Feature/litellm proxy (#27)

* feat: seed governance config and responses routing

* Add env-configurable timeout for proxy providers

* Integrate LiteLLM OTEL collector and update docs

* Make .env.litellm optional for LiteLLM proxy

* Add LiteLLM proxy integration with model-agnostic virtual keys

Changes:
- Bootstrap generates 3 virtual keys with individual budgets (CLI: $100, Task-Agent: $25, Cognee: $50)
- Task-agent loads config at runtime via entrypoint script to wait for bootstrap completion
- All keys are model-agnostic by default (no LITELLM_DEFAULT_MODELS restrictions)
- Bootstrap handles database/env mismatch after docker prune by deleting stale aliases
- CLI and Cognee configured to use LiteLLM proxy with virtual keys
- Added comprehensive documentation in volumes/env/README.md

Technical details:
- task-agent entrypoint waits for keys in .env file before starting uvicorn
- Bootstrap creates/updates TASK_AGENT_API_KEY, COGNEE_API_KEY, and OPENAI_API_KEY
- Removed hardcoded API keys from docker-compose.yml
- All services route through http://localhost:10999 proxy

* Fix CLI not loading virtual keys from global .env

Project .env files with empty OPENAI_API_KEY values were overriding
the global virtual keys. Updated _load_env_file_if_exists to only
override with non-empty values.

* Fix agent executor not passing API key to LiteLLM

The agent was initializing LiteLlm without api_key or api_base,
causing authentication errors when using the LiteLLM proxy. Now
reads from OPENAI_API_KEY/LLM_API_KEY and LLM_ENDPOINT environment
variables and passes them to LiteLlm constructor.

* Auto-populate project .env with virtual key from global config

When running 'ff init', the command now checks for a global
volumes/env/.env file and automatically uses the OPENAI_API_KEY
virtual key if found. This ensures projects work with LiteLLM
proxy out of the box without manual key configuration.

* docs: Update README with LiteLLM configuration instructions

Add note about LITELLM_GEMINI_API_KEY configuration and clarify that OPENAI_API_KEY default value should not be changed as it's used for the LLM proxy.

* Refactor workflow parameters to use JSON Schema defaults

Consolidates parameter defaults into JSON Schema format, removing the separate default_parameters field. Adds extract_defaults_from_json_schema() helper to extract defaults from the standard schema structure. Updates LiteLLM proxy config to use LITELLM_OPENAI_API_KEY environment variable.

* Remove .env.example from task_agent

* Fix MDX syntax error in llm-proxy.md

* fix: apply default parameters from metadata.yaml automatically

Fixed TemporalManager.run_workflow() to correctly apply default parameter
values from workflow metadata.yaml files when parameters are not provided
by the caller.

Previous behavior:
- When workflow_params was empty {}, the condition
  `if workflow_params and 'parameters' in metadata` would fail
- Parameters would not be extracted from schema, resulting in workflows
  receiving only target_id with no other parameters

New behavior:
- Removed the `workflow_params and` requirement from the condition
- Now explicitly checks for defaults in parameter spec
- Applies defaults from metadata.yaml automatically when param not provided
- Workflows receive all parameters with proper fallback:
  provided value > metadata default > None

This makes metadata.yaml the single source of truth for parameter defaults,
removing the need for workflows to implement defensive default handling.

Affected workflows:
- llm_secret_detection (was failing with KeyError)
- All other workflows now benefit from automatic default application

Co-authored-by: tduhamel42 <tduhamel@fuzzinglabs.com>

ai/agents/task_agent/README.md

@@ -43,18 +43,34 @@ cd task_agent
 # cp .env.example .env
 ```
 
-Edit `.env` (or `.env.example`) and add your API keys. The agent must be restarted after changes so the values are picked up:
+Edit `.env` (or `.env.example`) and add your proxy + API keys. The agent must be restarted after changes so the values are picked up:
 ```bash
-# Set default model
-LITELLM_MODEL=gemini/gemini-2.0-flash-001
+# Route every request through the proxy container (use http://localhost:10999 from the host)
+FF_LLM_PROXY_BASE_URL=http://llm-proxy:4000
 
-# Add API keys for providers you want to use
-GOOGLE_API_KEY=your_google_api_key
-OPENAI_API_KEY=your_openai_api_key
-ANTHROPIC_API_KEY=your_anthropic_api_key
-OPENROUTER_API_KEY=your_openrouter_api_key
+# Default model + provider the agent boots with
+LITELLM_MODEL=openai/gpt-4o-mini
+LITELLM_PROVIDER=openai
+
+# Virtual key issued by the proxy to the task agent (bootstrap replaces the placeholder)
+OPENAI_API_KEY=sk-proxy-default
+
+# Upstream keys stay inside the proxy. Store real secrets under the LiteLLM
+# aliases and the bootstrapper mirrors them into .env.litellm for the proxy container.
+LITELLM_OPENAI_API_KEY=your_real_openai_api_key
+LITELLM_ANTHROPIC_API_KEY=your_real_anthropic_key
+LITELLM_GEMINI_API_KEY=your_real_gemini_key
+LITELLM_MISTRAL_API_KEY=your_real_mistral_key
+LITELLM_OPENROUTER_API_KEY=your_real_openrouter_key
 ```
 
+> When running the agent outside of Docker, swap `FF_LLM_PROXY_BASE_URL` to the host port (default `http://localhost:10999`).
+
+The bootstrap container provisions LiteLLM, copies provider secrets into
+`volumes/env/.env.litellm`, and rewrites `volumes/env/.env` with the virtual key.
+Populate the `LITELLM_*_API_KEY` values before the first launch so the proxy can
+reach your upstream providers as soon as the bootstrap script runs.
+
 ### 2. Install Dependencies
 
 ```bash