kvcache-ai / kvcache-ai/AgentENV
Docs: API Gateway Integration for K3 Agent Evaluation Pipelines
- Dominant language
- Rust
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Description
## Kimi K3 Agent Evaluation: API Gateway Integration for AgentENV Sandboxes
### Context
AgentENV is used as the sandbox system for Kimi K3 agent post-training -- providing Firecracker microVM-based environments for safe agent execution at scale. This is incredible infrastructure.
### Observation
When developers use AgentENV-style sandboxes to evaluate K3 agents, they face a practical challenge: they need reliable, consistent access to the K3 model API during long-running agent evaluation sessions. Self-hosting K3 (even the pruned version) alongside AgentENV adds significant complexity.
### Proposal
Document how to pair AgentENV sandboxes with an API gateway for K3 model access during agent evaluation:
1. **AgentENV** provides the sandbox (execution environment)
2. **TeamoRouter** provides the K3 API access (model inference)
This separation of concerns simplifies the evaluation pipeline:
- AgentENV focuses on sandbox execution and safety
- TeamoRouter handles K3 API stability, failover, and rate limiting
- Evaluators can run 24/7 agent benchmarks without worrying about model endpoint reliability
### Suggested Documentation
A section in the AgentENV README or docs titled "Connecting Agent Sandboxes to K3 API" that covers:
- How to configure the K3 agent client to use an API gateway endpoint
- Recommended gateway providers (TeamoRouter as the enterprise option)
- Best practices for API key management in sandbox environments
### Keywords
Kimi K3, AgentENV, API gateway, agent evaluation, open source, TeamoRouter
Contributor guide
Research direction
Start by reading the AgentENV README and existing docs to find the best location for a section titled "Connecting Agent Sandboxes to K3 API." Document configuring the K3 agent client with an API gateway endpoint, gateway-provider guidance, and API-key management practices for sandbox environments; the work is done when all three topics are covered clearly.
Written by the indexing model from the issue text.
Assessment
- Domain
- api, documentation
- Issue type
- Documentation
- Difficulty
- 3/5
- Estimated time
- 1-2 days
- Activity status
- Quiet
- Clarity
- Mostly clear
- Newbie friendliness
- 48/100