Welcome to TameFlare

A transparent proxy firewall for AI agents. Intercept all outbound HTTP traffic, enforce per-agent permissions, require human approval for risky actions, and keep an audit trail of everything your agents do. Zero code changes required.

What is TameFlare?

AI agents are increasingly autonomous. They can create pull requests, send emails, transfer money, and provision infrastructure - all without a human in the loop. That's powerful, but it's also risky.

TameFlare sits between your agents and the tools they use. Every action an agent wants to take is sent to TameFlare first. TameFlare evaluates the action against your policies and returns one of three decisions:

Policies are configured in the dashboard via a 4-step builder and stored as JSON. They can match on action type, target environment, parameters, risk level, time of day, and more. TameFlare uses a deny-wins model: if any policy says deny, the action is denied regardless of other policies.

How it works

# 1. Sign up at tameflare.com/register

# 2. Create a gateway in the dashboard (Gateways → Create gateway)

# 3. Run your agent through the proxy
tf run -- python my_agent.py

# All outbound HTTP is now proxied:
# domain → connector → parse action → check permission
# allowed:          inject credentials → forward
# denied:           return 403
# require_approval: hold connection → wait for human

The cloud gateway at proxy.tameflare.com is a transparent HTTP/HTTPS proxy. The CLI sets HTTPS_PROXY and spawns your process. All outbound traffic is routed through the gateway, which parses requests via connectors (GitHub, OpenAI, Anthropic, Stripe, Slack, MCP, Webhook, or any HTTP API), checks per-gateway permissions, and injects credentials from an encrypted vault. The agent never sees real API keys.

The dashboard at tameflare.com is the control plane. Configure gateways, manage connectors and permissions, view traffic logs, and approve requests. The gateway fetches its config from the dashboard on startup and polls for updates every 60 seconds.

What can you control?

TameFlare's policy engine is generic. It evaluates fields like action_spec.type and parameters.* against conditions you define. This means you can write policies for any domain:

See the Writing Policies page for real-world examples across all these domains.

Key capabilities

Choose your path

Different roles need different information. Pick the path that matches your goal:

Documentation