Agno

Evaluate and improve an existing AgentOS agent or team. Repair failing eval cases, or explore behavior by generating simulated user scenarios from instructions and sanitized usage, running probes, inspecting evidence and iterating within a bounded budget. Use for failing evals, autonomous hardening or simulation-driven improvement. Use create-evals for coverage authoring alone and extend-agent for a requested new capability.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Review this AgentOS repository for public readiness after a refactor or before release. Compare documentation, skills, configuration, component wiring and scripts with the implementation; fix clear documentation drift when requested and report behavioral issues with evidence. Use for a repository consistency sweep, not agent-quality tuning or deployment.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Set up this AgentOS from a fresh clone — confirm Docker, configure .env, boot the containers, prove the MCP endpoint live, connect the AgentOS UI, then build the user's first agent. Use when the user asks to set up the platform, get started, or bring this repo up on a new machine.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Add a new agent to this AgentOS. Runs guided discovery or takes a concrete idea, then generates agents/slug.py, registers it in app/main.py, adds its manifest entry (description + quick prompts), reloads the selected development runtime, and smoke-tests it for you. Covers the product agent too — ingest a product's website/docs into the product knowledge base and build a knowledge-only agent over it. Use whenever the user wants to add or create a new agent, or names a product (theirs or one they use) they want an agent for.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Author eval coverage for an agent in this AgentOS — map what the agent promises, mine real sessions and eval history from Postgres for scenarios, propose capabilities worth testing, then write, run, and audit Case entries in evals/cases.py. Use when the user wants evals created, coverage added, or an agent's behavior pinned down as tests. For failing suites or exploratory simulation-driven hardening, use eval-and-improve instead.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Deploy this AgentOS using its own provider instructions, configure JWT verification, and verify the public service. Use for first deployment, redeployment, or production configuration changes.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

User-driven loop to change an existing agent in this AgentOS — add a tool/MCP server/toolkit, add a capability (knowledge base, learning/memory, sub-agent, scheduled task), grow the safe Studio registry so components built at runtime gain a new capability, refine its instructions, or fix a specific known bug, verifying each change against the selected development runtime. Use whenever the user names a concrete change to an agent, or wants a new building block available to the platform. For autonomous hardening with no specific change in mind, use eval-and-improve.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Set up this AgentOS from a fresh clone — confirm Docker, configure .env, boot the containers, prove the MCP endpoint live, connect the AgentOS UI, then build the user's first agent. Use when the user asks to set up the platform, get started, or bring this repo up on a new machine.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Add a new agent to this AgentOS. Runs guided discovery or takes a concrete idea, then generates agents/slug.py, registers it in app/main.py, adds its manifest entry (description + quick prompts), reloads the selected development runtime, and smoke-tests it for you. Covers the product agent too — ingest a product's website/docs into the product knowledge base and build a knowledge-only agent over it. Use whenever the user wants to add or create a new agent, or names a product (theirs or one they use) they want an agent for.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Author eval coverage for an agent in this AgentOS — map what the agent promises, mine real sessions and eval history from Postgres for scenarios, propose capabilities worth testing, then write, run, and audit Case entries in evals/cases.py. Use when the user wants evals created, coverage added, or an agent's behavior pinned down as tests. For failing suites or exploratory simulation-driven hardening, use eval-and-improve instead.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Deploy this AgentOS using its own provider instructions, configure JWT verification, and verify the public service. Use for first deployment, redeployment, or production configuration changes.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Evaluate and improve an existing AgentOS agent or team. Repair failing eval cases, or explore behavior by generating simulated user scenarios from instructions and sanitized usage, running probes, inspecting evidence and iterating within a bounded budget. Use for failing evals, autonomous hardening or simulation-driven improvement. Use create-evals for coverage authoring alone and extend-agent for a requested new capability.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

User-driven loop to change an existing agent in this AgentOS — add a tool/MCP server/toolkit, add a capability (knowledge base, learning/memory, sub-agent, scheduled task), grow the safe Studio registry so components built at runtime gain a new capability, refine its instructions, or fix a specific known bug, verifying each change against the selected development runtime. Use whenever the user names a concrete change to an agent, or wants a new building block available to the platform. For autonomous hardening with no specific change in mind, use eval-and-improve.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Review this AgentOS repository for public readiness after a refactor or before release. Compare documentation, skills, configuration, component wiring and scripts with the implementation; fix clear documentation drift when requested and report behavioral issues with evidence. Use for a repository consistency sweep, not agent-quality tuning or deployment.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Review this AgentOS repository for public readiness after a refactor or before release. Compare documentation, skills, configuration, component wiring and scripts with the implementation; fix clear documentation drift when requested and report behavioral issues with evidence. Use for a repository consistency sweep, not agent-quality tuning or deployment.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Set up this AgentOS from a fresh clone — confirm Docker, configure .env, boot the containers, prove the MCP endpoint live, connect the AgentOS UI, then build the user's first agent. Use when the user asks to set up the platform, get started, or bring this repo up on a new machine.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

User-driven loop to change an existing agent in this AgentOS — add a tool/MCP server/toolkit, add a capability (knowledge base, learning/memory, sub-agent, scheduled task), grow the safe Studio registry so components built at runtime gain a new capability, refine its instructions, or fix a specific known bug, verifying each change against the selected development runtime. Use whenever the user names a concrete change to an agent, or wants a new building block available to the platform. For autonomous hardening with no specific change in mind, use eval-and-improve.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Evaluate and improve an existing AgentOS agent or team. Repair failing eval cases, or explore behavior by generating simulated user scenarios from instructions and sanitized usage, running probes, inspecting evidence and iterating within a bounded budget. Use for failing evals, autonomous hardening or simulation-driven improvement. Use create-evals for coverage authoring alone and extend-agent for a requested new capability.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Deploy this AgentOS using its own provider instructions, configure JWT verification, and verify the public service. Use for first deployment, redeployment, or production configuration changes.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Author eval coverage for an agent in this AgentOS — map what the agent promises, mine real sessions and eval history from Postgres for scenarios, propose capabilities worth testing, then write, run, and audit Case entries in evals/cases.py. Use when the user wants evals created, coverage added, or an agent's behavior pinned down as tests. For failing suites or exploratory simulation-driven hardening, use eval-and-improve instead.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Add a new agent to this AgentOS. Runs guided discovery or takes a concrete idea, then generates agents/slug.py, registers it in app/main.py, adds its manifest entry (description + quick prompts), reloads the selected development runtime, and smoke-tests it for you. Covers the product agent too — ingest a product's website/docs into the product knowledge base and build a knowledge-only agent over it. Use whenever the user wants to add or create a new agent, or names a product (theirs or one they use) they want an agent for.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Review this AgentOS repository for public readiness after a refactor or before release. Compare documentation, skills, configuration, component wiring and scripts with the implementation; fix clear documentation drift when requested and report behavioral issues with evidence. Use for a repository consistency sweep, not agent-quality tuning or deployment.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Set up this AgentOS from a fresh clone — confirm Docker, configure .env, boot the containers, prove the MCP endpoint live, connect the AgentOS UI, then build the user's first agent. Use when the user asks to set up the platform, get started, or bring this repo up on a new machine.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Add a new agent to this AgentOS. Runs guided discovery or takes a concrete idea, then generates agents/slug.py, registers it in app/main.py, adds its manifest entry (description + quick prompts), reloads the selected development runtime, and smoke-tests it for you. Covers the product agent too — ingest a product's website/docs into the product knowledge base and build a knowledge-only agent over it. Use whenever the user wants to add or create a new agent, or names a product (theirs or one they use) they want an agent for.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.