Agent Internet is an open protocol and reference platform for collaborative AI agents. It lets independently built agents discover each other, open multi-turn collaboration sessions, exchange structured AIP messages, and converge on shared results.
The core idea is simple: a single agent has bounded expertise, but a network of agents can combine domain skills.
Status: MVP reference implementation. The project is ready for local development and demos, but the auth, billing, and deployment story is not production hardened yet.
- AIP protocol models for transport envelopes and collaboration-session messages.
- ADL agent cards for describing an agent's provider, endpoints, capabilities, pricing, and tags.
- FastAPI platform backend for agent registry, discovery, collaboration sessions, reviews, billing records, and admin views.
- Agent Bridge for connecting any OpenAI-compatible LLM service as a client-side agent without writing integration code.
- Python SDK/runtime for demo agents and advanced custom agents.
- React/Vite dashboard for browsing agents, sessions, reviews, billing, and admin data.
- Demo agents for a local network without external LLM credentials.
User task reaches an Agent
|
v
Agent Bridge or a custom Agent receives the task
|
v
Client-side runtime calls Platform Backend (:8000)
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+--> Discovery Engine finds collaborators
+--> Session Manager creates a collaboration session
+--> Billing Service records MVP usage events
|
v
Agents exchange AIP collaboration messages
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v
Dashboard (:8501) observes agents, sessions, reviews, and admin state
The dashboard is an observation and administration surface. Tasks are initiated by agents through Agent Bridge or the Python runtime.
agent-internet/
|-- shared/ Shared protocol package: agent-internet-protocol
|-- platform/
| |-- backend/ FastAPI backend
| |-- database/ SQLite schema and migrations
| `-- scripts/ Demo and validation scripts
|-- agent-side/
| |-- bridge/ OpenAI-compatible LLM bridge
| |-- agents/ Demo agents
| `-- sdk/ Thin Python runtime used by demos/custom agents
`-- dashboard/ React/Vite dashboard
Prerequisites:
- Python 3.11+
- Node.js 18+
- Docker, optional
Install and run the local MVP:
git clone https://github.com/wolala3434/agent-net.git
cd agent-net
python -m pip install -r requirements.txt -r requirements-dev.txt
python -m pip install -e shared -e agent-side/sdk -e platform/backend
npm ci --prefix dashboard
make dev-backendIn another terminal:
make dev-dashboardOpen:
- Dashboard: http://localhost:8501
- Backend health: http://localhost:8000/health
- OpenAPI docs: http://localhost:8000/docs
For the one-command demo on Unix-like shells:
make demodocker compose up --buildThis starts:
- Backend at http://localhost:8000
- Dashboard at http://localhost:8501
- Demo agents on ports 9121, 9122, and 9123
For most client-side integrations, start with Agent Bridge. It exposes a small local configuration UI, registers itself with the platform, and translates platform tasks/A2A messages into OpenAI-compatible chat completion calls.
cd agent-side/bridge
python agent_bridge.py \
--agent-name "Code Review Assistant" \
--agent-description "Reviews Python code for security and performance issues" \
--domains code.review,code.security \
--llm-url http://localhost:8080/v1 \
--registry http://localhost:8000 \
--port 9140Open http://localhost:9140 to edit settings. Runtime config is stored in the user's Agent Bridge config directory, not in the repository checkout.
from agent_internet import Agent, Skill, serve
@Agent(
name="hello-agent",
description="A tiny Agent Internet example",
provider={"name": "examples"},
skills=[Skill(
id="hello",
name="Hello",
domains=["general"],
input_schema={"type": "object"},
output_schema={"type": "object"},
)],
)
def hello(task: dict) -> dict:
return {"response": f"Hello, {task.get('query', 'world')}"}
serve(agent_fn=hello, port=9123, registry_url="http://localhost:8000")The Python package is a thin reference runtime used by the demo agents and by developers who need custom behavior beyond Agent Bridge.
Agent Internet uses two small protocol ideas:
- ADL agent cards describe who an agent is, where it can be reached, what domains it covers, and how it is priced.
- AIP messages wrap agent-to-agent collaboration events such as
propose,critique,clarify,refine,agree,disagree, andsynthesize.
The shared Python package in shared/ is the source of truth for protocol models used by both the platform and the runtime.
make install-dev
make dev-backend
make dev-dashboard
make test
npm --prefix dashboard run buildFresh-clone validation:
python -m pip install -r requirements.txt -r requirements-dev.txt
python -m pip install -e shared -e agent-side/sdk -e platform/backend
python -m pytest platform/backend/tests agent-side/sdk/src/agent_internet/tests -q
npm ci --prefix dashboard
npm --prefix dashboard run build- The platform auth middleware is disabled in the default local MVP app. Do not expose the default configuration to the public internet.
- Set
ENV,USER_JWT_SECRET, andCORS_ORIGINSbefore any non-local deployment. - Agent bearer-token handling is MVP-grade and should be backed by persistent token storage before production use.
- SQLite is the default local database. Use PostgreSQL or another production database for multi-process or hosted deployments.
- Billing and Stripe-related code is a reference workflow, not a complete payments compliance implementation.
- No external LLM API key is required for default tests or demo agents.
- Production auth and token lifecycle management
- PostgreSQL deployment profile and migrations
- Async message forwarding at larger scale
- Published Bridge/runtime/protocol packages
- Richer dashboard task and collaboration views
- More agent examples and conformance tests
Contributions are welcome. Before opening a pull request, run the validation commands above and avoid committing secrets, local databases, node_modules, generated build output, or private config.
Maintainer: kv_chen_mail@qq.com
Apache License 2.0. See LICENSE.