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FullCopilot

FullCopilot is a trustworthy biomedical tool-using agent framework. It parses a user request into a structured task, selects task-relevant tools, executes planned calls, and verifies whether the resulting evidence is sufficient to mark the task as complete.

Core Idea

FullCopilot is not about exposing more tools to a language model. It focuses on execution evidence: which tools were selected, why they were called, what they returned, which checks passed, and whether missing evidence should trigger repair, fallback, or an explicit incomplete status.

Features

  • Parse natural-language requests into ParsedTask.
  • Build task-conditioned PlannedWorkflow objects.
  • Execute tools or offline wrappers through a structured executor.
  • Normalize tool outputs into candidates and evidence records.
  • Verify outputs, scores, evidence fields, and provenance before declaring success.
  • Apply conservative repair or fallback when evidence is missing.
  • Save JSONL execution traces for reproducibility, audit, and future planner learning.
  • Provide benchmark runners, baseline runners, and release hygiene checks.

Repository Layout

FullCopilot/
├── CAi/
│   ├── CAi_agent/                 # compatible agent shell
│   └── toolkit/
│       ├── functions/             # agent-facing tool wrappers
│       ├── server/                # optional tool server wrappers
│       └── agent_planner/         # planning, execution, verification, tracing
├── tests/                         # unit tests, no API keys required
├── docs/                          # architecture and release notes
├── scripts/                       # release hygiene checks
└── pyproject.toml

Quick Start

conda create -n fullcopilot python=3.11
conda activate fullcopilot
pip install -e ".[dev]"
python -m pytest tests/test_domain_router.py tests/test_clinical_trial_verifier.py tests/test_drug_target_verifier.py

Run an offline benchmark example:

python -m CAi.toolkit.agent_planner.biomedical_benchmark_runner \
  --benchmark CAi/toolkit/agent_planner/benchmarks/clinical_trial_outcome_prediction_v2_offline.jsonl \
  --output /tmp/fullcopilot_offline_summary.json

Build a compact biomedical generalization table:

python -m CAi.toolkit.agent_planner.biomedical_generalization_table \
  --benchmark CAi/toolkit/agent_planner/benchmarks/clinical_trial_outcome_prediction_v2_offline.jsonl \
  --benchmark CAi/toolkit/agent_planner/benchmarks/drug_target_evidence_v2_offline.jsonl \
  --output /tmp/biomedical_generalization_table.json

Run a mock benchmark example:

python -m CAi.toolkit.agent_planner.benchmark_runner \
  --benchmark CAi/toolkit/agent_planner/benchmarks/molecular_agent_tasks.example.jsonl \
  --execution-mode mock \
  --output /tmp/fullcopilot_mock_summary.json

Optional Tool Server

Some real tool executions can use the local tool server:

python -m CAi.toolkit.server.app

Tool source code, model weights, generated workspaces, and large datasets are not included in this repository. Configure those paths locally and keep them outside version control.

Release Hygiene

Before publishing:

python scripts/audit_release_tree.py --root .

The audit checks for local absolute paths, credential-like tokens, runtime logs, tool workspaces, and large binary/model artifacts.