Agno + Orq: Production observability for Agno agents

Agno + Orq: Production observability for Agno agents

Use Orq.ai as the model control layer for Agno. Route LLM calls through one OpenAI-compatible endpoint, capture traces, monitor cost, and manage fallback behavior without rebuilding your agent logic.

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What is Agno?

Agno is an open-source Python framework for building agents and multi-agent systems, with components for tools, memory, knowledge, and reasoning. It helps teams focus on agent logic instead of assembling every infrastructure component from scratch. Learn more here.

Why use Orq with Agno

Trace agent behavior end to end

Trace Agno agent runs end to end, including prompts, tool calls, model responses, and errors in one place. You get clearer visibility into agent behavior without stitching together custom logging for each project.

Model flexibility without rewiring agents

Test new models, add providers, or assign different model tiers to different workflows from Orq.ai, while keeping your Agno application code stable.

Evaluate real production runs

Use real Agno conversations and traces to build datasets, compare prompt or model changes, and move from subjective tuning to measurable quality checks.

Control spend and access centrally

Track token usage and spend per agent, team, and workflow so you can see which Agno routes drive cost. Add budgets, rate limits, and approved-model lists at the platform layer instead of enforcing governance separately inside each agent.

Trace agent behavior end to end

Trace Agno agent runs end to end, including prompts, tool calls, model responses, and errors in one place. You get clearer visibility into agent behavior without stitching together custom logging for each project.

Model flexibility without rewiring agents

Test new models, add providers, or assign different model tiers to different workflows from Orq.ai, while keeping your Agno application code stable.

Evaluate real production runs

Use real Agno conversations and traces to build datasets, compare prompt or model changes, and move from subjective tuning to measurable quality checks.

Control spend and access centrally

Track token usage and spend per agent, team, and workflow so you can see which Agno routes drive cost. Add budgets, rate limits, and approved-model lists at the platform layer instead of enforcing governance separately inside each agent.

Trace agent behavior end to end

Trace Agno agent runs end to end, including prompts, tool calls, model responses, and errors in one place. You get clearer visibility into agent behavior without stitching together custom logging for each project.

Model flexibility without rewiring agents

Test new models, add providers, or assign different model tiers to different workflows from Orq.ai, while keeping your Agno application code stable.

Evaluate real production runs

Use real Agno conversations and traces to build datasets, compare prompt or model changes, and move from subjective tuning to measurable quality checks.

Control spend and access centrally

Track token usage and spend per agent, team, and workflow so you can see which Agno routes drive cost. Add budgets, rate limits, and approved-model lists at the platform layer instead of enforcing governance separately inside each agent.

Trace agent behavior end to end

Trace Agno agent runs end to end, including prompts, tool calls, model responses, and errors in one place. You get clearer visibility into agent behavior without stitching together custom logging for each project.

Model flexibility without rewiring agents

Test new models, add providers, or assign different model tiers to different workflows from Orq.ai, while keeping your Agno application code stable.

Evaluate real production runs

Use real Agno conversations and traces to build datasets, compare prompt or model changes, and move from subjective tuning to measurable quality checks.

Control spend and access centrally

Track token usage and spend per agent, team, and workflow so you can see which Agno routes drive cost. Add budgets, rate limits, and approved-model lists at the platform layer instead of enforcing governance separately inside each agent.

How the integration works

Step 1

Point Agno at Orq.ai’s router

Configure Agno’s model calls to use Orq.ai instead of calling each provider directly. This gives Orq the context it needs to apply routing rules, capture usage, and enforce fallback behavior.

Step 2

Enable tracing from Agno to Orq

Route Agno’s model calls through Orq.ai’s AI Router to capture Orq platform traces automatically for those calls.

Step 3

Define routes, fallbacks, and policies in Orq

Create routes for key Agno workflows and assign them model tiers, fallback chains, and region/data policies. Agno sends the model request to the configured Orq route, and Orq applies the routing rules you define.

Step 4

Monitor, evaluate, and tune

Once connected, use Orq’s dashboards to watch latency, errors, and cost for Agno agents, and run evals or experiments on their traces. You can then adjust routes, models, or prompt configurations centrally where supported.

Step 1

Point Agno at Orq.ai’s router

Configure Agno’s model calls to use Orq.ai instead of calling each provider directly. This gives Orq the context it needs to apply routing rules, capture usage, and enforce fallback behavior.

Step 2

Enable tracing from Agno to Orq

Route Agno’s model calls through Orq.ai’s AI Router to capture Orq platform traces automatically for those calls.

Step 3

Define routes, fallbacks, and policies in Orq

Create routes for key Agno workflows and assign them model tiers, fallback chains, and region/data policies. Agno sends the model request to the configured Orq route, and Orq applies the routing rules you define.

Step 4

Monitor, evaluate, and tune

Once connected, use Orq’s dashboards to watch latency, errors, and cost for Agno agents, and run evals or experiments on their traces. You can then adjust routes, models, or prompt configurations centrally where supported.

Step 1

Point Agno at Orq.ai’s router

Configure Agno’s model calls to use Orq.ai instead of calling each provider directly. This gives Orq the context it needs to apply routing rules, capture usage, and enforce fallback behavior.

Step 2

Enable tracing from Agno to Orq

Route Agno’s model calls through Orq.ai’s AI Router to capture Orq platform traces automatically for those calls.

Step 3

Define routes, fallbacks, and policies in Orq

Create routes for key Agno workflows and assign them model tiers, fallback chains, and region/data policies. Agno sends the model request to the configured Orq route, and Orq applies the routing rules you define.

Step 4

Monitor, evaluate, and tune

Once connected, use Orq’s dashboards to watch latency, errors, and cost for Agno agents, and run evals or experiments on their traces. You can then adjust routes, models, or prompt configurations centrally where supported.

Step 1

Point Agno at Orq.ai’s router

Configure Agno’s model calls to use Orq.ai instead of calling each provider directly. This gives Orq the context it needs to apply routing rules, capture usage, and enforce fallback behavior.

Step 2

Enable tracing from Agno to Orq

Route Agno’s model calls through Orq.ai’s AI Router to capture Orq platform traces automatically for those calls.

Step 3

Define routes, fallbacks, and policies in Orq

Create routes for key Agno workflows and assign them model tiers, fallback chains, and region/data policies. Agno sends the model request to the configured Orq route, and Orq applies the routing rules you define.

Step 4

Monitor, evaluate, and tune

Once connected, use Orq’s dashboards to watch latency, errors, and cost for Agno agents, and run evals or experiments on their traces. You can then adjust routes, models, or prompt configurations centrally where supported.

Use Cases

Multi‑agent products with real observability

Trace which agent, tool call, and model contributed to a failed workflow.

Cost‑aware internal tools

Route routine steps to lower-cost models while keeping complex reasoning on stronger routes.

Eval‑driven agent improvements

Reuse failed conversations as eval inputs before shipping prompt, tool, or model changes.

Safer experimentation across providers

Test a new model on a small share of traffic, then promote or roll back based on traces and evals.

Multi‑agent products with real observability

Trace which agent, tool call, and model contributed to a failed workflow.

Cost‑aware internal tools

Route routine steps to lower-cost models while keeping complex reasoning on stronger routes.

Eval‑driven agent improvements

Reuse failed conversations as eval inputs before shipping prompt, tool, or model changes.

Safer experimentation across providers

Test a new model on a small share of traffic, then promote or roll back based on traces and evals.

Multi‑agent products with real observability

Trace which agent, tool call, and model contributed to a failed workflow.

Cost‑aware internal tools

Route routine steps to lower-cost models while keeping complex reasoning on stronger routes.

Eval‑driven agent improvements

Reuse failed conversations as eval inputs before shipping prompt, tool, or model changes.

Safer experimentation across providers

Test a new model on a small share of traffic, then promote or roll back based on traces and evals.

Multi‑agent products with real observability

Trace which agent, tool call, and model contributed to a failed workflow.

Cost‑aware internal tools

Route routine steps to lower-cost models while keeping complex reasoning on stronger routes.

Eval‑driven agent improvements

Reuse failed conversations as eval inputs before shipping prompt, tool, or model changes.

Safer experimentation across providers

Test a new model on a small share of traffic, then promote or roll back based on traces and evals.

With Orq.ai vs without

Capability

Agno alone

Agno + Orq.ai

Model access

Provider integrations are managed inside each Agno project.

Model access is managed through Orq.ai’s router, keeping provider changes outside the agent code.

Observability

Teams need to assemble logs and traces across agent runs, tool calls, and model responses.

Orq centralizes traces for routed Agno traffic, including prompts, tools, responses, errors, and workflow context.

Reliability

Retries and fallback behavior live in application logic or provider-specific setup.

Fallback chains, retries, and routing rules can be configured centrally.

Evals

Prompt and model testing often happens in scripts, notebooks, or manual review.

Agno traces and run outputs can be used as inputs for eval datasets and experiments inside Orq.ai.

Cost and governance

Usage, budgets, and provider policies are spread across accounts and services.

Cost tracking, budgets, access rules, and approved-model policies can be managed from one control layer.

FAQ

Do I have to change my Agno agents to use Orq.ai?

In many cases, you can repoint Agno’s model calls to Orq.ai’s OpenAI-compatible endpoint and add tracing with minimal changes instead of rewriting your agents.

Does Orq.ai replace Agno’s own runtime or framework?

No. Agno remains your agent framework and runtime. Orq.ai sits alongside it as the control plane for models, routing, observability, and evaluation. 

You still design and run agents in Agno, but you use Orq to see how they behave, what they cost, and which models they should call.

Can I keep using my existing LLM providers with Agno if I move to Orq.ai?

Yes. You can bring your existing provider keys into Orq.ai and route Agno traffic through them, alongside any new models you add later. That way you centralize access, routing, and tracking without losing the providers you already rely on.

Create an account and start building today.

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Explore docs

Create an account and start building today.

Book a demo

Explore docs

Create an account and start building today.

Book a demo

Explore docs

Create an account and start building today.

Book a demo

Explore docs