AgentOps

What is AgentOps?

AgentOps is an observability platform for monitoring, debugging, and evaluating AI agents and LLM apps. It uses an SDK that developers can add with pip install agentops and agentops.init(), and it automatically instruments LLM calls, agent interactions, tool usage, and workflows across 400+ models and frameworks. AgentOps captures session data such as execution traces, performance metrics, token usage, costs, and errors in a dashboard with real-time monitoring, replays, and analytics. It is built for developers, indie builders, and enterprise teams that need to debug agent failures, track spending, and move from prototypes to production.

Key Features

• Automated Instrumentation: After agentops.init(), AgentOps detects installed LLM providers and instruments their API calls automatically, so teams can monitor agent and model interactions without manual setup.
• Sessions: Sessions group a single workflow execution into one trace with agents, LLMs, actions, tags, host environment, end state reason, and optional video, which helps users review full runs and find root causes faster.
• @agent Decorator: The @agent decorator in the Python SDK assigns names and IDs to agent classes, so users can trace and compare individual agent activity inside multi-agent systems.

Pricing

• Basic: $0/month. Perpetual free tier with a hard cap of 5,000 events/month. Includes the agent agnostic SDK, LLM cost tracking for 400+ LLMs, and replay analytics.
• Pro: $40/month (pay-as-you-go). Includes everything in Basic, unlimited events, unlimited log retention, session and event export, dedicated Slack and email support, and role-based permissioning.
• Enterprise: Custom (contact sales). Includes everything in Pro, plus SLA, Slack Connect, custom SSO, on-premise deployment, custom data retention policy, self-hosting on AWS, GCP, and Azure, SOC-2, HIPAA, and NIST AI RMF.

Pro pricing is usage-based according to the pricing calculator. Enterprise contract minimum is not publicly disclosed.

Who Is It For?

Ideal for:

• ML and AI engineers building multi-agent systems: AgentOps fits teams from a small team to enterprise that need observability for autonomous agents. Public materials say it can be added in just two lines of code and includes decorators for custom tracing, which helps teams monitor agent behavior without heavy instrumentation.
• Enterprise operations teams running mission-critical autonomous workflows: AgentOps is aimed at enterprise environments where non-deterministic LLM behavior and autonomous tool use create monitoring problems that standard application monitoring does not address. It is a match for growth-stage teams deploying agents into production workflows with high business impact.
• Compliance and governance leads in regulated industries: AgentOps is a fit for mid-market to enterprise teams in areas such as financial services, healthcare, and legal tech. Public information points to controlled tool access, human escalation rules, and confidence-threshold approvals for teams that need auditability and oversight.

Not ideal for:

• Solo founders or very small teams building simple automation: If you are building a single deterministic workflow, chatbot, or Q&A system, agent-specific observability is likely more than you need, and tools like Datadog or New Relic may be a better fit.
• Teams without human-in-the-loop processes: AgentOps treats human oversight as a core pattern, so organizations that do not use escalation rules, approvals, or exception handling may be better served by raw LangChain or AutoGen without observability overlays.

AgentOps is best suited to growth-stage and enterprise teams, often with a 3 to 15 person AI, ML, or operations team, that run autonomous agents in regulated or high-stakes settings. Use it when you need visibility into multi-agent behavior, decision logging, and oversight controls. Skip it for early prototyping, low-stakes use cases, or simple chatbot deployments.

Alternatives and Comparisons

• Helicone: AgentOps does agent-specific tracing better. It tracks LLM calls, tool use, errors, and costs across multi-agent workflows, and it includes replay for debugging failed runs. Helicone does open-source flexibility better with an MIT license, no usage-based limits on its free tier, and self-hosting without SDK latency overhead. Choose AgentOps if you need workflow-level monitoring for production multi-agent systems; choose Helicone if you want general AI app observability with more self-hosting control. Switching difficulty is medium.

• Arize: AgentOps does agent workflow debugging better. It focuses on real-time dashboards for token counts, latency, and cost per agent, and it integrates directly with frameworks such as LangChain. Arize does enterprise ML observability better with advanced LLM evaluation metrics and production model monitoring at larger team scale. Choose AgentOps if your main need is debugging production agent runs; choose Arize if you need broader model monitoring and evaluation.

• LlamaIndex: AgentOps does monitoring better. It is built for tracing agent activity, errors, and costs across existing workflows and frameworks, rather than for orchestration. LlamaIndex does agent development better with data ingestion and indexing tools for building context-aware agents from scratch. Choose AgentOps if you need visibility into agent runs already in production; choose LlamaIndex if you are building the agent system itself.

Getting Started

Setup:

• Signup: AgentOps supports signup with email only. A free trial is available and no credit card is required.
• Time to first result: Public research points to 5 to 15 minutes for a first result, after you add an API key and initialize a session.

Learning curve:

• Users with Python and agent-building experience often pick it up in under an hour. Basic Python and familiarity with agent frameworks are the main background requirements.
• Beginner: an afternoon to log and analyze first sessions. Experienced: immediate for core monitoring, and days for advanced dashboard features.

Where to get help:

• Discord is promoted as an active place for debugging, brainstorming, hackathons, and general support around agents.
• GitHub Discussions and email are also available as support channels.
• Community activity appears small but tight, and growing. Maintainers and staff are the main people answering, and third-party material is low to moderate with some YouTube demos and tutorials.

Watch out for:

• Forgetting to set the API key as an environment variable before initialization can block the first session.
• Starting from an empty dashboard and without a base agent script can slow down the first useful result.

AgentOps Integration Ecosystem

Users describe AgentOps as an API first tool with a focused integration set around observability and AI development workflows. Public reports suggest the ecosystem is still growing, and users generally view the current integrations as reliable for telemetry and tracing, though custom setups can take more technical work.

• LangSmith: Users report that LangSmith works well with AgentOps for API based ingestion of LLM traces and agent interaction data into shared observability dashboards.
• Azure AI Foundry: Users describe AgentOps working in Azure AI Foundry deployments where teams monitor agents, connect custom tools, and run CI/CD based production workflows.
• LangGraph: Users say the LangGraph connection works well for tracking multi agent graphs and state changes inside observability pipelines.
• GitHub: Public listings describe a GitHub workflow connection for logging agent events into repositories, though user discussion is limited.
• Discord: Public listings also mention Discord for bot and server based monitoring alerts, with limited user detail on day to day use.

User discussion points to a narrower ecosystem centered on developer observability tools rather than a wide set of business app connections. We did not find public requests that repeatedly pointed to specific missing integrations.

Developer Experience

AgentOps has a Python SDK for observability in AI agent workflows. Developers use it to log traces, sessions, LLM calls, tool usage, and custom metrics across frameworks such as LangChain, LlamaIndex, CrewAI, and AutoGen, usually through decorators or context managers. Public feedback describes the docs as concise and actionable, and reports place time to first traceable run at 5 to 15 minutes.

What developers like:

• Developers describe the Python SDK as dead simple and non-intrusive, with minimal dependencies.
• Public feedback often mentions effortless integration, including one-liner setup for full observability.
• Developers also point to the dashboard for cost breakdowns and latency graphs.

Common frustrations:

• Some users report rate limits on free tier dashboard views during high-volume testing.
• Some GitHub discussions mention sparse advanced configuration examples for custom metrics.
• Developers also mention occasional session truncation for long-running agents and unclear error messages when API keys are misconfigured.

Security and Privacy

• Audit logs: Audit logs are available, per the vendor's security information.
• Role-based access control: RBAC is available, per the vendor's security information.

Product Momentum

• Growth: Public GitHub activity shows an active open source codebase, with 25,295 stars, 5,552 forks, 450 contributors, and a last push on 2026-04-12.
• Search interest: Google Trends data shows unknown direction, with +0.0% change across the period. The latest interest score is 0/100, and the peak score is also 0/100.
• Risks: Public repository data shows 1,991 open issues, which may point to a sizable backlog.

FAQ

What is AgentOps?

AgentOps is the discipline of building, observing, and managing autonomous AI agents across their lifecycle. It extends LLMOps to cover decision paths, tool calls, state transitions, and side effects when agents act beyond text generation.

What is agentic ops?

Agentic ops, or AgentOps, refers to operations for AI systems where agents call tools, trigger workflows, maintain state, and make decisions to complete tasks. It focuses on observability, accountability, security, and control for those agent actions.

What is the difference between LLMOps and AgentOps?

LLMOps centers on managing large language models, including prompts, inference, output evaluation, safety, and token costs. AgentOps goes further by tracing agent actions, tool calls, workflows, and outcomes, including failures such as missed actions or unwanted side effects.

What is the difference between DevOps and AgentOps?

DevOps manages software delivery through practices such as CI/CD, versioning, and application operations. AgentOps adapts operational practices for AI agents, with attention to reasoning steps, tool integrations, workflow orchestration, and monitoring autonomous actions.

What is AgentOps used for?

AgentOps is used to observe and manage autonomous LLM-powered agents in production. Public sources describe tracking LLM calls, tool use, errors, costs, workflows, and replay analytics across agent systems.

Does AgentOps support automatic instrumentation?

Yes. Its SDK can automatically identify installed LLM providers after agentops.init() and instrument their API calls for dashboard data.

What frameworks and tools does AgentOps integrate with?

Public sources describe integrations with frameworks and tools such as AutoGen, CrewAI, LangChain, Agno, and LangSmith. The product is also described as agent agnostic.

Does AgentOps track LLM costs?

Yes. The Basic plan lists LLM cost tracking for more than 400 LLMs.

Is AgentOps free?

AgentOps has a Basic plan at $0 per month. Public pricing notes say this plan has a hard cap of 5,000 events per month.

How is Pro pricing handled for AgentOps?

Public pricing notes describe Pro as usage-based. A pricing calculator is available, and the exact cost depends on usage.

How do you get started with AgentOps?

Public setup details say signup requires only an email, and a free trial is available without a credit card. The essential configuration is an API key, and time to first result is listed as 5 to 15 minutes.

Does AgentOps include replay analytics?

Yes. Replay Analytics is listed on the Basic plan. Public positioning also describes replay and tracing as part of its observability workflow.

Is Chatgpt an agent or LLM?

ChatGPT is described in the research as a large language model, not an autonomous agent by itself. It does not have AgentOps traits such as tool calling or multi-step action loops unless other frameworks add them.