Akashi: Version Control for AI Decisions
Akashi helps companies managing multi-agent AI systems maintain an auditable record of agent decisions. It is designed for teams that need to coordinate multiple AI agents to help prevent contradictory outputs.
At a glance
- Category
- Software Development
- Best for
- 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.
- 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
- akashi.ai
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
Identifies contradictions between agents based on meaning rather than exact syntax, using LLM validation to classify relationships.
Captures the context of a decision, including reasoning, confidence scores, rejected alternatives, and supporting evidence.
Allows agents to query the audit trail for relevant past decisions, re-ranked by recency and assessment outcomes.
Provides native clients for Go, Python, and TypeScript to integrate the coordination layer into agent frameworks.
Uses SHA-256 content hashing and Merkle tree batch verification for tamper detection.
Supports marking past decisions as correct, incorrect, or partially correct to help improve future search re-ranking.
Use Cases
Supporting workflows where multiple agents must align on architectural or design decisions to help avoid production conflicts.
Maintaining a record of why an AI agent chose a specific outcome over other considered alternatives for compliance or review.
Enabling agents to reference a shared memory of settled questions so they may avoid repeating the same reasoning in every session.
Identifying and managing disagreements between agents using semantic analysis and LLM-based validation.
Best For
Integrations
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.
FAQ
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.
It is designed for self-hosting using Docker and requires a PostgreSQL database, though it can optionally use Qdrant and Ollama for search capabilities.
Akashi provides native SDKs for Go, Python, and TypeScript.
Source category: Software Development
Source subcategory: Code Repository
