haft vs Claude Agent SDK

Enforces a disciplined 5-mode engineering cycle (Understand → Explore → Choose → Execute → Verify) by requiring AI agents to frame problems before solving them, generate genuinely different solution variants, and compare options under parity constraints. Implements this via MCP tools (haft_problem, haft_solution, haft_decision) that validate reasoning artifacts against a formal specification before allowing progression to implementation.

Unique: Implements a formal specification-driven reasoning cycle with maturity (Unassessed → Shipped) and freshness (Healthy → Stale → At Risk) tracking, enforcing parity in comparisons via a knowledge graph that links decisions to codebase artifacts — unlike generic prompt engineering, this creates falsifiable contracts with evidence decay mechanics

vs alternatives: Differs from Cursor/Claude Code's native reasoning by adding governance layer that prevents decision drift and enforces structured comparison, whereas standard agents optimize for speed-to-code

Exposes Haft's reasoning capabilities as a Model Context Protocol (MCP) server via JSON-RPC transport, providing six specialized tools (haft_problem, haft_solution, haft_decision, haft_evidence, haft_check, haft_search) that AI agents can invoke natively within their execution environment. The server runs as a subprocess managed by the agent's MCP client, maintaining a persistent SQLite state store and knowledge graph indexed to the codebase.

Unique: Implements MCP as the primary delivery surface (not a secondary plugin), with six domain-specific tools designed for the FPF cycle rather than generic function calling — includes codebase-aware search and evidence decay scoring built into the protocol layer

vs alternatives: More specialized than generic MCP servers (e.g., Anthropic's file-system MCP) because tools are designed for reasoning governance, not file I/O; tighter integration with decision lifecycle than REST APIs

Enforces equal rigor in comparing competing solutions by requiring that all variants be evaluated against the same criteria, preventing bias toward preferred solutions. Implements parity checks via the haft_solution and haft_decision tools that validate solution descriptions follow the same structure and depth. Tracks comparison fairness metrics to ensure decisions are based on equivalent evidence.

Unique: Implements structural parity checks that validate all solutions follow the same evaluation template and depth — unlike generic decision frameworks, this prevents strawman alternatives and ensures fair comparison

vs alternatives: More rigorous than informal decision-making because it enforces structural equivalence; differs from decision matrices by focusing on comparison process rather than scoring

Monitors the health of engineering decisions across two axes: maturity (progress from Unassessed to Shipped) and freshness (Healthy → Stale → At Risk based on evidence age and drift detection). Implements R_eff (effective reasoning score) that decays over time as supporting evidence ages, triggering alerts when decisions drift from their original context. Uses SQLite schema with timestamp-based queries to identify stale decisions and prompt re-evaluation.

Unique: Implements a two-axis decision lifecycle model (maturity + freshness) with time-decay scoring (R_eff) that automatically degrades decision confidence — unlike static decision logs, this creates a living system where old decisions are flagged for re-evaluation without manual intervention

vs alternatives: More sophisticated than ADR (Architecture Decision Records) because it tracks decision health over time and flags staleness; differs from code review tools by focusing on decision validity rather than code quality

Builds a knowledge graph that links engineering decisions to codebase artifacts (modules, functions, files) using FPF Spec Search & Indexer. Enables semantic search over past decisions filtered by codebase context, allowing agents to query 'decisions affecting this module' or 'solutions tried for this problem pattern'. Stores graph in SQLite with projections that map decisions to code locations and vice versa.

Unique: Implements a bidirectional knowledge graph (decisions ↔ code artifacts) with FPF Spec Search that understands decision semantics and codebase structure simultaneously — unlike generic code search, this links reasoning to implementation and enables decision-centric queries

vs alternatives: More targeted than full-text search because it understands decision structure and codebase topology; differs from RAG systems by maintaining explicit decision-to-code mappings rather than embedding-based retrieval

Provides a terminal-based autonomous agent (haft agent command) that executes the engineering cycle without human intervention, using a ReAct-style coordinator to move through Understand → Explore → Choose → Execute → Verify phases. The coordinator maintains state in SQLite and can pause at checkpoints for human review. Implements a lemniscate cycle pattern that allows looping back to earlier phases if verification fails.

Unique: Implements a lemniscate cycle (figure-8 loop) that allows backtracking from Verify to earlier phases if verification fails, rather than linear progression — enables iterative refinement without restarting the entire cycle

vs alternatives: More structured than generic ReAct agents because it enforces FPF phases; differs from Devin/Claude Code by running autonomously in terminal without IDE, making it suitable for headless environments

Abstracts LLM provider differences (OpenAI Codex, Anthropic Claude, Google Gemini) behind a unified interface, allowing the same FPF reasoning cycle to work across different models. Routes tool calls and reasoning prompts to the configured provider via a provider adapter pattern, with fallback support for multiple models. Stores provider configuration in project policy files.

Unique: Implements provider abstraction at the reasoning level (not just API calls), allowing the same FPF cycle to work across Claude, Codex, and Gemini with different tool-calling conventions — uses adapter pattern to normalize provider differences

vs alternatives: More flexible than single-provider agents (Claude Code, Cursor) because it supports provider switching; differs from LangChain by focusing on reasoning governance rather than generic LLM chaining

Enforces project-level governance policies via .haft/ directory containing formal specifications (FPF Spec), provider configurations, and decision templates. Policies are versioned and can be checked via haft check command to ensure decisions comply with project standards. Implements a policy-as-code approach where governance rules are stored alongside the project and enforced by the Haft runtime.

Unique: Implements governance as versioned policy files in .haft/ directory (similar to .github/ workflows), making policies auditable and version-controlled alongside code — unlike external governance systems, policies live in the repository

vs alternatives: More integrated than external compliance tools because policies are co-located with code; differs from linters by enforcing reasoning discipline rather than code style

anthropics/claude-agent-sdk-python | DeepWiki Loading... Index your code with Devin DeepWiki DeepWiki anthropics/claude-agent-sdk-python Index your code with Devin Edit Wiki Share Loading... Last indexed: 5 June 2026 ( f83c87 ) Overview Quick Start Installation and Setup Version Information and Changelog Core Concepts Architecture Overview Type System and Message Architecture ClaudeAgentOptions Configuration Reference Bundled CLI Version Management Basic Usage query() Function ClaudeSDKClient Message Types and Content Blocks Transport and Communication Subprocess CLI Transport Control Protocol Message Streaming and Buffering Extension Points Custom Tools (SDK MCP Servers) Permission System and Callbacks Lifecycle Hooks Plugins and External MCP Servers Advanced Features Session Management and Forking SessionStore: Transcript Persistence File Checkpointing and Rewinding Resource Limits and Cost Control Sandbox Settings Model Selection, Thinking, and Output Formats Skills System Distributed Tracing (OpenTelemetry) Examples and Usage Patterns Interactive Streaming Examples Tool Integration Examples Error Handling Patterns Stderr Callback and Agents Examples Development Guide Project Structure Testing Strategy Build and Release Process Code Quality Standards Claude AI Integration in CI Glossary Menu Overview Relevant source files CHANGELOG.md CLAUDE.md

Core Concepts | anthropics/claude-agent-sdk-python | DeepWiki Loading... Index your code with Devin DeepWiki DeepWiki anthropics/claude-agent-sdk-python Index your code with Devin Edit Wiki Share Loading... Last indexed: 5 June 2026 ( f83c87 ) Overview Quick Start Installation and Setup Version Information and Changelog Core Concepts Architecture Overview Type System and Message Architecture ClaudeAgentOptions Configuration Reference Bundled CLI Version Management Basic Usage query() Function ClaudeSDKClient Message Types and Content Blocks Transport and Communication Subprocess CLI Transport Control Protocol Message Streaming and Buffering Extension Points Custom Tools (SDK MCP Servers) Permission System and Callbacks Lifecycle Hooks Plugins and External MCP Servers Advanced Features Session Management and Forking SessionStore: Transcript Persistence File Checkpointing and Rewinding Resource Limits and Cost Control Sandbox Settings Model Selection, Thinking, and Output Formats Skills System Distributed Tracing (OpenTelemetry) Examples and Usage Patterns Interactive Streaming Examples Tool Integration Examples Error Handling Patterns Stderr Callback and Agents Examples Development Guide Project Structure Testing Strategy Build and Release Process Code Quality Standards Claude AI Integration in CI Glossary Menu Core Concepts Relevant source files CHANG

Architecture Overview | anthropics/claude-agent-sdk-python | DeepWiki Loading... Index your code with Devin DeepWiki DeepWiki anthropics/claude-agent-sdk-python Index your code with Devin Edit Wiki Share Loading... Last indexed: 5 June 2026 ( f83c87 ) Overview Quick Start Installation and Setup Version Information and Changelog Core Concepts Architecture Overview Type System and Message Architecture ClaudeAgentOptions Configuration Reference Bundled CLI Version Management Basic Usage query() Function ClaudeSDKClient Message Types and Content Blocks Transport and Communication Subprocess CLI Transport Control Protocol Message Streaming and Buffering Extension Points Custom Tools (SDK MCP Servers) Permission System and Callbacks Lifecycle Hooks Plugins and External MCP Servers Advanced Features Session Management and Forking SessionStore: Transcript Persistence File Checkpointing and Rewinding Resource Limits and Cost Control Sandbox Settings Model Selection, Thinking, and Output Formats Skills System Distributed Tracing (OpenTelemetry) Examples and Usage Patterns Interactive Streaming Examples Tool Integration Examples Error Handling Patterns Stderr Callback and Agents Examples Development Guide Project Structure Testing Strategy Build and Release Process Code Quality Standards Claude AI Integration in CI Glossary Menu Architecture Overview Relevant source

anthropics/claude-agent-sdk-python | DeepWiki Loading... Index your code with Devin DeepWiki DeepWiki anthropics/claude-agent-sdk-python Index your code with Devin Edit Wiki Share Loading... Last indexed: 5 June 2026 ( f83c87 ) Overview Quick Start Installation and Setup Version Information and Changelog Core Concepts Architecture Overview Type System and Message Architecture ClaudeAgentOptions Configuration Reference Bundled CLI Version Management Basic Usage query() Function ClaudeSDKClient Message Types and Content Blocks Transport and Communication Subprocess CLI Transport Control Protocol Message Streaming and Buffering Extension Points Custom Tools (SDK MCP Servers) Permission System and Callbacks Lifecycle Hooks Plugins and External MCP Servers Advanced Features Session Management and Forking SessionStore: Transcript Persistence File Checkpointing and Rewinding Resource Limits and Cost Control Sandbox Settings Model Selection, Thinking, and Output Formats Skills System Distributed Tracing (OpenTelemetry) Examples and Usage Patterns Interactive Streaming Examples Tool Integration Examp