Your Own Truth-Grounded AI Research Assistant
Deploy a personal research agent that reads your papers,
discovers knowledge, verifies its own reasoning,
and grows with you over time.
Aletheia is a personal AI research agent designed to support the full research and learning process.
Research is not simply reading a few papers and asking questions.
A single scientific publication can require analyzing hundreds or even thousands of sources, comparing methodologies, identifying gaps in the literature, and synthesizing new ideas.
Aletheia is built to help navigate that complexity.
Instead of acting like a chatbot, Aletheia behaves like a long-term research collaborator that assists you with discovering knowledge, understanding complex materials, and developing ideas grounded in real sources.
Each Aletheia instance belongs to a single user.
You deploy your own agent, connect it to your papers, notes, and knowledge sources, and it becomes a research companion that grows with you over time.
Most AI tools struggle in serious academic environments for two fundamental reasons.
Large language models often generate answers based on their training data instead of the documents provided by the user.
This can lead to:
- fabricated citations
- incorrect definitions
- misleading explanations
Aletheia addresses this by enforcing strict truth-grounded reasoning.
The agent prioritizes information retrieved directly from verified sources.
Research involves many different activities:
- finding papers
- reading and summarizing literature
- connecting ideas across sources
- developing hypotheses
- organizing knowledge
- writing structured reports
Most AI tools only support one small part of this process.
Aletheia is designed to support the entire research workflow.
Aletheia helps you navigate large bodies of literature by:
- discovering relevant research papers
- organizing sources by topic
- identifying influential work in a field
- mapping citation relationships
This helps researchers quickly understand the landscape of a research domain.
The agent can analyze complex academic documents and extract:
- key ideas
- definitions
- methodologies
- experimental results
- limitations and open questions
It helps transform dense papers into structured, understandable knowledge.
Aletheia connects information across multiple sources.
It can:
- compare research approaches
- identify patterns across studies
- summarize consensus and disagreements
- highlight potential research gaps
This helps researchers move from information → insight.
Aletheia is designed to minimize hallucination.
When answering questions, the agent prioritizes:
- retrieved source material
- verified claims
- traceable reasoning
The goal is to ensure answers remain grounded in real documents rather than generated guesses.
Aletheia can learn how you write.
By analyzing your previous work (papers, notes, reports), the agent can generate content that reflects your:
- writing tone
- structure
- academic voice
This makes AI-assisted writing feel natural rather than generic.
Before producing responses, Aletheia can internally evaluate its own reasoning.
The agent can use multiple reasoning roles such as:
- Scholar — generates research analysis
- Critic — verifies reasoning against sources
- Archivist — manages document knowledge
This internal loop helps maintain accuracy and consistency.
Aletheia is designed as a modular research agent architecture that combines document grounding, long-term memory, and multi-agent reasoning.
The system is structured into several layers that work together to support complex research workflows.
User
│
▼
Agent Interface
│
▼
Task Planner
│
▼
Research Agent System
│
├── Retrieval Engine
├── Knowledge Graph
├── Memory System
├── Reasoning Agents
│
▼
Grounded Response
Each layer plays a different role in transforming user queries into verified research insights.
Research documents are processed through an ingestion pipeline.
The pipeline performs:
- document parsing
- semantic chunking
- embedding generation
- metadata extraction
Processed documents are stored inside a vector database for efficient retrieval.
Supported sources may include:
- research papers
- PDFs
- academic articles
- technical documentation
- research notes
Aletheia uses a retrieval engine to locate relevant knowledge when answering questions.
The retrieval system can combine multiple techniques:
- vector similarity search
- keyword search
- citation traversal
- topic filtering
This allows the agent to find the most relevant information across large research collections.
Research knowledge is stored not only as documents but also as relationships.
Aletheia builds a knowledge graph connecting:
Paper → Method → Dataset → Result → Citation
Example:
Paper A
├ Method: Diffusion Model
├ Dataset: CIFAR-10
└ Cites: Paper B
This structure helps the agent reason across papers rather than treating them as isolated documents.
Aletheia maintains several layers of memory to support long-term research collaboration.
Short-Term Memory
- current conversation context
- temporary reasoning steps
Project Memory
- research projects
- literature collections
- working notes
Long-Term Memory
- discovered insights
- frequently used concepts
- user research interests
Over time, this allows the agent to develop a deeper understanding of the user's research domain.
Aletheia can learn the user's writing style from previously written documents.
By analyzing prior work such as:
- papers
- reports
- essays
- research notes
the system builds a style memory.
Generated content can then reflect the user's:
- tone
- vocabulary
- academic structure
Aletheia is built as a multi-agent research system.
Instead of relying on a single language model response, the system uses specialized agents that collaborate to produce grounded and verifiable research outputs.
Each agent has a distinct responsibility in the reasoning process.
The Scholar Agent is responsible for research analysis.
Its tasks include:
- interpreting retrieved documents
- synthesizing ideas across papers
- generating structured explanations
- drafting research summaries
This agent focuses on knowledge synthesis.
The Critic Agent verifies the reasoning produced by the Scholar.
It checks whether:
- claims are supported by retrieved sources
- definitions match the original documents
- conclusions are logically consistent
If inconsistencies are detected, the response is rejected and regenerated.
This mechanism helps reduce hallucination.
The Archivist Agent manages the research knowledge base.
Responsibilities include:
- organizing papers
- updating document embeddings
- maintaining the knowledge graph
- managing user research libraries
Complex research tasks often require multiple steps.
The Planner Agent decomposes tasks such as:
- literature review
- topic exploration
- research comparison
into smaller reasoning steps that other agents can execute.
The Memory Agent manages long-term knowledge about the user and their research.
It records:
- research topics
- important insights
- previously explored ideas
- user feedback
Over time, this enables Aletheia to become a personal research intelligence system.
User Question
│
▼
Planner Agent
│
▼
Retriever
│
▼
Scholar Agent
│
▼
Critic Agent
│
▼
Memory Agent
│
▼
Grounded Response
This collaborative architecture allows Aletheia to reason about complex research questions while maintaining traceability and accuracy.
Aletheia is not designed as a stateless chatbot.
Over time the agent builds a long-term understanding of your research interests.
It can remember:
- topics you study
- papers you read
- ideas you explore
- insights discovered during research
This allows the agent to gradually evolve into a personal research intelligence system.
The more you work with it, the more useful it becomes.
Aletheia assists across multiple stages of the research process.
Find and collect relevant papers and sources.
Break down complex ideas and methodologies.
Structure knowledge across papers and topics.
Connect insights across multiple sources.
Explore hypotheses and research directions.
Generate structured reports, essays, and research papers.
Ensure claims remain grounded in original sources.
- Deploy your Aletheia agent
- Upload research papers and documents
- Ask questions or explore topics
- The agent retrieves and analyzes relevant material
- It synthesizes insights across sources
- Responses remain grounded in verified information
Over time, the agent also learns your interests and writing style.
Aletheia is designed for anyone who wants to explore knowledge deeply.
This includes:
- students
- graduate researchers
- PhD candidates
- independent researchers
- engineers and scientists
Anyone who reads, analyzes, and produces knowledge can benefit from a personal research agent.
The future of AI will not be a single global assistant.
Instead, every individual will have their own personal AI agents, trained on their documents, knowledge, and intellectual interests.
Aletheia aims to become one of those agents.
Not a chatbot.
But a long-term research partner dedicated to helping you discover truth.
git clone https://github.com/louisdevzz/aletheia
cd aletheia
bun install
bun run devMore setup instructions coming soon.
MIT License