> ## Documentation Index > Fetch the complete documentation index at: https://praison.ai/docs/llms.txt > Use this file to discover all available pages before exploring further. # Tool Config > Configure tool execution timeouts, retries, parallel execution, and artifact storage Tool Config controls how tools run — their timeout, retry behavior, output limits, and whether large results get stored as artifacts. ```python theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}} from praisonaiagents import Agent from praisonaiagents.config.feature_configs import ToolConfig agent = Agent( name="DataAgent", instructions="You analyze data using tools.", tools=[my_data_tool], tool_config=ToolConfig( timeout=60, parallel=True, enable_artifacts=True, ) ) result = agent.start("Analyze the sales data and generate a report") print(result) ``` The user asks the agent to use tools; ToolConfig controls timeouts, retries, parallel execution, and artifact storage for those calls. ```mermaid theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}} graph LR subgraph "Tool Execution" AGENT["🤖 Agent"] --> PARALLEL{"⚡ Parallel?"} PARALLEL -- Yes --> T1["🔧 Tool 1"] & T2["🔧 Tool 2"] PARALLEL -- No --> T1S["🔧 Tool\n(sequential)"] T1 & T2 --> CHECK{"📦 Output\n> limit?"} T1S --> CHECK CHECK -- Yes --> ARTIFACT["🗄️ Artifact\nStore"] CHECK -- No --> RESP["✅ Response"] ARTIFACT --> RESP end classDef agent fill:#8B0000,stroke:#7C90A0,color:#fff classDef tool fill:#189AB4,stroke:#7C90A0,color:#fff classDef check fill:#F59E0B,stroke:#7C90A0,color:#fff classDef success fill:#10B981,stroke:#7C90A0,color:#fff classDef storage fill:#6366F1,stroke:#7C90A0,color:#fff class AGENT agent class T1,T2,T1S tool class PARALLEL,CHECK check class ARTIFACT storage class RESP success ``` ## Quick Start Use `ToolConfig()` defaults to get retry protection and safe output limits: ```python theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}} from praisonaiagents import Agent from praisonaiagents.config.feature_configs import ToolConfig agent = Agent( name="Assistant", instructions="You are a helpful assistant with tools.", tools=[search_tool], tool_config=ToolConfig() ) agent.start("Search for recent AI news") ``` Configure timeout, retries, and parallel execution: ```python theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}} from praisonaiagents import Agent from praisonaiagents.config.feature_configs import ToolConfig agent = Agent( instructions="You are a multi-source research agent.", tool_config=ToolConfig( parallel=True, timeout=60, output_limit=32000, ), ) agent.start("Get weather, news, and stock data for San Francisco.") ``` ```python theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}} from praisonaiagents import Agent from praisonaiagents.config.feature_configs import ToolConfig agent = Agent( instructions="You are a data analysis agent.", tool_config=ToolConfig( enable_artifacts=True, artifact_retention_days=14, output_limit=32000, ), ) agent.start("Analyze this large CSV file and summarize the findings.") ``` *** ## How It Works ```mermaid theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}} sequenceDiagram participant User participant Agent participant ToolConfig participant Tool User->>Agent: Request with tools Agent->>ToolConfig: Apply timeout, limits Agent->>Tool: Execute (with timeout) alt Output within limit Tool-->>Agent: Direct output else Output exceeds limit Tool-->>ToolConfig: Spill to artifact store ToolConfig-->>Agent: Artifact reference end Agent-->>User: Response ``` | Phase | What happens | | ---------- | ------------------------------------------------ | | 1. Execute | Tool runs within the configured timeout | | 2. Retry | Failed executions retry with exponential backoff | | 3. Output | Large outputs spill to artifact storage | | 4. Respond | Agent uses tool output to form the final answer | *** ## Configuration Options Full parameter reference for ToolConfig **Precedence ladder:** ```python theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}} # Level 1: Bool (enable with defaults) agent = Agent(tool_config=True) # Level 2: ToolConfig (full control) agent = Agent(tool_config=ToolConfig( timeout=60, parallel=True, enable_artifacts=True, )) ``` | Option | Type | Default | Description | | ------------------------- | --------------------- | -------- | ----------------------------------------------------- | | `timeout` | `int \| None` | `None` | Per-tool timeout in seconds | | `retry_policy` | `RetryPolicy \| None` | `None` | Retry with exponential backoff | | `parallel` | `bool` | `False` | Run batched LLM tool calls concurrently | | `output_limit` | `int` | `16000` | Max bytes before spilling to artifact store | | `output_max_lines` | `int \| None` | `None` | Max lines before spilling | | `output_direction` | `str` | `"both"` | Truncation direction: `"head"`, `"tail"`, or `"both"` | | `enable_artifacts` | `bool` | `False` | Enable artifact storage for large outputs | | `artifact_retention_days` | `int` | `7` | Days to keep artifacts before cleanup | | `artifact_store` | `Any \| None` | `None` | Custom artifact store instance | | `redact_secrets` | `bool` | `True` | Redact secrets from artifacts | Legacy keyword arguments for tools now raise `TypeError`. Always use `ToolConfig` for tool configuration. See the migration guide if upgrading from an older version. *** ## Common Patterns ### Pattern 1 — Timeout for slow external APIs ```python theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}} from praisonaiagents import Agent from praisonaiagents.config.feature_configs import ToolConfig agent = Agent( name="DataAnalyzer", instructions="Analyze large datasets and return summaries.", tools=[run_sql_query], tool_config=ToolConfig( output_limit=32000, # Larger inline limit enable_artifacts=True, # Store huge outputs as files artifact_retention_days=14, # Keep for 2 weeks ) ) agent.start("Run the monthly sales report query") ``` **Parallel tool execution:** ```python theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}} from praisonaiagents import Agent from praisonaiagents.config.feature_configs import ToolConfig agent = Agent( name="ResearchAgent", instructions="Search multiple sources simultaneously.", tools=[search_news, search_arxiv, search_wikipedia], tool_config=ToolConfig( parallel=True, # Run all three searches at once timeout=15, # Fail fast if any tool is slow ) ) agent.start("Research the current state of quantum computing") ``` **Tail-only output for log tools:** ```python theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}} from praisonaiagents import Agent from praisonaiagents.config.feature_configs import ToolConfig agent = Agent( name="LogAnalyzer", instructions="Check system logs for errors.", tools=[get_system_logs], tool_config=ToolConfig( output_direction="tail", # Keep the most recent log lines output_max_lines=500, ) ) agent.start("Check recent server error logs for anomalies.") ``` *** ## Best Practices Any tool that calls an external API or runs a subprocess should have a `timeout`. Without one, a hung tool call blocks your agent indefinitely. Start with 30–60 seconds and adjust based on your tool's expected latency. Use `parallel=True` when your agent commonly calls multiple tools at once and those tools don't depend on each other's outputs. This can cut wall-clock time significantly. If your tools return large datasets (SQL queries, file reads, API responses), set `enable_artifacts=True`. This prevents large outputs from filling the LLM context window, which wastes tokens and can cause errors. Leave `redact_secrets=True` (the default) to prevent API keys, passwords, and tokens from being stored in artifact files or shown in tool outputs. *** ## Related How artifacts are stored and retrieved Safe concurrent tool execution Group and manage tools as sets Configure retry behavior for failing tools