Search2o Simplifies AI Agents
Search2o gives developers an agent framework and server for building, discovering, and running AI agents, with built-in runtime and conversation infrastructure. Enterprise teams can use it to connect agent workflows to their systems and manage deployment across environments.
Who it's for
- Developers building business agents who want the server to handle LLM calls, tool execution, and conversation state.
- Enterprise platform teams integrating agents with internal systems through Python, REST APIs, or server hooks.
- Teams building workflows that need multiple agents, human review, and preserved conversation context.
How it fits your workflow
Install the Search2o package and build, validate, and publish agents. The page says agents can be described in English and that the server supplies runtime infrastructure such as LLM calls, tool calling, and error handling.
A client discovers an agent through search and executes it. Agents can pause for a human response, preserve state and conversation context, and continue or pass work to another agent.
Use the bundled browser chat UI or build a client; the page also lists Slack, Teams, and Google Chat integrations. Connect other systems through Python expressions, the REST API, or server hooks.
Pricing
The vendor doesn't publish prices. Check with Search2o Simplifies AI Agents directly.
Prices checked on Oct 28, 2026 from the vendor's site. They can change; confirm before you buy.
Key features
- Agent framework and publishing
- Build, validate, and publish agents, including agents described in English. Agent versioning and multi-developer publishing are included.
- Agent discovery and multi-agent conversations
- Agents can be discovered through search and build on one another in a conversation, using relevant saved prompts and conversation context.
- Managed agent runtime
- The agent server handles LLM calls, tool-calling loops, API, database and MCP calls, connection pools, secrets, error handling, and runtime limits.
- Human-in-the-loop state
- Agent state can be stored while awaiting a user response, then continued when the user answers. Conversations can be saved and restored.
- Conversation memory and prompts
- The platform supports memory and externalized prompts, with conversation context available to agents.
- Multiple interfaces
- Use the bundled browser chat UI or build a client. The page also describes chat integrations for Slack, Teams, and Google Chat.
Works with
- Slack
- Microsoft Teams
- Google Chat
- MCP
- REST API
- Python
Alternatives to consider
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Sim
Choose it if you want a visual and code-based team workspace for building and managing agents with a stated catalog of 1,000+ integrations and support for major LLMs.
Getting started
pip install search2o- Install the package using the listed pip command.
- Build, validate, and publish an agent; the page says agents can be described in English.
- Use a client to discover and execute agents, or connect through the bundled browser chat UI.
- Integrate with systems through Python expressions, the REST API, or server hooks.