Identity & Memory side‑car for every LLM engine and multi‑agent framework. Add OIDC / DID SSO, A2A hand‑off, and a pluggable memory bus (Weaviate today) – all with one process.
LLM engines such as Ollama or vLLM ship with zero auth. Agent‑to‑agent protocols (Google A2A, MCP, OpenHands) assume a Bearer token is already present but don't tell you how to issue or validate it. Teams end up wiring ad‑hoc reverse proxies, leaking ports, and copy‑pasting JWT code.
Attach Gateway is that missing resource‑server:
- ✅ Verifies OIDC / JWT or DID‑JWT
- ✅ Stamps
X‑Attach‑User
+X‑Attach‑Session
headers so every downstream agent/tool sees the same identity - ✅ Implements
/a2a/tasks/send
+/tasks/status
for Google A2A & OpenHands hand‑off - ✅ Mirrors prompts & responses to a memory backend (Weaviate Docker container by default)
- ✅ Workflow traces (Temporal)
Run it next to any model server and get secure, shareable context in under 1 minute.
# 0) prerequisites: Python 3.12, Ollama installed, Auth0 account or DID token
# Install the package
pip install attach-dev
# 1) start memory in Docker (background tab)
# Mac M1/M2 users: use manual Docker command (see examples/README.md)
docker run --rm -d -p 6666:8080 \
-e AUTHENTICATION_ANONYMOUS_ACCESS_ENABLED=true \
semitechnologies/weaviate:1.30.5
# 2) export your short‑lived token
export JWT="<paste Auth0 or DID token>"
export OIDC_ISSUER=https://YOUR_DOMAIN.auth0.com
export OIDC_AUD=ollama-local
export MEM_BACKEND=weaviate
export WEAVIATE_URL=http://127.0.0.1:6666
# 3) run gateway
attach-gateway --port 8080 &
# 4) make a protected Ollama call via the gateway
curl -H "Authorization: Bearer $JWT" \
-d '{"model":"tinyllama","messages":[{"role":"user","content":"hello"}]}' \
http://localhost:8080/api/chat | jq .
# 0) prerequisites: Python 3.12, Ollama installed, Auth0 account or DID token
git clone https://github.com/attach-dev/attach-gateway.git && cd attach-gateway
python -m venv .venv && source .venv/bin/activate
pip install -r requirements.txt
# 1) start memory in Docker (background tab)
python script/start_weaviate.py &
# 2) export your short‑lived token
export JWT="<paste Auth0 or DID token>"
export OIDC_ISSUER=https://YOUR_DOMAIN.auth0.com
export OIDC_AUD=ollama-local
export MEM_BACKEND=weaviate
export WEAVIATE_URL=http://127.0.0.1:6666
# 3) run gateway
uvicorn main:app --port 8080 &
# The gateway exposes your Auth0 credentials for the demo UI at
# `/auth/config`. The values are read from `AUTH0_DOMAIN`,
# `AUTH0_CLIENT` and `OIDC_AUD`.
# 4) make a protected Ollama call via the gateway
curl -H "Authorization: Bearer $JWT" \
-d '{"model":"tinyllama","messages":[{"role":"user","content":"hello"}]}' \
http://localhost:8080/api/chat | jq .
In another terminal, try the Temporal demo:
pip install temporalio # optional workflow engine
python examples/temporal_adapter/worker.py &
python examples/temporal_adapter/client.py
You should see a JSON response plus X‑ATTACH‑Session‑Id
header – proof the pipeline works.
- Copy
.env.example
→.env
and fill in OIDC + backend URLs pip install attach-dev python-dotenv
attach-gateway
(reads .env automatically)
→ See docs/configuration.md for framework integration and examples/ for code samples.
flowchart TD
%%────────────────────────────────
%% COMPONENTS
%%────────────────────────────────
subgraph Front-end
UI["Browser<br/> demo.html"]
end
subgraph Gateway
GW["Attach Gateway<br/> (OIDC SSO + A2A)"]
end
subgraph Agents
PL["Planner Agent<br/>FastAPI :8100"]
CD["Coder Agent<br/>FastAPI :8101"]
end
subgraph Memory
WV["Weaviate (Docker)\nclass MemoryEvent"]
end
subgraph Engine
OL["Ollama / vLLM<br/>:11434"]
end
%%────────────────────────────────
%% USER FLOW
%%────────────────────────────────
UI -- ① POST /a2a/tasks/send<br/>Bearer JWT, prompt --> GW
%%─ Planner hop
GW -- ② Proxy → planner<br/>(X-Attach-User, Session) --> PL
PL -- ③ Write plan doc --> WV
PL -- ④ /a2a/tasks/send\nbody:{mem_id} --> GW
%%─ Coder hop
GW -- ⑤ Proxy → coder --> CD
CD -- ⑥ GET plan by mem_id --> WV
CD -- ⑦ POST /api/chat\nprompt(plan) --> GW
GW -- ⑧ Proxy → Ollama --> OL
OL -- ⑨ JSON response --> GW
GW -- ⑩ Write response to Weaviate --> WV
GW -- ⑪ /a2a/tasks/status = done --> UI
Key headers
Header | Meaning |
---|---|
Authorization: Bearer <JWT> |
OIDC or DID token proved by gateway |
X‑Attach‑User |
stable user ID (`auth0 |
X‑Attach‑Session |
deterministic hash (user + UA) for request trace |
# pane 1 – memory (Docker)
python script/start_weaviate.py
# pane 2 – gateway
uvicorn main:app --port 8080
# pane 3 – planner agent
uvicorn examples.agents.planner:app --port 8100
# pane 4 – coder agent
uvicorn examples.agents.coder:app --port 8101
# pane 5 – static chat UI
cd examples/static && python -m http.server 9000
open http://localhost:9000/demo.html
Type a request like "Write Python to sort a list." The browser shows:
- Planner message → logged in gateway, plan row appears in memory.
- Coder reply → code response, second memory row, status
done
.
Path | Purpose |
---|---|
auth/ |
OIDC & DID‑JWT verifiers |
middleware/ |
JWT middleware, session header, mirror trigger |
a2a/ |
/tasks/send & /tasks/status routes |
mem/ |
pluggable memory writers (weaviate.py , sakana.py ) |
proxy/ |
Engine-agnostic HTTP proxy logic |
examples/agents/ |
examples – Planner & Coder FastAPI services |
examples/static/ |
demo.html chat page |
auth.verify_jwt()
accepts three token formats and routes them automatically:
- Standard OIDC JWTs
did:key
tokensdid:pkh
tokens
Example DID-JWT request:
curl -X POST /v1/resource \
-H "Authorization: Bearer did:key:z6Mki...<sig>.<payload>.<sig>"
Example OIDC JWT request:
curl -X POST /v1/resource \
-H "Authorization: Bearer $JWT"
To use your OIDC provider, set the following environment variables:
OIDC_ISSUER=<your-oidc-issuer>
OIDC_AUD=<your-oidc-audience>
AUTH_BACKEND=<your-authentication-provider>
Descope-specific environment variables:
DESCOPE_BASE_URL=https://api.descope.com or <your-descope-base-url>
DESCOPE_PROJECT_ID=<your-descope-project-id>
DESCOPE_CLIENT_ID=<your-descope-inbound-app-client-id>
DESCOPE_CLIENT_SECRET=<your-descope-inbound-app-client-secret>
Send Sakana-formatted logs to the gateway and they will be stored as
MemoryEvent
objects in Weaviate.
curl -X POST /v1/logs \
-H "Authorization: Bearer $JWT" \
-d '{"run_id":"abc","level":"info","message":"hi"}'
# => HTTP/1.1 202 Accepted
Emit token usage metrics for every request. Choose a backend via
USAGE_METERING
(alias USAGE_BACKEND
):
export USAGE_METERING=prometheus # or null
A Prometheus counter attach_usage_tokens_total{user,direction,model}
is
exposed for Grafana dashboards.
Set USAGE_METERING=null
(the default) to disable metering entirely.
⚠️ Usage hooks depend on the quota middleware.
Make sureMAX_TOKENS_PER_MIN
is set (any positive number) so the
TokenQuotaMiddleware
is enabled; the middleware is what records usage
events that feed Prometheus.
# Enable usage tracking (set any reasonable limit)
export MAX_TOKENS_PER_MIN=60000
export USAGE_METERING=prometheus
# No additional dependencies needed - uses direct HTTP API
export MAX_TOKENS_PER_MIN=60000 # Required: enables quota middleware
export USAGE_METERING=openmeter # Required: activates OpenMeter backend
export OPENMETER_API_KEY=your-api-key-here # Required: API authentication
export OPENMETER_URL=https://openmeter.cloud # Optional: defaults to https://openmeter.cloud
Events are sent directly to OpenMeter's HTTP API and are processed by the LLM tokens meter for billing integration with Stripe.
⚠️ All three variables are required for OpenMeter to work:
MAX_TOKENS_PER_MIN
enables the quota middleware that records usage eventsUSAGE_METERING=openmeter
activates the OpenMeter backendOPENMETER_API_KEY
provides authentication to OpenMeter's API
The gateway gracefully falls back to NullUsageBackend
if any required variable is missing.
curl -H "Authorization: Bearer $JWT" http://localhost:8080/metrics
Attach Gateway can enforce per-user token limits. Install the optional
dependency with pip install attach-dev[quota]
and set
MAX_TOKENS_PER_MIN
in your environment to enable the middleware. The
counter defaults to the cl100k_base
encoding; override with
QUOTA_ENCODING
if your model uses a different tokenizer. The default
in-memory store works in a single process and is not shared between
workers—requests retried across processes may be double-counted. Use Redis
for production deployments.
If tiktoken
is missing, a byte-count fallback is used which counts about
four times more tokens than the cl100k
tokenizer – install tiktoken
in
production.
# Optional: Enable token quotas
export MAX_TOKENS_PER_MIN=60000
pip install tiktoken # or pip install attach-dev[quota]
To customize the tokenizer:
export QUOTA_ENCODING=cl100k_base # default
MIT