chore: Bump version to 0.8.2

Users can now access the Caido web UI from their browser to inspect traffic,
replay requests, and perform manual testing alongside the automated scan.

- Map Caido port (48080) to a random host port in DockerRuntime
- Add caido_port to SandboxInfo and track across container lifecycle
- Display Caido URL in TUI sidebar stats panel with selectable text
- Bind Caido to 0.0.0.0 in entrypoint (requires image rebuild)
- Bump sandbox image to 0.1.12
- Restore discord link in exit screen

CONTRIBUTING.md

@@ -30,7 +30,7 @@ Thank you for your interest in contributing to Strix! This guide will help you g
 
 3. **Configure your LLM provider**
    ```bash
-   export STRIX_LLM="anthropic/claude-sonnet-4-6"
+   export STRIX_LLM="openai/gpt-5"
    export LLM_API_KEY="your-api-key"
    ```
 

README.md

@@ -15,7 +15,7 @@
 
 <a href="https://docs.strix.ai"><img src="https://img.shields.io/badge/Docs-docs.strix.ai-2b9246?style=for-the-badge&logo=gitbook&logoColor=white" alt="Docs"></a>
 <a href="https://strix.ai"><img src="https://img.shields.io/badge/Website-strix.ai-f0f0f0?style=for-the-badge&logoColor=000000" alt="Website"></a>
-[![](https://dcbadge.limes.pink/api/server/8Suzzd9z)](https://discord.gg/strix-ai)
+[![](https://dcbadge.limes.pink/api/server/strix-ai)](https://discord.gg/strix-ai)
 
 <a href="https://deepwiki.com/usestrix/strix"><img src="https://deepwiki.com/badge.svg" alt="Ask DeepWiki"></a>
 <a href="https://github.com/usestrix/strix"><img src="https://img.shields.io/github/stars/usestrix/strix?style=flat-square" alt="GitHub Stars"></a>
@@ -82,11 +82,8 @@ Strix are autonomous AI agents that act just like real hackers - they run your c
 # Install Strix
 curl -sSL https://strix.ai/install | bash
 
-# Or via pipx
-pipx install strix-agent
-
 # Configure your AI provider
-export STRIX_LLM="anthropic/claude-sonnet-4-6"  # or "strix/claude-sonnet-4.6" via Strix Router (https://models.strix.ai)
+export STRIX_LLM="openai/gpt-5"  # or "strix/gpt-5" via Strix Router (https://models.strix.ai)
 export LLM_API_KEY="your-api-key"
 
 # Run your first security assessment
@@ -203,7 +200,7 @@ jobs:
 ### Configuration
 
 ```bash
-export STRIX_LLM="anthropic/claude-sonnet-4-6"
+export STRIX_LLM="openai/gpt-5"
 export LLM_API_KEY="your-api-key"
 
 # Optional
@@ -217,8 +214,8 @@ export STRIX_REASONING_EFFORT="high"  # control thinking effort (default: high,
 
 **Recommended models for best results:**
 
-- [Anthropic Claude Sonnet 4.6](https://claude.com/platform/api) — `anthropic/claude-sonnet-4-6`
 - [OpenAI GPT-5](https://openai.com/api/) — `openai/gpt-5`
+- [Anthropic Claude Sonnet 4.6](https://claude.com/platform/api) — `anthropic/claude-sonnet-4-6`
 - [Google Gemini 3 Pro Preview](https://cloud.google.com/vertex-ai) — `vertex_ai/gemini-3-pro-preview`
 
 See the [LLM Providers documentation](https://docs.strix.ai/llm-providers/overview) for all supported providers including Vertex AI, Bedrock, Azure, and local models.

containers/docker-entrypoint.sh

@@ -9,7 +9,7 @@ if [ ! -f /app/certs/ca.p12 ]; then
   exit 1
 fi
 
-caido-cli --listen 127.0.0.1:${CAIDO_PORT} \
+caido-cli --listen 0.0.0.0:${CAIDO_PORT} \
           --allow-guests \
           --no-logging \
           --no-open \

docs/advanced/configuration.mdx

@@ -8,7 +8,7 @@ Configure Strix using environment variables or a config file.
 ## LLM Configuration
 
 <ParamField path="STRIX_LLM" type="string" required>
-  Model name in LiteLLM format (e.g., `anthropic/claude-sonnet-4-6`, `openai/gpt-5`).
+  Model name in LiteLLM format (e.g., `openai/gpt-5`, `anthropic/claude-sonnet-4-6`).
 </ParamField>
 
 <ParamField path="LLM_API_KEY" type="string">
@@ -51,7 +51,7 @@ Configure Strix using environment variables or a config file.
 
 ## Docker Configuration
 
-<ParamField path="STRIX_IMAGE" default="ghcr.io/usestrix/strix-sandbox:0.1.11" type="string">
+<ParamField path="STRIX_IMAGE" default="ghcr.io/usestrix/strix-sandbox:0.1.12" type="string">
   Docker image to use for the sandbox container.
 </ParamField>
 
@@ -86,7 +86,7 @@ strix --target ./app --config /path/to/config.json
 ```json
 {
   "env": {
-    "STRIX_LLM": "anthropic/claude-sonnet-4-6",
+    "STRIX_LLM": "openai/gpt-5",
     "LLM_API_KEY": "sk-...",
     "STRIX_REASONING_EFFORT": "high"
   }
@@ -97,7 +97,7 @@ strix --target ./app --config /path/to/config.json
 
 ```bash
 # Required
-export STRIX_LLM="anthropic/claude-sonnet-4-6"
+export STRIX_LLM="openai/gpt-5"
 export LLM_API_KEY="sk-..."
 
 # Optional: Enable web search

docs/contributing.mdx

@@ -32,7 +32,7 @@ description: "Contribute to Strix development"
   </Step>
   <Step title="Configure LLM">
     ```bash
-    export STRIX_LLM="anthropic/claude-sonnet-4-6"
+    export STRIX_LLM="openai/gpt-5"
     export LLM_API_KEY="your-api-key"
     ```
   </Step>

docs/index.mdx

@@ -78,7 +78,7 @@ Strix uses a graph of specialized agents for comprehensive security testing:
 curl -sSL https://strix.ai/install | bash
 
 # Configure
-export STRIX_LLM="anthropic/claude-sonnet-4-6"
+export STRIX_LLM="openai/gpt-5"
 export LLM_API_KEY="your-api-key"
 
 # Scan

docs/integrations/github-actions.mdx

@@ -35,7 +35,7 @@ Add these secrets to your repository:
 
 | Secret | Description |
 |--------|-------------|
-| `STRIX_LLM` | Model name (e.g., `anthropic/claude-sonnet-4-6`) |
+| `STRIX_LLM` | Model name (e.g., `openai/gpt-5`) |
 | `LLM_API_KEY` | API key for your LLM provider |
 
 ## Exit Codes

docs/llm-providers/anthropic.mdx

@@ -6,7 +6,7 @@ description: "Configure Strix with Claude models"
 ## Setup
 
 ```bash
-export STRIX_LLM="anthropic/claude-sonnet-4-6"
+export STRIX_LLM="openai/gpt-5"
 export LLM_API_KEY="sk-ant-..."
 ```
 
@@ -14,7 +14,7 @@ export LLM_API_KEY="sk-ant-..."
 
 | Model | Description |
 |-------|-------------|
-| `anthropic/claude-sonnet-4-6` | Best balance of intelligence and speed (recommended) |
+| `anthropic/claude-sonnet-4-6` | Best balance of intelligence and speed |
 | `anthropic/claude-opus-4-6` | Maximum capability for deep analysis |
 
 ## Get API Key

docs/llm-providers/models.mdx

@@ -25,7 +25,7 @@ Strix Router is currently in **beta**. It's completely optional — Strix works
 
 ```bash
 export LLM_API_KEY='your-strix-api-key'
-export STRIX_LLM='strix/claude-sonnet-4.6'
+export STRIX_LLM='strix/gpt-5'
 ```
 
 3. Run a scan:

docs/llm-providers/overview.mdx

@@ -10,7 +10,7 @@ Strix uses [LiteLLM](https://docs.litellm.ai/docs/providers) for model compatibi
 The fastest way to get started. [Strix Router](/llm-providers/models) gives you access to tested models with the highest rate limits and zero data retention.
 
 ```bash
-export STRIX_LLM="strix/claude-sonnet-4.6"
+export STRIX_LLM="strix/gpt-5"
 export LLM_API_KEY="your-strix-api-key"
 ```
 
@@ -22,12 +22,12 @@ You can also use any LiteLLM-compatible provider with your own API keys:
 
 | Model             | Provider      | Configuration                    |
 | ----------------- | ------------- | -------------------------------- |
-| Claude Sonnet 4.6 | Anthropic     | `anthropic/claude-sonnet-4-6`    |
 | GPT-5             | OpenAI        | `openai/gpt-5`                   |
+| Claude Sonnet 4.6 | Anthropic     | `anthropic/claude-sonnet-4-6`    |
 | Gemini 3 Pro      | Google Vertex | `vertex_ai/gemini-3-pro-preview` |
 
 ```bash
-export STRIX_LLM="anthropic/claude-sonnet-4-6"
+export STRIX_LLM="openai/gpt-5"
 export LLM_API_KEY="your-api-key"
 ```
 
@@ -52,7 +52,7 @@ See the [Local Models guide](/llm-providers/local) for setup instructions and re
     GPT-5 and Codex models.
   </Card>
   <Card title="Anthropic" href="/llm-providers/anthropic">
-    Claude Sonnet 4.6, Opus, and Haiku.
+    Claude Opus, Sonnet, and Haiku.
   </Card>
   <Card title="OpenRouter" href="/llm-providers/openrouter">
     Access 100+ models through a single API.
@@ -76,8 +76,8 @@ See the [Local Models guide](/llm-providers/local) for setup instructions and re
 Use LiteLLM's `provider/model-name` format:
 
 ```
-anthropic/claude-sonnet-4-6
 openai/gpt-5
+anthropic/claude-sonnet-4-6
 vertex_ai/gemini-3-pro-preview
 bedrock/anthropic.claude-4-5-sonnet-20251022-v1:0
 ollama/llama4

docs/quickstart.mdx

@@ -30,20 +30,20 @@ Set your LLM provider:
 <Tabs>
   <Tab title="Strix Router">
     ```bash
-    export STRIX_LLM="strix/claude-sonnet-4.6"
+    export STRIX_LLM="strix/gpt-5"
     export LLM_API_KEY="your-strix-api-key"
     ```
   </Tab>
   <Tab title="Bring Your Own Key">
     ```bash
-    export STRIX_LLM="anthropic/claude-sonnet-4-6"
+    export STRIX_LLM="openai/gpt-5"
     export LLM_API_KEY="your-api-key"
     ```
   </Tab>
 </Tabs>
 
 <Tip>
-For best results, use `strix/claude-sonnet-4.6`, `strix/claude-opus-4.6`, or `strix/gpt-5.2`.
+For best results, use `strix/gpt-5`, `strix/claude-opus-4.6`, or `strix/gpt-5.2`.
 </Tip>
 
 ## Run Your First Scan

poetry.lock

@@ -190,7 +190,7 @@ description = "Python graph (network) package"
 optional = false
 python-versions = "*"
 groups = ["dev"]
-markers = "python_version <= \"3.14\""
+markers = "python_version < \"3.15\""
 files = [
     {file = "altgraph-0.17.5-py2.py3-none-any.whl", hash = "sha256:f3a22400bce1b0c701683820ac4f3b159cd301acab067c51c653e06961600597"},
     {file = "altgraph-0.17.5.tar.gz", hash = "sha256:c87b395dd12fabde9c99573a9749d67da8d29ef9de0125c7f536699b4a9bc9e7"},
@@ -324,7 +324,7 @@ description = "LTS Port of Python audioop"
 optional = true
 python-versions = ">=3.13"
 groups = ["main"]
-markers = "extra == \"sandbox\" and python_version >= \"3.13\""
+markers = "python_version >= \"3.13\" and extra == \"sandbox\""
 files = [
     {file = "audioop_lts-0.2.2-cp313-abi3-macosx_10_13_universal2.whl", hash = "sha256:fd3d4602dc64914d462924a08c1a9816435a2155d74f325853c1f1ac3b2d9800"},
     {file = "audioop_lts-0.2.2-cp313-abi3-macosx_10_13_x86_64.whl", hash = "sha256:550c114a8df0aafe9a05442a1162dfc8fec37e9af1d625ae6060fed6e756f303"},
@@ -622,7 +622,7 @@ description = "Extensible memoizing collections and decorators"
 optional = true
 python-versions = ">=3.7"
 groups = ["main"]
-markers = "extra == \"vertex\" or extra == \"sandbox\""
+markers = "extra == \"sandbox\""
 files = [
     {file = "cachetools-5.5.2-py3-none-any.whl", hash = "sha256:d26a22bcc62eb95c3beabd9f1ee5e820d3d2704fe2967cbe350e20c8ffcd3f0a"},
     {file = "cachetools-5.5.2.tar.gz", hash = "sha256:1a661caa9175d26759571b2e19580f9d6393969e5dfca11fdb1f947a23e640d4"},
@@ -890,7 +890,7 @@ files = [
     {file = "colorama-0.4.6-py2.py3-none-any.whl", hash = "sha256:4f1d9991f5acc0ca119f9d443620b77f9d6b33703e51011c16baf57afb285fc6"},
     {file = "colorama-0.4.6.tar.gz", hash = "sha256:08695f5cb7ed6e0531a20572697297273c47b8cae5a63ffc6d6ed5c201be6e44"},
 ]
-markers = {main = "sys_platform == \"win32\" and extra == \"sandbox\" or platform_system == \"Windows\"", dev = "platform_system == \"Windows\" or sys_platform == \"win32\""}
+markers = {main = "extra == \"sandbox\" and sys_platform == \"win32\" or platform_system == \"Windows\"", dev = "platform_system == \"Windows\" or sys_platform == \"win32\""}
 
 [[package]]
 name = "contourpy"
@@ -1850,50 +1850,51 @@ grpcio-gcp = ["grpcio-gcp (>=0.2.2,<1.0.0)"]
 
 [[package]]
 name = "google-auth"
-version = "2.43.0"
+version = "2.48.0"
 description = "Google Authentication Library"
 optional = true
-python-versions = ">=3.7"
+python-versions = ">=3.8"
 groups = ["main"]
 markers = "extra == \"vertex\""
 files = [
-    {file = "google_auth-2.43.0-py2.py3-none-any.whl", hash = "sha256:af628ba6fa493f75c7e9dbe9373d148ca9f4399b5ea29976519e0a3848eddd16"},
+    {file = "google_auth-2.48.0-py3-none-any.whl", hash = "sha256:2e2a537873d449434252a9632c28bfc268b0adb1e53f9fb62afc5333a975903f"},
-    {file = "google_auth-2.43.0.tar.gz", hash = "sha256:88228eee5fc21b62a1b5fe773ca15e67778cb07dc8363adcb4a8827b52d81483"},
+    {file = "google_auth-2.48.0.tar.gz", hash = "sha256:4f7e706b0cd3208a3d940a19a822c37a476ddba5450156c3e6624a71f7c841ce"},
 ]
 
 [package.dependencies]
-cachetools = ">=2.0.0,<7.0"
+cryptography = ">=38.0.3"
 pyasn1-modules = ">=0.2.1"
 requests = {version = ">=2.20.0,<3.0.0", optional = true, markers = "extra == \"requests\""}
 rsa = ">=3.1.4,<5"
 
 [package.extras]
 aiohttp = ["aiohttp (>=3.6.2,<4.0.0)", "requests (>=2.20.0,<3.0.0)"]
-enterprise-cert = ["cryptography", "pyopenssl"]
+cryptography = ["cryptography (>=38.0.3)"]
-pyjwt = ["cryptography (<39.0.0) ; python_version < \"3.8\"", "cryptography (>=38.0.3)", "pyjwt (>=2.0)"]
+enterprise-cert = ["pyopenssl"]
-pyopenssl = ["cryptography (<39.0.0) ; python_version < \"3.8\"", "cryptography (>=38.0.3)", "pyopenssl (>=20.0.0)"]
+pyjwt = ["pyjwt (>=2.0)"]
+pyopenssl = ["pyopenssl (>=20.0.0)"]
 reauth = ["pyu2f (>=0.1.5)"]
 requests = ["requests (>=2.20.0,<3.0.0)"]
-testing = ["aiohttp (<3.10.0)", "aiohttp (>=3.6.2,<4.0.0)", "aioresponses", "cryptography (<39.0.0) ; python_version < \"3.8\"", "cryptography (<39.0.0) ; python_version < \"3.8\"", "cryptography (>=38.0.3)", "cryptography (>=38.0.3)", "flask", "freezegun", "grpcio", "mock", "oauth2client", "packaging", "pyjwt (>=2.0)", "pyopenssl (<24.3.0)", "pyopenssl (>=20.0.0)", "pytest", "pytest-asyncio", "pytest-cov", "pytest-localserver", "pyu2f (>=0.1.5)", "requests (>=2.20.0,<3.0.0)", "responses", "urllib3"]
+testing = ["aiohttp (<3.10.0)", "aiohttp (>=3.6.2,<4.0.0)", "aioresponses", "flask", "freezegun", "grpcio", "oauth2client", "packaging", "pyjwt (>=2.0)", "pyopenssl (<24.3.0)", "pyopenssl (>=20.0.0)", "pytest", "pytest-asyncio", "pytest-cov", "pytest-localserver", "pyu2f (>=0.1.5)", "requests (>=2.20.0,<3.0.0)", "responses", "urllib3"]
 urllib3 = ["packaging", "urllib3"]
 
 [[package]]
 name = "google-cloud-aiplatform"
-version = "1.129.0"
+version = "1.133.0"
 description = "Vertex AI API client library"
 optional = true
 python-versions = ">=3.9"
 groups = ["main"]
 markers = "extra == \"vertex\""
 files = [
-    {file = "google_cloud_aiplatform-1.129.0-py2.py3-none-any.whl", hash = "sha256:b0052143a1bc05894e59fc6f910e84c504e194fadf877f84fc790b38a2267739"},
+    {file = "google_cloud_aiplatform-1.133.0-py2.py3-none-any.whl", hash = "sha256:dfc81228e987ca10d1c32c7204e2131b3c8d6b7c8e0b4e23bf7c56816bc4c566"},
-    {file = "google_cloud_aiplatform-1.129.0.tar.gz", hash = "sha256:c53b9d6c529b4de2962b34425b0116f7a382a926b26e02c2196e372f9a31d196"},
+    {file = "google_cloud_aiplatform-1.133.0.tar.gz", hash = "sha256:3a6540711956dd178daaab3c2c05db476e46d94ac25912b8cf4f59b00b058ae0"},
 ]
 
 [package.dependencies]
 docstring_parser = "<1"
 google-api-core = {version = ">=1.34.1,<2.0.dev0 || >=2.8.dev0,<3.0.0", extras = ["grpc"]}
-google-auth = ">=2.14.1,<3.0.0"
+google-auth = ">=2.47.0,<3.0.0"
 google-cloud-bigquery = ">=1.15.0,<3.20.0 || >3.20.0,<4.0.0"
 google-cloud-resource-manager = ">=1.3.3,<3.0.0"
 google-cloud-storage = [
@@ -1905,7 +1906,6 @@ packaging = ">=14.3"
 proto-plus = ">=1.22.3,<2.0.0"
 protobuf = ">=3.20.2,<4.21.0 || >4.21.0,<4.21.1 || >4.21.1,<4.21.2 || >4.21.2,<4.21.3 || >4.21.3,<4.21.4 || >4.21.4,<4.21.5 || >4.21.5,<7.0.0"
 pydantic = "<3"
-shapely = "<3.0.0"
 typing_extensions = "*"
 
 [package.extras]
@@ -1918,21 +1918,21 @@ cloud-profiler = ["tensorboard-plugin-profile (>=2.4.0,<2.18.0)", "werkzeug (>=2
 datasets = ["pyarrow (>=10.0.1) ; python_version == \"3.11\"", "pyarrow (>=14.0.0) ; python_version >= \"3.12\"", "pyarrow (>=3.0.0,<8.0.0) ; python_version < \"3.11\""]
 endpoint = ["requests (>=2.28.1)", "requests-toolbelt (<=1.0.0)"]
 evaluation = ["jsonschema", "litellm (>=1.72.4,!=1.77.2,!=1.77.3,!=1.77.4)", "pandas (>=1.0.0)", "pyyaml", "ruamel.yaml", "scikit-learn (<1.6.0) ; python_version <= \"3.10\"", "scikit-learn ; python_version > \"3.10\"", "tqdm (>=4.23.0)"]
-full = ["docker (>=5.0.3)", "explainable-ai-sdk (>=1.0.0) ; python_version < \"3.13\"", "fastapi (>=0.71.0,<=0.114.0)", "google-cloud-bigquery", "google-cloud-bigquery-storage", "google-vizier (>=0.1.6)", "httpx (>=0.23.0,<=0.28.1)", "immutabledict", "jsonschema", "lit-nlp (==0.4.0) ; python_version < \"3.14\"", "litellm (>=1.72.4,!=1.77.2,!=1.77.3,!=1.77.4)", "mlflow (>=1.27.0) ; python_version >= \"3.13\"", "mlflow (>=1.27.0,<=2.16.0) ; python_version < \"3.13\"", "numpy (>=1.15.0)", "pandas (>=1.0.0)", "pyarrow (>=10.0.1) ; python_version == \"3.11\"", "pyarrow (>=14.0.0) ; python_version >= \"3.12\"", "pyarrow (>=3.0.0,<8.0.0) ; python_version < \"3.11\"", "pyarrow (>=6.0.1)", "pyyaml", "pyyaml (>=5.3.1,<7)", "ray[default] (>=2.4,<2.5.dev0 || >2.9.0,!=2.9.1,!=2.9.2,<2.10.dev0 || ==2.33.* || >=2.42.dev0,<=2.42.0) ; python_version < \"3.11\"", "ray[default] (>=2.5,<=2.47.1) ; python_version == \"3.11\"", "requests (>=2.28.1)", "requests-toolbelt (<=1.0.0)", "ruamel.yaml", "scikit-learn (<1.6.0) ; python_version <= \"3.10\"", "scikit-learn ; python_version > \"3.10\"", "starlette (>=0.17.1)", "tensorboard-plugin-profile (>=2.4.0,<2.18.0)", "tensorflow (>=2.3.0,<3.0.0) ; python_version < \"3.13\"", "tensorflow (>=2.3.0,<3.0.0) ; python_version < \"3.13\"", "tqdm (>=4.23.0)", "urllib3 (>=1.21.1,<1.27)", "uvicorn[standard] (>=0.16.0)", "werkzeug (>=2.0.0,<4.0.0)"]
+full = ["docker (>=5.0.3)", "explainable-ai-sdk (>=1.0.0) ; python_version < \"3.13\"", "fastapi (>=0.71.0,<=0.124.4)", "google-cloud-bigquery", "google-cloud-bigquery-storage", "google-vizier (>=0.1.6)", "httpx (>=0.23.0,<=0.28.1)", "immutabledict", "jsonschema", "lit-nlp (==0.4.0) ; python_version < \"3.13\"", "litellm (>=1.72.4,!=1.77.2,!=1.77.3,!=1.77.4)", "mlflow (>=1.27.0) ; python_version >= \"3.13\"", "mlflow (>=1.27.0,<=2.16.0) ; python_version < \"3.13\"", "numpy (>=1.15.0)", "pandas (>=1.0.0)", "pyarrow (>=10.0.1) ; python_version == \"3.11\"", "pyarrow (>=14.0.0) ; python_version >= \"3.12\"", "pyarrow (>=3.0.0,<8.0.0) ; python_version < \"3.11\"", "pyarrow (>=6.0.1)", "pyyaml", "pyyaml (>=5.3.1,<7)", "ray[default] (>=2.4,<2.5.dev0 || >2.9.0,!=2.9.1,!=2.9.2,<2.10.dev0 || ==2.33.* || >=2.42.dev0,<=2.42.0) ; python_version < \"3.11\"", "ray[default] (>=2.5,<=2.47.1) ; python_version == \"3.11\"", "requests (>=2.28.1)", "requests-toolbelt (<=1.0.0)", "ruamel.yaml", "scikit-learn (<1.6.0) ; python_version <= \"3.10\"", "scikit-learn ; python_version > \"3.10\"", "starlette (>=0.17.1)", "tensorboard-plugin-profile (>=2.4.0,<2.18.0)", "tensorflow (>=2.3.0,<3.0.0) ; python_version < \"3.13\"", "tensorflow (>=2.3.0,<3.0.0) ; python_version < \"3.13\"", "tqdm (>=4.23.0)", "urllib3 (>=1.21.1,<1.27)", "uvicorn[standard] (>=0.16.0)", "werkzeug (>=2.0.0,<4.0.0)"]
 langchain = ["langchain (>=0.3,<0.4)", "langchain-core (>=0.3,<0.4)", "langchain-google-vertexai (>=2.0.22,<3)", "langgraph (>=0.2.45,<0.4)", "openinference-instrumentation-langchain (>=0.1.19,<0.2)"]
 langchain-testing = ["absl-py", "cloudpickle (>=3.0,<4.0)", "google-cloud-trace (<2)", "langchain (>=0.3,<0.4)", "langchain-core (>=0.3,<0.4)", "langchain-google-vertexai (>=2.0.22,<3)", "langgraph (>=0.2.45,<0.4)", "openinference-instrumentation-langchain (>=0.1.19,<0.2)", "opentelemetry-exporter-gcp-logging (>=1.11.0a0,<2.0.0)", "opentelemetry-exporter-gcp-trace (<2)", "opentelemetry-exporter-otlp-proto-http (<2)", "opentelemetry-sdk (<2)", "pydantic (>=2.11.1,<3)", "pytest-xdist", "typing_extensions"]
-lit = ["explainable-ai-sdk (>=1.0.0) ; python_version < \"3.13\"", "lit-nlp (==0.4.0) ; python_version < \"3.14\"", "pandas (>=1.0.0)", "tensorflow (>=2.3.0,<3.0.0) ; python_version < \"3.13\""]
+lit = ["explainable-ai-sdk (>=1.0.0) ; python_version < \"3.13\"", "lit-nlp (==0.4.0) ; python_version < \"3.13\"", "pandas (>=1.0.0)", "tensorflow (>=2.3.0,<3.0.0) ; python_version < \"3.13\""]
 llama-index = ["llama-index", "llama-index-llms-google-genai", "openinference-instrumentation-llama-index (>=3.0,<4.0)"]
 llama-index-testing = ["absl-py", "cloudpickle (>=3.0,<4.0)", "google-cloud-trace (<2)", "llama-index", "llama-index-llms-google-genai", "openinference-instrumentation-llama-index (>=3.0,<4.0)", "opentelemetry-exporter-gcp-logging (>=1.11.0a0,<2.0.0)", "opentelemetry-exporter-gcp-trace (<2)", "opentelemetry-exporter-otlp-proto-http (<2)", "opentelemetry-sdk (<2)", "pydantic (>=2.11.1,<3)", "pytest-xdist", "typing_extensions"]
 metadata = ["numpy (>=1.15.0)", "pandas (>=1.0.0)"]
 pipelines = ["pyyaml (>=5.3.1,<7)"]
-prediction = ["docker (>=5.0.3)", "fastapi (>=0.71.0,<=0.114.0)", "httpx (>=0.23.0,<=0.28.1)", "starlette (>=0.17.1)", "uvicorn[standard] (>=0.16.0)"]
+prediction = ["docker (>=5.0.3)", "fastapi (>=0.71.0,<=0.124.4)", "httpx (>=0.23.0,<=0.28.1)", "starlette (>=0.17.1)", "uvicorn[standard] (>=0.16.0)"]
 private-endpoints = ["requests (>=2.28.1)", "urllib3 (>=1.21.1,<1.27)"]
 ray = ["google-cloud-bigquery", "google-cloud-bigquery-storage", "immutabledict", "pandas (>=1.0.0)", "pyarrow (>=6.0.1)", "ray[default] (>=2.4,<2.5.dev0 || >2.9.0,!=2.9.1,!=2.9.2,<2.10.dev0 || ==2.33.* || >=2.42.dev0,<=2.42.0) ; python_version < \"3.11\"", "ray[default] (>=2.5,<=2.47.1) ; python_version == \"3.11\""]
 ray-testing = ["google-cloud-bigquery", "google-cloud-bigquery-storage", "immutabledict", "pandas (>=1.0.0)", "pyarrow (>=6.0.1)", "pytest-xdist", "ray[default] (>=2.4,<2.5.dev0 || >2.9.0,!=2.9.1,!=2.9.2,<2.10.dev0 || ==2.33.* || >=2.42.dev0,<=2.42.0) ; python_version < \"3.11\"", "ray[default] (>=2.5,<=2.47.1) ; python_version == \"3.11\"", "ray[train]", "scikit-learn (<1.6.0)", "tensorflow ; python_version < \"3.13\"", "torch (>=2.0.0,<2.1.0)", "xgboost", "xgboost_ray"]
 reasoningengine = ["cloudpickle (>=3.0,<4.0)", "google-cloud-trace (<2)", "opentelemetry-exporter-gcp-logging (>=1.11.0a0,<2.0.0)", "opentelemetry-exporter-gcp-trace (<2)", "opentelemetry-exporter-otlp-proto-http (<2)", "opentelemetry-sdk (<2)", "pydantic (>=2.11.1,<3)", "typing_extensions"]
 tensorboard = ["tensorboard-plugin-profile (>=2.4.0,<2.18.0)", "werkzeug (>=2.0.0,<4.0.0)"]
-testing = ["Pillow", "aiohttp", "bigframes ; python_version >= \"3.10\" and python_version < \"3.14\"", "docker (>=5.0.3)", "explainable-ai-sdk (>=1.0.0) ; python_version < \"3.13\"", "fastapi (>=0.71.0,<=0.114.0)", "google-api-core (>=2.11,<3.0.0)", "google-cloud-bigquery", "google-cloud-bigquery-storage", "google-vizier (>=0.1.6)", "google-vizier (>=0.1.6)", "grpcio-testing", "grpcio-tools (>=1.63.0) ; python_version >= \"3.13\"", "httpx (>=0.23.0,<=0.28.1)", "immutabledict", "immutabledict", "ipython", "jsonschema", "kfp (>=2.6.0,<3.0.0) ; python_version < \"3.13\"", "lit-nlp (==0.4.0) ; python_version < \"3.14\"", "litellm (>=1.72.4,!=1.77.2,!=1.77.3,!=1.77.4)", "mlflow (>=1.27.0) ; python_version >= \"3.13\"", "mlflow (>=1.27.0,<=2.16.0) ; python_version < \"3.13\"", "mock", "nltk", "numpy (>=1.15.0)", "pandas (>=1.0.0)", "protobuf (<=5.29.4)", "pyarrow (>=10.0.1) ; python_version == \"3.11\"", "pyarrow (>=14.0.0) ; python_version >= \"3.12\"", "pyarrow (>=3.0.0,<8.0.0) ; python_version < \"3.11\"", "pyarrow (>=6.0.1)", "pytest-asyncio", "pytest-cov", "pytest-xdist", "pyyaml", "pyyaml (>=5.3.1,<7)", "ray[default] (>=2.4,<2.5.dev0 || >2.9.0,!=2.9.1,!=2.9.2,<2.10.dev0 || ==2.33.* || >=2.42.dev0,<=2.42.0) ; python_version < \"3.11\"", "ray[default] (>=2.5,<=2.47.1) ; python_version == \"3.11\"", "requests (>=2.28.1)", "requests-toolbelt (<=1.0.0)", "requests-toolbelt (<=1.0.0)", "ruamel.yaml", "scikit-learn (<1.6.0) ; python_version <= \"3.10\"", "scikit-learn (<1.6.0) ; python_version <= \"3.10\"", "scikit-learn ; python_version > \"3.10\"", "scikit-learn ; python_version > \"3.10\"", "sentencepiece (>=0.2.0)", "starlette (>=0.17.1)", "tensorboard-plugin-profile (>=2.4.0,<2.18.0)", "tensorboard-plugin-profile (>=2.4.0,<2.18.0)", "tensorflow (==2.14.1) ; python_version <= \"3.11\"", "tensorflow (==2.19.0) ; python_version > \"3.11\" and python_version < \"3.13\"", "tensorflow (>=2.3.0,<3.0.0) ; python_version < \"3.13\"", "tensorflow (>=2.3.0,<3.0.0) ; python_version < \"3.13\"", "torch (>=2.0.0,<2.1.0) ; python_version <= \"3.11\"", "torch (>=2.2.0) ; python_version > \"3.11\" and python_version < \"3.13\"", "tqdm (>=4.23.0)", "urllib3 (>=1.21.1,<1.27)", "uvicorn[standard] (>=0.16.0)", "werkzeug (>=2.0.0,<4.0.0)", "werkzeug (>=2.0.0,<4.0.0)", "xgboost"]
+testing = ["Pillow", "aiohttp", "bigframes ; python_version >= \"3.10\" and python_version < \"3.14\"", "docker (>=5.0.3)", "explainable-ai-sdk (>=1.0.0) ; python_version < \"3.13\"", "fastapi (>=0.71.0,<=0.124.4)", "google-api-core (>=2.11,<3.0.0)", "google-cloud-bigquery", "google-cloud-bigquery-storage", "google-vizier (>=0.1.6)", "google-vizier (>=0.1.6)", "grpcio-testing", "grpcio-tools (>=1.63.0) ; python_version >= \"3.13\"", "httpx (>=0.23.0,<=0.28.1)", "immutabledict", "immutabledict", "ipython", "jsonschema", "kfp (>=2.6.0,<3.0.0) ; python_version < \"3.13\"", "lit-nlp (==0.4.0) ; python_version < \"3.13\"", "litellm (>=1.72.4,!=1.77.2,!=1.77.3,!=1.77.4)", "mlflow (>=1.27.0) ; python_version >= \"3.13\"", "mlflow (>=1.27.0,<=2.16.0) ; python_version < \"3.13\"", "mock", "nltk", "numpy (>=1.15.0)", "pandas (>=1.0.0)", "protobuf (<=5.29.4)", "pyarrow (>=10.0.1) ; python_version == \"3.11\"", "pyarrow (>=14.0.0) ; python_version >= \"3.12\"", "pyarrow (>=3.0.0,<8.0.0) ; python_version < \"3.11\"", "pyarrow (>=6.0.1)", "pytest-asyncio", "pytest-cov", "pytest-xdist", "pyyaml", "pyyaml (>=5.3.1,<7)", "ray[default] (>=2.4,<2.5.dev0 || >2.9.0,!=2.9.1,!=2.9.2,<2.10.dev0 || ==2.33.* || >=2.42.dev0,<=2.42.0) ; python_version < \"3.11\"", "ray[default] (>=2.5,<=2.47.1) ; python_version == \"3.11\"", "requests (>=2.28.1)", "requests-toolbelt (<=1.0.0)", "requests-toolbelt (<=1.0.0)", "ruamel.yaml", "scikit-learn (<1.6.0) ; python_version <= \"3.10\"", "scikit-learn (<1.6.0) ; python_version <= \"3.10\"", "scikit-learn ; python_version > \"3.10\"", "scikit-learn ; python_version > \"3.10\"", "sentencepiece (>=0.2.0)", "starlette (>=0.17.1)", "tensorboard-plugin-profile (>=2.4.0,<2.18.0)", "tensorboard-plugin-profile (>=2.4.0,<2.18.0)", "tensorflow (==2.14.1) ; python_version <= \"3.11\"", "tensorflow (==2.19.0) ; python_version > \"3.11\" and python_version < \"3.13\"", "tensorflow (>=2.3.0,<3.0.0) ; python_version < \"3.13\"", "tensorflow (>=2.3.0,<3.0.0) ; python_version < \"3.13\"", "torch (>=2.0.0,<2.1.0) ; python_version <= \"3.11\"", "torch (>=2.2.0) ; python_version > \"3.11\" and python_version < \"3.13\"", "tqdm (>=4.23.0)", "urllib3 (>=1.21.1,<1.27)", "uvicorn[standard] (>=0.16.0)", "werkzeug (>=2.0.0,<4.0.0)", "werkzeug (>=2.0.0,<4.0.0)", "xgboost"]
 tokenization = ["sentencepiece (>=0.2.0)"]
 vizier = ["google-vizier (>=0.1.6)"]
 xai = ["tensorflow (>=2.3.0,<3.0.0) ; python_version < \"3.13\""]
@@ -3298,7 +3298,7 @@ description = "Mach-O header analysis and editing"
 optional = false
 python-versions = "*"
 groups = ["dev"]
-markers = "sys_platform == \"darwin\" and python_version <= \"3.14\""
+markers = "python_version < \"3.15\" and sys_platform == \"darwin\""
 files = [
     {file = "macholib-1.16.4-py2.py3-none-any.whl", hash = "sha256:da1a3fa8266e30f0ce7e97c6a54eefaae8edd1e5f86f3eb8b95457cae90265ea"},
     {file = "macholib-1.16.4.tar.gz", hash = "sha256:f408c93ab2e995cd2c46e34fe328b130404be143469e41bc366c807448979362"},
@@ -3882,7 +3882,7 @@ description = "Fundamental package for array computing in Python"
 optional = true
 python-versions = ">=3.11"
 groups = ["main"]
-markers = "extra == \"sandbox\" or extra == \"vertex\""
+markers = "extra == \"sandbox\""
 files = [
     {file = "numpy-2.3.2-cp311-cp311-macosx_10_9_x86_64.whl", hash = "sha256:852ae5bed3478b92f093e30f785c98e0cb62fa0a939ed057c31716e18a7a22b9"},
     {file = "numpy-2.3.2-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:7a0e27186e781a69959d0230dd9909b5e26024f8da10683bd6344baea1885168"},
@@ -4347,7 +4347,7 @@ description = "Python PE parsing module"
 optional = false
 python-versions = ">=3.6.0"
 groups = ["dev"]
-markers = "sys_platform == \"win32\" and python_version <= \"3.14\""
+markers = "python_version < \"3.15\" and sys_platform == \"win32\""
 files = [
     {file = "pefile-2024.8.26-py3-none-any.whl", hash = "sha256:76f8b485dcd3b1bb8166f1128d395fa3d87af26360c2358fb75b80019b957c6f"},
     {file = "pefile-2024.8.26.tar.gz", hash = "sha256:3ff6c5d8b43e8c37bb6e6dd5085658d658a7a0bdcd20b6a07b1fcfc1c4e9d632"},
@@ -4360,7 +4360,7 @@ description = "Pexpect allows easy control of interactive console applications."
 optional = true
 python-versions = "*"
 groups = ["main"]
-markers = "sys_platform != \"win32\" and sys_platform != \"emscripten\" and extra == \"sandbox\""
+markers = "extra == \"sandbox\" and sys_platform != \"win32\" and sys_platform != \"emscripten\""
 files = [
     {file = "pexpect-4.9.0-py2.py3-none-any.whl", hash = "sha256:7236d1e080e4936be2dc3e326cec0af72acf9212a7e1d060210e70a47e253523"},
     {file = "pexpect-4.9.0.tar.gz", hash = "sha256:ee7d41123f3c9911050ea2c2dac107568dc43b2d3b0c7557a33212c398ead30f"},
@@ -4769,7 +4769,7 @@ description = "Run a subprocess in a pseudo terminal"
 optional = true
 python-versions = "*"
 groups = ["main"]
-markers = "sys_platform != \"win32\" and sys_platform != \"emscripten\" and extra == \"sandbox\""
+markers = "extra == \"sandbox\" and sys_platform != \"win32\" and sys_platform != \"emscripten\""
 files = [
     {file = "ptyprocess-0.7.0-py2.py3-none-any.whl", hash = "sha256:4b41f3967fce3af57cc7e94b888626c18bf37a083e3651ca8feeb66d492fef35"},
     {file = "ptyprocess-0.7.0.tar.gz", hash = "sha256:5c5d0a3b48ceee0b48485e0c26037c0acd7d29765ca3fbb5cb3831d347423220"},
@@ -5085,7 +5085,7 @@ description = "PyInstaller bundles a Python application and all its dependencies
 optional = false
 python-versions = "<3.15,>=3.8"
 groups = ["dev"]
-markers = "python_version <= \"3.14\""
+markers = "python_version < \"3.15\""
 files = [
     {file = "pyinstaller-6.17.0-py3-none-macosx_10_13_universal2.whl", hash = "sha256:4e446b8030c6e5a2f712e3f82011ecf6c7ead86008357b0d23a0ec4bcde31dac"},
     {file = "pyinstaller-6.17.0-py3-none-manylinux2014_aarch64.whl", hash = "sha256:aa9fd87aaa28239c6f0d0210114029bd03f8cac316a90bab071a5092d7c85ad7"},
@@ -5121,7 +5121,7 @@ description = "Community maintained hooks for PyInstaller"
 optional = false
 python-versions = ">=3.8"
 groups = ["dev"]
-markers = "python_version <= \"3.14\""
+markers = "python_version < \"3.15\""
 files = [
     {file = "pyinstaller_hooks_contrib-2025.10-py3-none-any.whl", hash = "sha256:aa7a378518772846221f63a84d6306d9827299323243db890851474dfd1231a9"},
     {file = "pyinstaller_hooks_contrib-2025.10.tar.gz", hash = "sha256:a1a737e5c0dccf1cf6f19a25e2efd109b9fec9ddd625f97f553dac16ee884881"},
@@ -5239,9 +5239,10 @@ diagrams = ["jinja2", "railroad-diagrams"]
 name = "pypdf"
 version = "6.7.1"
 description = "A pure-python PDF library capable of splitting, merging, cropping, and transforming PDF files"
-optional = false
+optional = true
 python-versions = ">=3.9"
 groups = ["main"]
+markers = "extra == \"sandbox\""
 files = [
     {file = "pypdf-6.7.1-py3-none-any.whl", hash = "sha256:a02ccbb06463f7c334ce1612e91b3e68a8e827f3cee100b9941771e6066b094e"},
     {file = "pypdf-6.7.1.tar.gz", hash = "sha256:6b7a63be5563a0a35d54c6d6b550d75c00b8ccf36384be96365355e296e6b3b0"},
@@ -5502,7 +5503,7 @@ description = "A (partial) reimplementation of pywin32 using ctypes/cffi"
 optional = false
 python-versions = ">=3.6"
 groups = ["dev"]
-markers = "sys_platform == \"win32\" and python_version <= \"3.14\""
+markers = "python_version < \"3.15\" and sys_platform == \"win32\""
 files = [
     {file = "pywin32-ctypes-0.2.3.tar.gz", hash = "sha256:d162dc04946d704503b2edc4d55f3dba5c1d539ead017afa00142c38b9885755"},
     {file = "pywin32_ctypes-0.2.3-py3-none-any.whl", hash = "sha256:8a1513379d709975552d202d942d9837758905c8d01eb82b8bcc30918929e7b8"},
@@ -6149,81 +6150,6 @@ enabler = ["pytest-enabler (>=2.2)"]
 test = ["build[virtualenv] (>=1.0.3)", "filelock (>=3.4.0)", "ini2toml[lite] (>=0.14)", "jaraco.develop (>=7.21) ; python_version >= \"3.9\" and sys_platform != \"cygwin\"", "jaraco.envs (>=2.2)", "jaraco.path (>=3.7.2)", "jaraco.test (>=5.5)", "packaging (>=24.2)", "pip (>=19.1)", "pyproject-hooks (!=1.1)", "pytest (>=6,!=8.1.*)", "pytest-home (>=0.5)", "pytest-perf ; sys_platform != \"cygwin\"", "pytest-subprocess", "pytest-timeout", "pytest-xdist (>=3)", "tomli-w (>=1.0.0)", "virtualenv (>=13.0.0)", "wheel (>=0.44.0)"]
 type = ["importlib_metadata (>=7.0.2) ; python_version < \"3.10\"", "jaraco.develop (>=7.21) ; sys_platform != \"cygwin\"", "mypy (==1.14.*)", "pytest-mypy"]
 
-[[package]]
-name = "shapely"
-version = "2.1.2"
-description = "Manipulation and analysis of geometric objects"
-optional = true
-python-versions = ">=3.10"
-groups = ["main"]
-markers = "extra == \"vertex\""
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-    {file = "shapely-2.1.2.tar.gz", hash = "sha256:2ed4ecb28320a433db18a5bf029986aa8afcfd740745e78847e330d5d94922a9"},
-]
-
-[package.dependencies]
-numpy = ">=1.21"
-
-[package.extras]
-docs = ["matplotlib", "numpydoc (==1.1.*)", "sphinx", "sphinx-book-theme", "sphinx-remove-toctrees"]
-test = ["pytest", "pytest-cov", "scipy-doctest"]
-
 [[package]]
 name = "six"
 version = "1.17.0"
@@ -6532,7 +6458,7 @@ description = "Standard library aifc redistribution. \"dead battery\"."
 optional = true
 python-versions = "*"
 groups = ["main"]
-markers = "extra == \"sandbox\" and python_version >= \"3.13\""
+markers = "python_version >= \"3.13\" and extra == \"sandbox\""
 files = [
     {file = "standard_aifc-3.13.0-py3-none-any.whl", hash = "sha256:f7ae09cc57de1224a0dd8e3eb8f73830be7c3d0bc485de4c1f82b4a7f645ac66"},
     {file = "standard_aifc-3.13.0.tar.gz", hash = "sha256:64e249c7cb4b3daf2fdba4e95721f811bde8bdfc43ad9f936589b7bb2fae2e43"},
@@ -6549,7 +6475,7 @@ description = "Standard library chunk redistribution. \"dead battery\"."
 optional = true
 python-versions = "*"
 groups = ["main"]
-markers = "extra == \"sandbox\" and python_version >= \"3.13\""
+markers = "python_version >= \"3.13\" and extra == \"sandbox\""
 files = [
     {file = "standard_chunk-3.13.0-py3-none-any.whl", hash = "sha256:17880a26c285189c644bd5bd8f8ed2bdb795d216e3293e6dbe55bbd848e2982c"},
     {file = "standard_chunk-3.13.0.tar.gz", hash = "sha256:4ac345d37d7e686d2755e01836b8d98eda0d1a3ee90375e597ae43aaf064d654"},

pyproject.toml

@@ -1,6 +1,6 @@
 [tool.poetry]
 name = "strix-agent"
-version = "0.8.0"
+version = "0.8.2"
 description = "Open-source AI Hackers for your apps"
 authors = ["Strix <hi@usestrix.com>"]
 readme = "README.md"

scripts/install.sh

@@ -4,7 +4,7 @@ set -euo pipefail
 
 APP=strix
 REPO="usestrix/strix"
-STRIX_IMAGE="ghcr.io/usestrix/strix-sandbox:0.1.11"
+STRIX_IMAGE="ghcr.io/usestrix/strix-sandbox:0.1.12"
 
 MUTED='\033[0;2m'
 RED='\033[0;31m'
@@ -340,7 +340,7 @@ echo -e "     ${MUTED}https://models.strix.ai${NC}"
 echo ""
 echo -e "  ${CYAN}2.${NC} Set your environment:"
 echo -e "     ${MUTED}export LLM_API_KEY='your-api-key'${NC}"
-echo -e "     ${MUTED}export STRIX_LLM='strix/claude-sonnet-4.6'${NC}"
+echo -e "     ${MUTED}export STRIX_LLM='strix/gpt-5'${NC}"
 echo ""
 echo -e "  ${CYAN}3.${NC} Run a penetration test:"
 echo -e "     ${MUTED}strix --target https://example.com${NC}"

strix/agents/StrixAgent/system_prompt.jinja

@@ -314,13 +314,37 @@ CRITICAL RULES:
 4. Use ONLY the exact format shown above. NEVER use JSON/YAML/INI or any other syntax for tools or parameters.
 5. When sending ANY multi-line content in tool parameters, use real newlines (actual line breaks). Do NOT emit literal "\n" sequences. Literal "\n" instead of real line breaks will cause tools to fail.
 6. Tool names must match exactly the tool "name" defined (no module prefixes, dots, or variants).
-   - Correct: <function=think> ... </function>
-   - Incorrect: <thinking_tools.think> ... </function>
-   - Incorrect: <think> ... </think>
-   - Incorrect: {"think": {...}}
 7. Parameters must use <parameter=param_name>value</parameter> exactly. Do NOT pass parameters as JSON or key:value lines. Do NOT add quotes/braces around values.
 8. Do NOT wrap tool calls in markdown/code fences or add any text before or after the tool block.
 
+CORRECT format — use this EXACTLY:
+<function=tool_name>
+<parameter=param_name>value</parameter>
+</function>
+
+WRONG formats — NEVER use these:
+- <invoke name="tool_name"><parameter name="param_name">value</parameter></invoke>
+- <function_calls><invoke name="tool_name">...</invoke></function_calls>
+- <tool_call><tool_name>...</tool_name></tool_call>
+- {"tool_name": {"param_name": "value"}}
+- ```<function=tool_name>...</function>```
+- <function=tool_name>value_without_parameter_tags</function>
+
+EVERY argument MUST be wrapped in <parameter=name>...</parameter> tags. NEVER put values directly in the function body without parameter tags. This WILL cause the tool call to fail.
+
+Do NOT emit any extra XML tags in your output. In particular:
+- NO <thinking>...</thinking> or <thought>...</thought> blocks
+- NO <scratchpad>...</scratchpad> or <reasoning>...</reasoning> blocks
+- NO <answer>...</answer> or <response>...</response> wrappers
+If you need to reason, use the think tool. Your raw output must contain ONLY the tool call — no surrounding XML tags.
+
+Notice: use <function=X> NOT <invoke name="X">, use <parameter=X> NOT <parameter name="X">, use </function> NOT </invoke>.
+
+Example (terminal tool):
+<function=terminal_execute>
+<parameter=command>nmap -sV -p 1-1000 target.com</parameter>
+</function>
+
 Example (agent creation tool):
 <function=create_agent>
 <parameter=task>Perform targeted XSS testing on the search endpoint</parameter>

strix/agents/base_agent.py

@@ -333,6 +333,14 @@ class BaseAgent(metaclass=AgentMeta):
 
                 if "agent_id" in sandbox_info:
                     self.state.sandbox_info["agent_id"] = sandbox_info["agent_id"]
+
+                caido_port = sandbox_info.get("caido_port")
+                if caido_port:
+                    from strix.telemetry.tracer import get_global_tracer
+
+                    tracer = get_global_tracer()
+                    if tracer:
+                        tracer.caido_url = f"localhost:{caido_port}"
             except Exception as e:
                 from strix.telemetry import posthog
 

strix/config/config.py

@@ -40,7 +40,7 @@ class Config:
     strix_disable_browser = "false"
 
     # Runtime Configuration
-    strix_image = "ghcr.io/usestrix/strix-sandbox:0.1.11"
+    strix_image = "ghcr.io/usestrix/strix-sandbox:0.1.12"
     strix_runtime_backend = "docker"
     strix_sandbox_execution_timeout = "120"
     strix_sandbox_connect_timeout = "10"
@@ -187,6 +187,9 @@ def resolve_llm_config() -> tuple[str | None, str | None, str | None]:
 
     Returns:
         tuple: (model_name, api_key, api_base)
+        - model_name: Original model name (strix/ prefix preserved for display)
+        - api_key: LLM API key
+        - api_base: API base URL (auto-set to STRIX_API_BASE for strix/ models)
     """
     model = Config.get("strix_llm")
     if not model:
@@ -195,10 +198,8 @@ def resolve_llm_config() -> tuple[str | None, str | None, str | None]:
     api_key = Config.get("llm_api_key")
 
     if model.startswith("strix/"):
-        model_name = "openai/" + model[6:]
         api_base: str | None = STRIX_API_BASE
     else:
-        model_name = model
         api_base = (
             Config.get("llm_api_base")
             or Config.get("openai_api_base")
@@ -206,4 +207,4 @@ def resolve_llm_config() -> tuple[str | None, str | None, str | None]:
             or Config.get("ollama_api_base")
         )
 
-    return model_name, api_key, api_base
+    return model, api_key, api_base

strix/interface/assets/tui_styles.tcss

@@ -77,12 +77,21 @@ Toast.-information .toast--title {
     margin-bottom: 0;
 }
 
-#stats_display {
+#stats_scroll {
     height: auto;
     max-height: 15;
     background: transparent;
     padding: 0;
     margin: 0;
+    border: round #333333;
+    scrollbar-size: 0 0;
+}
+
+#stats_display {
+    height: auto;
+    background: transparent;
+    padding: 0 1;
+    margin: 0;
 }
 
 #vulnerabilities_panel {

strix/interface/main.py

@@ -18,6 +18,8 @@ from rich.panel import Panel
 from rich.text import Text
 
 from strix.config import Config, apply_saved_config, save_current_config
+from strix.config.config import resolve_llm_config
+from strix.llm.utils import resolve_strix_model
 
 
 apply_saved_config()
@@ -99,7 +101,7 @@ def validate_environment() -> None:  # noqa: PLR0912, PLR0915
                 error_text.append("• ", style="white")
                 error_text.append("STRIX_LLM", style="bold cyan")
                 error_text.append(
-                    " - Model name to use with litellm (e.g., 'anthropic/claude-sonnet-4-6')\n",
+                    " - Model name to use with litellm (e.g., 'openai/gpt-5')\n",
                     style="white",
                 )
 
@@ -139,9 +141,9 @@ def validate_environment() -> None:  # noqa: PLR0912, PLR0915
 
         error_text.append("\nExample setup:\n", style="white")
         if uses_strix_models:
-            error_text.append("export STRIX_LLM='strix/claude-sonnet-4.6'\n", style="dim white")
+            error_text.append("export STRIX_LLM='strix/gpt-5'\n", style="dim white")
         else:
-            error_text.append("export STRIX_LLM='anthropic/claude-sonnet-4-6'\n", style="dim white")
+            error_text.append("export STRIX_LLM='openai/gpt-5'\n", style="dim white")
 
         if missing_optional_vars:
             for var in missing_optional_vars:
@@ -204,12 +206,12 @@ def check_docker_installed() -> None:
 
 
 async def warm_up_llm() -> None:
-    from strix.config.config import resolve_llm_config
-
     console = Console()
 
     try:
         model_name, api_key, api_base = resolve_llm_config()
+        litellm_model, _ = resolve_strix_model(model_name)
+        litellm_model = litellm_model or model_name
 
         test_messages = [
             {"role": "system", "content": "You are a helpful assistant."},
@@ -219,7 +221,7 @@ async def warm_up_llm() -> None:
         llm_timeout = int(Config.get("llm_timeout") or "300")
 
         completion_kwargs: dict[str, Any] = {
-            "model": model_name,
+            "model": litellm_model,
             "messages": test_messages,
             "timeout": llm_timeout,
         }
@@ -460,7 +462,7 @@ def display_completion_message(args: argparse.Namespace, results_path: Path) ->
     console.print("\n")
     console.print(panel)
     console.print()
-    console.print("[#60a5fa]strix.ai[/]  [dim]·[/]  [#60a5fa]discord.gg/strix-ai[/]")
+    console.print("[#60a5fa]models.strix.ai[/]  [dim]·[/]  [#60a5fa]discord.gg/strix-ai[/]")
     console.print()
 
 

strix/interface/streaming_parser.py

@@ -3,8 +3,11 @@ import re
 from dataclasses import dataclass
 from typing import Literal
 
+from strix.llm.utils import normalize_tool_format
+
 
 _FUNCTION_TAG_PREFIX = "<function="
+_INVOKE_TAG_PREFIX = "<invoke "
 
 _FUNC_PATTERN = re.compile(r"<function=([^>]+)>")
 _FUNC_END_PATTERN = re.compile(r"</function>")
@@ -21,9 +24,8 @@ def _get_safe_content(content: str) -> tuple[str, str]:
         return content, ""
 
     suffix = content[last_lt:]
-    target = _FUNCTION_TAG_PREFIX  # "<function="
 
-    if target.startswith(suffix):
+    if _FUNCTION_TAG_PREFIX.startswith(suffix) or _INVOKE_TAG_PREFIX.startswith(suffix):
         return content[:last_lt], suffix
 
     return content, ""
@@ -42,6 +44,8 @@ def parse_streaming_content(content: str) -> list[StreamSegment]:
     if not content:
         return []
 
+    content = normalize_tool_format(content)
+
     segments: list[StreamSegment] = []
 
     func_matches = list(_FUNC_PATTERN.finditer(content))

strix/interface/tui.py

@@ -687,7 +687,7 @@ class StrixTUIApp(App):  # type: ignore[misc]
     CSS_PATH = "assets/tui_styles.tcss"
     ALLOW_SELECT = True
 
-    SIDEBAR_MIN_WIDTH = 140
+    SIDEBAR_MIN_WIDTH = 120
 
     selected_agent_id: reactive[str | None] = reactive(default=None)
     show_splash: reactive[bool] = reactive(default=True)
@@ -829,11 +829,11 @@ class StrixTUIApp(App):  # type: ignore[misc]
             agents_tree.guide_style = "dashed"
 
             stats_display = Static("", id="stats_display")
-            stats_display.ALLOW_SELECT = False
+            stats_scroll = VerticalScroll(stats_display, id="stats_scroll")
 
             vulnerabilities_panel = VulnerabilitiesPanel(id="vulnerabilities_panel")
 
-            sidebar = Vertical(agents_tree, vulnerabilities_panel, stats_display, id="sidebar")
+            sidebar = Vertical(agents_tree, vulnerabilities_panel, stats_scroll, id="sidebar")
 
             content_container.mount(chat_area_container)
             content_container.mount(sidebar)
@@ -1272,6 +1272,9 @@ class StrixTUIApp(App):  # type: ignore[misc]
         if not self._is_widget_safe(stats_display):
             return
 
+        if self.screen.selections:
+            return
+
         stats_content = Text()
 
         stats_text = build_tui_stats_text(self.tracer, self.agent_config)
@@ -1281,15 +1284,7 @@ class StrixTUIApp(App):  # type: ignore[misc]
         version = get_package_version()
         stats_content.append(f"\nv{version}", style="white")
 
-        from rich.panel import Panel
+        self._safe_widget_operation(stats_display.update, stats_content)
-
-        stats_panel = Panel(
-            stats_content,
-            border_style="#333333",
-            padding=(0, 1),
-        )
-
-        self._safe_widget_operation(stats_display.update, stats_panel)
 
     def _update_vulnerabilities_panel(self) -> None:
         """Update the vulnerabilities panel with current vulnerability data."""

strix/interface/utils.py

@@ -390,6 +390,12 @@ def build_tui_stats_text(tracer: Any, agent_config: dict[str, Any] | None = None
         stats_text.append(" · ", style="white")
         stats_text.append(f"${total_stats['cost']:.2f}", style="white")
 
+    caido_url = getattr(tracer, "caido_url", None)
+    if caido_url:
+        stats_text.append("\n")
+        stats_text.append("Caido: ", style="bold white")
+        stats_text.append(caido_url, style="white")
+
     return stats_text
 
 

strix/llm/config.py

@@ -1,5 +1,6 @@
 from strix.config import Config
 from strix.config.config import resolve_llm_config
+from strix.llm.utils import resolve_strix_model
 
 
 class LLMConfig:
@@ -17,6 +18,10 @@ class LLMConfig:
         if not self.model_name:
             raise ValueError("STRIX_LLM environment variable must be set and not empty")
 
+        api_model, canonical = resolve_strix_model(self.model_name)
+        self.litellm_model: str = api_model or self.model_name
+        self.canonical_model: str = canonical or self.model_name
+
         self.enable_prompt_caching = enable_prompt_caching
         self.skills = skills or []
 

strix/llm/dedupe.py

@@ -6,6 +6,7 @@ from typing import Any
 import litellm
 
 from strix.config.config import resolve_llm_config
+from strix.llm.utils import resolve_strix_model
 
 
 logger = logging.getLogger(__name__)
@@ -156,6 +157,8 @@ def check_duplicate(
         comparison_data = {"candidate": candidate_cleaned, "existing_reports": existing_cleaned}
 
         model_name, api_key, api_base = resolve_llm_config()
+        litellm_model, _ = resolve_strix_model(model_name)
+        litellm_model = litellm_model or model_name
 
         messages = [
             {"role": "system", "content": DEDUPE_SYSTEM_PROMPT},
@@ -170,7 +173,7 @@ def check_duplicate(
         ]
 
         completion_kwargs: dict[str, Any] = {
-            "model": model_name,
+            "model": litellm_model,
             "messages": messages,
             "timeout": 120,
         }

strix/llm/llm.py

@@ -14,6 +14,7 @@ from strix.llm.memory_compressor import MemoryCompressor
 from strix.llm.utils import (
     _truncate_to_first_function,
     fix_incomplete_tool_call,
+    normalize_tool_format,
     parse_tool_invocations,
 )
 from strix.skills import load_skills
@@ -63,7 +64,7 @@ class LLM:
         self.agent_name = agent_name
         self.agent_id: str | None = None
         self._total_stats = RequestStats()
-        self.memory_compressor = MemoryCompressor(model_name=config.model_name)
+        self.memory_compressor = MemoryCompressor(model_name=config.litellm_model)
         self.system_prompt = self._load_system_prompt(agent_name)
 
         reasoning = Config.get("strix_reasoning_effort")
@@ -143,10 +144,10 @@ class LLM:
             delta = self._get_chunk_content(chunk)
             if delta:
                 accumulated += delta
-                if "</function>" in accumulated:
+                if "</function>" in accumulated or "</invoke>" in accumulated:
-                    accumulated = accumulated[
+                    end_tag = "</function>" if "</function>" in accumulated else "</invoke>"
-                        : accumulated.find("</function>") + len("</function>")
+                    pos = accumulated.find(end_tag)
-                    ]
+                    accumulated = accumulated[: pos + len(end_tag)]
                     yield LLMResponse(content=accumulated)
                     done_streaming = 1
                     continue
@@ -155,6 +156,7 @@ class LLM:
         if chunks:
             self._update_usage_stats(stream_chunk_builder(chunks))
 
+        accumulated = normalize_tool_format(accumulated)
         accumulated = fix_incomplete_tool_call(_truncate_to_first_function(accumulated))
         yield LLMResponse(
             content=accumulated,
@@ -184,6 +186,9 @@ class LLM:
         conversation_history.extend(compressed)
         messages.extend(compressed)
 
+        if messages[-1].get("role") == "assistant":
+            messages.append({"role": "user", "content": "<meta>Continue the task.</meta>"})
+
         if self._is_anthropic() and self.config.enable_prompt_caching:
             messages = self._add_cache_control(messages)
 
@@ -194,7 +199,7 @@ class LLM:
             messages = self._strip_images(messages)
 
         args: dict[str, Any] = {
-            "model": self.config.model_name,
+            "model": self.config.litellm_model,
             "messages": messages,
             "timeout": self.config.timeout,
             "stream_options": {"include_usage": True},
@@ -229,8 +234,8 @@ class LLM:
     def _update_usage_stats(self, response: Any) -> None:
         try:
             if hasattr(response, "usage") and response.usage:
-                input_tokens = getattr(response.usage, "prompt_tokens", 0)
+                input_tokens = getattr(response.usage, "prompt_tokens", 0) or 0
-                output_tokens = getattr(response.usage, "completion_tokens", 0)
+                output_tokens = getattr(response.usage, "completion_tokens", 0) or 0
 
                 cached_tokens = 0
                 if hasattr(response.usage, "prompt_tokens_details"):
@@ -238,14 +243,11 @@ class LLM:
                     if hasattr(prompt_details, "cached_tokens"):
                         cached_tokens = prompt_details.cached_tokens or 0
 
+                cost = self._extract_cost(response)
             else:
                 input_tokens = 0
                 output_tokens = 0
                 cached_tokens = 0
-
-            try:
-                cost = completion_cost(response) or 0.0
-            except Exception:  # noqa: BLE001
                 cost = 0.0
 
             self._total_stats.input_tokens += input_tokens
@@ -256,6 +258,18 @@ class LLM:
         except Exception:  # noqa: BLE001, S110  # nosec B110
             pass
 
+    def _extract_cost(self, response: Any) -> float:
+        if hasattr(response, "usage") and response.usage:
+            direct_cost = getattr(response.usage, "cost", None)
+            if direct_cost is not None:
+                return float(direct_cost)
+        try:
+            if hasattr(response, "_hidden_params"):
+                response._hidden_params.pop("custom_llm_provider", None)
+            return completion_cost(response, model=self.config.canonical_model) or 0.0
+        except Exception:  # noqa: BLE001
+            return 0.0
+
     def _should_retry(self, e: Exception) -> bool:
         code = getattr(e, "status_code", None) or getattr(
             getattr(e, "response", None), "status_code", None
@@ -275,13 +289,13 @@ class LLM:
 
     def _supports_vision(self) -> bool:
         try:
-            return bool(supports_vision(model=self.config.model_name))
+            return bool(supports_vision(model=self.config.canonical_model))
         except Exception:  # noqa: BLE001
             return False
 
     def _supports_reasoning(self) -> bool:
         try:
-            return bool(supports_reasoning(model=self.config.model_name))
+            return bool(supports_reasoning(model=self.config.canonical_model))
         except Exception:  # noqa: BLE001
             return False
 
@@ -302,7 +316,7 @@ class LLM:
         return result
 
     def _add_cache_control(self, messages: list[dict[str, Any]]) -> list[dict[str, Any]]:
-        if not messages or not supports_prompt_caching(self.config.model_name):
+        if not messages or not supports_prompt_caching(self.config.canonical_model):
             return messages
 
         result = list(messages)

strix/llm/memory_compressor.py

@@ -91,7 +91,7 @@ def _summarize_messages(
     if not messages:
         empty_summary = "<context_summary message_count='0'>{text}</context_summary>"
         return {
-            "role": "assistant",
+            "role": "user",
             "content": empty_summary.format(text="No messages to summarize"),
         }
 
@@ -123,7 +123,7 @@ def _summarize_messages(
             return messages[0]
         summary_msg = "<context_summary message_count='{count}'>{text}</context_summary>"
         return {
-            "role": "assistant",
+            "role": "user",
             "content": summary_msg.format(count=len(messages), text=summary),
         }
     except Exception:
@@ -158,7 +158,7 @@ class MemoryCompressor:
     ):
         self.max_images = max_images
         self.model_name = model_name or Config.get("strix_llm")
-        self.timeout = timeout or int(Config.get("strix_memory_compressor_timeout") or "30")
+        self.timeout = timeout or int(Config.get("strix_memory_compressor_timeout") or "120")
 
         if not self.model_name:
             raise ValueError("STRIX_LLM environment variable must be set and not empty")

strix/llm/utils.py

@@ -3,11 +3,75 @@ import re
 from typing import Any
 
 
+_INVOKE_OPEN = re.compile(r'<invoke\s+name=["\']([^"\']+)["\']>')
+_PARAM_NAME_ATTR = re.compile(r'<parameter\s+name=["\']([^"\']+)["\']>')
+_FUNCTION_CALLS_TAG = re.compile(r"</?function_calls>")
+_STRIP_TAG_QUOTES = re.compile(r"<(function|parameter)\s*=\s*([^>]*?)>")
+
+
+def normalize_tool_format(content: str) -> str:
+    """Convert alternative tool-call XML formats to the expected one.
+
+    Handles:
+      <function_calls>...</function_calls>  → stripped
+      <invoke name="X">                     → <function=X>
+      <parameter name="X">                  → <parameter=X>
+      </invoke>                             → </function>
+      <function="X">                        → <function=X>
+      <parameter="X">                       → <parameter=X>
+    """
+    if "<invoke" in content or "<function_calls" in content:
+        content = _FUNCTION_CALLS_TAG.sub("", content)
+        content = _INVOKE_OPEN.sub(r"<function=\1>", content)
+        content = _PARAM_NAME_ATTR.sub(r"<parameter=\1>", content)
+        content = content.replace("</invoke>", "</function>")
+
+    return _STRIP_TAG_QUOTES.sub(
+        lambda m: f"<{m.group(1)}={m.group(2).strip().strip(chr(34) + chr(39))}>", content
+    )
+
+
+STRIX_MODEL_MAP: dict[str, str] = {
+    "claude-sonnet-4.6": "anthropic/claude-sonnet-4-6",
+    "claude-opus-4.6": "anthropic/claude-opus-4-6",
+    "gpt-5.2": "openai/gpt-5.2",
+    "gpt-5.1": "openai/gpt-5.1",
+    "gpt-5": "openai/gpt-5",
+    "gpt-5.2-codex": "openai/gpt-5.2-codex",
+    "gpt-5.1-codex-max": "openai/gpt-5.1-codex-max",
+    "gpt-5.1-codex": "openai/gpt-5.1-codex",
+    "gpt-5-codex": "openai/gpt-5-codex",
+    "gemini-3-pro-preview": "gemini/gemini-3-pro-preview",
+    "gemini-3-flash-preview": "gemini/gemini-3-flash-preview",
+    "glm-5": "openrouter/z-ai/glm-5",
+    "glm-4.7": "openrouter/z-ai/glm-4.7",
+}
+
+
+def resolve_strix_model(model_name: str | None) -> tuple[str | None, str | None]:
+    """Resolve a strix/ model into names for API calls and capability lookups.
+
+    Returns (api_model, canonical_model):
+    - api_model: openai/<base> for API calls (Strix API is OpenAI-compatible)
+    - canonical_model: actual provider model name for litellm capability lookups
+    Non-strix models return the same name for both.
+    """
+    if not model_name or not model_name.startswith("strix/"):
+        return model_name, model_name
+
+    base_model = model_name[6:]
+    api_model = f"openai/{base_model}"
+    canonical_model = STRIX_MODEL_MAP.get(base_model, api_model)
+    return api_model, canonical_model
+
+
 def _truncate_to_first_function(content: str) -> str:
     if not content:
         return content
 
-    function_starts = [match.start() for match in re.finditer(r"<function=", content)]
+    function_starts = [
+        match.start() for match in re.finditer(r"<function=|<invoke\s+name=", content)
+    ]
 
     if len(function_starts) >= 2:
         second_function_start = function_starts[1]
@@ -18,6 +82,7 @@ def _truncate_to_first_function(content: str) -> str:
 
 
 def parse_tool_invocations(content: str) -> list[dict[str, Any]] | None:
+    content = normalize_tool_format(content)
     content = fix_incomplete_tool_call(content)
 
     tool_invocations: list[dict[str, Any]] = []
@@ -47,12 +112,14 @@ def parse_tool_invocations(content: str) -> list[dict[str, Any]] | None:
 
 
 def fix_incomplete_tool_call(content: str) -> str:
-    """Fix incomplete tool calls by adding missing </function> tag."""
+    """Fix incomplete tool calls by adding missing closing tag.
-    if (
+
-        "<function=" in content
+    Handles both ``<function=…>`` and ``<invoke name="…">`` formats.
-        and content.count("<function=") == 1
+    """
-        and "</function>" not in content
+    has_open = "<function=" in content or "<invoke " in content
-    ):
+    count_open = content.count("<function=") + content.count("<invoke ")
+    has_close = "</function>" in content or "</invoke>" in content
+    if has_open and count_open == 1 and not has_close:
         content = content.rstrip()
         content = content + "function>" if content.endswith("</") else content + "\n</function>"
     return content
@@ -73,6 +140,7 @@ def clean_content(content: str) -> str:
     if not content:
         return ""
 
+    content = normalize_tool_format(content)
     content = fix_incomplete_tool_call(content)
 
     tool_pattern = r"<function=[^>]+>.*?</function>"

strix/runtime/docker_runtime.py

@@ -22,6 +22,7 @@ from .runtime import AbstractRuntime, SandboxInfo
 HOST_GATEWAY_HOSTNAME = "host.docker.internal"
 DOCKER_TIMEOUT = 60
 CONTAINER_TOOL_SERVER_PORT = 48081
+CONTAINER_CAIDO_PORT = 48080
 
 
 class DockerRuntime(AbstractRuntime):
@@ -37,6 +38,7 @@ class DockerRuntime(AbstractRuntime):
         self._scan_container: Container | None = None
         self._tool_server_port: int | None = None
         self._tool_server_token: str | None = None
+        self._caido_port: int | None = None
 
     def _find_available_port(self) -> int:
         with socket.socket(socket.AF_INET, socket.SOCK_STREAM) as s:
@@ -78,6 +80,10 @@ class DockerRuntime(AbstractRuntime):
         if port_bindings.get(port_key):
             self._tool_server_port = int(port_bindings[port_key][0]["HostPort"])
 
+        caido_port_key = f"{CONTAINER_CAIDO_PORT}/tcp"
+        if port_bindings.get(caido_port_key):
+            self._caido_port = int(port_bindings[caido_port_key][0]["HostPort"])
+
     def _wait_for_tool_server(self, max_retries: int = 30, timeout: int = 5) -> None:
         host = self._resolve_docker_host()
         health_url = f"http://{host}:{self._tool_server_port}/health"
@@ -121,6 +127,7 @@ class DockerRuntime(AbstractRuntime):
                     time.sleep(1)
 
                 self._tool_server_port = self._find_available_port()
+                self._caido_port = self._find_available_port()
                 self._tool_server_token = secrets.token_urlsafe(32)
                 execution_timeout = Config.get("strix_sandbox_execution_timeout") or "120"
 
@@ -130,7 +137,10 @@ class DockerRuntime(AbstractRuntime):
                     detach=True,
                     name=container_name,
                     hostname=container_name,
-                    ports={f"{CONTAINER_TOOL_SERVER_PORT}/tcp": self._tool_server_port},
+                    ports={
+                        f"{CONTAINER_TOOL_SERVER_PORT}/tcp": self._tool_server_port,
+                        f"{CONTAINER_CAIDO_PORT}/tcp": self._caido_port,
+                    },
                     cap_add=["NET_ADMIN", "NET_RAW"],
                     labels={"strix-scan-id": scan_id},
                     environment={
@@ -152,6 +162,7 @@ class DockerRuntime(AbstractRuntime):
                 if attempt < max_retries:
                     self._tool_server_port = None
                     self._tool_server_token = None
+                    self._caido_port = None
                     time.sleep(2**attempt)
             else:
                 return container
@@ -173,6 +184,7 @@ class DockerRuntime(AbstractRuntime):
                 self._scan_container = None
                 self._tool_server_port = None
                 self._tool_server_token = None
+                self._caido_port = None
 
         try:
             container = self.client.containers.get(container_name)
@@ -260,7 +272,7 @@ class DockerRuntime(AbstractRuntime):
             raise RuntimeError("Docker container ID is unexpectedly None")
 
         token = existing_token or self._tool_server_token
-        if self._tool_server_port is None or token is None:
+        if self._tool_server_port is None or self._caido_port is None or token is None:
             raise RuntimeError("Tool server not initialized")
 
         host = self._resolve_docker_host()
@@ -273,6 +285,7 @@ class DockerRuntime(AbstractRuntime):
             "api_url": api_url,
             "auth_token": token,
             "tool_server_port": self._tool_server_port,
+            "caido_port": self._caido_port,
             "agent_id": agent_id,
         }
 
@@ -314,6 +327,7 @@ class DockerRuntime(AbstractRuntime):
             self._scan_container = None
             self._tool_server_port = None
             self._tool_server_token = None
+            self._caido_port = None
         except (NotFound, DockerException):
             pass
 
@@ -323,6 +337,7 @@ class DockerRuntime(AbstractRuntime):
             self._scan_container = None
             self._tool_server_port = None
             self._tool_server_token = None
+            self._caido_port = None
 
             if container_name is None:
                 return

strix/runtime/runtime.py

@@ -7,6 +7,7 @@ class SandboxInfo(TypedDict):
     api_url: str
     auth_token: str | None
     tool_server_port: int
+    caido_port: int
     agent_id: str
 
 

strix/telemetry/tracer.py

@@ -56,6 +56,7 @@ class Tracer:
         self._next_message_id = 1
         self._saved_vuln_ids: set[str] = set()
 
+        self.caido_url: str | None = None
         self.vulnerability_found_callback: Callable[[dict[str, Any]], None] | None = None
 
     def set_run_name(self, run_name: str) -> None: