Agentic Memory for Scientific Discovery¶
A Tutorial on Using Persistent Memory in AI-Powered Research Workflows
🎯 What is Agentic Memory?¶
Agentic memory enables AI agents to remember, learn, and build knowledge across sessions—transforming them from stateless tools into cumulative research collaborators.
The Problem¶
Traditional AI agents are like researchers with amnesia: - ❌ Forget everything between sessions - ❌ Can't learn from past experiments - ❌ Can't connect findings across time - ❌ Can't provide historical context
Result: You spend time explaining context every session.
The Solution¶
With agentic memory, agents become knowledge-building partners: - ✅ Remember experimental results - ✅ Learn what works (and what doesn't) - ✅ Connect patterns across studies - ✅ Provide full provenance for every claim
Result: Agents that get smarter over time, just like human researchers.
🧬 Why This Matters for Genomics¶
Use Case 1: Novel Isoform Discovery¶
Without Memory:
Day 1: "I found a novel splice site in BRCA1 exon 11"
Day 2: Agent forgets → starts validation from scratch
Day 3: Repeat validation → waste time
With Memory:
Day 1: Agent stores discovery + evidence
Day 2: Agent remembers → "I validated this last week, confidence 0.92"
Day 3: Agent builds on it → "Let's check nearby exons for similar patterns"
Use Case 2: Experiment Tracking¶
Without Memory:
Researcher: "Did adding chromatin modality help?"
Agent: "I don't have that information"
Researcher: *Searches through 50 log files manually*
With Memory:
Researcher: "Did adding chromatin modality help?"
Agent: "Yes. In 3 experiments (Feb 2026), F1 improved by 8% (0.87→0.91).
Especially effective for tissue-specific splicing.
Evidence: experiments/chromatin_ablation.json"
Use Case 3: Literature Knowledge¶
Without Memory:
Researcher: "Has anyone found cryptic sites in BRCA1 exon 11?"
Agent: *Searches PubMed again (5 minutes)*
With Memory:
Researcher: "Has anyone found cryptic sites in BRCA1 exon 11?"
Agent: "Yes. Wang et al. (2023, Cell) reported 127 novel junctions
including BRCA1 exon 11. I stored this in
memory/literature/Wang2023Cell.md last month.
Would you like the full citation?"
🏗️ How It Works¶
Architecture Overview¶
┌─────────────────────────────────────────────┐
│ Your Research Workflow │
│ (Run experiments, analyze data, write) │
└──────────────────┬──────────────────────────┘
│
│ stores results
▼
┌─────────────────────────────────────────────┐
│ Agentic Memory Layer │
│ │
│ ┌──────────────┐ ┌──────────────┐ │
│ │ Experiments │ │ Discoveries │ │
│ │ (.md) │ │ (.md) │ │
│ └──────────────┘ └──────────────┘ │
│ │
│ ┌──────────────┐ ┌──────────────┐ │
│ │ Literature │ │ Hypotheses │ │
│ │ (.md) │ │ (.md) │ │
│ └──────────────┘ └──────────────┘ │
└──────────────────┬──────────────────────────┘
│
│ traces to
▼
┌─────────────────────────────────────────────┐
│ Raw Data & Artifacts │
│ (logs, configs, papers, plots, tables) │
└─────────────────────────────────────────────┘
Key Principle: Memory preserves the full chain from claim → evidence → raw data.
Memory as Markdown Files¶
Memory is stored as human-readable Markdown files (not opaque databases):
memory/
├── experiments/
│ ├── chromatin_modality_2026Q1.md
│ └── histone_marks_ablation.md
├── discoveries/
│ ├── brca1_novel_isoforms.md
│ └── tp53_cryptic_sites.md
├── literature/
│ ├── wang2023_cell_splicing.md
│ └── key_papers_alternative_splicing.md
└── hypotheses/
└── high_delta_predicts_novel.md
Benefits: - ✅ Human-readable: You can open and read any memory file - ✅ Git-versionable: Track how knowledge evolves over time - ✅ Editable: Correct mistakes or add notes - ✅ Transparent: See exactly what the agent remembers
📝 Example: Storing an Experiment¶
Step 1: Run Your Experiment¶
from agentic_spliceai import MetaModel
# Run experiment
model = MetaModel(modalities=["base", "sequence", "chromatin"])
results = model.train(data="chr21", epochs=50)
print(f"F1 Score: {results.f1}") # 0.91
Step 2: Store in Memory¶
from agentic_spliceai.memory import AgenticMemory
memory = AgenticMemory()
# Store experiment
memory.store_experiment({
"date": "2026-02-15",
"hypothesis": "Chromatin modality improves tissue-specific predictions",
"config": {
"model": "meta-v2",
"modalities": ["base", "sequence", "chromatin"],
"test_set": "chr21"
},
"results": {
"f1": 0.91,
"precision": 0.89,
"recall": 0.93
},
"conclusion": "Chromatin effective (+8% F1 improvement)",
"artifacts": [
"logs/chromatin_exp.log",
"plots/chromatin_roc.png"
]
})
Step 3: Memory File Created¶
File: memory/experiments/chromatin_modality_2026Q1.md
# Chromatin Modality Experiment (Q1 2026)
## Experiment: chromatin-chr21-validation
**Date**: 2026-02-15
**Hypothesis**: Chromatin modality improves tissue-specific predictions
### Setup
- Model: meta-v2
- Modalities: base + sequence + chromatin (ATAC-seq)
- Test set: chr21 validation
### Results
- **F1**: 0.91 (baseline: 0.83, improvement: +8%)
- **Precision**: 0.89
- **Recall**: 0.93
### Conclusion
Chromatin accessibility significantly improves predictions for
tissue-specific splice sites. Most effective in genes with
dynamic chromatin state (e.g., immune response genes).
### Evidence
- [logs/chromatin_exp.log]
- [plots/chromatin_roc.png]
- [configs/chromatin_exp.yaml]
### Related
- See: memory/modality_effectiveness/chromatin.md
🔍 Example: Querying Memory¶
Natural Language Queries¶
# Ask a question
results = memory.query(
"What have we learned about chromatin modality effectiveness?"
)
# Agent finds relevant memories
for result in results:
print(f"Category: {result.category}")
print(f"Finding: {result.content}")
print(f"Evidence: {result.links}")
print("---")
Output:
Category: experiments
Finding: Chromatin modality improved F1 by 8% (0.83 → 0.91) in chr21 validation
Evidence: [logs/chromatin_exp.log, plots/chromatin_roc.png]
---
Category: modality_effectiveness
Finding: Chromatin most effective for tissue-specific splicing (F1=0.89)
Least effective for housekeeping genes (F1=0.71)
Evidence: [47 experiments across 12 tissues]
---
Category: literature
Finding: Wang et al. (2023) showed chromatin remodeling activates cryptic sites
Evidence: [papers/Wang_2023_Cell.pdf]
---
Complex Logical Queries¶
MemU uses LLM-based search (not just embeddings), enabling complex logic:
# Find experiments where:
# - Chromatin was used
# - AND F1 > 0.9
# - AND tested on cancer samples
results = memory.query(
"experiments with chromatin modality, F1 above 0.9, on cancer samples"
)
Why this matters: Semantic search would miss the logical constraints (F1 > 0.9, cancer-only).
🔬 Example: Novel Discovery Workflow¶
Full Workflow with Memory¶
from agentic_spliceai import MetaModel, NexusAgent, AgenticMemory
# Initialize
model = MetaModel()
agent = NexusAgent(memory=AgenticMemory())
# 1. Make predictions
predictions = model.predict(gene="BRCA1")
# 2. Find high-delta sites (potential novel isoforms)
novel_sites = [s for s in predictions if s.delta_score > 0.5]
# 3. Validate with memory context
for site in novel_sites:
# Agent searches memory for similar cases
similar = agent.memory.query(
f"novel splice sites in {site.gene} with high chromatin signal"
)
# Agent validates with historical context
validation = agent.validate(
site,
context=similar, # ← Uses past discoveries!
sources=["literature", "rnaseq", "databases"]
)
# 4. Store discovery if high confidence
if validation.confidence > 0.8:
agent.memory.store_discovery(
site=site,
evidence=validation.evidence,
confidence=validation.confidence
)
print(f"✅ Novel site discovered: {site.location}")
print(f" Confidence: {validation.confidence}")
print(f" Evidence: {len(validation.evidence)} sources")
print(f" Similar cases: {len(similar)} in memory")
Output:
✅ Novel site discovered: chr17:43,094,582
Confidence: 0.92
Evidence: 4 sources (literature + RNA-seq + databases + chromatin)
Similar cases: 3 in memory (BRCA1 exon 10, exon 12)
What Got Stored¶
File: memory/discoveries/brca1_novel_isoforms.md
# BRCA1 Novel Isoforms
## Discovery: BRCA1_exon11_cryptic_donor_20260220
**Location**: chr17:43,094,582 (GRCh38)
**Type**: Cryptic donor (GT motif)
**Discovery Date**: 2026-02-20
### Prediction Scores
- Base model: 0.15 (LOW - not in training data)
- Meta model: 0.88 (HIGH - strong multimodal evidence)
- **Delta: 0.73** (large gap → novel signal)
### Evidence
**Sequence**: AGGTAAGT (canonical GT donor, 0.90 strength)
**Chromatin**: 0.82 (open in breast tissue)
**Histone**: H3K36me3 = 0.76 (active transcription)
**RNA-seq**: 24 junction reads in TCGA-BRCA
**Literature**: Confirmed by Venkitaraman (2014, Nature)
### Validation
- **Confidence**: 0.92 (HIGH)
- **Agent**: nexus-v2
- **Status**: ✅ Validated
- **Clinical**: Likely pathogenic (familial breast cancer)
### Provenance
- [rnaseq/TCGA-BRCA/junction_counts.tsv]
- [papers/Venkitaraman_2014_Nature.pdf]
- [predictions/meta-v2/BRCA1_predictions.tsv]
- [igv_screenshots/BRCA1_exon11_junction.png]
🧪 Scientific Workflows¶
Workflow 1: Hypothesis Testing¶
# Propose hypothesis
agent.memory.store_hypothesis({
"title": "High-delta sites predict novel isoforms",
"definition": "Sites with delta > 0.5 are enriched for novel sites",
"rationale": "Base model hasn't seen them, meta model has context",
"proposed": "2026-01-30"
})
# Run experiments
for experiment in ["chr21", "brca1", "tp53"]:
results = run_experiment(experiment)
agent.memory.add_experiment_to_hypothesis(
hypothesis_id="high_delta_predicts_novel",
experiment=results
)
# Query hypothesis status
status = agent.memory.query("status of high-delta hypothesis")
# → "Validated in 3/3 experiments. Precision = 0.85, Recall = 0.73"
Workflow 2: Error Pattern Analysis¶
# Analyze false positives
fps = model.get_false_positives(test_set="chr21")
# Store pattern
agent.memory.store_error_pattern({
"type": "false_positive",
"pattern": "High FP rate in repetitive regions",
"frequency": 0.23, # 23% of all FPs
"cause": "Base model trained on canonical sites",
"mitigation": "Add repeat masking + chromatin filter",
"effectiveness": "78% FP reduction"
})
# Later experiments automatically benefit
agent.memory.query("how to reduce false positives?")
# → Returns stored mitigation strategies
Workflow 3: Cross-Study Validation¶
# Discovery in Study 1 (BRCA1)
agent.memory.store_discovery({
"gene": "BRCA1",
"site": "chr17:43,094,582",
"evidence": ["TCGA-BRCA RNA-seq"],
"confidence": 0.85
})
# Study 2 (TP53) - Agent checks for similar patterns
similar = agent.memory.query(
"discoveries with cryptic donors and chromatin evidence"
)
# Agent: "Found similar pattern in BRCA1. Let me check TP53..."
# → Cross-validates across studies
# → Builds confidence through independent replication
📊 Benefits for Your Research¶
1. Time Savings¶
- No more searching through old log files
- No more re-reading papers you've already processed
- No more explaining context to tools each session
Estimate: Save 2-5 hours/week
2. Better Science¶
- Reproducibility: Full provenance for every claim
- Cumulative learning: Build on past experiments
- Cross-validation: Connect findings across studies
- Error avoidance: Remember what didn't work
3. Publication Ready¶
Memory files can directly become: - Methods sections (full experimental details) - Supplementary materials (all evidence chains) - Data availability statements (all artifacts linked)
4. Collaborative¶
- Share memory with lab members
- Build institutional knowledge
- Onboard new researchers faster
🚀 Getting Started¶
Installation¶
# Install Agentic-SpliceAI with memory support
pip install agentic-spliceai[memory]
# Initialize memory
agentic-spliceai memory init --dir ./memory
Basic Usage¶
from agentic_spliceai.memory import AgenticMemory
# Create memory
memory = AgenticMemory()
# Store something
memory.store(
category="experiments",
item={"name": "My first experiment", "result": "success"}
)
# Query it
results = memory.query("my first experiment")
print(results) # Finds it!
CLI Commands¶
# Search memory
agentic-spliceai memory search \
--query "chromatin modality effectiveness"
# Store experiment
agentic-spliceai memory store \
--category experiments \
--file experiment_results.json
# Export for paper
agentic-spliceai memory export \
--categories discoveries,literature \
--gene BRCA1 \
--output supplementary_materials/
🎓 Best Practices¶
1. Store Early, Store Often¶
Don't wait until "final results"—store intermediate findings too.
Why: Negative results and failed experiments are valuable knowledge!
2. Always Link to Raw Data¶
Every memory item should link back to source files.
memory.store({
"finding": "Chromatin improved F1 by 8%",
"evidence": [
"logs/experiment_123.log", # ← Link to raw
"results/metrics.json"
]
})
3. Use Descriptive Categories¶
Organize memory by scientific theme, not tool structure.
Good: memory/tissue_specific_splicing/
Bad: memory/model_outputs/
4. Review High-Stakes Memories¶
Validate discoveries before storing as "confirmed."
5. Query Before You Start¶
Check memory before running experiments.
# Before experiment
prior_work = memory.query("chromatin modality on chr21")
if prior_work:
print("We tried this before! Here's what we learned:")
print(prior_work)
📚 Advanced Topics¶
Memory Provenance Chains¶
Every claim traces back through full evidence chain:
Discovery: "BRCA1 exon 11 cryptic donor"
↓
Evidence: "24 RNA-seq junction reads"
↓
Experiment: "TCGA-BRCA analysis"
↓
Raw Data: "tcga_brca_junctions.bam"
Query any level:
Memory Versioning¶
Memory files are Markdown → version with Git:
Collaborative Memory¶
Share memory across team:
# Clone lab memory
git clone lab-server:/memory ./shared_memory
# Use it
memory = AgenticMemory(root_dir="./shared_memory")
# Contribute discoveries
memory.store_discovery(...)
git commit -m "Added TP53 cryptic site validation"
git push
🔗 Learn More¶
- Framework: MemU (https://github.com/NevaMind-AI/memU)
- Use Cases: See
docs/agency/MEMORY_PATTERNS.md - API Reference: See
docs/api/memory.md
🎉 Summary¶
Agentic memory transforms AI agents from tools into research collaborators that: - ✅ Remember what you've learned - ✅ Build cumulative knowledge - ✅ Provide full provenance - ✅ Get smarter over time
The result: Better science, faster discovery, stronger publications.
Try it today: pip install agentic-spliceai[memory]
Questions? Open an issue on GitHub or email research@agentic-spliceai.org
"The best research assistant is one that remembers everything you've taught it."