AI TOOL PROFILE
Akashi: Version Control for AI Decisions
- Software Development
- Code Repository
- Software companies
- AI developers
- Teams building multi-agent AI systems
- Organizations requiring AI decision audit trails
Pricing
Akashi is released under the Apache 2.0 open-source license. Pricing was not clearly available from the provided evidence. Buyers should confirm current pricing on the vendor website.
At a glance
- Best for
- Software companies, AI developers, Teams building multi-agent AI systems, Organizations requiring AI decision audit trails
- Key use cases
- Multi-Agent Coordination, AI Decision Auditing, Reducing Logic Repetition, Conflict Resolution
- Integrations
- Claude Code, LangChain, CrewAI, Vercel AI SDK, MCP-compatible agents
- Official website
- Visit akashi official website
How AI is used
Akashi is a coordination layer for multi-agent AI environments. It functions as a version control system for AI decisions, allowing agents to check for precedents and record their reasoning process before finalizing an action.
The tool is designed for software companies and developers building complex AI agent workflows. It provides a way to make AI decisions visible and auditable, which may help prevent situations where different agents work toward conflicting goals.
By utilizing two main primitives, akashi_check and akashi_trace, the software supports a workflow where agents query past decisions and log their outcomes, confidence scores, and the evidence used. This may reduce the likelihood of agents relitigating settled questions across different sessions.
Buyers should confirm their technical capacity for self-hosting, as the tool is deployed via Docker and requires a PostgreSQL database. As an open-source project under the Apache 2.0 license, teams should evaluate their internal support for managing open-source infrastructure.
Key Features
Semantic Conflict Detection
Identifies contradictions between agents based on meaning rather than exact syntax, using LLM validation to classify relationships.
Audit Trail Recording
Captures the context of a decision, including reasoning, confidence scores, rejected alternatives, and supporting evidence.
Precedent Lookup
Allows agents to query the audit trail for relevant past decisions, re-ranked by recency and assessment outcomes.
Multi-Language SDKs
Provides native clients for Go, Python, and TypeScript to integrate the coordination layer into agent frameworks.
Integrity Verification
Uses SHA-256 content hashing and Merkle tree batch verification for tamper detection.
Decision Assessment
Supports marking past decisions as correct, incorrect, or partially correct to help improve future search re-ranking.
Use Cases
Multi-Agent Coordination
Supporting workflows where multiple agents must align on architectural or design decisions to help avoid production conflicts.
AI Decision Auditing
Maintaining a record of why an AI agent chose a specific outcome over other considered alternatives for compliance or review.
Reducing Logic Repetition
Enabling agents to reference a shared memory of settled questions so they may avoid repeating the same reasoning in every session.
Conflict Resolution
Identifying and managing disagreements between agents using semantic analysis and LLM-based validation.
Integrations
- Claude Code
- LangChain
- CrewAI
- Vercel AI SDK
- MCP-compatible agents
- PostgreSQL
FAQ
What is Akashi used for in AI systems?
- Akashi acts as a coordination layer that allows multi-agent AI systems to record decisions, query past precedents, and detect semantic conflicts between different agents.
How is Akashi deployed?
- It is designed for self-hosting using Docker and requires a PostgreSQL database, though it can optionally use Qdrant and Ollama for search capabilities.
Which programming languages are supported for integration?
- Akashi provides native SDKs for Go, Python, and TypeScript.
Source category: Software Development
Source subcategory: Code Repository
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