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Documentation

Reference

Technical reference for access, model tiers, safeguards, and troubleshooting.

Access Requirements

Primary Access Credential (Required)

A primary access credential is required to run analyses through a hosted inference service.

Typical workflow:

  1. Create an account with your chosen service
  2. Generate an access credential
  3. Store it securely and pass it to your integration

Illustrative usage:

Python 
result = await run_analysis(
    query="Your analysis query",
    api_key="your-access-key",
)
Optional Research Credential

Some deployments support optional live research or fresh-data retrieval. When available, an additional credential can unlock those capabilities.

Python 
result = await run_analysis(
    query="Analyze recent developments in AI regulation",
    api_key="your-access-key",
    research_key="your-research-key",
)
Optional Image Credential

Some environments support optional image generation for report artwork. When available, a separate credential can enable that feature.

Python 
result = await run_analysis(
    query="Analyze the future of sustainable architecture",
    api_key="your-access-key",
    image_key="your-image-key",
)

Access Summary

Credential Required? Purpose Illustrative Name
Primary access credential Required Model access api_key
Research credential Optional Live research features research_key
Image credential Optional Image generation image_key

Available Models

GPT-OSS 20B

Identifier: openai/gpt-oss-20b

Description Compact open-weight Mixture of Experts (MoE) model optimized for cost-efficient deployment
Size 21 billion total parameters, 3.6 billion active per token (32 experts, Top-4 routing)
Architecture MoE with 24 layers, Grouped Query Attention, RMSNorm
Context Window 128K tokens
Speed High-throughput hosted inference
License Apache 2.0 (fully open for commercial use)

Hardware Requirements: Can run on high-end consumer GPUs with at least 16-20 GB VRAM (NVIDIA RTX 4090/5090). Using MXFP4 quantization enables fast, efficient local inference. 24+ GB VRAM recommended for optimal performance.

Best For: Cost-efficient agentic workflows, tool calling, web browsing, code execution.

GPT-OSS 120B

Identifier: openai/gpt-oss-120b

Description Larger open-weight MoE model for complex tasks
Size 120 billion total parameters
Context Window 128K tokens
License Apache 2.0 (fully open for commercial use)

Hardware Requirements: Requires a single 80GB H100 GPU (typically accessed via data center or cloud).

Best For: Complex reasoning, advanced code generation, research tasks.

Why These Models?

This program relies extensively on advanced structured data output—the ability for LLMs to return responses in precise, validated formats (Pydantic models). The GPT-OSS models were specifically trained by OpenAI to handle structured data as part of their training process, making them ideal for this application where every response must conform to a specific schema.

Error Handling

Cost Safeguards

The system tracks costs continuously and enforces limits:

Python 
# Internal cost checking (from source)
current_cost = await cost_tracker.get_total_cost()
if current_cost > max_cost:
    raise CostFailsafeError(
        message=f"Cost limit exceeded: ${current_cost:.4f} > ${max_cost}",
        current_cost=current_cost,
        cost_limit=max_cost,
    )

Task Evaluation Recovery

%%{init: {'theme': 'base', 'themeVariables': { 'primaryColor': '#1a1a2e', 'primaryTextColor': '#fff', 'primaryBorderColor': '#4a4a6a', 'lineColor': '#6c63ff', 'fontFamily': 'JetBrains Mono, monospace'}}}%%
flowchart TD
    A["Task Completes"] --> B{"Status
Check"}
    B -->|"Error"| C["Automatic Rejection
no LLM call"]
    B -->|"Success"| D["LLM Evaluation"]
    D --> E{"Quality
Assessment"}
    E -->|"Accepted"| F["Write to
Analysis History"]
    E -->|"Rejected"| G["Log Reason
Don't Write"]

    C --> H(["Continue Pipeline"])
    F --> H
    G --> H

    style A fill:#16213e,stroke:#4a4a6a,color:#fff
    style B fill:#0f3460,stroke:#e94560,stroke-width:2px,color:#fff
    style C fill:#6b2737,stroke:#e94560,color:#fff
    style D fill:#16213e,stroke:#4a4a6a,color:#fff
    style E fill:#0f3460,stroke:#e94560,stroke-width:2px,color:#fff
    style F fill:#0d7377,stroke:#14ffec,stroke-width:2px,color:#fff
    style G fill:#6b2737,stroke:#e94560,color:#fff
    style H fill:#4a4a6a,stroke:#6c63ff,color:#fff

Only accepted tasks inform subsequent analysis, preventing error propagation.

Troubleshooting

Possible causes:

  • Invalid or missing access credential
  • Cost limit set too low
  • Content filter blocking query

Solutions:

  • Verify API key is valid and has credits
  • Increase cost_limit parameter
  • Review query for content policy issues

Possible causes:

  • Complex query with many iterations
  • Network issues
  • API rate limiting

Solutions:

  • Reduce max_iterations
  • Use quick mode for testing
  • Check your provider's service status page

Possible causes:

  • Using 20B model for complex task
  • Insufficient context
  • Too few iterations

Solutions:

  • Upgrade to gpt-oss-120b
  • Provide more focused context
  • Increase max_iterations and use thorough mode

Possible causes:

  • Safety violations in generated code
  • Runtime errors in calculations
  • Timeout during execution

Solutions:

  • Check error details in output
  • Simplify the computational request
  • Increase python_tool_timeout

Symptoms:

  • Slow execution
  • Timeout errors
  • Partial results

Solutions:

  • Use shared state manager for concurrent runs
  • Reduce concurrent analysis count
  • Add delays between requests