Complete reference for running Agentica SDK with a local Claude proxy. This enables Python agents to use Claude CLI as their inference backend.
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Before installing skills in Cursor, ensure your development environment meets these requirements:
node --versionagentica-serverExecute the skills CLI command in your project's root directory to begin installation:
Fetches agentica-server from parcadei/continuous-claude-v3 and configures it for Cursor.
The CLI shows a list of agents. Use arrow keys and space to select Cursor:
Confirm successful installation by checking the skill directory location:
Restart Cursor to activate agentica-server. Access via /agentica-server in your agent's command palette.
We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.
Skills execute code in your environment. Always review source, verify the publisher, and test in isolation before production.
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Complete reference for running Agentica SDK with a local Claude proxy. This enables Python agents to use Claude CLI as their inference backend.
Use this skill when:
Agentica SDK (client code)
| S_M_BASE_URL=http://localhost:2345
v
ClientSessionManager
|
v
Agentica Server (agentica-server)
| INFERENCE_ENDPOINT_URL=http://localhost:8080/v1/chat/completions
v
Claude Proxy (claude_proxy.py)
|
v
Claude CLI (claude -p)
| Variable | Set By | Used By | Purpose |
|---|---|---|---|
INFERENCE_ENDPOINT_URL |
Human | agentica-server | Where server sends LLM inference requests |
S_M_BASE_URL |
Human | Agentica SDK client | Where SDK connects to session manager |
KEY: These are NOT the same endpoint!
Must start in this order (each in a separate terminal):
uv run python scripts/agentica/claude_proxy.py --port 8080
MUST run from its directory:
cd workspace/agentica-research/agentica-server
INFERENCE_ENDPOINT_URL=http://localhost:8080/v1/chat/completions uv run agentica-server --port 2345
S_M_BASE_URL=http://localhost:2345 uv run python scripts/agentica/your_script.py
# Claude proxy health
curl http://localhost:8080/health
# Agentica server health
curl http://localhost:2345/health
Symptom: Agent spawns successfully but fails on first call with connection error.
Cause: Claude proxy returning plain JSON instead of SSE format.
Fix: Proxy must return Server-Sent Events format:
data: {"choices": [...]}\n\n
Symptom: ModuleNotFoundError: No module named 'agentica_server'
Cause: Running uv run agentica-server from wrong directory.
Fix: Must cd workspace/agentica-research/agentica-server first.
Symptom: Agent asks for file contents instead of reading them.
Cause: Missing --allowedTools in claude_proxy.py CLI call.
Fix: Proxy must pass tool permissions:
claude -p ... --allowedTools Read Write Edit Bash
Symptom: Agent says "I've created the file" but file doesn't exist.
Cause: Hallucination - agent describing intended actions without executing.
Fix: Added emphatic anti-hallucination prompt in REPL_BASELINE:
CRITICAL: Use ACTUAL tools. Never DESCRIBE using tools.
Symptom: Call hangs for 30+ seconds then times out.
Cause: Claude CLI taking too long or stuck in a loop.
Fix: Check proxy logs for the actual CLI output. May need to simplify prompt.
| File | Purpose |
|---|---|
scripts/agentica/claude_proxy.py |
OpenAI-compatible proxy with SSE streaming |
workspace/agentica-research/agentica-server/ |
Local agentica-server installation |
scripts/agentica/PATTERNS.md |
Multi-agent pattern documentation |
Test the full stack:
# 1. Verify proxy responds
curl http://localhost:8080/health
# 2. Verify server responds
curl http://localhost:2345/health
# 3. Test inference through proxy
curl http://localhost:8080/v1/chat/completions \
-H "Content-Type: application/json" \
-d '{"model":"claude","messages":[{"role":"user","content":"Say hello"}]}'
Before running agents:
S_M_BASE_URL set for client scriptsINFERENCE_ENDPOINT_URL set for serverPrerequisites
Time Estimate
15-45 minutes depending on use case complexity
Steps
Common Pitfalls
✓ Do
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💡 Pro Tips
✓ Use when
Use when skill capabilities match your task, clear ROI on time saved, and you can validate outputs. Best for repetitive tasks, learning, and quality improvement.
✗ Avoid when
Avoid when task requires deep expertise you can't validate, involves sensitive decisions, or when learning process is more valuable than speed of completion.
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agentica-server has been reliable in day-to-day use. Documentation quality is above average for community skills.
Useful defaults in agentica-server — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
Solid pick for teams standardizing on skills: agentica-server is focused, and the summary matches what you get after install.
Registry listing for agentica-server matched our evaluation — installs cleanly and behaves as described in the markdown.
agentica-server fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
agentica-server is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
Keeps context tight: agentica-server is the kind of skill you can hand to a new teammate without a long onboarding doc.
Solid pick for teams standardizing on skills: agentica-server is focused, and the summary matches what you get after install.
agentica-server has been reliable in day-to-day use. Documentation quality is above average for community skills.
We added agentica-server from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
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