The open-source platform for human-agent collaboration in pharma
Define processes. Assign humans and AI agents to each step. Ship compliant workflows — fast.
Why Mediforce | How It Works | See It in Action | Get Involved
Pharma is ready for AI. The models are capable, the budgets exist, and the pressure to modernize is real. What's missing is the infrastructure — a way to deploy AI agents into regulated workflows with the compliance, auditability, and human oversight that GxP demands.
Mediforce is that infrastructure. Open-source, built for pharma, designed so your compliance team says yes on the first review.
One platform, every process. From clinical operations to pharmacovigilance to supply chain — define a process once, configure autonomy levels per step, and deploy. The first process is the hardest. Every one after that is incremental.
Your rules, your control. You decide how much autonomy each agent gets. An agent can draft and a human approves. Or the agent acts and a human reviews after the fact. The process stays the same; the configuration adapts to your organization's risk tolerance.
Compliance is not a bolt-on. Audit trails, accountability, data integrity, and scoped access are built into the platform from day one — not layered on top.
Read the full vision — why this needs to exist and where we're headed
Processes are made of steps. Each step can be performed by a human, an AI agent, or both — with clear rules about who decides what.
| Level | Agent Role | Human Involvement |
|---|---|---|
| L1 — Observer | Watches and surfaces insights | Informational only |
| L2 — Advisor | Suggests actions | Human decides and acts |
| L3 — Drafter | Does the work, submits for review | Human approves or sends back |
| L4 — Executor | Acts autonomously | Human reviews periodically |
At any level, an agent can signal uncertainty and escalate to a human. This isn't a failure mode — it's how the system maintains safety in production.
These aren't chatbots. Mediforce agents perform real cognitive work inside structured processes:
- Document analysis — review consent forms, flag missing fields, simplify language
- Anomaly detection — monitor metrics, alert on unusual patterns across sites
- Report generation — draft clinical summaries, compile safety narratives
- Supply intelligence — forecast demand, detect risk signals, optimize inventory
- Quality checks — validate data integrity, cross-reference against standards
Every agent operates under human oversight, with every action recorded in a complete audit trail.
All your workflows in one place — run counts, active status, and one-click access to any process execution.
The core decision point. Reviewers see full context from the agent's work and submit their verdict — approve, revise, or escalate.
Each step displays its autonomy configuration (L1–L4) so operators always know what's agent-driven and what requires human action.
See all features with recordings — task management, workflow editor, run reports, agent catalog, escalation handling, and more.
In regulated industries, trust and transparency are non-negotiable. Open source is the right model:
- Full transparency — your compliance team can inspect every line of code
- Zero vendor lock-in — you own your deployment, your data, your customizations
- Shared standard — instead of every company building their own AI integration layer, we build one together
- Community-driven quality — battle-tested by the people who use it
We're Appsilon — we've been building open-source tools for life sciences for over a decade. Mediforce applies that same philosophy to the biggest opportunity in pharma today.
We're building the standard for human-agent collaboration in pharma — and we're doing it in the open.
- Getting Started — set up your development environment
- Join our Discord — follow progress, ask questions, shape the roadmap
- Star this repo — helps others in pharma find us
- Open an issue — tell us what processes matter most to you
Getting Started Guide — Quick start with emulators and demo data, no setup required.
Quick start:
pnpm install
cd packages/platform-ui
python3 scripts/bootstrap-dev.py # Create .env.local, start emulators
pnpm seed:dev # Seed demo data
NEXT_PUBLIC_USE_EMULATORS=true pnpm dev:uiDemo credentials: test@mediforce.dev / test123456
For production Firebase setup, see the Getting Started Guide.
Run tests:
pnpm typecheck # type checking
pnpm test # unit + integration
cd packages/platform-ui && pnpm test:e2e # E2E (Playwright)Workflows with script executor steps need Docker images built locally:
# Community Digest workflow
docker build -t mediforce-agent:community-digest -f apps/community-digest/container/Dockerfile .
# Protocol to TFL workflow
docker build -t mediforce-agent:protocol-to-tfl -f apps/protocol-to-tfl/container/Dockerfile .Skip this if you only use human or agent executor steps, or run with MOCK_AGENT=true.
By default, agents execute inside Docker containers. To run them using your local claude CLI instead (useful for development and reducing costs):
pnpm dev:ui:local # platform UI only
pnpm dev:local # platform UI + supply intelligenceRequires
claudeto be available on yourPATH. Use the:localscripts (notALLOW_LOCAL_AGENTS=true pnpm dev:ui) — the env var doesn't propagate reliably through pnpm script aliases.
Full guide: docs/development.md
| Getting Started | Set up your development environment with Firebase |
| Vision | Why this needs to exist, what agents actually do in pharma, and where we're headed |
| Architecture | Processes, steps, agents, compliance — the technical foundation |
| How We Work | Building bottom-up, in public, with real processes |
| Development | Setup, monorepo structure, testing, deployment |
| Features | Full feature gallery with recorded walkthroughs |
Apache License 2.0 — see LICENSE.
Built by Appsilon — data solutions for life sciences since 2013.