Skill: lrg913427-dot/agent-lens (version 2.16.0)

--- name: agent-lens description: "Track AI agent API calls, analyze token usage, and optimize costs. Use when user wants to monitor LLM spending, debug API calls, track token consumption, or generate cost reports for OpenAI/Anthropic/Google/DeepSeek APIs." version: 2.15.0 author: lrg913427-dot license: MIT metadata: hermes: tags: [llm, cost, tracking, observability, tokens, api, monitoring, agent] related_skills: [db-explorer] --- # Agent Lens Track every AI API call, analyze token usage, and optimize costs. ## When to Use Activate this skill when the user: - Says "how much am I spending", "token usage", "API costs" - Wants to know which model is most expensive - Needs to optimize prompt costs - Wants to track API call latency or error rates - Mentions "budget", "cost optimization", or "token counting" - Asks "why is my API bill so high" ## Quick Start ```bash # Install pip install git+https://github.com/lrg913427-dot/agent-lens.git # Generate demo data and see it in action agent-lens demo # View stats agent-lens stats agent-lens cost agent-lens recent ``` ## Three Ways to Track ### 1. Decorator (easiest) ```python from agent_lens import AgentLens lens = AgentLens(agent_name="my-agent") @lens.track(model="gpt-4o") def call_api(prompt): return client.chat.completions.create( model="gpt-4o", messages=[{"role": "user", "content": prompt}], ) # Token usage is auto-extracted from OpenAI-style responses result = call_api("Hello") ``` ### 2. Context Manager (flexible) ```python from agent_lens import AgentLens lens = AgentLens(agent_name="my-agent") with lens.trace(model="claude-3.5-sonnet") as t: result = client.chat.completions.create(...) t.input_tokens = result.usage.prompt_tokens t.output_tokens = result.usage.completion_tokens ``` ### 3. Direct Record (manual) ```python from agent_lens import AgentLens lens = AgentLens(agent_name="my-agent") lens.record( model="gpt-4o", input_tokens=1500, output_tokens=800, latency_ms=2300, ) ``` ### Global Shortcuts ```python from agent_lens import record, trace, track record(model="gpt-4o", input_tokens=100, output_tokens=50) with trace(model="gpt-4o") as t: ... @track(model="gpt-4o") def my_func(): ... ``` ## CLI Commands | Command | Description | |---------|-------------| | `agent-lens stats` | Overview: total calls, tokens, cost | | `agent-lens report --by model` | Breakdown by model/provider/agent | | `agent-lens cost` | Cost ranking with percentage bars | | `agent-lens recent -n 10` | Latest API calls | | `agent-lens top` | Most expensive calls | | `agent-lens export --json` | Export to JSON | | `agent-lens export -o data.csv` | Export to CSV | | `agent-lens clean --before <ts>` | Clean old data | | `agent-lens demo` | Generate sample data | ## Cost Optimization Workflow When user asks "how can I save money": 1. **Run cost report**: `agent-lens cost` 2. **Identify expensive models**: Which models cost the most? 3. **Check token efficiency**: Are prompts too long? 4. **Suggest cheaper alternatives**: - gpt-4o → gpt-4o-mini (10x cheaper) - claude-3.5-sonnet → claude-3.5-haiku (4x cheaper) - gpt-4 → gpt-4o (2x cheaper) 5. **Check caching**: Are there repeated prompts? 6. **Check error rate**: `agent-lens report --by status` ## Token Counting ```python import tiktoken def count_tokens(text: str, model: str = "gpt-4o") -> int: """Count tokens for a given model.""" try: enc = tiktoken.encoding_for_model(model) except KeyError: enc = tiktoken.get_encoding("cl100k_base") return len(enc.encode(text)) # Check before sending prompt = "Your long prompt here..." tokens = count_tokens(prompt) print(f"Prompt: {tokens} tokens") print(f"Estimated cost: ${tokens * 2.50 / 1_000_000:.4f}") ``` ## Supported Models Pricing data for: OpenAI (GPT-4o, o1, o3), Anthropic (Claude 3.5/4), Google (Gemini 2.x), DeepSeek, Mistral, Qwen, GLM, MiMo. Unknown models are tracked but cost shows "—". ## Integration with Hermes ```python # Track Hermes agent API calls from agent_lens import AgentLens lens = AgentLens(agent_name="hermes-main") # In your agent loop: with lens.trace(model=config.model) as t: response = agent.run_conversation(message) t.input_tokens = response.get("input_tokens", 0) t.output_tokens = response.get("output_tokens", 0) ``` ## Data Storage SQLite at `~/.agent-lens/traces.db`. Fully local, no cloud service needed. ## Pitfalls - Token extraction auto-works only for OpenAI-compatible response format - For non-OpenAI providers, manually set `t.input_tokens` and `t.output_tokens` - Cost estimates use list prices; actual costs may differ with discounts - Database grows over time; use `agent-lens clean` periodically ## Verification ```bash agent-lens demo # Generate 20 sample records agent-lens stats # Should show 20 calls agent-lens cost # Should show cost breakdown by model ```


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)