Think deeper.
Reason further.
Verify everything.
Some questions need breadth. Others need proof. MIRA brings both — NAE agents that reason across your entire problem space, and RLM as the executor that won't stop until the numbers hold. Open-source. Any LLM. Nothing leaves your machine.
Adapt to every session — instantly
Every session is yours to shape — chat freely, focus with a Skill, or automate with a Workflow.
SaaS contract · IP review
now
EMEA Q3 gap analysis
2h ago
What can I help you with?
Multi-step reasoning across research, code, data and documents.
Security review
Audit code for vulnerabilities
Research synthesis
Summarise papers with citations
Analyse data
Verify findings with real code
Debug & explain
Trace errors step by step
Drop files here
PDF, DOCX, CSV…
§12, §14, §18, §19, §21, §22
Schedule B §3 · IP assignment
Who it's for
Built for anyone who needs
to be right.
Whether you're running data analysis, reviewing documents, or synthesising research — MIRA adapts to your domain so you don't have to adapt to it.
Data Analysts & Scientists
Ask questions about your data in plain language. MIRA computes the answer from your actual data — every number earned, every result auditable.
Researchers & Academics
Upload papers, datasets, and reports. Run multi-step research pipelines that synthesise evidence and cite sources — not statistical plausibility.
Legal & Compliance
Cross-reference contracts, flag conflicting clauses, and surface jurisdiction risks. RLM runs the analysis in code so every finding is traceable, not inferred.
Financial & Business Analysts
Model, forecast, and cross-reference data with an engine that verifies its own calculations before reporting them — not an engine that guesses.
Security Professionals
Threat modelling, secure review, and CVE analysis with an engine that doesn't skip steps or paper over uncertainty.
Anyone Handling Sensitive Data
Everything runs on your machine. Documents, credentials, and conversations never leave — by design, not by policy.
Capabilities
Ask anything.
MIRA figures out the rest.
Analyse a csv, review a contract, analyse financial reports, simulate scenarios, or synthesise research papers — MIRA decomposes the problem, reasons through each part with the right engine, and shows you exactly what it did and why.
Two Reasoning Engines
The Native Agent Engine spawns parallel sub-agents for open-ended research, managing long context and episodic memory automatically. The RLM Engine investigates using real computation, observes the actual output, and refines until the answer is verified — not inferred.
Document Intelligence
Upload PDFs, Word docs, CSVs, Markdown, code files — up to 50 MB each. Every file is parsed locally, chunked intelligently, and injected into the active reasoning context. Toggle documents in or out per session without restarting.
Skills — Instant Domain Expert
Activate a Skill and MIRA adopts a specialist mindset for that field. Five built-in Skills cover Life Sciences, Software Architecture, Financial Analysis, Security Review, and Research Synthesis — each tuning the model's temperature, iteration depth, and tool access to match domain norms.
Deterministic Workflows
Build repeatable multi-step pipelines where each step receives the previous step's output via template variables. Add conditional routing rules to branch, retry, or skip based on actual results. Three production pipelines ship out of the box — Research Report, Code Review, Document Deep Dive.
MCP Tool Integration
Connect any Model Context Protocol server — web search, SQL databases, REST APIs, or your own internal tools. Both reasoning engines can invoke MCP tools mid-chain and fold the live results straight back into their reasoning loop.
Live REPL Console
Every line of code the RLM Engine writes and executes streams to the REPL Console in real time — including stdout, stderr, and engine status messages. Watch reasoning happen step by step. Nothing runs in a black box, and session state persists across iterations.
Built-in Eval Framework
Automatically grade every response using LLM judges, rule-based checks, embedding similarity scores, and custom metrics. Run evals on demand after any session to track answer quality over time and catch regressions before they become habits.
Zero Telemetry. Fully On-Device.
No analytics pipeline, no crash reports, no cloud sync — ever. API keys are stored in your OS keychain (macOS Keychain, Windows Credential Manager, libsecret). The renderer runs with contextIsolation and has no direct filesystem access. Your data is private by design.
How it works
From question to
verified answer.
Install once, connect any LLM, and follow four steps to get answers you can trust and trace.
Install — one click, then done
Download the macOS, Windows, or Linux installer from GitHub Releases. On first launch MIRA automatically provisions a bundled Python 3.11 virtual environment. No terminal. No pip. No PATH configuration. Nothing to maintain.
Connect the AI you trust
Open Settings (⌘,) and paste an Anthropic, OpenAI, or AWS Bedrock API key — or point MIRA at a local Ollama instance for fully air-gapped inference. Credentials are written directly to your OS keychain; never stored on disk or sent anywhere.
Load your context
Drop in any PDF, DOCX, CSV, Markdown, or code file (up to 50 MB each). Attach a Skill to give the AI a specialised reasoning persona — analyst, reviewer, security auditor. Wire up Workflows for repeatable multi-step pipelines. Connect external data via MCP tools.
Ask. Reason. Verify.
Choose NAE to decompose your question into parallel sub-tasks with automatic context compaction and episodic memory. Choose RLM to investigate with real computation, observe the actual output, and iterate until the answer is proven — not guessed.
Stay in the loop.
Be the first to know when MIRA ships new reasoning features, engine improvements, and releases. No spam — ever.