git:20260321.e9d57a6 to git:20260322.96ffe9a
23 added, 1 removed. Audit A to A.
# Ollama Provider Configuration
This file contains Ollama-specific instructions for Claude Octopus workflows.
## Provider Information
- **Provider**: Ollama (Local LLM)
- **Emoji**: (none — local provider, no cost indicator needed)
- **API Key**: None required — fully local
- **CLI Command**: `ollama`
## Detection
- CLI: `command -v ollama`
- - Server health: `curl -s http://localhost:11434/api/tags`
+ - Server health: `curl -sf http://localhost:11434/api/tags`
+ - Available models: `ollama list`
If the CLI is installed but the server is not running, suggest: `ollama serve`
+
+ ## Integration Paths
+
+ ### Primary: Direct CLI Dispatch (recommended)
+
+ The orchestrator dispatches via `ollama run <model> "<prompt>"`. This gives full visibility
+ into provider status, model selection, and error handling through the standard provider pipeline
+ (doctor checks, circuit breaker, fallback history).
+
+ ### Secondary: Anthropic-Compatible API Bridge
+
+ Ollama exposes an Anthropic-compatible endpoint. Set these environment variables to make
+ Ollama act as a Claude drop-in (useful for tools that only speak the Anthropic API):
+
+ ```bash
+ export ANTHROPIC_BASE_URL=http://localhost:11434
+ export ANTHROPIC_AUTH_TOKEN=ollama
+ ```
+
+ The MCP and OpenClaw adapters forward these env vars automatically. This path is user-configurable
+ but not the primary integration — it hides Ollama's identity from the orchestrator's observability.
## Role Assignment
Ollama models serve as:
- **Research assistant** — local model for exploration and brainstorming
- **Implementation reviewer** — code review without API costs
- **Fallback** — when external providers are unavailable
## Model Selection
Use `ollama list` to detect available models. Prefer:
- `llama3.3` or `llama3.1` for general tasks
- `codellama` for code-specific tasks
- `mistral` as a lightweight alternative
## Usage Patterns
### Invoking Ollama
```bash
# Basic query
ollama run <model> "<prompt>"
# List available models
ollama list
# Pull a model
ollama pull llama3.3
```
### Ollama Strengths
Ollama excels at:
1. **Zero-Cost Iteration** — Unlimited queries with no API spend
2. **Offline Workflows** — No internet connection required
3. **Privacy-Sensitive Tasks** — All data stays on-device
4. **Rapid Prototyping** — Quick brainstorming without budget concerns
5. **Fallback Coverage** — Available when cloud providers are down or rate-limited
### When to Use Ollama
Use Ollama for:
- Exploration and brainstorming when cost matters
- Offline or air-gapped environments
- Privacy-sensitive code review
- Fallback when Codex/Gemini auth is expired or unavailable
- Local testing of prompt patterns before sending to cloud providers
## Dispatch Pattern
```bash
ollama run <model> "<prompt>"
```
## Cost
Zero — fully local, no API keys needed.
## Limitations
- Quality varies significantly by model size
- No streaming in CLI mode (batch output)
- Limited context window compared to cloud providers
- Not suitable for primary orchestration role
- Requires sufficient local hardware (RAM/GPU) for larger models
- Model download required before first use
## Timeout Configuration
Default timeout: 120 seconds (local models can be slower on first load)
Can be configured in orchestrate.sh:
```bash
OLLAMA_TIMEOUT=180 # 3 minutes for large models
```
## Error Handling
Common errors:
- `connection refused`: Ollama server not running — run `ollama serve`
- `model not found`: Model not pulled — run `ollama pull <model>`
- `out of memory`: Model too large for available RAM/VRAM — try a smaller model
- `Timeout`: Model loading on first run — increase timeout or use a smaller model
## Integration with Workflows
Ollama can be used in:
- **Discover Phase**: Local brainstorming and exploration
- **Develop Phase**: Code review without API costs
- **Fallback**: Any phase when cloud providers are unavailable