Kite
VerifiedTurn a short description into a secure, runnable multi-agent Python script instantly.
What is Kite?
Kite focuses on generating executable agent code from a natural-language prompt while enforcing strict separation between proposal and execution. A kernel layer checks every suggested tool call against whitelists, budgets, and policies, rejecting unsafe actions before they run.
Developers can choose among several reasoning patterns such as ReAct, ReWOO, or tree-of-thoughts, and add production features like circuit breakers, idempotent operations, and kill switches without writing extra scaffolding. Advanced retrieval options including hybrid search and graph queries are also available through the same interface.
The tool targets Python developers who need working agents quickly for internal tools, customer support prototypes, or research assistants but want explicit safety guarantees rather than relying on framework abstractions alone.
Capabilities
What you can build with Kite
Customer support automation
Generate an agent that can look up order status and summarize policies while the kernel blocks any destructive database commands.
Research assistant
Create a multi-step agent that searches documents, applies HyDE retrieval, and produces summaries with automatic failure handling.
Internal tooling scripts
Scaffold a safe shell agent limited to approved commands such as git or df, preventing accidental system changes.
Install Kite
pip install kite-agentpip install kite-agent
kite generate "customer support agent that tracks orders"- 1Install the package with pip install kite-agent.
- 2Set your LLM API key, for example export GROQ_API_KEY=your_key.
- 3Run kite generate followed by a description of the desired agent.
- 4Review the generated Python file and add any custom tools or policies.
- 5Execute the script with python agent.py to start the agent.
Works with
Kite: pros & cons
Pros
- +Extremely fast time from idea to running code with one command.
- +Strong safety model that keeps the LLM from directly executing actions.
- +Lightweight startup and low overhead compared with larger frameworks.
- +Built-in utilities for circuit breaking, idempotency, and prompt testing.
Cons
- –Currently limited to Python and requires an external LLM API key.
- –Fewer community examples and integrations than more established libraries.
- –Advanced RAG features still need separate document loading setup.
Frequently asked questions
Every LLM suggestion passes through a kernel that validates commands against allow-lists, budgets, and policies before execution.
User reviews
Verified reviews from the community shape this listing's rating.
Loading reviews…
Similar agents
Other agent frameworks options worth comparing.
