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ai-agents-for-science/code-search-agent

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App README

💻 Code Search Agent

A chat demo of the CARE v2 Scientific Code Discovery Agent. Describe a scientific task and the agent discovers relevant public code repositories (NASA-verified repo search, the Science Discovery Engine, optional ASCL/ADS citation evidence, and the web), then returns a ranked, evidence-backed comparison. Each answer keeps a visible agent-activity timeline and a collapsible reasoning trace, and you can keep chatting to refine the results.

Artifacts-driven: the agent's instructions are NOT hardcoded. They are loaded at startup from the agent's CARE workspace artifacts bundled in ./artifact — a copy of NASA-IMPACT/akd-plugins → plugins/code-search-assistant (the skill's SKILL.md is bundled as agents.md; references/ — per-domain contexts, guardrails, tool specs, output spec — ride along and are exposed to the agent through a read_reference tool).

Runtime: pydantic-ai (OpenAI Responses API) + the plugin's hosted FastMCP discovery servers + OpenAI hosted web search. Bring your own OpenAI key — entered in the UI, used only for your session, never stored. The Space owner supplies the MCP tokens (secrets).

Guardrailed (pydantic-ai v2 harness): every turn is checked by the NASA-IMPACT/akd-guardrails service, attached as InputGuard / OutputGuard capabilities on the agent — gliguard (GLiNER) screens each user prompt before the model is invoked (hard block, zero tokens), and risk_agent (LLM judge) reviews the final answer (with the run's tool returns as grounding context) before it renders. No guard logic lives in this app; it only relays verdicts. Blocked turns show ⛔ Blocked by AKD input/output guardrails: <risks>, and blocked answers never enter the conversation memory. If the guardrails service itself is unreachable, checks fail open (logged).

Run locally

bash

cd code-search-agent
pip install -r requirements.txt
# .env (not committed): CODE_SEARCH_MCP_KEY=… (+ optional vars below)
python app.py

Deploy (private Docker Space)

See DEPLOY.md for the full walkthrough. In short:

1.Create a private Space with SDK: Docker.
2.Set runtime secrets in Settings → Secrets (no build secrets needed — all dependencies are public):
3.CODE_SEARCH_MCP_KEY — FastMCP token for the primary discovery server.
4.optional: CODE_SIGNALS_MCP_KEY, ADS_ASCL_MCP_KEY.
5.Push Dockerfile, app.py, requirements.txt, bot-avatar-v2.png, artifact/, and this README.md to the Space.

The OpenAI key is supplied by each visitor at runtime (bring-your-own-key).

Configuration (runtime env vars / Space secrets)

Variable	Required	Purpose
`CODE_SEARCH_MCP_KEY`	yes	Token for the primary discovery server (`repository_search_tool`, `sde_search_tool`).
`CODE_SEARCH_MCP_URL`	no	Override the primary server URL (default: the plugin's `sde-repo-search` server).
`CODE_SIGNALS_MCP_KEY`	no	Token for the code-signals server (static code inspection channel).
`CODE_SIGNALS_MCP_URL`	no	Override the code-signals server URL.
`ADS_ASCL_MCP_KEY`	no	Token for the ASCL/ADS server (Astrophysics citation channel).
`ADS_ASCL_MCP_URL`	no	Override the ASCL/ADS server URL.
`AKD_GUARDRAILS_URL`	no	AKD guardrails service base URL (default: the dev ALB).
`ARTIFACT_DIR`	no	Artifact folder override (default `./artifact`).
`AGENT_MODEL`	no	Default model id (default `gpt-5.2`).

Servers are probed once at startup: a channel whose token is missing or rejected is dropped and disclosed to the agent, which notes it in Search Notes instead of fabricating results.