Define agents in YAML, write tools in Python, push to Git. We run them. Deployment, scaling, and the connectors into your stack are already wired up. No Kubernetes, no glue code.
Define agents in YAML. Add custom tools when you need them.
# Define the whole agent in one YAML filename: invoice-processormodel: gemini/gemini-2.5-protemperature: 0.3system_prompt: |You are an expert accountant.Extract every field from the invoiceand verify the totals add up.tools: - documents.parse - documents.extract_entities - database.store_invoicePredefined integrations, your own code tools, or any MCP server.
connic dev streams every save to a live runner. Test in seconds, not minutes.
~/invoice-bot ❯ connic dev watching agents/ and tools/ connected to dev runner · session a1b2c3d4 ✓ ready in 412ms ↻ tools/documents.py changed ✓ reloaded invoice-processor in 2.4s → invoice-processor #a1b2c3d4 parsed 3 entities, stored to db ✓ run completed in 2.3s❯Knowledge base, database, session memory. Nothing for you to provision.
Write assertions for agent behavior. Run them in CI.
Dev / staging / prod with isolated secrets and config.
Reach internal services without opening firewall ports.
docker run -d --name connic-bridge \
-e BRIDGE_TOKEN=••••••••••••••••
-e ALLOWED_HOSTS=postgres:5432,kafka:9092 \
connicorg/bridge:latestWebhooks, queues, databases, schedules. Wire agents to the systems you already run.
Every run traced. Tokens, latency, errors, costs.
LLM-as-judge evaluation built into the platform.
Route traffic between agent variants. Compare outcomes.
A/B test · 4,128 runs
Pause tool calls for human review before they execute.
Use your own API keys with any provider. Connect agents to the infrastructure you already run.
No credit card required. Upgrade as your agents grow.
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Tutorials, product updates, and engineering insights.
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