Pyttechat

Minimal Go scaffold for a server-rendered LLM chat web app. The app is intended to act as a Backend for Frontend between a browser chat UI and an enterprise LLM proxy API.

Status: early backend/web POC. The repository currently has a streaming pyttechat CLI, a minimal server-rendered web chat UI, a dummy LLM implementation, and an OpenResponses-compatible proxy client.

Quick Start

go test ./...
make test
make build

The Makefile keeps Go and pre-commit caches under .cache/ so normal project checks do not need to write to user-level cache directories. For a bare go test ./... in restricted shells, export the same cache locations first:

export GOCACHE="$PWD/.cache/go-build"
export GOMODCACHE="$PWD/.cache/go-mod"
export GOTMPDIR="$PWD/.cache/tmp"
mkdir -p "$GOCACHE" "$GOMODCACHE" "$GOTMPDIR"
go test ./...

Build the CLI and inspect build information:

make build
./bin/pyttechat version

Send a prompt to the dummy LLM:

./bin/pyttechat ask "hello"

Start a single ephemeral multi-turn chat session. Each input line is sent as the next user turn:

printf 'hello\nagain\n' | ./bin/pyttechat chat

Start the web chat POC:

./bin/pyttechat serve --addr :3000

Then open http://localhost:3000. Browser sessions are in memory and reset when the process restarts. A submitted turn starts on the server first, and the browser subscribes to that turn with EventSource, so closing the stream subscription does not cancel the upstream request. Use the Stop button to cancel an active turn.

For local browser checks, the Makefile can run the web server in the background and stop it by PID:

make serve-start
make serve-status
make serve-stop

Override the default address with WEB_ADDR=127.0.0.1:3017 make serve-start.

To test browser streaming and the Stop button against the local fake OpenResponses provider, run both services together:

make serve-fake-start
make serve-fake-stop

The fake provider streams reasoning first, then answer text, with FAKE_RESPONSES_STREAM_DELAY=150ms by default.

Use an OpenResponses-compatible LLM proxy:

PYTTECHAT_LLM_PROXY_URL=http://localhost:8080 \
PYTTECHAT_LLM_PROXY_TOKEN=token-value \
  ./bin/pyttechat --model gpt-example chat

PYTTECHAT_LLM_PROXY_URL=http://localhost:8080 \
PYTTECHAT_LLM_PROXY_TOKEN=token-value \
  ./bin/pyttechat --model gpt-example serve

Set --reasoning-effort when you want to request model reasoning options. Assistant answer text streams to stdout. Reasoning events, when returned, stream separately to stderr. Proxy requests default to a 5-minute timeout; override it with --proxy-timeout or PYTTECHAT_LLM_PROXY_TIMEOUT.

OpenTelemetry

Telemetry is disabled by default. If no OTLP endpoint is configured, pyttechat and fake-responses run without an OpenTelemetry Collector and use no external telemetry network calls.

To enable traces and metrics locally, run an OpenTelemetry Collector with the provided development config:

otelcol --config docs/otel-collector.yaml

Then start the app with an OTLP/gRPC endpoint:

OTEL_EXPORTER_OTLP_ENDPOINT=http://localhost:4317 \
OTEL_SERVICE_NAME=pyttechat \
  ./bin/pyttechat serve --addr :3000

Supported telemetry environment variables:

• OTEL_SERVICE_NAME: service name, default pyttechat.
• OTEL_EXPORTER_OTLP_ENDPOINT: shared OTLP endpoint for traces and metrics.
• OTEL_EXPORTER_OTLP_TRACES_ENDPOINT: traces-only OTLP endpoint.
• OTEL_EXPORTER_OTLP_METRICS_ENDPOINT: metrics-only OTLP endpoint.
• OTEL_RESOURCE_ATTRIBUTES: additional resource attributes, for example deployment.environment=local.
• OTEL_SDK_DISABLED=true: force telemetry off.

Emitted signals:

• HTTP server spans and HTTP client spans, including trace context propagation to the LLM proxy.
• Manual spans for CLI commands, chat turn start, SSE streaming, abort/cancel, OpenResponses proxy requests, stream consumption, and fake provider streaming.
• Counters for chat turns started/completed/cancelled/failed and streamed LLM/SSE events.
• Histograms for upstream LLM request duration and chat turn duration.

Do not attach prompt text, user messages, OAuth tokens, API keys, bearer tokens, cookies, session IDs, user IDs, turn IDs, model output, or raw LLM responses to telemetry.

Fake OpenResponses Provider

The repo includes a deterministic fake OpenAI-compatible Responses API provider for tests and local development. Prefer internal/llm/openresponses/fakeprovider.NewHandler() in Go tests instead of hand-written happy-path SSE stubs. Keep custom httptest handlers for malformed streams, auth assertions, and narrow parser edge cases.

Run the local harness manually:

go run ./cmd/fake-responses --addr :8080

Verify the streaming endpoint:

curl -N http://localhost:8080/v1/responses \
  -H 'Content-Type: application/json' \
  -d '{"model":"dummy-responses","input":"hello world","stream":true}'

Expected: an SSE stream with typed response lifecycle events, text deltas, response.completed, and final data: [DONE].

Pre-Commit

Install the hooks after cloning:

pre-commit install

Run the same hooks manually:

make pre-commit

The pre-commit checks are intentionally fast: formatting, module tidiness, fast linting, and unit tests.

Local CI

Run the commit-gating checks locally:

make all-tests

Run the broader PR-quality checks locally:

make ci

The Makefile installs pinned Go tools into .bin/ when needed.

make coverage enforces 95.0% Go statement coverage. Container-level smoke coverage is kept separate from all-tests/ci because it requires Docker:

make container-smoke

The CI workflow in .github/workflows/ci.yml runs make ci, and the smoke workflow in .github/workflows/smoke.yml runs container smoke as its own CI job.

Reference Skills

Initialize the read-only reference implementations after cloning:

git submodule update --init --depth 1 --recursive

Reference repositories live under .agent/skills/references/*/repo and are accessed through their concrete skills and .agent/skills/references/registry.yaml. See AGENTS.md before using the references.