Every observability tool can show you a gorgeous trace of the agent that just approved the wrong refund. Detailed, timestamped, color-coded, and about four seconds too late to matter.
This guide compares 12 tools across 7 dimensions so CTOs and AI platform leads can match a tool to their stack, their risk profile and their stage, not just their RFP checklist.
Get the short answer
- ✓Lyzr Open ControllerBest overall for enterprises. Observes and governs every agent, from any framework, and refuses a bad call before it runs. Flat annual fee, runs in your cloud.
- ✓LangSmithBest for teams building on LangChain and LangGraph.
- ✓LangfuseBest open-source tracing and evals you can self-host.
- ✓Arize AIBest for classical ML and LLM agents on one platform.
- ✓BraintrustBest for eval-driven development and CI regression gates.
- ✓Splunk Agent ObservabilityBest for Splunk and Cisco shops (formerly Galileo).
- ✓Datadog LLM ObservabilityBest for teams already standardized on Datadog APM.
- ✓Fiddler AIBest for explainability and model-risk reporting.
- ✓Maxim AIBest for pre-launch simulation with product teams involved.
- ✓Comet OpikBest lightweight open-source eval and tracing.
- ✓Confident AIBest for research-grade output scoring on DeepEval.
- ✓MLflowBest for Databricks and existing MLflow teams.
Know what changed in this market in 2026
- Gartner named the category. Gartner published its first Market Guide for AI Evaluation and Observability Platforms (AEOPs) on 2 February 2026, and projects that 60% of software engineering teams will use AEOPs by 2028, up from 18% in 2025.
- Evaluation and observability merged. A trace tells you what happened. A score tells you whether it was right. Buyers now expect both in one purchase.
- The question moved from watching to controlling. Once agents move money, change records or send email, “what did it do” is no longer enough. Risk teams ask “what is it allowed to do, and who stops it.”
- Three well-known tools changed owners. Vendor independence is now a real evaluation criterion:
- ClickHouse acquired Langfuse alongside its $400M Series D. Langfuse remains MIT-licensed and self-hostable.
- Mintlify acquired Helicone. Helicone moved to maintenance mode: fixes continue, new development stopped.
- Cisco completed its acquisition of Galileo, announced in April.
- Galileo was renamed Splunk Agent Observability.
Tell LLM observability and agent observability apart
Most tools on this list started with LLM observability: one call in, one completion out. Agents are a different shape of problem.
| LLM observability | Agent observability | Agent control | |
|---|---|---|---|
| Unit you watch | A single model call | A tree of calls, tools, retrievals and handoffs | Every agent, its owner and its permissions |
| Typical failure | Error, timeout, bad output | Sound reasoning over the wrong retrieved input | An agent doing something it was never approved to do |
| When you find out | After the call | After the run | Before the action executes |
| Key question | Was it fast and correct | Which step went wrong | Should this call be allowed at all |
| Primary buyer | ML engineering | AI platform engineering | Platform, security, risk and compliance |
| Example tools | Helicone, Datadog | LangSmith, Langfuse, Arize | Lyzr Open Controller |
See how we scored every tool
- 12 tools scored on the same 7 dimensions, using vendor documentation, public announcements, G2 and Gartner Peer Insights, and community discussion.
- Lyzr is on this list and we say so plainly. Lyzr Open Controller is scored on the same dimensions, and we name where other tools are the better pick.
- Scores are our editorial assessment on a 1 to 5 scale, dated September 2026. Verify against current vendor docs before you buy.
| Dimension | What we checked | Why it matters |
|---|---|---|
| Policy enforcement | Can the tool refuse a call before it executes, or only alert afterward | An alert after a wire transfer does not reverse it |
| Agent-native tracing | Depth on multi-step runs, tool calls, retrievals and multi-agent handoffs | Root cause in agents is an attribution problem, not an error log |
| Evaluation depth | Pre-launch and production scoring, custom metrics, regression gates | A trace without a quality score tells you what happened, not whether it was right |
| Deployment control | Self-host, your cloud, on-prem, air-gapped | Prompts and completions often contain regulated data |
| Framework neutrality | Coverage across LangChain, CrewAI, AutoGen, custom code and vendor-embedded agents | Large companies never run one framework |
| Cost predictability | Pricing model and how it behaves as agent volume grows | Trace-volume pricing grows with the success of your agents |
| Vendor independence | Ownership changes, roadmap status, maintenance mode | Your control layer should outlive your next renewal |
1. Lyzr Open Controller: control every agent in the request path
Lyzr Open Controller is the universal control plane for enterprise AI agents. It finds every agent you run, watches what each one does, and enforces what each one may do, across any cloud, framework, model and runtime.
Know the brand and the product
Lyzr, the company
- Enterprise AI agent platform: build in Agent Studio and Architect, govern in Open Controller
- Headquartered in New York, engineering hub in Bengaluru, teams in the US, India, UK and Dubai
- Serves banking, financial services, insurance, healthcare and the public sector
- Technology partners: AWS, Google Cloud, Microsoft Azure, NVIDIA
- Series A backers include Accenture
Open Controller, the product
- Control plane for the whole agent system, not one framework
- Runs inside your cloud or on-prem, fully air-gapped if needed
- Governs agents you already have, with no rewrite
- Covers graph agents and coding agents
- Fixed annual fee: unlimited agents, users, logs and usage
See why “in the path” is the difference
- Most tools on this list sit beside the request path. They copy the trace, score it and alert you.
- Open Controller sits in the path. It checks identity, permission, spend and policy before the call reaches the tool, and refuses it when policy says no.
- A dashboard, a policy document or an alert cannot stop an agent. Control has to happen where the call is made.
Follow the four-stage lifecycle
- FindDiscover agents, models, tools, data and workflows across clouds, SaaS, Kubernetes and devices.
- ShipEvaluate and validate every agent before production, with ordered promotion and separation of duties.
- RunMonitor agents, applications, APIs and infrastructure in real time, and enforce policy on every call.
- ImproveTurn usage, performance, cost and security signals into changes you can act on.
Check the core capabilities
| Capability | What it does | Why it matters to an enterprise |
|---|---|---|
| Agent discovery and registry | Finds agents across clouds and teams, including third-party agents from Salesforce, Snowflake, Databricks, ServiceNow and SAP | Ends shadow agents. You can ask “show me every agent reading from production Databricks” and get an answer |
| Attributable identity | Gives every agent an identity, an owner and role-based permissions | Answers the auditor’s first question: who is accountable for this agent |
| In-path policy enforcement | Checks each call against policy and refuses it in the request path | Prevents the incident instead of documenting it |
| Stop, restrict or isolate | Lets you halt an unauthorized or misbehaving agent immediately | Turns a live incident into a controlled one |
| Spend limits and cost attribution | Shows which agents call which models, where those models run, and cost by developer, project and agent | Gives finance a real number and stops runaway loops before the invoice |
| Multi-step agent observability | Traces agent activity, tool calls, APIs and infrastructure in real time | Root cause across a whole run, not a single call |
| Controlled promotion | Ordered promotion to production with separation of duties on prod | Maps to change-management controls your auditors already test |
| Immutable versions and rollback | Every agent version is immutable; rollback is a pointer move | Recovery in seconds, with a clean record of what ran when |
| Deploys into your cluster | Deploys your container into your own cluster, and into Bedrock, Vertex and Foundry runtimes | Control without moving agents onto a vendor’s cloud |
| Sovereign by default | Runs in your cloud or on-prem, fully air-gapped; logs, tool integrations and metadata stay with you | Clears data-residency review in regulated industries |
Pick it if you are
- A CTO or CISO in a regulated industry who needs every agent action attributable and enforceable
- An AI platform lead governing agents across several frameworks, clouds and vendor platforms
- A Head of AI scaling past the first handful of agents, where ownership starts to blur
- A risk or compliance lead who must prove who approved what
Skip it if you are
- A solo developer who wants a hosted trace viewer for one prototype
- A team whose only need is prompt-level eval scoring today. Start with an eval tool and add Open Controller when agents start taking actions
- Looking for a tool that builds your containers. Open Controller governs agents; your team keeps owning the code and images
Weigh the numbers that change the business case
See who runs on Lyzr
- Published case studies include a leading fintech innovator, a leading energy provider, a global IT company, an HR and workforce leader, a customer service leader and an industrial manufacturer
- Industry focus: banking, insurance, healthcare, fintech and government
- Partner ecosystem: AWS, Google Cloud, Microsoft Azure, NVIDIA and global systems integrators
Compare it with platform registries
You may also be weighing agent registries from cloud and SaaS platforms. The difference, as Lyzr frames it:
| Approach | Examples | What it does |
|---|---|---|
| Cloud runtimes | AWS AgentCore, Microsoft Agent 365, Google’s agent platform | Run and list agents on that vendor’s own cloud |
| SaaS governance consoles | ServiceNow AI Control Tower, Salesforce Agent Fabric | Catalogue agents from a SaaS console |
| Lyzr Open Controller | Neutral control plane | Deploys your container on your cluster under your keys, enforces on agents in other vendors’ clouds, and works without buying a wider platform |
Hear it from the market
Pick Lyzr Open Controller if your agents take real actions and you need to decide what they are allowed to do, not just read what they did.
See Open Controller govern your agentsAgent tracing and evaluation
2. LangSmith: trace LangChain and LangGraph agents in depth
Agent engineering platform from the LangChain team, covering tracing, evaluation and prompt management. SDKs work outside LangChain, with the deepest integration in LangGraph.
- Best for
- Engineering teams standardized on LangChain or LangGraph
- Not ideal if
- Data must stay in your environment and you are not on an enterprise plan
- Scale signal
- Built by LangChain, Inc., maintainer of one of the most widely adopted agent frameworks
- Full execution-tree traces showing tool selection, retrieved documents and parameters at each step
- Datasets and evaluations tied directly to production traces
- Prompt playground and versioning inside the same workflow
- Teams sometimes note that self-hosting sits behind the enterprise tier
- Access control is organized around projects and workspaces rather than per-agent identity and runtime policy
LangSmith is the right call for LangGraph-heavy engineering teams.Pair it with Lyzr Open Controller when those agents need ownership, spend limits and enforced policy in production.
3. Langfuse: self-host open-source tracing and evals
Open-source LLM engineering platform founded in 2023, MIT-licensed. Acquired by ClickHouse in January 2026 and remains open source and self-hostable.
- Best for
- Teams that want full hosting control and are comfortable running their own infrastructure
- Not ideal if
- You need a managed governance layer with policy and ownership out of the box
- Scale signal
- 2,000+ paying customers, used at 19 of the Fortune 50 (company-reported at acquisition)
- Self-hosted version keeps close parity with the cloud product
- Tracing, prompt management and LLM-as-judge evals in one tool
- Large open-source community and OpenTelemetry support
- Built for developer debugging; agent ownership and runtime policy are outside its scope
- Self-hosting at scale means operating ClickHouse, Postgres and supporting services yourself
Langfuse is the right call for open-source tracing under your own roof.Consider Lyzr Open Controller when you also need to decide which agents may run and what they may touch.
4. Arize AI: monitor classical ML and LLM agents together
ML monitoring company extended to LLM and agent observability. Open-source Phoenix (Elastic License 2.0) is the entry point; Arize AX is the enterprise platform. Built on OpenTelemetry and OpenInference.
- Best for
- ML platform teams running predictive models and generative AI side by side
- Not ideal if
- You want the deepest capabilities without an enterprise contract
- Scale signal
- Established MLOps vendor with a large open-source Phoenix community
- One view across model drift, data quality and LLM traces
- Strong evaluation tooling and online evals
- OpenTelemetry-native instrumentation
- Teams sometimes note that session grouping, agent trajectories and scale dashboards sit in the paid AX tier
- Governance is observational: it reports on behavior rather than enforcing policy on calls
Arize is the right call for unified ML and LLM monitoring.Consider Lyzr Open Controller when the need moves from measuring agents to controlling them.
5. Braintrust: gate releases on evaluation results
Evaluation-first platform with tracing built in, designed so a quality regression can be traced to the step that caused it and caught in CI before release.
- Best for
- Product engineering teams practicing eval-driven development
- Not ideal if
- You need runtime enforcement on live agent actions
- Scale signal
- Customers include Notion and Stripe, per Braintrust
- CI/CD regression gates tied to production traces
- Fast experiment comparison across prompts and models
- Well-regarded developer experience
- Hosting options beyond SaaS sit in enterprise plans; confirm data-plane terms
- Focused on quality, not on who owns an agent or what it may access
Braintrust is the right call when output quality is your hardest problem.Consider Lyzr Open Controller when agents that pass your evals still need permission to act.
6. Splunk Agent Observability: add evals and guardrails to Splunk formerly Galileo
Galileo, founded in 2021, built evaluation and real-time guardrails for agents, including its Luna-2 evaluation models. Cisco completed its acquisition in May 2026 and renamed the product Splunk Agent Observability in August 2026.
- Best for
- Enterprises already standardized on Splunk and Cisco
- Not ideal if
- You want an independent vendor or framework-neutral control outside the Splunk portfolio
- Scale signal
- Enterprise customers reported include Comcast, HP and NTT
- Low-latency evaluation metrics usable as production guardrails
- Coverage from prompt optimization through production monitoring
- Telemetry alongside Splunk security and infrastructure data
- Roadmap, pricing and support now follow the Splunk portfolio; existing Galileo customers should confirm renewal and migration terms
Splunk Agent Observability is the right call for Splunk-centered security and ops teams.Consider Lyzr Open Controller for a neutral control plane that runs in your cloud on a flat fee.
APM platforms with AI extensions
7. Datadog LLM Observability: keep AI signals in your APM
AI module within Datadog’s observability platform that correlates LLM and agent spans with application and infrastructure telemetry. Dynatrace offers a comparable extension for Dynatrace customers.
- Best for
- Platform teams already standardized on Datadog
- Not ideal if
- You need the deepest agent reasoning traces or on-prem data control
- Scale signal
- Public company with a large enterprise installed base
- No new vendor relationship for existing Datadog customers
- LLM spans correlated with APM, logs and infrastructure metrics
- Reuses existing alerting and on-call workflows
- Teams sometimes note that agent-specific eval and reasoning depth is lighter than dedicated tools
- Ingestion-based pricing grows with agent traffic
Datadog is the right call when consolidation matters most.Consider Lyzr Open Controller when alerts need to become enforced policy.
Explainability and model risk
8. Fiddler AI: produce explainability and audit evidence
Built on ML explainability, bias detection and compliance reporting, since extended to LLM and agent monitoring with guardrails.
- Best for
- Model-risk and compliance teams in finance, healthcare and insurance
- Not ideal if
- Multi-agent trace visualization is your main need
- Scale signal
- Long-standing vendor to regulated enterprises
- Explainability and bias analysis for model-risk reviews
- Reporting aligned to regimes such as SR 11-7
- Self-hosted deployment options
- Rooted in tabular, single-inference ML, so complex agent workflows can feel less native
Fiddler is the right call for model-risk reporting.Consider Lyzr Open Controller for per-agent identity and enforcement across the whole agent system.
Simulation and evaluation specialists
9. Maxim AI: simulate agents before launch
Evaluation, simulation and observability platform that tests agents across many scenarios and user personas before deployment, with interfaces for product managers as well as engineers.
- Best for
- Cross-functional teams where product owns agent quality
- Not ideal if
- You need runtime governance and on-prem control
- Scale signal
- Growing vendor focused on agent quality
- Scenario and persona simulation before release
- Evals usable by non-engineers
- Production monitoring linked back to test suites
- Policy enforcement and agent ownership are outside its scope
Maxim is the right call for pre-launch simulation.Consider Lyzr Open Controller for what happens after launch.
10. Comet Opik: start with lightweight open-source evals
Open-source tracing and evaluation from Comet, covering LLM-as-judge scoring, human annotation and workflow-level logging.
- Best for
- Teams that want open-source evals with an easy start
- Not ideal if
- You need enterprise governance features
- Scale signal
- Backed by Comet, an established ML experiment-tracking vendor
- Open source and self-hostable
- Evals and tracing across components and whole workflows
- Fits teams already using Comet
- Teams sometimes note that managed enterprise features sit in Comet’s paid platform
Opik is the right call for a low-cost eval start.Consider Lyzr Open Controller as agents move into regulated production.
11. Confident AI: score output quality with DeepEval
Commercial platform on the open-source DeepEval library, scoring traces, spans and conversation threads with research-backed metrics.
- Best for
- Teams whose main fear is a confident but wrong answer
- Not ideal if
- You only want a fast trace viewer
- Scale signal
- DeepEval is widely used as an open-source eval framework
- Deep metric library beyond pass or fail
- Open-source core you can run locally
- Conversation-level evaluation for multi-turn agents
- Heavier workflow than teams expect if they only need tracing
Confident AI is the right call for rigorous output scoring.Consider Lyzr Open Controller to enforce what happens when scores drop.
Open-source ML lifecycle
12. MLflow: extend your ML lifecycle to agents
Long-standing open-source ML lifecycle platform (Apache 2.0), backed by Databricks, now with GenAI and agent tracing and evaluation.
- Best for
- Databricks customers and teams already running MLflow
- Not ideal if
- You need agent-specific views across many frameworks
- Scale signal
- One of the most widely deployed open-source ML tools
- Free to self-host, managed option on Databricks
- One system for classical ML and GenAI experiments
- Large community and integrations
- Tool-selection and reasoning-chain views are thinner than agent-native tools
MLflow is the right call if you live in Databricks.Consider Lyzr Open Controller to govern agents across Databricks and everything outside it.
Compare all 12 tools side by side
● Strong ◐ Partial ○ Limited or not the focus. Editorial assessment, September 2026. Verify every cell against current vendor documentation before you buy.
| Tool | Best for | Stops a bad call before it runs | Per-agent identity and owner | Agent trace depth | Evaluation | Runs in your cloud | Pricing model | Vendor status, 2026 |
|---|---|---|---|---|---|---|---|---|
| Lyzr Open Controller | Enterprise agent control and observability | ● In the request path | ● Built in | ● Cross-framework | ◐ Pre-production validation | ● Your cloud, on-prem, air-gapped | Flat annual fee, unlimited | Independent |
| LangSmith | LangChain and LangGraph teams | ○ | ◐ Workspace roles | ● | ● | ◐ Enterprise tier | Seats plus trace volume | Independent |
| Langfuse | Open-source self-hosted tracing | ○ | ○ | ● | ● | ● Open source | Free self-host, usage-based cloud | Acquired by ClickHouse |
| Arize AI | Classical ML plus LLM | ○ | ○ | ● | ● | ◐ Phoenix, AX enterprise | Usage and enterprise | Independent |
| Braintrust | Eval-driven development | ○ | ○ | ● | ● | ◐ Enterprise | Usage-based | Independent |
| Splunk Agent Observability | Splunk and Cisco shops | ◐ Guardrails | ◐ | ● | ● | ◐ | Splunk licensing | Acquired by Cisco |
| Datadog LLM Observability | Datadog-standardized teams | ○ | ○ | ◐ | ◐ | ○ SaaS | Ingestion and hosts | Public company |
| Fiddler AI | Explainability and model risk | ◐ Guardrails | ◐ | ◐ | ● | ● | Enterprise | Independent |
| Maxim AI | Pre-launch simulation | ○ | ○ | ● | ● | ◐ | Seats and usage | Independent |
| Comet Opik | Open-source evals | ○ | ○ | ● | ● | ● Open source | Free self-host, Comet plans | Independent |
| Confident AI | Output quality scoring | ○ | ○ | ◐ | ● Deep | ◐ DeepEval OSS | Usage-based | Independent |
| MLflow | Databricks and MLflow teams | ○ | ○ | ◐ | ◐ | ● Open source | Free self-host, Databricks managed | Open source, Databricks-backed |
Read the scores on one chart
Toggle tools to compare against Lyzr Open Controller:
- Where Lyzr leads: policy enforcement, deployment control, framework neutrality and cost predictability
- Where others lead: LangSmith and Langfuse on raw tracing depth; Arize, Braintrust and Splunk on evaluation
- Why that split is healthy: tracing and eval tools feed a control plane. They answer different questions
| Score, 1 to 5 | Lyzr | LangSmith | Langfuse |
|---|---|---|---|
| Policy enforcement | 5 | 2 | 1.5 |
| Agent-native tracing | 4 | 5 | 4.5 |
| Evaluation depth | 4 | 4.5 | 4 |
| Deployment control | 5 | 2.5 | 5 |
| Framework neutrality | 5 | 3 | 4.5 |
| Cost predictability | 5 | 3 | 4 |
Match your scenario to a tool
| If your situation is | Pick | Why |
|---|---|---|
| Agents move money, change records or touch customer data | Lyzr Open Controller | Refuses non-compliant calls before they execute |
| Security asks who owns each agent and what it can access | Lyzr Open Controller | Attributable identity and permissions per agent |
| Agents span LangChain, CrewAI, custom code and SaaS platforms | Lyzr Open Controller | Any framework, model, cloud and runtime, no rewrite |
| Finance wants a fixed AI tooling cost as agents scale | Lyzr Open Controller | Flat annual fee with unlimited agents and logs |
| Your developers live in LangGraph | LangSmith | Deepest native tracing for that framework |
| Open source and self-hosting are hard requirements for dev tracing | Langfuse, Phoenix, Opik or MLflow | Free to self-host, strong communities |
| Output quality is the core problem | Braintrust or Confident AI | Built around scoring and regression gates |
| You run classical ML and LLMs together | Arize AI | One monitoring stack for both |
| You are a Datadog or Splunk shop | Datadog or Splunk Agent Observability | AI signals next to your existing telemetry |
| Model-risk teams need explainability reports | Fiddler AI | Explainability and regulatory reporting heritage |
Walk the decision tree
Stack tools the way production teams do
Several tools on this list work together. Common pairings we see:
| Layer | Job | Typical choice |
|---|---|---|
| Control | Identity, permissions, spend limits, policy on every call, production observability | Lyzr Open Controller |
| Developer tracing | Debugging runs during development | Langfuse or LangSmith, fully intact |
| Evaluation | Quality scoring and regression gates in CI | Braintrust, Confident AI or Opik |
| Infrastructure | Hosts, latency, uptime | Your existing APM |
Run these five tests in every vendor demo
- The misroute test. Feed the agent a wrong retrieval and ask the vendor to show which step caused the bad answer.
- The stop test. Ask them to block an agent from calling a payment API it should not use, live, before the call executes.
- The owner test. Pick any running agent and ask who owns it and what it is permitted to access.
- The bill test. Ask for your cost at ten times today’s agent volume, in writing.
- The exit test. Ask where your logs live, who can read them, and what happens to the roadmap if the vendor is acquired.
Get answers to common questions
What is the best AI agent observability tool for enterprises in 2026?
For agents that take real actions, Lyzr Open Controller: it pairs observability with identity, spend limits and in-path policy enforcement, runs in your cloud and has a flat annual fee. For developer tracing alone, Langfuse and LangSmith lead. For evaluation, Braintrust and Confident AI.
What is the difference between agent observability and an agent control plane?
Observability records and scores what an agent did. A control plane decides what the agent may do and can refuse a call before it runs. Lyzr Open Controller does both.
Is Langfuse still open source after the ClickHouse acquisition?
Yes. ClickHouse acquired Langfuse in January 2026, and Langfuse says it stays open source and self-hostable, with Langfuse Cloud unchanged.
What happened to Galileo AI?
Cisco announced the acquisition in April 2026 and completed it in May. The product was renamed Splunk Agent Observability in August 2026.
Is Helicone still maintained?
Helicone moved to maintenance mode after Mintlify acquired it in March 2026. Fixes continue; new feature development has stopped.
Can Lyzr Open Controller govern agents built on LangChain, CrewAI or AutoGen?
Yes. It supports any framework, model, cloud and runtime, and governs existing containerized agents without a rewrite. Connectors also cover agents from Salesforce, Snowflake, Databricks, ServiceNow and SAP.
How is Lyzr Open Controller priced?
A fixed annual fee with unlimited usage, logs, users and agents. Most alternatives price by trace volume, ingestion, seats or hosts.
Can agent observability data stay inside our own cloud?
Yes, with self-hosted open-source tools or with Lyzr Open Controller, which runs in your cloud or on-prem, including fully air-gapped.
How long does it take to deploy an agent control plane?
Lyzr states Open Controller can be set up in less than a week.
Do I need both a tracing tool and a control plane?
Often, yes. Developers debug in a tracing tool; production and compliance teams govern in a control plane.
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