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Phren — Governed Terminals for AI Agents

Phren compiles any website into a terminal — a governed, navigable layer between AI agents and web APIs. Agents navigate terminals via MCP to complete tasks like purchasing, booking, or requesting documents.

Why not just give agents the API?

┌──────────────────────────────────────────────┐
│              MCP (protocol)                  │
│  How the agent talks to tools                │
│                                              │
│  ┌────────────────────────────────────────┐  │
│  │         TERMINAL (governance)          │  │
│  │  What the agent is allowed to do       │  │
│  │  Where it should go next               │  │
│  │  What it already did                   │  │
│  │                                        │  │
│  │  ┌──────────────────────────────────┐  │  │
│  │  │         API (execution)          │  │  │
│  │  │  The actual HTTP calls           │  │  │
│  │  └──────────────────────────────────┘  │  │
│  └────────────────────────────────────────┘  │
└──────────────────────────────────────────────┘

MCP is the WIRE.  The API is the DESTINATION.
The terminal is the RULES + MAP + MEMORY in between.

Raw API

MCP Tools

Browser Agent

Phren Terminal

Governance

None

None

None

Trust tiers, spend limits, rate limits

Flow guidance

None

None

None

Roles, hints, path trace

Min model size

N/A (code)

~7B

~70B (vision)

0.8B

Speed

Instant

~1s/call

5-30s/action

0.3-1s/action

Cost per task

¢

¢

$$$

¢

Terminal vs Browser Agent

Related MCP server: ai-agent-layer

Agent eval results

Terminal design, not model size, determines agent success. The same models that score 0% without terminal guidance score 100% with it — the improvement comes entirely from the terminal, not the model.

Design Iteration Impact

Six models from 0.8B to 8B, 126/126 on linear flows across three domains (e-commerce, hospitality, government). All tasks follow the same shape (login → search → act), tested with 3 trials per model. Branching, error recovery, and adversarial paths remain untested.

Model

Params

Family

pass@1

Avg Steps

Avg Invalid

Avg Time

qwen3.5:0.8b

0.8B

Alibaba

21/21

10.1

3.5

41s

lfm2.5-thinking

1.2B

Liquid

21/21

10.1

3.3

60s

llama3.2:3b

3B

Meta

21/21

10.4

3.1

4.4s

granite4.1:3b

3B

IBM

21/21

10.4

1.8

6.9s

qwen3:4b

4B

Alibaba

21/21

8.1

2.6

175s

qwen3:8b

8B

Alibaba

21/21

7.1

1.8

266s

Speed per Task

Steps Breakdown

Key finding: terminal design — not model size — determines agent success. The same models that scored 0% without the terminal's path trace now score 100%.

Design Iteration Impact

What the terminal does for agents

1. Flow map with roles. Actions are marked as primary (move forward), secondary (optional), or navigation (go back). Each screen carries a flow hint from the contract.

2. Path trace. The agent sees where it's been and what it found:

PATH SO FAR:
  ✅ [0] login → got auth token
  ✅ [1] search → found 3: Budget Laptop ($399) [id:prod_004]
  ✅ [2] view_product → name=Budget Laptop

SUGGESTED NEXT: add_to_cart (needs: product_id, quantity)

3. Governance enforcement. Every contract field binds at runtime: trust tiers, spend limits, rate limits, confirmation gates. The terminal checks all constraints before proxying any action to the real API.

How it works

your-website/api/main.py
        │
        ▼  source parser (10 frameworks)
discovered routes
        │
        ▼  terminal compiler
contract.json (governance + flow map + tools)
        │
        ▼  terminal engine
MCP server (JSON-RPC, stdio + HTTP)
        │
        ▼  agent connects, navigates, completes tasks

Install

pip install -e ".[all]"
pytest evals/ conformance/ -v        # 183 tests

CLI

phren info <contract.json>          # Show contract summary: screens, actions, flow hints, limits
phren verify <contract.json>        # Validate a contract (fail-open → error)
phren compile <path> -o out.json    # Compile website source into a contract
phren serve <contract.json>         # Start MCP terminal server (HTTP or stdio)
phren eval <contract.json>          # Run agent eval against a simulation
phren demo                          # One-command demo: start simulation, connect, browse
# Quick start
phren demo
phren info simulations/shopping/contract.json
phren compile simulations/shopping/api -o /tmp/test.json --site-name "My Shop"

Run agent evals

ollama pull llama3.2:3b
python3 -m evals.agent_eval --task all --model llama3.2:3b --trials 3 -v

Docker

# Start simulation + Phren terminal server
docker compose up

# Simulation on :8080, Phren MCP on :8000
curl http://localhost:8000/mcp

See docs/quickstart.md for the full getting-started guide.

Trust model

Trust is derived from Ed25519-signed credentials, never self-declared. The terminal verifies the signature, checks expiration, validates the audience, rejects replayed tokens, and caps the trust tier at the operator's registered maximum.

Supported frameworks

FastAPI, Rails, Go (Chi/Echo), NestJS, Next.js (files/App Router/pages), tRPC, PHP/Laravel. 13 GitHub repos tested: 2,203 routes, 100% precision.

Positioning

Stripe ACP / Google UCP handle the payment rail. Phren handles what the agent is permitted to do, proves what it did, and guides it through the flow. They are complementary.

Known limitations

Results cover linear flows (login → search → act) on 3 simulations mimicking real website characteristics. Not yet tested on more dense and complex websites. Tested simulations:

Simulation

Domain

Characteristics

Shopping

E-commerce

Auth, search, cart state, checkout flow, order confirmation

Booking

Hospitality

Date-range queries, room availability, reservation lifecycle

Library

Government

Document catalog, department taxonomy, service request submission

License

Apache-2.0. Open core never imports the commercial layer.

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Maintenance

ActivityMaintained
ResponsivenessNo issues

Resources

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