python, TypeScript, Java, and C#, with integrations for OpenAI, LangChain, LlamaIndex, and 30+ frameworks. Apache 2.0 licensed, self-hostable or cloud-managed. -
9
Lucidic AI
Lucidic AI
Lucidic AI is a specialized analytics and simulation platform built for AI agent development that brings much-needed transparency, interpretability, and efficiency to often opaque workflows. It provides developers with visual, interactive insights, including searchable workflow replays, step-by-step video, and graph-based replays of agent decisions, decision tree visualizations, and side‑by‑side simulation comparisons, that enable you to observe exactly how your agent reasons and why it succeeds or fails. The tool dramatically reduces iteration time from weeks or days to mere minutes by streamlining debugging and optimization through instant feedback loops, real‑time “time‑travel” editing, mass simulations, trajectory clustering, customizable evaluation rubrics, and prompt versioning. Lucidic AI integrates seamlessly with major LLMs and frameworks and offers advanced QA/QC mechanisms like alerts, workflow sandboxing, and more. -
10
Atla
Atla
Atla is the agent observability and evaluation platform that dives deeper to help you find and fix AI agent failures. It provides real‑time visibility into every thought, tool call, and interaction so you can trace each agent run, understand step‑level errors, and identify root causes of failures. Atla automatically surfaces recurring issues across thousands of traces, stops you from manually combing through logs, and delivers specific, actionable suggestions for improvement based on detected error patterns. You can experiment with models and prompts side by side to compare performance, implement recommended fixes, and measure how changes affect completion rates. Individual traces are summarized into clean, readable narratives for granular inspection, while aggregated patterns give you clarity on systemic problems rather than isolated bugs. Designed to integrate with tools you already use, OpenAI, LangChain, Autogen AI, Pydantic AI, and more. -
11
Netra
Netra
AI agents fail silently in production. Wrong answers, broken loops, cost spikes, behavior drift after a prompt change, and no stack trace to explain why. Netra gives engineering teams full visibility into every agent decision. Trace every LLM call, evaluate quality automatically, simulate edge cases before launch, and manage prompts with complete version history. Built on OpenTelemetry so setup takes minutes, not days. SOC2 Type II certified. GDPR and HIPAA compliant. US and EU data residency. Integrates with: LangChain, LangGraph, CrewAI, LlamaIndex, OpenAI, Anthropic, Gemini, AWS Bedrock, and 30+ more.Starting Price: $39/month -
12
LayerLens
LayerLens
LayerLens is an independent AI model evaluation platform for understanding how models perform through verified results across benchmarks, prompt-level results, agentic benchmarks, and audit-ready comparisons across vendors. It helps teams compare more than 200 AI models side by side, with transparent benchmarks, model comparison tools, and consistent evaluation methods for accuracy, latency, behavior, and real-world applicability. LayerLens is built for deep model analysis through Spaces, where teams can group benchmarks and evaluations, explore task strengths, and track performance patterns in context. It supports continuous evaluation by running ongoing evals across model versions, prompt changes, judge updates, and live traces, helping teams detect quality regressions, drift, silent failures, contamination, and policy issues before they affect production. -
13
Flowise
Flowise AI
Flowise is an open-source platform that enables developers and teams to build AI agents and LLM-powered applications through a visual interface. The platform provides modular building blocks that allow users to create everything from simple chatbot workflows to complex multi-agent systems. With its drag-and-drop design environment, developers can rapidly prototype and deploy AI-powered applications without extensive coding. Flowise supports integrations with more than 100 large language models, embeddings, and vector databases. It also includes features such as human-in-the-loop workflows, observability tools, and execution tracing for monitoring agent behavior. Developers can extend applications through APIs, SDKs, and embedded chat interfaces using TypeScript or Python. By combining visual development tools with scalable infrastructure, Flowise simplifies the process of building and deploying production-ready AI agents.Starting Price: Free -
14
Coval
Coval
Coval is a simulation and evaluation platform designed to accelerate the development of reliable AI agents across chat, voice, and other modalities. By automating the testing process, Coval enables engineers to simulate thousands of scenarios from a few test cases, allowing for comprehensive assessments without manual intervention. Users can create test sets by adding customer transcripts or describing user intents in natural language, with Coval handling the formatting. The platform supports both text and voice simulations, facilitating the testing of AI agents against a set of scorecard metrics. Comprehensive evaluations of agent interactions are provided, enabling performance tracking over time and root cause analysis of specific runs. Coval also offers workflow metrics that provide observability into system processes, aiding in the optimization of AI agents.Starting Price: $300 per month -
15
Agent Builder
OpenAI
Agent Builder is part of OpenAI’s tooling for constructing agentic applications, systems that use large language models to perform multi-step tasks autonomously, with governance, tool integration, memory, orchestration, and observability baked in. The platform offers a composable set of primitives—models, tools, memory/state, guardrails, and workflow orchestration- that developers assemble into agents capable of deciding when to call a tool, when to act, and when to halt and hand off control. OpenAI provides a new Responses API that combines chat capabilities with built-in tool use, along with an Agents SDK (Python, JS/TS) that abstracts the control loop, supports guardrail enforcement (validations on inputs/outputs), handoffs between agents, session management, and tracing of agent executions. Agents can be augmented with built-in tools like web search, file search, or computer use, or custom function-calling tools. -
16
Latitude
Latitude
Latitude is an open-source prompt engineering platform designed to help product teams build, evaluate, and deploy AI models efficiently. It allows users to import and manage prompts at scale, refine them with real or synthetic data, and track the performance of AI models using LLM-as-judge or human-in-the-loop evaluations. With powerful tools for dataset management and automatic logging, Latitude simplifies the process of fine-tuning models and improving AI performance, making it an essential platform for businesses focused on deploying high-quality AI applications.Starting Price: $0 -
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Cortex AgentiX
Palo Alto Networks
Cortex AgentiX is the next-generation evolution of Cortex XSOAR®, designed by Palo Alto Networks to securely build, deploy, and govern AI-powered security agents. It enables organizations to unleash agentic AI that acts as intelligent teammates, capable of planning and executing complex workflows around the clock. Cortex AgentiX is powered by over 1.2 billion real-world playbook executions, providing agents with proven operational intelligence. The platform offers a rich library of ready-to-use agents while also supporting custom, no-code agent creation tailored to specific security needs. With built-in guardrails, Cortex AgentiX ensures agents operate with the appropriate level of autonomy, including human-in-the-loop approvals for critical actions. Full transparency allows teams to trace every agent decision, action, and outcome for audit and compliance purposes. Cortex AgentiX integrates seamlessly across the Cortex ecosystem to help organizations stay ahead of evolving threats. -
18
Microsoft Agent Framework
Microsoft
Microsoft Agent Framework is an open source SDK and runtime designed to help developers build, orchestrate, and deploy AI agents and multi-agent workflows using languages such as .NET and Python. It combines the simple agent abstractions of AutoGen with the enterprise-grade capabilities of Semantic Kernel, including session-based state management, type safety, middleware, telemetry, and broad model and embedding support, creating a unified platform for both experimentation and production use. It introduces graph-based workflows that give developers explicit control over how multiple agents interact, execute tasks, and coordinate complex processes, enabling structured orchestration across sequential, concurrent, or branching scenarios. It supports long-running and human-in-the-loop workflows through robust state management, allowing agents to maintain context, reason through multi-step problems, and operate continuously over time.Starting Price: Free -
19
Plurai
Plurai
Plurai is the real-world trust platform for AI agents, built for simulation-driven evaluation, protection, and optimization that turns agents into trusted, continuously improving production systems. It helps teams train evals and guardrails tailored to their use case, bridging the gap from prototype to reliable production at scale. Plurai’s simulation platform prepares agents for the real world, not the lab, with hyper-realistic, product-tailored experimentation and evaluation that covers production complexity. It generates authentic multi-turn scenarios, personas, required artifacts, and tool mocking, using organizational PRDs, relevant sources, and policies to build a knowledge graph and expand edge-case coverage. Instead of relying on static datasets, manual test creation, or inconsistent LLM-as-a-judge methods, Plurai groups evaluations into structured, runnable experiments so teams can test new versions, measure regressions, and validate improvements before release.Starting Price: Free -
20
HoneyHive
HoneyHive
AI engineering doesn't have to be a black box. Get full visibility with tools for tracing, evaluation, prompt management, and more. HoneyHive is an AI observability and evaluation platform designed to assist teams in building reliable generative AI applications. It offers tools for evaluating, testing, and monitoring AI models, enabling engineers, product managers, and domain experts to collaborate effectively. Measure quality over large test suites to identify improvements and regressions with each iteration. Track usage, feedback, and quality at scale, facilitating the identification of issues and driving continuous improvements. HoneyHive supports integration with various model providers and frameworks, offering flexibility and scalability to meet diverse organizational needs. It is suitable for teams aiming to ensure the quality and performance of their AI agents, providing a unified platform for evaluation, monitoring, and prompt management. -
21
Inquir Compute
Inquir Compute
Inquir Compute is a cloud platform for deploying and running server-side code without managing servers, Kubernetes, CI/CD, or DevOps infrastructure. It lets developers create functions, APIs, webhooks, cron jobs, background tasks, and multi-step workflows directly from a browser-based editor or API. Users can write code in Node.js, Python, or Go, configure runtime settings such as memory, CPU, timeout, environment variables, and network access, then deploy and invoke it in isolated containers. Functions can be exposed through an API Gateway, triggered manually, scheduled, or combined into pipelines where one step passes data to another. The platform is designed for long-running workloads such as AI agents, scraping, document processing, data enrichment, integrations, and automation. It includes logs, traces, invocation history, error tracking, route management, API keys, tenant isolation, and observability tools. -
22
Laminar
Laminar
Laminar is an open source all-in-one platform for engineering best-in-class LLM products. Data governs the quality of your LLM application. Laminar helps you collect it, understand it, and use it. When you trace your LLM application, you get a clear picture of every step of execution and simultaneously collect invaluable data. You can use it to set up better evaluations, as dynamic few-shot examples, and for fine-tuning. All traces are sent in the background via gRPC with minimal overhead. Tracing of text and image models is supported, audio models are coming soon. You can set up LLM-as-a-judge or Python script evaluators to run on each received span. Evaluators label spans, which is more scalable than human labeling, and especially helpful for smaller teams. Laminar lets you go beyond a single prompt. You can build and host complex chains, including mixtures of agents or self-reflecting LLM pipelines.Starting Price: $25 per month -
23
Teammately
Teammately
Teammately is an autonomous AI agent designed to revolutionize AI development by self-iterating AI products, models, and agents to meet your objectives beyond human capabilities. It employs a scientific approach, refining and selecting optimal combinations of prompts, foundation models, and knowledge chunking. To ensure reliability, Teammately synthesizes fair test datasets and constructs dynamic LLM-as-a-judge systems tailored to your project, quantifying AI capabilities and minimizing hallucinations. The platform aligns with your goals through Product Requirement Docs (PRD), enabling focused iteration towards desired outcomes. Key features include multi-step prompting, serverless vector search, and deep iteration processes that continuously refine AI until objectives are achieved. Teammately also emphasizes efficiency by identifying the smallest viable models, reducing costs, and enhancing performance.Starting Price: $25 per month -
24
Swarm
OpenAI
Swarm is an experimental, educational framework developed by OpenAI to explore ergonomic, lightweight multi-agent orchestration. It is designed to be scalable and highly customizable, making it suitable for scenarios involving a large number of independent capabilities and instructions that are challenging to encode into a single prompt. Swarm operates entirely on the client side and, like the Chat Completions API it utilizes, does not store state between calls. This stateless nature allows for the construction of scalable, real-world solutions without a steep learning curve. Swarm agents are distinct from assistants in the assistants API; they are named similarly for convenience but are otherwise completely unrelated. It includes examples demonstrating fundamentals such as setup, function calling, handoffs, and context variables, as well as more complex scenarios like a multi-agent setup for handling different customer service requests in an airline context.Starting Price: Free -
25
Portia
Portia
Portia AI is an open source developer framework (with optional cloud services) that lets teams rapidly build, deploy, and monitor stateful, authenticated AI agents with full visibility and control. Developers start by prompting the SDK to generate explicit, structured multi-step “plans” that weave together LLM reasoning and tool calls, then run those plans step-by-step, enriching plan state at each stage and pausing for clarifications (human or machine) whenever authentication or missing data is required. With its unified auth framework and plug-and-play tool catalog, Portia handles credentials and permissions for remote API and MCP tool invocations automatically. The complementary cloud offering adds persistent storage of plan run states, historical logs, telemetry dashboards, and managed scaling so production deployments stay reliable, auditable, and compliant in regulated environments.Starting Price: $30 per month -
26
AgentScope
AgentScope
AgentScope is an AI-driven agent observability and operations platform that provides visibility, control, and performance analytics for autonomous AI agents across production workloads. It enables engineering and DevOps teams to monitor, diagnose, and optimize complex multi-agent applications in real time by capturing detailed telemetry on agent actions, decisions, resource usage, and outcome quality. With rich dashboards and timelines, AgentScope helps teams trace execution flows, identify bottlenecks, and understand how agents interact with external systems, APIs, and data sources, improving debugging and reliability for autonomous workflows. It supports customizable alerting, log aggregation, and structured event views so teams can quickly surface anomalous behavior or errors across distributed agent fleets. In addition to real-time monitoring, AgentScope provides historical analysis and reporting that help teams measure performance trends, model drift, etc.Starting Price: Free -
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Foundry
Foundry
Build, evaluate, and improve AI agents that deliver reliable outcomes, blending automation speed with human quality. Build your AI agents with simple prompts and logic, no coding. Or through our API if you prefer that. Track, manage, and evaluate your agents with easy access to metrics and trends in real-time. Improve your models based on the insights from your evaluation. Steer your agents towards desirable outcomes. Use simple prompts and logic to set up main and supporting agents for your tasks. Define when agents require human review to keep standards high. Gather feedback and refine performance for constant improvement. Experiment with approaches to ensure the best results. Use a comprehensive dashboard for instant access to performance insights. Discover flexible solutions for seamless AI management and human oversight. Our system continuously refines agents based on human feedback to keep quality high. -
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Opik
Comet
Confidently evaluate, test, and ship LLM applications with a suite of observability tools to calibrate language model outputs across your dev and production lifecycle. Log traces and spans, define and compute evaluation metrics, score LLM outputs, compare performance across app versions, and more. Record, sort, search, and understand each step your LLM app takes to generate a response. Manually annotate, view, and compare LLM responses in a user-friendly table. Log traces during development and in production. Run experiments with different prompts and evaluate against a test set. Choose and run pre-configured evaluation metrics or define your own with our convenient SDK library. Consult built-in LLM judges for complex issues like hallucination detection, factuality, and moderation. Establish reliable performance baselines with Opik's LLM unit tests, built on PyTest. Build comprehensive test suites to evaluate your entire LLM pipeline on every deployment.Starting Price: $39 per month -
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Weavel
Weavel
Meet Ape, the first AI prompt engineer. Equipped with tracing, dataset curation, batch testing, and evals. Ape achieves an impressive 93% on the GSM8K benchmark, surpassing both DSPy (86%) and base LLMs (70%). Continuously optimize prompts using real-world data. Prevent performance regression with CI/CD integration. Human-in-the-loop with scoring and feedback. Ape works with the Weavel SDK to automatically log and add LLM generations to your dataset as you use your application. This enables seamless integration and continuous improvement specific to your use case. Ape auto-generates evaluation code and uses LLMs as impartial judges for complex tasks, streamlining your assessment process and ensuring accurate, nuanced performance metrics. Ape is reliable, as it works with your guidance and feedback. Feed in scores and tips to help Ape improve. Equipped with logging, testing, and evaluation for LLM applications.Starting Price: Free -
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Openlayer
Openlayer
Openlayer is the AI governance and observability platform that accelerates the evaluation and observability of agentic systems through 100+ automated tests and real-time guardrails that prevent prompt injections, PII leakage, bias, toxicity, and hallucinations, powering secure enterprise innovation. Designed to support both traditional ML and GenAI systems, Openlayer helps teams seamlessly handle everything from data-quality detection to automating comprehensive model evaluations, with full traceability across RAG, agents, and complex multi-step workflows. Trusted by Fortune 500 companies from early experimentation through production deployment and automated governance capabilities (NIST, EU AI Act, etc.)., Openlayer enables safe, reliable, and responsible AI operations. -
31
Hamming
Hamming
Prompt optimization, automated voice testing, monitoring, and more. Test your AI voice agent against 1000s of simulated users in minutes. AI voice agents are hard to get right. A small change in prompts, function call definitions or model providers can cause large changes in LLM outputs. We're the only end-to-end platform that supports you from development to production. You can store, manage, version, and keep your prompts synced with voice infra providers from Hamming. This is 1000x more efficient than testing your voice agents by hand. Use our prompt playground to test LLM outputs on a dataset of inputs. Our LLM judges the quality of generated outputs. Save 80% of manual prompt engineering effort. Go beyond passive monitoring. We actively track and score how users are using your AI app in production and flag cases that need your attention using LLM judges. Easily convert calls and traces into test cases and add them to your golden dataset. -
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NVIDIA Agent Toolkit
NVIDIA
NVIDIA Agent Toolkit is a solution stack designed to build, deploy, and scale autonomous AI agents that can reason, plan, and execute complex tasks across enterprise systems. Unlike traditional generative AI, which responds to single prompts, agentic AI uses sophisticated reasoning and iterative planning to solve multi-step problems independently, enabling systems to analyze data, develop strategies, and complete workflows without continuous human input. It integrates multiple components of the NVIDIA AI ecosystem, including pretrained models, microservices, and development frameworks, allowing organizations to create context-aware AI agents that operate using their own data. These agents can ingest large volumes of structured and unstructured data from enterprise systems, interpret context, and coordinate actions across applications to automate processes such as customer service, software development, analytics, and operational workflows. -
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Mistral AI Studio
Mistral AI
Mistral AI Studio is a unified builder-platform that enables organizations and development teams to design, customize, deploy, and manage advanced AI agents, models, and workflows from proof-of-concept through to production. The platform offers reusable blocks, including agents, tools, connectors, guardrails, datasets, workflows, and evaluations, combined with observability and telemetry capabilities so you can track agent performance, trace root causes, and govern production AI operations with visibility. With modules like Agent Runtime to make multi-step AI behaviors repeatable and shareable, AI Registry to catalogue and manage model assets, and Data & Tool Connections for seamless integration with enterprise systems, Studio supports everything from fine-tuning open source models to embedding them in your infrastructure and rolling out enterprise-grade AI solutions.Starting Price: $14.99 per month -
34
Deepsona
Deepsona
Deepsona is an AI-powered market research platform that uses synthetic audience simulations to generate predictive consumer behaviour insights. Built on behavioural science and advanced AI modeling, the platform enables marketers, market researchers and product teams to evaluate commercial viability, test messaging strategies and assess market acceptance before launch. The platform combines large-scale persona generation, interaction modeling, and sentiment analysis into a unified simulation engine. Users can run concept tests, pricing experiments, and positioning evaluations that produce high-fidelity predictive data on consumer responses. Key capabilities include multi-trait synthetic AI personas, automated sentiment evaluation, and conversion likelihood modeling. Deepsona transforms traditional market research from retrospective analysis into forward-looking simulation, enabling faster validation cycles and data-driven go-to-market decisions.Starting Price: $79/month -
35
MAIHEM
MAIHEM
MAIHEM creates AI agents that continuously test your AI applications. We enable you to automate your AI quality assurance, ensuring AI performance and safety from development all the way to deployment. Avoid hours of manual testing and randomly probing for AI model weaknesses. MAIHEM automates your AI quality assurance and provides you with comprehensive coverage of thousands of edge cases. Generate thousands of realistic personas to interact with your conversational AI. Automatically evaluate entire conversations with a customizable set of performance and risk metrics. Leverage the simulation data for targeted improvements of your conversational AI. Independent of your conversational AI application, MAIHEM can help you improve its performance. Integrate AI quality assurance seamlessly into your developer workflow with a few lines of code. User-friendly web app with dashboards offering AI quality assurance in a few clicks. -
36
Symbiotic EDA Suite
Symbiotic EDA
Find bugs early and raise the confidence in your design by using formal checks and formal properties. Apply formal early on in the design process wherever it makes sense for your application. Use formal cover traces to further your design understanding and answer hard questions about the design under test. Apply formal safety properties to produce shorter and more insightful traces than simulation could ever generate. Employ formal proofs to ensure correctness of your design, use mutation cover to gain confidence in your simulation-based verification strategy, and speed up writing of test cases by guiding the process with formal cover traces. Unbounded and bounded verification of safety properties. Reachability-check and bounds-detection for cover properties -
37
LLMWise
LLMWise
LLMWise is a multi-model AI platform that lets you access 52+ models from 18 providers using a single credit wallet and one API key. It’s designed to replace multiple separate AI subscriptions by offering GPT, Claude, Gemini, and many more models in one dashboard and API. Users can compare model answers side-by-side, blend outputs, judge responses, and set up failover routing for reliability. The platform supports multiple data paths per prompt, evaluating options like speed and cost to return the best response. It offers usage-settled billing so you pay for actual token consumption rather than a flat monthly fee, with free starter credits that never expire. Developers can integrate quickly using REST, cURL, or SDKs for Python and TypeScript with streaming support. LLMWise also emphasizes production readiness with features like audit-ready routing traces, encrypted key storage, and optional zero-retention mode. -
38
Forsy
Forsy
Forsy is built around authentic human signal from real agent workflows, helping teams capture, understand, and trade agent trajectory data across the agent stack. It tracks agent work in real time as it happens, rather than reconstructing activity afterward, creating native capture for traces, tasks, and toolchain activity. It is designed for full coverage across everyday tasks, specialized workflows, and different domains, giving teams one engine for trajectory data across the agents they already use. Forsy turns AI agents into strategic assets by making authentic workflow data discoverable, licensable, and sellable through a market for agent data. Its high-fidelity data is purpose-built for teams building more capable and reliable agents, helping them access the kinds of real workflow traces needed to improve agent behavior, reliability, and evaluation. -
39
Respan
Respan
Respan is a self-driving observability and evaluation platform built specifically for AI agents. It enables teams to trace full execution flows, including messages, tool calls, routing decisions, memory usage, and outcomes. The platform connects observability, evaluations, and optimization into a continuous improvement loop. Metric-first evaluations allow teams to define performance standards such as accuracy, cost, reliability, and safety. Respan also includes capability and regression testing to protect stable behaviors while improving new ones. An AI-powered evaluation agent analyzes failures, identifies root causes, and recommends next steps automatically. With compliance certifications including ISO 27001, SOC 2, GDPR, and HIPAA, Respan supports secure, large-scale AI deployments across industries.Starting Price: $0/month -
40
Koog
JetBrains
Koog is a Kotlin‑based framework for building and running AI agents entirely in idiomatic Kotlin, supporting both single‑run agents that process individual inputs and complex workflow agents with custom strategies and configurations. It features pure Kotlin implementation, seamless Model Control Protocol (MCP) integration for enhanced model management, vector embeddings for semantic search, and a flexible system for creating and extending tools that access external systems and APIs. Ready‑to‑use components address common AI engineering challenges, while intelligent history compression optimizes token usage and preserves context. A powerful streaming API enables real‑time response processing and parallel tool calls. Persistent memory allows agents to retain knowledge across sessions and between agents, and comprehensive tracing facilities provide detailed debugging and monitoring.Starting Price: Free -
41
Origon
Origon
Origon is a full-stack AI agent development and operations platform engineered as a unified “Agentic Operating System” that supports the entire lifecycle of autonomous AI systems from design to deployment and observability. It offers an intuitive Studio for visual, drag-and-drop agent creation and configuration, Sessions for real-time observation, behavior tracing, and debugging, and Insights dashboards for performance analytics, reliability tracking, and outcome measurement in one place. Origon runs natively on dedicated infrastructure optimized for low-latency performance and security, avoiding dependency on external cloud APIs, and includes a built-in knowledge engine that connects agents to contextual memory and domain data so responses stay grounded and consistent. It supports hundreds of connectors and APIs, including chat, voice, WhatsApp, SMS, email, and telephony, and lets agents execute code and interact with real systems with a single click.Starting Price: $200 per month -
42
CAMEL-AI
CAMEL-AI
CAMEL-AI is the first LLM-based multi-agent framework and an open-source community dedicated to exploring the scaling laws of agents. It enables the creation of customizable agents using modular components tailored for specific tasks, facilitating the development of multi-agent systems that address challenges in autonomous cooperation. The framework serves as a generic infrastructure for various applications, including task automation, data generation, and world simulations. By studying agents on a large scale, CAMEL-AI.org aims to gain valuable insights into their behaviors, capabilities, and potential risks. The community emphasizes rigorous research, balancing urgency with patience, and encourages contributions that enhance infrastructure, improve documentation, and implement research ideas. The platform offers components such as models, tools, memory, and prompts to empower agents, and supports integrations with various external tools and services. -
43
Knolli
Knolli
Knolli is an AI copilot platform that enables users to build, launch, and scale custom AI copilots and agents without writing code by turning knowledge, documents, datasets, and proprietary content into interactive, conversational assistants. It provides a no-code workspace where creators, teams, and businesses can describe their idea in plain language and have Knolli automatically structure uploaded content into a usable AI copilot, organize and protect data securely with encrypted private knowledge bases, and connect to tools such as CRMs, file storage, and databases to pull in live data for context-aware responses. It supports multi-agent architecture to run specialized agents inside one copilot, pre-built templates for common use cases, custom branding and white-labeling, and advanced analytics so users can monitor performance, usage, and ROI. Knolli also offers workflow automation, letting copilots automate multi-step tasks and integrate with existing systems.Starting Price: $39 per month -
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Kayba
Kayba
Kayba makes AI agents self-improve from experience. It learns from an agent’s execution traces to detect failures, fix them, and measure whether the fix actually worked. Instead of relying on generic evals that cannot explain why an agent failed, Kayba derives failure modes from the agent’s own traces and builds custom benchmarks for the user’s domain, so teams can measure improvement against real production failure patterns. Kayba wires tracing into an agent with one line of setup, watches it around the clock, and flags the moment a step stops being recorded. Even good tracing rots as teams ship changes, and steps can quietly stop being captured; Kayba checks the tracing users already have, shows exactly what is broken, points to the file that needs attention, and sends the gap to a coding agent through MCP. The coding agent patches the issue, and Kayba verifies that the trace is actually closed.Starting Price: Free -
45
AG2
AG2
AG2 is the open source AgentOS for building production-ready AI agents and multi-agent systems in minutes, not months. Formerly AutoGen, it provides an open source Python framework for building, orchestrating, and scaling AI agents that can collaborate through shared context, use tools, execute workflows, and support both autonomous and human-in-the-loop patterns. AG2 is designed for developers who want to build systems, not prompts, with simple and intuitive syntax, built-in conversation patterns, and a flexible platform for multi-agent automation. Agents in AG2 can extend their capabilities with tools, allowing them to interact with external systems, fetch real-time data, execute code, search the web, process documents, and complete complex tasks beyond a model’s internal knowledge. It supports many LLM providers and local models, including OpenAI-compatible endpoints, Anthropic Claude, Gemini through Vertex AI, DeepSeek, and LM Studio.Starting Price: Free -
46
Parea
Parea
The prompt engineering platform to experiment with different prompt versions, evaluate and compare prompts across a suite of tests, optimize prompts with one-click, share, and more. Optimize your AI development workflow. Key features to help you get and identify the best prompts for your production use cases. Side-by-side comparison of prompts across test cases with evaluation. CSV import test cases, and define custom evaluation metrics. Improve LLM results with automatic prompt and template optimization. View and manage all prompt versions and create OpenAI functions. Access all of your prompts programmatically, including observability and analytics. Determine the costs, latency, and efficacy of each prompt. Start enhancing your prompt engineering workflow with Parea today. Parea makes it easy for developers to improve the performance of their LLM apps through rigorous testing and version control. -
47
Claude Managed Agents
Anthropic
Claude Managed Agents is a pre-built, configurable agent system from Anthropic designed to run long-running, asynchronous tasks on managed infrastructure without requiring developers to build their own agent loops. It acts as a complete “agent harness,” allowing developers to define goals while the system handles execution, orchestration, and state management behind the scenes. Unlike direct model prompting, which requires step-by-step interaction, Managed Agents are designed for tasks that unfold over time, such as research, automation, or multi-step workflows, where the agent can continue working independently after being started. It supports advanced capabilities such as multi-agent orchestration, where a primary agent can coordinate specialized sub-agents that operate in parallel with isolated contexts, improving both speed and output quality. -
48
Oracle AI Agent Platform
Oracle
Oracle AI Agent Platform is a fully-managed service that enables the creation, deployment, and management of intelligent virtual agents powered by large language models and integrated AI technologies. Agents can be set up through a simple few-step process, and can orchestrate tools such as natural‐language-to‐SQL conversion, retrieval-augmented generation from enterprise knowledge bases, custom function or API calling, and even the ability to coordinate sub-agents. They support multi-turn conversational experiences with context retention across sessions, enabling agents to handle follow‐up questions and maintain personalised, consistent interactions. Built-in guardrails help enforce content moderation, prompt-injection prevention, and protection of PII (personally identifiable information), while optional human-in-the-loop workflows allow real-time supervision and escalation.Starting Price: $0.003 per 10,000 transactions -
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Prompt flow
Microsoft
Prompt Flow is a suite of development tools designed to streamline the end-to-end development cycle of LLM-based AI applications, from ideation, prototyping, testing, and evaluation to production deployment and monitoring. It makes prompt engineering much easier and enables you to build LLM apps with production quality. With Prompt Flow, you can create flows that link LLMs, prompts, Python code, and other tools together in an executable workflow. It allows for debugging and iteration of flows, especially tracing interactions with LLMs with ease. You can evaluate your flows, calculate quality and performance metrics with larger datasets, and integrate the testing and evaluation into your CI/CD system to ensure quality. Deployment of flows to the serving platform of your choice or integration into your app’s code base is made easy. Additionally, collaboration with your team is facilitated by leveraging the cloud version of Prompt Flow in Azure AI. -
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Orq.ai
Orq.ai
Orq.ai is the #1 platform for software teams to operate agentic AI systems at scale. Optimize prompts, deploy use cases, and monitor performance, no blind spots, no vibe checks. Experiment with prompts and LLM configurations before moving to production. Evaluate agentic AI systems in offline environments. Roll out GenAI features to specific user groups with guardrails, data privacy safeguards, and advanced RAG pipelines. Visualize all events triggered by agents for fast debugging. Get granular control on cost, latency, and performance. Connect to your favorite AI models, or bring your own. Speed up your workflow with out-of-the-box components built for agentic AI systems. Manage core stages of the LLM app lifecycle in one central platform. Self-hosted or hybrid deployment with SOC 2 and GDPR compliance for enterprise security.