docs: align Getting Started and examples with Google API key requirement (#56)

Signed-off-by: Aleksandr Suvorov <asuvorov@hensu.io>

Author: alxsuv

README.md

@@ -58,8 +58,11 @@ working-dir/
 
 ### 3. Set an API key
 
+The bundled example workflows use Google Gemini models. You can obtain a free
+`GOOGLE_API_KEY` from [Google AI Studio](https://aistudio.google.com/app/apikey).
+
 ```bash
-hensu credentials set ANTHROPIC_API_KEY   # or OPENAI_API_KEY / GEMINI_API_KEY
+hensu credentials set GOOGLE_API_KEY
 ```
 
 ### 4. Run a workflow
@@ -78,6 +81,9 @@ Pre-built binaries are available for **Linux x86_64**. On macOS or Windows,
 curl -L https://github.com/hensu-project/hensu/releases/download/server/v0.1.0-beta.1/hensu-server-linux-x86_64 \
   -o hensu-server-v0.1.0-beta.1 && chmod +x hensu-server-v0.1.0-beta.1
 
+# The server needs provider API keys as environment variables
+export GOOGLE_API_KEY=<your-key>
+
 # Start in in-memory mode (no database, no JWT)
 QUARKUS_PROFILE=inmem ./hensu-server-v0.1.0-beta.1
 
@@ -102,8 +108,8 @@ approves or sends it back for revision.
 ```kotlin
 fun contentPipeline() = workflow("content-pipeline") {
     agents {
-        agent("writer")   { role = "Content Writer";   model = Models.CLAUDE_SONNET_4_5 }
-        agent("reviewer") { role = "Content Reviewer"; model = Models.GPT_4O }
+        agent("writer")   { role = "Content Writer";   model = Models.GEMINI_3_1_FLASH_LITE }
+        agent("reviewer") { role = "Content Reviewer"; model = Models.GEMINI_3_1_PRO }
     }
 
     rubrics { rubric("content-quality", "content-quality.md") }
@@ -291,7 +297,7 @@ MCP split-pipe connectivity, stub tool implementations, SSE event streaming, and
 human-in-the-loop review gate.
 
 **Scenario:** a credit risk analyst agent evaluates a fictional credit-limit increase for
-customer `C-42`.
+customer `C-42`. Complete the [Getting Started](#getting-started) steps before running this example.
 
 ```bash
 QUARKUS_PROFILE=inmem ./hensu-server-v0.1.0-beta.1
@@ -312,6 +318,7 @@ Hensu is in **pre-beta**, working toward beta stability.
 - Pre-built server binaries are Linux x86_64 only. macOS and ARM targets are planned.
 - No observability integration yet (OpenTelemetry, metrics export).
 - No native image integration tests yet. The native build is verified by CI, but test coverage runs in JVM mode only.
+- OpenAI and DeepSeek models are defined in the DSL but have not been tested end-to-end. Only Anthropic and Google Gemini models are verified.
 - APIs and DSL surface may change before beta.
 
 ---

assets/logo.png

@@ -1218,8 +1218,6 @@ HensuFactory.builder()
 | `OPENAI_API_KEY`     | OpenAI GPT       |
 | `GOOGLE_API_KEY`     | Google Gemini    |
 | `DEEPSEEK_API_KEY`   | DeepSeek         |
-| `OPENROUTER_API_KEY` | OpenRouter       |
-| `AZURE_OPENAI_KEY`   | Azure OpenAI     |
 
 Environment variables matching `*_API_KEY`, `*_KEY`, `*_SECRET`, or `*_TOKEN` patterns are auto-discovered.
 

docs/developer-guide-core.md

@@ -991,7 +991,6 @@ fun contentPipeline() = workflow("ContentPipeline") {
             agent = "researcher"
             prompt = """
                 Research {topic} and provide key facts.
-                Output as JSON: {"fact1": "...", "fact2": "...", "fact3": "..."}
             """.trimIndent()
 
             writes("fact1", "fact2", "fact3")

docs/dsl-reference.md

@@ -29,7 +29,7 @@ fun myWorkflow() = workflow("ContentPipeline") {
         }
         agent("reviewer") {
             role = "Quality Reviewer"
-            model = Models.GPT_4O
+            model = Models.GEMINI_3_1_PRO
         }
     }
 

hensu-dsl/README.md

@@ -44,7 +44,10 @@ flowchart LR
 
 ### 1 — Start hensu-server in inmem profile
 
+The workflow uses Gemini models — export your API key before starting the server:
+
 ```bash
+export GOOGLE_API_KEY=<your-key>
 ./gradlew hensu-server:quarkusDev -Dquarkus.profile=inmem
 ```
 

integrations/spring-reference-client/README.md

@@ -13,7 +13,7 @@ fun contentPipeline() = workflow("content-pipeline") {
 
     agents {
         agent("writer")   { role = "Content Writer";   model = Models.GEMINI_3_1_FLASH_LITE }
-        agent("reviewer") { role = "Content Reviewer"; model = Models.CLAUDE_SONNET_4_6 }
+        agent("reviewer") { role = "Content Reviewer"; model = Models.GEMINI_3_1_PRO }
     }
 
     rubrics { rubric("content-quality", "content-quality.md") }