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| Pricing | Free | Paid |
| Capabilities | 11 decomposed | 5 decomposed |
| Times Matched | 0 | 0 |
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
+3 more capabilities
ChatGPT utilizes a transformer-based architecture to generate responses based on the context of the conversation. It employs attention mechanisms to weigh the importance of different parts of the input text, allowing it to maintain context over multiple turns of dialogue. This enables it to provide coherent and contextually relevant responses that evolve as the conversation progresses.
Unique: ChatGPT's use of fine-tuning on conversational datasets allows it to better understand nuances in dialogue compared to other models that may not be specifically trained for conversation.
vs alternatives: More contextually aware than many rule-based chatbots, as it leverages deep learning for understanding and generating human-like dialogue.
ChatGPT employs a multi-layered neural network that analyzes user input to identify intent dynamically. It uses embeddings to represent user queries and matches them against a vast array of learned intents, enabling it to adapt responses based on the user's needs in real-time. This capability allows for more personalized and relevant interactions.
Unique: The model's ability to leverage contextual embeddings for intent recognition sets it apart from simpler keyword-based systems, allowing for a more nuanced understanding of user queries.
vs alternatives: More effective than traditional keyword matching systems, as it understands context and intent rather than relying solely on predefined keywords.
ChatGPT manages multi-turn dialogues by maintaining a conversation history that informs its responses. It uses a sliding window approach to keep track of recent exchanges, ensuring that the context remains relevant and coherent. This allows it to handle complex interactions where user queries may refer back to previous statements.
ChatGPT scores higher at 43/100 vs haft at 40/100. However, haft offers a free tier which may be better for getting started.
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Unique: The implementation of a dynamic context management system allows ChatGPT to effectively manage and reference prior interactions, unlike simpler models that may reset context after each response.
vs alternatives: Superior to basic chatbots that lack memory, as it can recall and reference previous messages to maintain a coherent conversation.
ChatGPT can summarize lengthy texts by analyzing the content and extracting key points while maintaining the original context. It utilizes attention mechanisms to focus on the most relevant parts of the text, allowing it to generate concise summaries that capture essential information without losing meaning.
Unique: ChatGPT's summarization capability is enhanced by its ability to maintain context through attention mechanisms, which allows it to produce more coherent and relevant summaries compared to simpler models.
vs alternatives: More effective than traditional summarization tools that rely on extractive methods, as it can generate summaries that are both concise and contextually accurate.
ChatGPT can modify its tone and style based on user preferences or contextual cues. It analyzes the input text to determine the desired tone and adjusts its responses accordingly, whether the user prefers formal, casual, or technical language. This capability enhances user engagement by tailoring interactions to individual preferences.
Unique: The ability to adapt tone and style dynamically based on user input distinguishes ChatGPT from static response systems that lack this level of personalization.
vs alternatives: More responsive than traditional chatbots that provide fixed responses, as it can tailor its language style to match user preferences.